Earth doi: 10.3390/earth7050141

Authors: Ivica Milevski Bojana Aleksova Pece Gorsevski

Flash floods are among the most damaging hydrometeorological hazards in the Western Balkans (WB), yet regionally consistent, cross-border susceptibility assessments remain scarce because of fragmented national datasets and differing methodological standards. This study develops a harmonized, cloud-based flash-flood susceptibility framework for the WB (208,052 km2) by implementing a physiography-based modified Flash-Flood Potential Index (FFPI) in Google Earth Engine (GEE) at 30 m resolution. The modified FFPI integrates slope, land cover, soil texture, vegetation exposure (Bare-Soil Index), and soil erodibility, and is aggregated across 9524 EU-Hydro sub-basins to produce an operational catchment-level ranking. Additionally, CHIRPS-derived maximum daily precipitation is used to derive a rainfall-triggered hotspot layer that highlights sub-basins where terrain-controlled susceptibility coincides with strong observed rainfall extremes over the 2001–2025 period. Enhanced susceptibility is concentrated in Adriatic and Aegean-facing mountain basins of Albania, Montenegro, and North Macedonia, with 44.2% of sub-basins classified as high or very-high susceptibility. Multi-source validation against inventoried torrential catchments, published GIS-based susceptibility maps, and flood records yielded moderate to very strong agreement (68.6–92.0%), together with an AUC-ROC of 0.79 and F1-score of 0.77 for the pooled orthophoto-based validation dataset (n = 336 sub-basins). The framework provides a reproducible transboundary tool for regional flood-risk screening and demonstrates the potential of cloud-based geospatial platforms to overcome cross-border data fragmentation in hazard assessment. Its main limitations are the static physiographic nature of the FFPI, the coarser resolution of CHIRPS and SoilGrids relative to small sub-basins, and possible overestimation in karst terrains where subsurface drainage reduces surface runoff.

Earth doi: 10.3390/earth7040140

Authors: Salis Deris Artikanur Widiatmaka Widiatmaka Wiwin Ambarwulan Yusuf Surachman Djajadihardja Nawa Suwedi Darmawan Listya Cahya Lena Sumargana Bambang Winarno Heri Sadmono Andri Purwandani Fanny Meliani Teguh Arif Pianto Harun Idham Akbar Elenora Gita Alamanda Sapan

Population growth is increasing food demand, while watershed food systems face intense pressure from land-use/land-cover (LULC) change. Therefore, this study aimed to identify the critical threshold for food sufficiency in the Cimanuk Watershed. The methods used include dynamic analysis and prediction of LULC changes in line with four scenarios using Support Vector Machine (SVM)–Markov model, ecosystem service-based food production modeling through Crop Production module in Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, food demand estimation based on population projections, and calculation of Food Sufficiency Index (FSI) to identify the critical threshold at which the food system shifts from surplus to deficit. The results showed that paddy rice production is projected to continue meeting food demand across all scenarios through 2042. However, food demand is expected to increase as the population grows, specifically in the Accelerated Population Growth (APGS) scenario. FSI analysis indicated that the Cimanuk Watershed remained in food surplus (FSI > 1) up to 2042. The watershed may transition to a food-deficit condition after 2062 in the absence of intervention measures. Therefore, this study recommends protecting productive paddy fields, conserving forests, and managing population growth to maintain long-term food sufficiency.

Earth doi: 10.3390/earth7040139

Authors: Carlos Emérico Nieto Ramos Rosario Marilu Bernaola-Paucar Bayron Alexander Ruiz-Blandon Efrén Hernández-Alvarez Leonor Neda Carbajal Cuadros Luis Armando Nieto Ramos Marcos Alama-Flores Walter Javier Cuadrado-Campó Eduardo Salcedo-Pérez Deysi Alina Colachagua-Calderon

Tropical mining landscapes require plot-based structural and carbon baselines to support restoration planning and environmental monitoring. This study estimated aboveground, root, and total tree carbon stocks across six mining concessions located in the Inambari River basin, Madre de Dios, southeastern Peruvian Amazon. Field inventories were conducted in 39 plots of 0.1 ha, where all trees with DBH ≥ 10 cm were measured. Aboveground biomass was estimated using a pantropical allometric equation based on wood density, diameter, and height; root biomass was estimated using a baseline root-to-shoot ratio of 0.24; and biomass was converted to carbon using a baseline carbon fraction of 0.47. Structural attributes, biomass stocks, carbon stocks, diameter-size profiles, basal-area contribution by diameter class, and multivariate structural-carbon patterns were evaluated among concessions. The concessions differed significantly in measured structural attributes, and these differences translated into contrasting derived biomass and tree carbon estimates. Edmilot I showed the highest basal area per hectare, total biomass, and total tree carbon, reaching 118.34 Mg C ha−1, while Yesica recorded the lowest total tree carbon, with 58.54 Mg C ha−1. Most trees were concentrated in the 10–20 cm and 20–30 cm DBH classes, but intermediate and larger trees contributed disproportionately to basal area. Principal component analysis summarized the concessions according to a reduced set of non-redundant structural and carbon variables, with the first two components explaining 99.21% of the total variation. These findings show that mining concessions retain contrasting forest conditions and should not be treated as homogeneous units. Structural and carbon baselines can help identify internal differences among concessions, prioritize restoration actions, and improve environmental monitoring in tropical mining landscapes.

Earth doi: 10.3390/earth7040138

Authors: Mattia Napoletano Alessandro Bellino Alessio Langella Mariano Mercurio Vincenzo Baldi Antonio Ernesto Detta Daniela Baldantoni

Urban and industrial activities have lasting effects on Earth ecosystems, impairing their functionality. Technosols offer a sustainable solution for restoring degraded urban and industrial ecosystems. In a 30-day microcosm experiment, a mixture of six pioneer plant species was sown in three substrates: a sewage sludge-based Technosol (GF), a zeolite-enriched Technosol (GZ), and a commercial potting mix control (GC). The plant population dynamics, final performance, and substrate biochemical properties were monitored. A strong environmental filter allowed only two species (Bromus inermis Leyss. and Lolium perenne L.) to establish. Technosols exerted a demographic bottleneck, delaying emergence and reducing the total biomass relative to the control. Zeolites in the GZ Technosol mitigated this delay, accelerating early establishment due to their microporous structure and high cation exchange capacity. However, GZ caused the greatest reduction in individual biomass and functional plant performance index, corresponding to a microbial shift toward oxidative activity at the expense of hydrolytic nutrient mineralization. These results show that sewage sludge Technosols can initiate functional ecological succession. While zeolites positively affect germination, their microbial interaction suggests a temporary decoupling between the establishment speed and final productivity. Integrated monitoring of demographic and biochemical dynamics is therefore essential to optimize Technosol-based environmental restoration.

Earth doi: 10.3390/earth7040137

Authors: Marwa Zerrouk Siham Fellahi Asmaa Moussaoui Imane Sebari Kenza Aitelkadi

Reliable coastal wetland habitat mapping is often constrained by the scarcity and the cost of reliable reference data, especially in data-limited coastal environments. We propose a validation-controlled, label-efficient framework pairing multi-season Sentinel-1 and Sentinel-2 predictors with a CatBoost teacher and a lightweight MLP student. A candidate is pseudo-labeled only when both separately calibrated models agree and exceed class-specific thresholds; accepted labels are class-balanced and down-weighted. The framework was evaluated at the Sidi Moussa–Oualidia wetland complex and Merja Zerga lagoon in Morocco. At Sidi Moussa–Oualidia, 62 configurations were compared through nested polygon-grouped validation and then frozen before a five-seed held-out evaluation. The supervised MLP and Agreement-augmented MLP achieved mean Macro-F1 values of 0.9518±0.0044 and 0.9509±0.0062, indicating that augmentation did not materially change the already strong full-data baseline. Under a stricter budget of 30 training and 20 validation observations per class, Agreement yielded a mean Macro-F1 of 0.9092±0.0102 compared with 0.9023±0.0093 for the supervised baseline and produced pseudo-labels in all five seeds. A spatial-range sensitivity analysis further showed that both models retained Macro-F1 values of 0.9391 and 0.9403 for test observations located beyond the largest estimated within-class autocorrelation range. At Merja Zerga, the native six-class supervised MLP achieved 0.9456±0.0050, compared with 0.9401±0.0047 after Agreement augmentation. Spatially blocked four-class experiments nevertheless showed that 20 to 30 local training labels per class recovered approximately 96–98% of the corresponding full-data performance. The framework therefore supplies an operational criterion for using unlabeled observations: augmentation is adopted only where calibrated filtering yields adequate class coverage, and validation confirms a downstream effect; otherwise the supervised model is retained. For the strict Sidi Moussa–Oualidia reduced-label experiment, the reported development budgets count every site-specific label used for fitting, early stopping, and calibration. The Merja Zerga blocked experiments separately quantify training-label sensitivity while retaining their blocked validation resources.

Earth doi: 10.3390/earth7040136

Authors: Carlos Roberto Ávila-Acosta Marivel Domínguez-Domínguez César Jesús Vázquez-Navarrete Rocío Guadalupe Acosta-Pech Pablo Martínez-Zurimendi

Mangroves are highly productive ecosystems due to their great capacity to store carbon, but they are also vulnerable to human activities and adverse environmental conditions. Their conservation is often constrained by the opportunity costs of shifting from traditional economic activities to blue carbon projects. This work analyzes the prices of the different carbon markets in ecosystems, compares them with the benefits obtained from other productive activities, and evaluates the viability of implementing carbon projects in mangroves. An exhaustive literature search is conducted to assess the carbon prices of ecosystems worldwide. The opportunity costs of mangrove carbon capture projects in Mexico are estimated from site-specific and regionally relevant economic data; additionally, broader national benchmarks are presented for contextual comparison but are not interpreted as direct opportunity costs where they do not represent realistic land-use alternatives for the mangrove sites analyzed. The price of carbon ranges from 4 to 86 USD per Mg CO2e. The most studied natural ecosystems are forests. The highest gross annual profit (GAP) from carbon sales is observed in Tabasco and Campeche. GAP with mangrove wood harvesting ranges from 628.0 USD ha−1 year−1 to 3917.7 USD ha−1 year−1. The highest GAP for crops is obtained for white corn in the state of Hidalgo. GAP of the economic activity of livestock ranges from 3167.59 USD ha−1 year−1 to 3365.71 USD ha−1 year−1. The blue carbon projects are competitive with other productive activities at relatively high prices (86 USD per Mg CO2e). In Tabasco, under certain high-price and high-sequestration scenarios, blue carbon projects can be competitive with local agricultural activities; however, this competitiveness is highly conditional on carbon price, sequestration rates, and local opportunity costs, and therefore cannot be generalized to all mangrove owners without site-specific appraisal. Fair carbon prices are required to make mangrove conservation projects attractive to producers.

Earth doi: 10.3390/earth7040135

Authors: Kun Li Peirui Liu Zhehao Huang Zilin Chen Junfeng Wang

Per- and polyfluoroalkyl substances (PFASs) are persistent synthetic chemicals of global concern. While most research has focused on their occurrence and toxicity, far less attention has been paid to their impacts on the global carbon cycle. This review synthesizes current evidence on how PFASs influence carbon cycling across soils, aquatic systems, and the atmosphere. In soils, PFASs alter organic carbon inputs by affecting plant biomass and root exudates and shift microbial community composition and enzyme activities, thereby modulating organic matter decomposition. In aquatic ecosystems, PFASs biologically impair carbon sequestration by inhibiting plankton, and abiotically interact with extracellular polymeric substances to prolong the cycling of dissolved organic carbon. The atmosphere acts as a key mediator as follows: thermal treatment of PFASs generates perfluorocarbons, potent greenhouse gases that exacerbate global warming and further disturb carbon cycling. Despite clear disruptive effects, major knowledge gaps remain. Future research should use quantitative structure–property relationship modeling to assess PFAS alternatives (e.g., PFHxS), and employ advanced molecular tracking (e.g., isotopic labeling, NanoSIMS) and machine learning to unravel nonlinear PFAS–carbon dynamics. Improved detection technologies are needed to identify greenhouse gas byproducts from PFAS thermal treatment. Ultimately, deploying high-resolution flux observation networks and integrating PFAS dynamics into Earth system models and carbon-accounting frameworks are critical for predicting carbon–climate feedback and supporting global carbon neutrality goals.

Earth doi: 10.3390/earth7040134

Authors: Jorge Luis Ojeda-García Francisco Guadalupe Echavarría-Cháirez Rómulo Bañuelos-Valenzuela Ricardo Alonso Sánchez-Gutiérrez Alejandro Espinoza-Canales Héctor Gutiérrez-Bañuelos

The abundant rainfall and rugged topography characteristic of southern Zacatecas promoted soil leaching. This differentiation in soil physicochemical properties, driven by leaching, results in higher-altitude areas having soils with high sand and aluminum (Al3+) content. The influence of altitude and soil units on acidity levels and the quantity of amendments required for the study region (the municipality of Momax) was determined. Samples were collected from sixty-one agricultural sites, based on pH data reported by INEGI in 1979 and variance estimated from an interpolated map. Principal component analysis was used to divide the samples into four contrasting groups. Group 4 exhibited the highest values for clay, organic matter, exchangeable cations, and cation exchange capacity (p < 0.05). Group 1 and 2 served as a transition zone; Group 3 showed the lowest pH values (mean of 4.7) (p < 0.05) and the highest levels of sand (65.5%), aluminum (0.83 cmol kg-1), and hydrogen (0.09 cmol kg−1) (p < 0.05). Given that 80% of the study area contains exchangeable aluminum, it is necessary to implement a future technological intervention plan, incorporating this study’s recommendations, as well as, the possibility of reducing cost by applying less CO3 amendment rates within a range from 0 to 1.76 t ha−1.

Earth doi: 10.3390/earth7040133

Authors: Nudthawud Homtong Jirawat Kasmanee

Agricultural drought threatens rainfed agriculture in northeast Thailand, where variable monsoon rainfall, limited irrigation access, and extensive cropland increase vulnerability. This study developed a Google Earth Engine-based Agricultural Drought Risk Index (ADRI) for the Chi River Basin using six benchmark years (2000, 2005, 2010, 2015, 2020, and 2025). CHIRPS precipitation, MODIS-derived vegetation health, ERA5-Land soil moisture, irrigation accessibility, and agricultural land exposure were normalized and integrated by weighted linear combination. The analysis quantified risk-class areas, irrigated–rainfed contrasts, persistent hotspots, weight sensitivity, and spatial agreement with the official Land Development Department recurring-drought map. Moderate risk dominated most years, but high-risk area expanded to 60.7% in 2015, coincident with severe rainfall deficits during the 2015–2016 El Niño event. Conditions improved in 2020 and 2025 as rainfall, vegetation health, and soil moisture recovered. Rainfed areas consistently had higher ADRI values than irrigated areas, and persistent hotspots were concentrated in southeastern and downstream agricultural zones. The principal spatial and temporal patterns remained stable under ±10% weight perturbations. External validation identified ADRI > 2.90 as the optimal threshold, with raster-level precision, recall, and F1 of 0.779, 0.884, and 0.828, respectively; the 998-point sample produced an F1 of 0.832. ADRI therefore provides a practical basin-scale screening framework for drought monitoring, adaptation prioritization, and agricultural water-management planning.

Earth doi: 10.3390/earth7040132

Authors: Fernando García-Ávila José Lalvay-Naula Verónica Tigre-Remache Irina Tapia-Peralta Diana Siguencia-Calle Rodrigo Mendieta-Muñoz Lorgio Valdiviezo-Gonzales

Natural resource management under climate change generates interdependent economic, social, and environmental impacts. However, the scientific evidence remains fragmented. This fragmentation limits the design of integrated policies capable of reducing vulnerability and preventing the degradation of natural capital. The objective of this study is to analyze recent scientific literature to assess how natural resource management in the context of climate change simultaneously influences economic stability, social resilience, and environmental sustainability. To this end, a systematic review of literature published in indexed journals on environmental economics, climate change, and natural resource management was conducted, selecting quantitative and mixed-methods studies that examine economic, social, or biophysical impacts associated with environmental degradation, extractive dependence, and adaptation and mitigation strategies. The review integrated research at macroeconomic, microeconomic, and ecological scales, organized using comparative matrices that allowed for the identification of common patterns in indicators of economic loss, emissions, natural capital depreciation, and effects on social welfare. Subsequently, a comparative analysis was conducted to detect relationships between management failures, social vulnerability, and long-term costs, as well as to identify conceptual, methodological, and geographical gaps in the literature. The results show that the degradation of natural resources under climate change produces simultaneous effects on macroeconomic stability, household income, and ecosystem resilience, increasing the costs of inaction when policies are designed sectorally. The evidence synthesized in this review indicates that dependence on extractive activities, limited productive diversification, and institutional weaknesses are frequently associated with greater economic and social vulnerability, particularly in communities dependent on natural resources. The reviewed studies also suggest that adaptation and mitigation strategies that incorporate participatory governance, social capital, and natural capital conservation may contribute to strengthening resilience. However, given the heterogeneity of methodologies, spatial scales, and indicators among the analyzed studies, these findings should be interpreted as evidence of consistent patterns rather than causal relationships. Therefore, integrated approaches that consider economic, social, and environmental dimensions represent a promising direction for sustainable natural resource management under climate change, although further empirical research is required to evaluate their effectiveness across different contexts.

Earth doi: 10.3390/earth7040131

Authors: Dahyra Sofía Mercado-Velasco María del Refugio Castañeda-Chávez Alejandro Granados-Barba Fabiola Lango-Reynoso Aracely Isabel Amaro-Espejo María de Lourdes Fernández-Peña Rosa Elena Zamudio-Alemán

Sandy beaches in the Central Region of Veracruz (CRV) face constant anthropogenic pressure from port and urban activities. This study aimed to evaluate total hydrocarbon (TH) concentrations in the intertidal interstitial water of five beaches in the CRV, analyzing their variability by depth (15 and 30 cm) and seasonality (northerly winds, dry, and rainy seasons). TH determination was performed using gas chromatography (GC-FID), following the NMX-AA-117-SCFI-2001 and NOM-138-SEMARNAT/SSA1-2012 standards. Results showed concentrations ranging from 0.86 to 6.53 µg L−1. Significant spatial differences were identified (p < 0.05); Antepuerto beach presented the highest levels due to its proximity to the port, while Farallón showed the lowest concentrations, confirming its role as a reference site. No significant variations were detected by depth or season (p > 0.05), indicating temporal stability associated with continuous anthropogenic inputs. Although levels comply with Mexican regulations, the continuous presence of TH represents a potential risk to benthic biota and the integrity of the Veracruz Reef System (SAV). This study provides a critical baseline for strengthening coastal ecosystem management strategies in the Gulf of Mexico.

Earth doi: 10.3390/earth7040130

Authors: Mehmet Ali Çelik Adile Bilik Figen Akpınar Yasin Paşa

This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) along precipitation gradients across Türkiye by integrating high-resolution satellite-based precipitation datasets (CHIRPS, TerraClimate, and TRMM) with the European Space Agency (ESA) WorldCover (10 m) product and multi-sensor Normalized Difference Vegetation Index (NDVI) composites (Landsat, MODIS, Sentinel-2). Türkiye’s heterogeneous climate, characterized by a sharp contrast between humid coastal belts and semi-arid interiors, serves as a natural laboratory to assess ecosystem responses to moisture availability. The results reveal a systematic and non-linear transformation of LULC classes as precipitation increases. In low-rainfall zones (200–400 mm), agricultural activities and bare surfaces predominate, reflecting human-induced land management in water-constrained environments. A critical ecological threshold was identified between 400 mm and 700 mm, where grassland areas expand rapidly, becoming the dominant class. Beyond the 900 mm isohyet, forest cover exhibits a sharp increase, approaching nearly 100% dominance in regions exceeding 1200 mm, effectively displacing other LULC categories. Comparative analysis of precipitation products shows that while all datasets capture the “coastal-wet/inland-dry” pattern, TRMM tends to overestimate winter precipitation (exceeding 100 mm), whereas CHIRPS and TerraClimate provide more conservative estimates (75–80 mm). Overlay analyses between seasonal NDVI and precipitation confirm a pronounced “time-lag effect” in vegetation phenology. Despite peak precipitation occurring in winter (~75 mm), NDVI reaches its minimum (~0.03) due to thermal limitations and dormancy. Conversely, vegetation greenness peaks during the dry summer months (NDVI ~0.14 to 0.40), utilizing antecedent soil moisture stored during the spring recharge. High-resolution Sentinel-2 data proved superior in delineating micro-topographic vegetation responses compared to Landsat and MODIS. These findings provide a scientific baseline for sustainable land management and climate adaptation strategies, highlighting that precipitation thresholds are the primary determinants of Türkiye’s ecological boundaries.

Earth doi: 10.3390/earth7040129

Authors: Carmela Vennari Graziella Emanuela Scarcella Loredana Antronico Deborah Biondino Francesco Chiaravalloti Roberto Coscarelli

Mediterranean coastal regions are increasingly affected by hydrometeorological hazards associated with high-impact weather events, including cyclones. Between 18 and 21 January 2026, the intense extratropical cyclone Harry affected Sicily, Sardinia, and Calabria, producing severe weather conditions including heavy precipitation, strong winds, and extreme wave activity. This study investigates both the meteo-marine characteristics of the event and its associated damage in Calabria, where the cyclone triggered multiple hazards (wave storms, landslides, flooding, and strong winds). Meteo-marine forcing was characterized using integrated rainfall data, wave parameters, and wind data. In situ observations, radar-derived precipitation estimates, satellite measurements, and model-based reanalysis products were combined to provide a comprehensive evaluation of the event. A georeferenced database of 195 damage records was compiled and classified according to the EU Floods Directive (2007/60/EC), allowing spatial analyses within a GIS framework. Although the cyclone produced exceptional rainfall totals, locally exceeding 580 mm in 90 h, the distribution of impacts reveals the predominance of coastal processes. Wave storm-related damage accounted for 68% of all recorded impacts, mainly affecting transportation and communication infrastructures, tourism facilities, and population. The prevalence of coastal damage appears to be linked not only to the intensity of marine forcing but also to its persistence which locally exceeded the maximum climatological persistence, suggesting that event duration plays a critical role in determining impact severity. Geomorphological analyses indicate that short-term coastal vulnerability is influenced not only by long-term shoreline evolution but also by local topographic characteristics and exposure to marine forcing. These findings contribute to improving risk assessment and mitigation strategies for Mediterranean coastal regions under a changing climate.

Earth doi: 10.3390/earth7040128

Authors: Eduardo Antonio Muñoz-Jácome Pedro Vicente Vaca-Cárdenas Carlos Cajas-Bermeo Purificación Galindo Villardón Leticia Vaca-Cardenas Roberth Alcivar-Cevallos Marcela Yolanda Brito-Mancero Guicela Margoth Ati-Cutiupala Karen Lizbeth Yumi-Criollo Maritza Lucia Vaca-Cárdenas Diego Francisco Cushquicullma-Colcha Francesco Chiaravalloti

Climate change and glacial retreat in the tropical Andes—as evidenced by environmental changes in the Carihuairazo volcano area and human-induced alterations to the páramo—reveal complex environmental dynamics that require an integrated understanding. This study evaluates edaphic variation along a periglacial–agricultural spatial gradient, relating soil properties to sustainable management strategies. Using a methodological approach that includes multi-criteria spatial delineation, altitude-stratified sampling, and multivariate modeling via HJ-Biplot, the physical, chemical, and biological properties were analyzed, with a focus on basal microbial respiration. The data show that periglacial soils exhibit geochemical–mineral control and high basal respiration, potentially influenced by moisture peaks, despite their low organic matter content. In contrast, lowland andisols exhibit biological–structural control conditioned by organic matter accumulation, reflecting distinct conditions associated with agricultural management and land use. It is concluded that understanding these spatial edaphic patterns and their vulnerability to human intervention is essential for designing sustainable management and conservation frameworks that mitigate the impact of climate change on high-mountain ecosystems.

Earth doi: 10.3390/earth7040127

Authors: Andrés Caballero-Calvo Yolanda Jiménez Olivencia Raúl Pérez-Arévalo

Over the past two decades, the notion of landscape observatories has gained prominence as a strategic tool for monitoring, documenting and interpreting landscape change. These entities combine scientific research, policy advice and public engagement, aiming to bridge the gap between territorial knowledge and decision-making. This paper presents a systematic review of the scientific literature on landscape observatories, complemented by an original comparative database of 188 landscape observatories and related initiatives worldwide. Bibliographic searches were conducted in Web of Science, Scopus and ProQuest, using the term “Landscape Observatory” and related expressions. The quantitative analysis reveals a steady growth of publications since the early 2000s, with a marked concentration in Europe, particularly France, Italy, and Spain, where the implementation of the European Landscape Convention fostered institutionalisation. Regional-scale observatories dominate the dataset, while national examples illustrate standardised approaches to landscape monitoring. Methodological diversity is evident, as most initiatives rely on GIS and remote sensing, while others emphasise photographic documentation or participatory perception studies. Despite this richness, the review identifies persistent gaps in impact evaluation, long-term institutional sustainability and methodological integration across physical and social dimensions of the landscape. Landscape observatories are thus positioned as promising but still evolving instruments for multi-scalar governance, capable of connecting observation, policy and collective awareness. To consolidate their role, future efforts should focus on harmonising indicators, ensuring continuity beyond political cycles, and promoting interdisciplinary and participatory frameworks that capture both the material and experiential facets of landscape transformation.

Earth doi: 10.3390/earth7040126

Authors: François Duse Dukuku Médard Mpanda Mukenza John Kikuni Tchowa Joel Mobunda Tiko Julien Bwazani Balandi Jan Bogaert Dieu-donné N’tambwe Nghonda Yannick Useni Sikuzani

Understanding how land-use change affects habitat fragmentation and connectivity is essential for assessing landscape degradation and conservation effectiveness in protected areas globally. It is particularly acute in tropical protected areas where anthropogenic pressures are intensifying. This study investigated long-term landscape dynamics, forest fragmentation, and structural connectivity in the Bena Mulumbu Hunting Domain, a Category VI protected area located in the Miombo woodland region of southeastern Democratic Republic of the Congo. Landsat imagery acquired in 1995, 2005, 2015, and 2025 was classified using the Random Forest algorithm into six land-cover classes (Miombo woodland, savanna, agricultural land, mining areas, built-up/bare land, and water bodies) to quantify land-cover changes over 30 years. Landscape composition was assessed using the percentage of landscape (PLAND), Shannon diversity metrics, and transition analyses. At the same time, fragmentation and structural connectivity of Miombo woodland were evaluated along an edge-to-core gradient (0–2 km, 2–4 km, 4–6 km, and >6 km) using landscape metrics. Results showed that savanna remained the dominant land-cover type throughout the study period. However, the landscape underwent progressive reorganization characterized by recurrent transitions among Miombo woodland, savanna, and agricultural land, leading to increased spatial heterogeneity. Fragmentation analyses revealed significant spatial differences in total core area among zones (Kruskal–Wallis: H = 8.12, p = 0.044); however, after normalization by zone area, no consistent edge-to-core gradient was observed for core habitat proportion, indicating that raw differences primarily reflect zone size rather than a systematic ecological gradient. Despite increasing fragmentation, structural connectivity remained high across the hunting domain. The CONNECT index increased significantly from the edge toward the core zone (p = 0.003), highlighting better-connected forest networks in interior sectors. These findings suggest that the Bena Mulumbu Hunting Domain is experiencing an intermediate stage of landscape transformation, where forest fragmentation is evident but has not yet resulted in widespread connectivity loss. Maintaining existing forest cores and connectivity corridors should therefore be prioritized to prevent further degradation of ecological integrity. These findings challenge the assumption that landscape degradation in protected tropical Miombo woodlands necessarily follows a simple edge-to-core gradient.

Earth doi: 10.3390/earth7040125

Authors: Muhammad Zeeshan Ali Mohammed Benaafi Mahfuzur Rahman Golden Odey Husam Musa Baalousha

The overexploitation of groundwater resources is a significant concern due to the potential risks associated with a decline in freshwater availability. Future planning and policymaking should consider long-term groundwater availability and urban expansion patterns to understand urban growth. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the land surface temperature (LST) and vegetation change using Normalized Difference Vegetation Index NDVI analysis have been performed to find the spatial spread of urbanization and its impact on surface temperature in the area. For groundwater assessment, the Gravity Recovery and Climate Experiment (GRACE) data have been used, while for land cover, NDVI, and LST assessment, Landsat data have been used. The GRACE-based groundwater storage (GWS) anomaly has been correlated with Global Precipitation Measurement (GPM) data. An annual groundwater storage decline of ~7.01 mm/year was identified. Groundwater and land-cover changes were evaluated at five-year intervals from 1990 to 2025. The urban expansion from 838 to 1470 km2 coverage shows the rapid expansion and its impact on vegetation and groundwater recharge in the area. The results demonstrate a rapid increase in the urban area, which affected the vegetation and increased the surface temperature in the area. Urban expansion reduced vegetation cover and infiltration, contributing to elevated land surface temperature and groundwater depletion. This study focused on integrating the groundwater impacts due to other environmental variables, i.e., temperature increase and vegetation decrease. The temporal increase in urban expansion decreases the infiltration rate, which impacts the groundwater storage and depletion, as shown by the linear trend. These findings underscore the urgent need for effective groundwater management and vegetation management policies and integrated urban planning strategies to ensure the long-term sustainability of freshwater resources.

Earth doi: 10.3390/earth7040124

Authors: Hyun-Sil Shin Xiongzhi Hu

Non-grain production of cultivated land has increasingly affected regional food security and the stability of agricultural ecosystems. In traditional rice-producing regions, changes associated with non-rice cultivation, fallow land, rice-fishery integrated farming, and intensive agricultural management are reshaping the spatial structure of paddy landscapes. To identify the long-term spatiotemporal evolution of paddy systems, this study investigated Northern Jiangxi, China, using Landsat surface reflectance imagery from 2000, 2005, 2010, 2015, and 2020 on the Google Earth Engine (GEE) platform. The Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) were used to construct a phenology-based Flooding Frequency (FF) indicator. Based on the annual frequency with which pixels satisfied the condition LSWI > EVI, cultivated land was classified into three categories: non-flooded cropland, standard rice paddy, and high-frequency flooded cropland. In this study, non-flooded cropland was used as an indicator of potential non-rice cultivation rather than as direct evidence of confirmed non-grain production. Landscape metrics, transition matrices, gravity center migration, standard deviation ellipses, and geographically weighted regression (GWR) were then used to examine paddy landscape dynamics, fragmentation patterns, and county-level spatial associations with socioeconomic factors. The results suggest that the paddy system in Northern Jiangxi experienced marked stage-based fluctuations between 2000 and 2020. Standard rice paddy recovered during 2005–2010, whereas non-flooded cropland expanded considerably during 2010–2015, accompanied by intensified paddy landscape fragmentation. Non-flooded cropland was mainly distributed around urban fringes, transport corridors, and some hilly margins. Standard rice paddy was concentrated in traditional grain-producing areas, including the Poyang Lake Plain and the Gan-Fu Plain. High-frequency flooded cropland was primarily located in low-lying lake areas, where its dynamics were likely associated with rice-fishery integrated farming, continuous irrigation, and hydrological fluctuations. Landscape metrics showed that the largest patch index and mean patch size of standard rice paddy declined after 2010, indicating reduced spatial continuity of core paddy fields. The GWR analysis provided auxiliary evidence that total population, per capita gross domestic product (GDP), and urbanization rate were spatially associated with changes in non-flooded cropland at the county level; however, the results should be interpreted as exploratory associations rather than causal mechanisms. Overall, paddy landscape change in Northern Jiangxi was expressed not only through changes in cultivated land area, but also through the reorganization of paddy function, spatial continuity, and land-use intensity. Future cropland protection should therefore move beyond area-based control toward integrated management of quantity, quality, function, and spatial configuration. Future research should further verify these findings using dynamic cropland boundaries, higher-resolution imagery, and more detailed socioeconomic data.

Earth doi: 10.3390/earth7040123

Authors: Xiaohe Liang Jiayu Zhuang Jiajia Liu Qi Wang Ailian Zhou

Nature-based solutions (NbS) are increasingly recognized as important strategies for advancing agricultural sustainability and supporting the United Nations Sustainable Development Goals (SDGs). Much research has highlighted the biophysical potential of NbS; however, the literature remains fragmented regarding their socio-economic and governance implications. This study synthesizes existing research, identifies major knowledge structures, and develops a forward-looking research agenda. We analyzed 1198 academic articles using Structural Topic Modeling (STM) to identify ten latent topics. These topics were subsequently mapped onto the adapted Agro-Ecotopia model based on two dimensions: Ecosystem State & Control and Governance Logic & Goal Alignment. This approach established an Agro-NbS analytical framework and identified four exploratory scenarios: Green Regulation, Engineered Ecotopia, Vulnerable Wilderness, and Grassroots Resilience. These scenarios characterize diverse pathways of agricultural NbS development, highlighting potential spatial trade-offs and emerging system vulnerabilities associated with social equity. Furthermore, this study extends traditional biophysical assessments by emphasizing the importance of polycentric governance and Traditional Ecological Knowledge (TEK) in addressing complex climate challenges. The findings contribute to the understanding of agricultural Socio-Ecological Systems (SESs) and provide insights for policymakers seeking to promote a more equitable transition toward sustainable agriculture.

Earth doi: 10.3390/earth7040122

Authors: Aldo A. Tapia Andrew Bennett

Crop monitoring is an important aspect of agricultural management, as it provides insights into cultivated area, crop health, growth patterns, and yields potential. Mapping cultivated areas and identifying crop types was historically conducted through field surveys and manual mapping, which are time-consuming and labor-intensive. Remote sensing classification has transformed large-scale land cover mapping, including crop identification. This work aims to: (1) compare the performance of Dynamic Time Warping (DTW) and two machine learning families (artificial neural networks and decision trees) for crop classification using Sentinel-2 data; (2) assess whether reflectance data, spectral indices, or both yield better classification results; and (3) evaluate the effect of hyperparameters on model performance. Among the DTW variants evaluated, dynamic time warping without a time constraint performed the best, with an overall accuracy of 0.921 using the combination of both reflectance and spectral indices. Most machine learning methods outperformed DTW. Although the convolutional neural network reached the highest single accuracy (0.948), the transformer was selected as the best model overall (accuracy of 0.944), as it combined a comparable accuracy with the lowest sensitivity to hyperparameter variations, making it a reliable option when testing machine learning architectures applied to crop mapping. This work also provides insights for model architecture development based on an exhaustive hyperparameter search for the machine learning models.

Earth doi: 10.3390/earth7040121

Authors: Sri Nurdiati Mohamad Khoirun Najib Elis Khatizah Lailan Syaufina Mirza Farhan Azhari Raihan Akbar

Wildfires pose increasing environmental and socio-economic risks, particularly in climate-sensitive and tropical regions, necessitating reliable satellite-based monitoring and predictive frameworks. This study presents a comprehensive survey of satellite-derived wildfire indicators and spatiotemporal modeling approaches, covering their historical development, current methodologies, and emerging research directions. We review major active fire and hotspot datasets derived from MODIS, VIIRS, and related platforms, along with key environmental drivers such as vegetation indices, meteorological variables, and land-surface with a specific case study for the Indonesian region. Modeling approaches are synthesized from classical statistical regression and time-series analysis to contemporary machine learning and deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer-based models. The analysis highlights the transition toward multi-source data integration and spatiotemporal deep learning frameworks capable of capturing complex wildfire dynamics. Finally, we identify future research challenges, including hybrid physical–AI modeling, uncertainty quantification, and scalable real-time wildfire intelligence systems. This survey provides a structured reference for researchers and practitioners seeking to advance satellite-based wildfire monitoring and prediction.

Earth doi: 10.3390/earth7040120

Authors: Miho Awamura Shin-ichi Onodera Kelly Tiku Tarh Mitsuyo Saito Sharon Bih Kimbi

The objective of this study was to propose a simple, low-cost in-situ remediation method for NO3−-N that effectively utilizes natural denitrification processes. We verified the inflow of surrounding groundwater containing high concentrations of NO3−-N when groundwater at the denitrification depth was pumped, as well as the denitrification effect at that depth, under two pumping flow-rate conditions (low and high) at a site where denitrification had been confirmed. The results suggest that pumping groundwater at the denitrification depth enables the inflow of surrounding groundwater, thereby enabling denitrification of high-concentration NO3−-N in the surrounding groundwater under oxidizing conditions. The denitrification amounts were 72 mg-N/h for the high-flow Pumped Denitrification Test (PDT) and 3.3 mg-N/h for the low-flow PDT. Additionally, the nitrate removal efficiency of the high-flow PDT was higher than the results obtained in previous studies at the same site and season under natural groundwater flow. It was also comparable to that of artificially created denitrification environments at other sites when assuming conditions with high NO3−-N concentrations in shallow groundwater. This study demonstrated that the pumping of reductive groundwater transports high concentrations of NO3−-N along with the surrounding groundwater, and that denitrification occurs without impairing denitrification capacity.

Earth doi: 10.3390/earth7040119

Authors: Uroš Durlević Velibor Ilić Milan M. Radovanović Ana Milanović Pešić Marko D. Petrović Milan Milenković Jasmina M. Jovanović Emin Atasoy

Long-term wildfire susceptibility mapping represents a significant component of disaster prevention and the protection of human communities, public health, and local ecosystems. In this study, a wildfire inventory was developed through multi-sensor fusion of satellite data (MODIS and VIIRS), comprising 153,305 fire events across China for the period 2001–2024. In addition to historical incidents, 14 predictive variables were processed, representing geomorphological, climatological, hydrological, vegetative, and anthropogenic conditions. This study evaluates long-term spatial wildfire susceptibility based on long-term mean environmental and climatic conditions. Methodologically, the research applies six models from machine learning (ML), deep learning (DL), and transformer-based approaches: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), Fourier Multi-Layer Perceptron (F-MLP), Kolmogorov–Arnold Network (KAN), and Feature Tokenizer (FT) Transformer. The results were integrated into an ensemble susceptibility map with a spatial resolution of 500 m using Geographic Information Systems (GIS), indicating that 7.4% of China’s territory is classified as having a very high wildfire susceptibility. In addition to the national-scale assessment, a local differentiation was conducted across 34 province-level divisions, revealing that Fujian Province (86.8%) and the Guangxi Zhuang Autonomous Region (82.9%) had the largest shares of areas classified as high and very high wildfire susceptibility. Performance evaluation under spatial block-based validation demonstrated that the Random Forest model achieved the highest predictive power, with an area under the curve (AUC) of 87.8%, followed by XGBoost (87.3%) and Fourier MLP (86.6%). Based on the combined SHAP (Shapley additive explanations) analysis of all applied models, soil moisture, elevation, and terrain slope were identified as the most influential factors affecting wildfire occurrence in China. Overall, the findings contribute to more effective wildfire prevention and risk management strategies at both the local and national levels.

Earth doi: 10.3390/earth7040118

Authors: Yu Feng Batunacun Chang An Boyu Wang Yong Mei Dandan Zhou Kaixin Liu

Grassland degradation (GD) varies markedly across space. Identifying potential drivers at the county level enables precise grassland conservation and supports a win–win between economic development and ecological protection. However, most existing studies adopt a single, region-wide lens and lack county-level analyses. Focusing on Ordos, we conduct a county-level assessment and rank potential driver groups using partial-order theory. The results indicated the following: (1) from 2000 to 2020, a total of 6.9% (6026 km2) of grassland was restored, while approximately 5.0% (4372 km2) underwent degradation, with grassland recovery outpacing degradation; (2) urbanisation and economic development were identified as the leading drivers in five counties, followed by human activities and climate (three), and livelihood development (one); and (3) Ordos should adopt county-level differentiated management strategies: controlling urban and industrial expansion in urbanisation and economic-development-dominated counties, regulating grazing and land-use activities in human-activity-dominated counties, implementing dynamic grazing bans and drought preparedness in climate-dominated counties, promoting livelihood diversification in livelihood-development-dominated counties, and applying priority-based integrated governance in multi-driver counties.

Earth doi: 10.3390/earth7040117

Authors: Angie Riascos-España Heiber Andres Trujillo Fernando H. Silva García Jairo H. Mosquera Guerrero Claudia E. Salazar González Pedro A. Velasquez-Vasconez

Agricultural sustainability assessment is increasingly required to characterize how food production systems interact with land, soil, water, carbon dynamics, and broader environmental change. However, the extent to which scientific assessment methods capture these environmental-system interactions remains unclear. This study mapped methodological and thematic trends in agricultural sustainability research through text mining of 3302 bibliographic records retrieved from the Web of Science Core Collection, which was selected because of its standardized metadata structure and suitability for reproducible text-mining analysis, covering publications from 2003 to 1 March 2025. After corpus preprocessing and tokenization, term-frequency analysis, dimension-specific lexical classification, co-occurrence networks, and temporal bibliometric trends were used to identify dominant environmental themes and assessment approaches. The results revealed a clear predominance of the environmental dimension in the analyzed literature, particularly through terms associated with land, carbon, soil, and water resources, whereas social and economic dimensions displayed lower lexical representation. Food, production, and systems formed a central semantic cluster linking environmental assessment with food security. Life Cycle Assessment (LCA) was the most frequently identified methodology, reflecting the prominence of impact-oriented environmental evaluation. In contrast, integrative and farm-scale frameworks, including Driver–Pressure–State–Impact–Response (DPSIR), Sustainability Assessment of Food and Agriculture Systems (SAFA), and the Tool for Agroecology Performance Evaluation (TAPE), among others, indicated increasing attention to governance, resilience, and agroecological transitions. These findings show that text mining can support environmental research by identifying methodological biases and emerging priorities in agriculture–environment interactions. Strengthening integrated assessment approaches will be essential for managing natural resources and supporting resilient and environmentally sustainable food systems.

Earth doi: 10.3390/earth7040116

Authors: Tsomo Wangchuk Rohan Mukerjee James D. Ford Anita Varghese

This study examines how climate change interacts with social, ecological, and policy factors to shape food security among Indigenous tribal communities in Jharkhand, focusing on Saraikela Kharsawan district. It combines a scoping review, policy analysis, and a climate–agriculture case study of Saraikela Kharsawan to identify vulnerabilities and pathways for more resilient Indigenous food systems. The research is qualitative, using a scoping review of 28 studies on Indigenous food security and climate impacts in Jharkhand, thematic analysis of nine national and state policies, and a district-level case study using land use, climate trends/projections, and crop statistics for Saraikela Kharsawan. Additionally, findings from participant observation were integrated into how tribal communities in Saraikela Kharsawan experience and respond to climate variability and its implications for local food systems and nutrition. The study identifies a nutrition paradox, where Indigenous communities experience micronutrient deficiencies and anaemia despite rich biodiversity and Indigenous knowledge. This is accompanied by a decrease in the consumption of nutrient-dense Indigenous foods and a predominance of rainfed monoculture rice cultivation. Marked by rising temperatures and erratic rainfall, climate variability is destabilising agroforestry systems, narrowing dietary options and reducing adaptive capacity. Additionally, policy and institutional gaps reveal fragmented support—strong rights laws and calorie-focused welfare schemes but weak integration of Indigenous foods, agroforestry, and traditional ecological knowledge into nutrition and climate programmes. The paper argues that climate change acts as a threat multiplier on already fragile Indigenous food systems and calls for nutrition-sensitive safety nets, community-based agroforestry, gender-inclusive Indigenous knowledge governance, and cross-sectoral policy alignment to support resilient, culturally appropriate food systems in Jharkhand.

Earth doi: 10.3390/earth7040115

Authors: Jemma Nolan Azza Silotry Naik

Freshwater is essential for sustaining the health of humans, animals, and ecosystems; however, agricultural activities remain a major source of water pollution globally. This review examines how crop production, livestock farming, and aquaculture contribute to water contamination, the effectiveness of current European policies, and the potential of sustainable mitigation strategies. Evidence from the research identified pesticides, herbicides, veterinary antibiotics, nutrient runoff, aquaculture effluents, and microplastics as the primary agricultural pollutants affecting surface and groundwater quality. These contaminants have been linked to ecosystem degradation, biodiversity loss, endocrine disruption, antimicrobial resistance, and adverse human health outcomes. Despite extensive regulatory frameworks, including the Water Framework Directive, Nitrates Directive, Farm to Fork Strategy, and European Green Deal, significant implementation and monitoring challenges remain. Current evidence indicates that only 40% of European surface waters achieve “good” ecological status, highlighting persistent water quality concerns across the region. The review further identified precision irrigation, Internet of Things (IoT)-enabled monitoring, biopesticides, hydroponic systems, and integrated multi-trophic aquaculture as promising solutions for reducing agricultural impacts on water resources. However, barriers, including high implementation costs, technological limitations, and inconsistent policy enforcement, continue to hinder widespread adoption. Overall, the findings demonstrate that while existing policies have improved water governance, stronger regulatory enforcement, greater investment in sustainable technologies, and increased adoption of nature-based solutions are required to reduce agricultural water pollution. An integrated approach combining technological innovation, policy support, and sustainable farming practices is essential to protect freshwater resources and ensure long-term environmental sustainability.

Earth doi: 10.3390/earth7040114

Authors: Roberta Farina Muhammad Ilyas Mariangela Diacono Claudia Di Bene Valentina Baratella Claudia De Santis Ulderico Neri Alessandro Persiani Francesco Montemurro Chiara Piccini Carlos Alberto Torres-Guerrero Silvia Vanino

Mediterranean agroecosystems are highly vulnerable to climate change, soil degradation, and declining soil organic carbon (SOC), threatening long-term agricultural sustainability. Carbon farming and regenerative agriculture have emerged as complementary approaches to restore soil functionality while contributing to climate change mitigation. This review synthesizes peer-reviewed literature published between 2015 and 2025 to assess the agronomic effectiveness of key regenerative and carbon farming practices in Mediterranean systems. A structured bibliographic analysis using Scopus and Web of Science evaluated practices influencing SOC dynamics, erosion control, water regulation, and associated ecosystem services. Evidence indicates that the introduction of cover crops in the crop rotation and reduced or no-tillage are the most consistently effective practices for enhancing SOC stocks, particularly when combined with organic amendments and diversified rotations. Crop diversification, intercropping, and agroforestry further support SOC accumulation and erosion control, especially in perennial systems such as vineyards and olive orchards. Organic inputs stimulate microbial-mediated carbon stabilization, while regenerative grazing contributes to nutrient cycling under context-specific conditions. Across practices, integrated management consistently delivers greater and more stable benefits than single interventions. Regenerative agriculture thus provides a systems-based foundation for carbon farming in Mediterranean agroecosystems. Long-term field experiments and improved monitoring frameworks remain essential to quantify carbon persistence and support policy implementation.

Earth doi: 10.3390/earth7040113

Authors: Rifdah Octavi Azzahra Afina Aristiani Zahra Bintang Lamra Soetopo Muhammad Dimyati Iwa Garniwa Hyunjin Lee Josaphat Tetuko Sri Sumantyo Pranda Mulya Putra Garniwa

Accurate and spatially representative estimation of Global Horizontal Irradiance (GHI) is critical for solar energy planning in tropical regions characterized by strong atmospheric variability and complex topography. This study aims to evaluate the performance and robustness of four semi-empirical satellite-derived GHI models, Beyer, Perez, Hammer, and Rigollier, under heterogeneous tropical conditions in West Java, Indonesia. Hourly GHI data for 2022 were derived from GK2A satellite observations and validated against ground measurements from eight stations representing coastal, lowland, and mountainous areas. Model performance was assessed at annual and seasonal scales using relative Root Mean Square Error (rRMSE) and relative Mean Bias Error (rMBE). The results show significant variability in model performance across locations, with the average annual rRMSE computed per model and averaged over the eight stations being similar among models: 41.10% (Perez), 41.18% (Beyer), 42.44% (Hammer), and 42.49% (Rigollier). Perez showed the most consistent performance, with station-level rRMSE values ranging from 35.36% to 43.32% and rMBE ranging from −18.20% to 22.09%. Seasonal analysis indicates higher errors during the rainy season, 41.16% (Perez), 45.23% (Beyer), 42.74% (Hammer), and 46.34% (Rigollier), while lower errors were observed during the dry season, particularly for Beyer (36.16%) and Rigollier (36.29%). Spatial analysis indicates higher irradiance in coastal and lowland areas compared to mountainous regions. These findings emphasize the importance of climate- and topography-aware model selection for reliable solar resource assessment in tropical environments.

Earth doi: 10.3390/earth7040112

Authors: Bosy A. El-Haddad Ahmed M. Youssef Alaa Ramadan El-Sayed M. Robaa Shaymaa Rizk

Long-term monitoring of surface-water dynamics in hyper-arid reservoir systems requires consistent remote-sensing methods that can distinguish open water from bright desert surfaces, shallow water, wet sand, and mixed shoreline pixels. This study evaluates Landsat-derived spectral water indices for delineating surface water in Lake Nasser and the adjacent Tushka Lakes, generates a multi-decadal record of surface-water extent using Google Earth Engine, and places the resulting surface-water patterns in the context of available hydrogeological observations. Landsat TM and OLI surface reflectance imagery was used to compare seven commonly applied water indices (NDWI, EWI, NDX, WRI, AWEInsh, TCW, and NWI) based on mapped water area, relative area differences, and classification accuracy metrics derived from 1000 stratified reference samples. Among the tested indices, NDWI provided stable water–land separation (overall accuracy ≈ 93.6%; κ ≈ 0.898) and was selected for long-term mapping. The NDWI-based workflow was implemented in Google Earth Engine to generate quarterly composites of surface-water extent for the period 1987–2026. The resulting time series reveals stable, persistent surface water in the central and southern sectors of Lake Nasser, in contrast to pronounced seasonal and interannual variability in the shallow, intermittently connected Tushka basins. Total mapped water area increased from 2631 km2 in 1987 to 8923 km2 in early 2026, with Lake Nasser ranging from 2411 to 6060.7 km2 and the Tushka Lakes expanding from no mapped water before 1998 to more than 3300 km2 during 2025. To assess possible surface–subsurface interaction, daily lake-stage records (1965–2014) and monthly groundwater levels from 44 observation wells were used to estimate potential seepage losses from Lake Nasser to the Nubian Sandstone Aquifer System using Darcy’s law. Annual seepage estimates ranged from 15.58 × 106 to 36.68 × 106 m3/year, suggesting spatial variability in potential lake–aquifer seepage along the western lake margin. The combined remote-sensing and hydrogeologic results provide complementary, non-causal evidence for interpreting where surface-water persistence and estimated seepage may co-occur. Because spatial correlation analysis, calibrated ground-water modeling, full water-budget analysis, and independent field validation were not performed, the inferred seepage–surface-water relation should be regarded as a cautious hypothesis rather than proof of causality.

Earth doi: 10.3390/earth7040111

Authors: Caihong Yu Ru Liu Wei Huang Zhubin Zheng Shifan Qiu Chunmei Xiao Wenshuo Yu Manzhu Cai Yang Liu Lihong Meng

Seasonal delineation represents a critical interface between the natural environment and human activities. However, the conventional climate-temperature (C–T) method, which relies solely on thermal thresholds, has limited applicability in regions with complex climatic regimes. In this study, we develop and apply a composite seasonal index (CSI) integrating temperature and precipitation, using meteorological observations from 298 stations across mainland China during 1980–2020, with CSI calculation based on 278 stations that had valid paired temperature and precipitation records, to investigate spatial patterns of seasonal variability. The results show that incorporating precipitation improves the identification of regional heterogeneity in seasonal dynamics. In northeastern and northwestern China, spring rainfall advances spring onset, while autumn rainfall delays autumn termination, producing a CSI–defined spring duration 1–2 months longer than that derived from the C–T method and an autumn duration about one month longer. In some arid regions, concentrated precipitation prolongs summer by approximately 1–2 months. An independent comparison with land surface phenology metrics during 1982–2018 further shows that CSI–derived seasonal transition dates are broadly consistent with the spatial patterns of SOS, maturity, senescence, and EOS, especially in monsoonal and hydrothermally complex regions. Differences between the CSI and C–T methods are generally small (approximately ±1 month) where precipitation and temperature vary synchronously, but increase to approximately ±2 months where precipitation exerts stronger control. Overall, the CSI preserves the structure of the traditional C–T classification while accounting for hydrological influences, thereby enhancing seasonal delineation in climatically Eastern Monsoon Region and improving the ecological interpretability of hydrothermal seasonality assessment.

Earth doi: 10.3390/earth7040110

Authors: Mostafa El-Nazer Ali Wheida Amira Mostafa Moetasm H. ElTaweel M. M. Abdel Wahab Guillaume Siour Stephane C. Alfaro

The continuum of arid and semi-arid lands spanning from the western coast of the Sahara to the Chinese deserts (the Dust Belt) contains the most active dust sources on Earth. Understanding how their emissions are influenced by human activities and by natural climate variations is crucial for the prediction of our future climate. This work analyzes the correlation between the decadal variability in the dust surface concentrations in 23 representative sub-regions of the Dust Belt and 6 major climate oscillations. A simple parameterization assuming that this evolution can be considered a linear temporal trend, due to human activities but modulated by the effects of the natural climate variability, reproduces well (R2 > 0.70) the variations in the concentrations at 18 locations. In the western part of the Sahel, concentrations are large, decreasing, and influenced by the North Atlantic Oscillation (NAO, positive correlation). Conversely, concentrations increase in East and Northeast Africa where the Pacific decadal Oscillation (PDO) and the East Atlantic/Western Russia oscillations (EAWR, negative correlation) play a leading role. In Asia, the situation is more contrasted: temporal trends can be positive or negative, and are mostly modulated by the NAO (in the west) or by the EAWR (positive or negative correlation) in the south and east.

Earth doi: 10.3390/earth7040109

Authors: Bayron Alexander Ruiz-Blandon Rosario Marilu Bernaola-Paucar Bertha Carolina Sotelo Alcántara Leonor Neda Carbajal Cuadros Kenyi Paul Hinostroza Mendoza Julián Leonardo Mantari Mallqui Yubel Mayela Carrasco Nuñez Hebert Ernesto Ramos Acuña

Teak plantations are widely promoted as productive forest systems with potential contributions to carbon storage and climate-oriented land management. However, plantation carbon estimates often rely on generic biomass-to-carbon conversion factors, which may overlook variation in carbon concentration among tree components. This study refined carbon estimates in tropical teak plantations in Nayarit, western Mexico, by combining pantropical allometry with measured carbon fractions. Carbon concentration was determined in leaves, branches, stem, roots, and total biomass, and carbon stocks were compared using the generic 0.47 factor and measured total biomass carbon fractions. Carbon concentration differed among biomass components, with leaves and branches remaining below 47%, while stem, roots, and total biomass exceeded this value. The measured total biomass carbon fraction averaged 48.24%, producing refined carbon estimates that were consistently higher than those obtained with the generic factor. Across plantation-age records, the refined approach increased carbon estimates by 1.33 Mg C ha−1, equivalent to a mean relative adjustment of 2.67%. When projected as an illustrative scenario, this difference represented 133, 665, and 1330 Mg C over 100, 500, and 1000 ha, respectively. These findings show that measured carbon fractions can reduce one source of conversion-related uncertainty and refine plantation-level carbon estimates. Broader regional application would require larger and more representative plantation inventories.

Earth doi: 10.3390/earth7040108

Authors: Walter Manuel Hoyos-Alayo Jorge Luis Leiva-Piedra Emilio Ramirez-Juidias José-Lázaro Amaro-Mellado

Climate change is intensifying droughts, heatwaves, and hydrological extremes, increasing crop vulnerability and threatening global food security. This study analyzes the scientific evolution of research on remote sensing-based crop vulnerability to climate, focusing on temporal trends, geographical patterns, thematic structures, remote sensing data, and methodological approaches. A quantitative, exploratory, descriptive, longitudinal, and retrospective bibliometric analysis was applied to 2343 Scopus-indexed documents published between 1985 and 2026. Bibliometrix 5.1.1 and VOSviewer 1.6.20 were used to assess productivity, collaboration, intellectual structure, keyword co-occurrence, thematic evolution, and Reference Publication Year Spectroscopy. Results show sustained growth, with a 4% annual growth rate and a sharp acceleration after 2015, reaching 487 publications in 2025. This trend reflects a transition from descriptive crop monitoring toward predictive and operational geospatial intelligence. China, the United States, and India lead scientific production, while specialized journals concentrate dissemination. The most common remote sensing data and indicators include NDVI, MODIS, Landsat, Sentinel imagery, SAR, drought indices, vegetation condition metrics, and Google Earth Engine. Frequent methods include bibliometric mapping, keyword co-occurrence analysis, thematic clustering, machine learning, time-series analysis, and multi-sensor integration. Overall, the field is mature but still faces challenges in interoperability, geographical representation, validation, and decision-oriented applications.

Earth doi: 10.3390/earth7040107

Authors: Mohammed Benaafi Ali Q. Alorabi Ali Y. Alzahrani Husam Musa Baalousha Mahfuzur Rahman

The Wadi Al-Ahsaba watershed is an arid to semi-arid catchment situated in southwestern Saudi Arabia, characterized by intermittent surface flow, high evaporation and low rainfall, and a dam reservoir built for flood control. The work aims to assess hydrological and anthropogenic controls on surface and groundwater quality, pollution status, and human health risks using an integrated approach of hydrogeochemical analysis, multivariable statistics, and water quality and contamination indices. A total of 21 water samples (15 surface water, 6 groundwater) were analyzed for general chemistry, major ions, and trace elements. Hydrogeochemical analysis and principal component analysis (PCA) were implemented to differentiate the geogenic from anthropogenic control on water quality. The pollution status and associated risk were evaluated using water quality index (WQI), contamination degree (Cd), Hazard Quotient (HQ), and Hazard Index (HI). Results suggest limited surface–groundwater interaction, with surface water dominated by Ca–Mg–HCO3 facies, indicating recent recharge and limited water–rock interaction, whereas groundwater exhibits mixed Ca–Mg–Cl and Ca–Na–Cl–SO4 types, revealing longer residence time and water–rock interaction. Nitrate (9.5–109 mg/L) and TDS (522–1003 mg/L) exceeded drinking water standards in 90% and 95% of tested samples, respectively, and WQI ranged from 43 to 134, reflecting excellent to poor water. High non-carcinogenic risk from nitrate was observed, especially for infants. The study concluded that the geogenic processes (water–rock interaction, evaporation, and mineral dissolution) control the general chemistry of tested water, while anthropogenic input from wastewater and agriculture input are likely contributors to nitrate contamination. The study contributes to the understanding of arid wadi-dam systems by revealing how limited recharge, hydrological connectivity, and episodic flow control contaminant transport and persistence, underscoring the critical role of integrated hydrological analysis and land use management in safeguarding freshwater resources in arid environments.

Earth doi: 10.3390/earth7040106

Authors: Tolulope Elizabeth Aniyikaiye Akinola Ikudayisi Motebang Dominic Vincent Nakin

Estuaries along South Africa’s coastline are increasingly subjected to anthropogenic pressures that disrupt their biogeochemical function and increase the risk of contamination. This study presents the first seasonal assessment of heavy metal contamination and water quality indices in the Qholora Estuary, Eastern Cape Province. Surface water samples collected during wet and dry seasons were analysed for physicochemical properties and heavy metals (As, Cd, Cu, Fe, Hg, and Pb). Multiple pollution metrics (Pollution Index (PI), Nemerow Pollution Index (NPI), Heavy Metal Evaluation Index (HEI), Heavy Metal Pollution Index (HPI)), ecological risk indices ((Ecological Risk Index (ERI), and Potential Ecological Risk Index (PERI)), and the Water Quality Index (WQI) were applied and supported by Principal Component and Cluster Analyses to identify dominant pollutant, contamination sources and seasonal hydro-geochemical controls. Results reveal strong seasonal contrasts: wet-season conditions showed elevated ionic concentrations and enhanced mobilisation of Cu, Pb, Cd, Hg, and Fe due to storm-driven runoff and sediment resuspension, while dry-season patterns reflected evapo-concentration, prolonged residence times, and pH-mediated metal partitioning. Across indices, heavy metal contamination remained low in the dry season but increased significantly in the wet season, especially for Hg, which posed moderate to considerable ecological risk at most sites, indicating emerging ecological pressure under high-flow conditions. These findings highlight a generally low risk under average conditions but a pronounced seasonally vulnerable estuarine system, underscoring the need for intensified monitoring during periods of increased runoff. The study establishes an important baseline for regional water resource management.

Earth doi: 10.3390/earth7040105

Authors: Lorena Ceballos-Pérez Juvenal Villanueva-Maldonado Erick Dante Mattos-Villarroel Víktor Iván Rodríguez-Abdalá Remberto Sandoval-Aréchiga Carlos Francisco Bautista-Capetillo

Water erosion is a critical degradation process that reduces fertility and agricultural sustainability, especially in semi-arid regions. The Universal Soil Loss Equation (USLE) allows for the quantification of this phenomenon using factors such as rainfall erosivity (R) and topography (length-slope, LS). In this study, both factors were estimated and analyzed in the Cañitas sub-basin, located in the semi-desert area of the state of Zacatecas, Mexico, characterized by irregular precipitation and limited data availability. The objective of this study is to estimate and analyze the R factor and LS factor to evaluate their influence on soil water erosion processes. Records from five meteorological stations (1986–2022) were used, along with the Modified Fournier Index (MFI) and Geographic Information Systems (GIS) tools, generating spatial maps of rainfall erosivity and topography. An average R factor of 81.69 MJ∙mm/ha∙h∙year was estimated, consistent with the values obtained using the MFI. The LS factor shows that the northwestern area of the study zone has the most extensive and steepest slopes (up to 20). This study analyzes the R and LS factors to identify areas vulnerable to water erosion and to understand the influence of climate and topography in a semi-arid region, which can serve as a reference for planning conservation actions and managing watersheds in semi-arid areas with high climatic variability.

Earth doi: 10.3390/earth7030104

Authors: Samir Saadane Lahcen Hassine Hatim Kharraz Aroussi Rachid Saadane

Accurate weather-regime classification is increasingly important for climate-sensitive decision-making in agriculture, aviation, disaster preparedness, and territorial planning, particularly in regions where strong climatic heterogeneity complicates conventional operational workflows. This study proposes a machine learning-based framework for broad-regime weather classification over Morocco using hourly METAR observations collected from 22 meteorological stations between July 2022 and February 2024. The proposed workflow integrates data cleaning, missing-value imputation, feature transformation, categorical encoding, class-imbalance handling, and model optimization under a leakage-safe experimental protocol. To preserve temporal integrity, observations were chronologically split into training, validation, and independent test subsets; SMOTE and random undersampling were applied exclusively to the training subset, whereas the validation and test subsets retained their original class distributions. Seven classifiers were evaluated, including XGBoost, LightGBM, CatBoost, Random Forest, Gradient Boosting, Support Vector Machine, and Logistic Regression, with hyperparameters optimized using Optuna. The results show that optimized boosting models are particularly effective for Moroccan station-based weather classification. XGBoost achieved the highest test-set accuracy of 95.1%, followed by LightGBM at 94.7% and CatBoost at 93.8%, with optimization improving accuracy by approximately 8–12 percentage points compared with baseline configurations. Because the dataset exhibits class imbalance, macro-averaged precision, recall, and F1-score were emphasized alongside accuracy to provide a more reliable assessment across weather classes. Confusion-matrix analysis indicates improved recognition of underrepresented regimes, especially Dust/Sand events, while residual confusion between Fog/Haze and Rain/Storm reflects both physical overlap and the limits of a four-class METAR taxonomy. Overall, the findings demonstrate that optimized ensemble learning can provide a robust, computationally efficient, and operationally relevant classification layer for regional meteorological decision support in Morocco, while future work should extend the framework to longer time series, finer weather taxonomies, and external regional validation.

Earth doi: 10.3390/earth7030103

Authors: Uroš Durlević Tanja Srejić Sanja Manojlović Marko V. Milošević Natalija Batoćanin Milica Dobrić Jelena Svetozarević Velibor Ilić

In this study, a soil erosion intensity map for the territory of Serbia was produced using the Modernized Erosion Intensity Coefficient (MEIC-Z), combined with remote sensing data (Sentinel-2) and Geographic Information Systems (GIS). The analysis was based on contemporary geospatial data on lithology, land use, and terrain slope, with a spatial resolution of 30 m. Particular emphasis was placed on modifying the φ coefficient, which significantly improved estimates of erosion intensity. The average erosion intensity at the national level is 0.239, corresponding to the weak erosion class. Multivariate analysis of geographical conditions showed that the highest values of the erosion coefficient (Z) were determined by agricultural land (r = 0.826), while the lowest values were associated with terrain slope (r = −0.805) and forest cover (r = −0.767). In addition to the national-scale assessment, spatial differentiation of the results was performed at the local (municipal) level. Municipalities were differentiated into four clusters using Agglomerative Hierarchical Clustering. The advantage of the modified φ coefficient lies in the integration of land use and terrain slope, enabling a more realistic assessment of the intensity of erosion processes. Validation results demonstrated strong agreement between the modernized Z-derived erosion coefficient and the expert-defined erosion inventory, supporting the internal consistency of the model-derived erosion susceptibility patterns. This study significantly contributes to decision-making at both national and local levels by providing a scientific basis for developing strategies for sustainable forest management and soil conservation.

Earth doi: 10.3390/earth7030102

Authors: Igor Trišić

This study investigated the influence of the National Marine Park of Zakynthos (NMP) on overall tourist satisfaction, with a particular focus on the conservation of the endangered loggerhead sea turtle (Caretta caretta). Using a quantitative methodology on a hybrid sample of 1216 respondents, the research framework was validated via exploratory factor analysis (EFA). The measurement model analyzed visitor attitudes across two primary dimensions: ecological destination factors and institutional management factors. The multiple linear regression analysis indicated that both groups of latent factors contributed significantly to tourist satisfaction (R2 = 0.359, p < 0.001). The study revealed high environmental awareness among visitors, who supported spatial–behavioural restrictions and expressed a strong willingness to contribute to protection programs through monetary donations. In conclusion, the results demonstrate that strict biodiversity conservation is not a barrier but rather a critical asset that enhances the destination’s sustainable tourism value.

Earth doi: 10.3390/earth7030101

Authors: Kai Wang Huizhou Zuo Jinzhu Ji Xinpeng Wang Qi Cao

Siziwang Banner in Inner Mongolia is a typical arid and semi-arid grassland region where ecological environmental quality is highly sensitive to climate variability and land use and land cover change (LULCC). Clarifying the long-term coupling relationship between LULCC and ecological environmental quality is essential for regional ecological protection and sustainable land management. Based on the Google Earth Engine (GEE) platform, this study integrated multi-temporal Landsat imagery and CLCD-based land use datasets, including an updated 2024 land use layer, to construct a Remote Sensing Ecological Index (RSEI) using standardized and direction-corrected principal component analysis. land use transition matrix analysis, spatial autocorrelation analysis, ecological contribution rate calculation, and GeoDetector were further applied to reveal the spatiotemporal evolution patterns, ecological effects, and driving mechanisms of LULCC in Siziwang Banner from 2000 to 2024. The results showed that: (1) grassland was consistently the dominant land use type, accounting for more than 90% of the total area. The overall land use pattern was characterized by stable grassland dominance, decreasing farmland and unused land, and slight increases in grassland and construction land; forestland showed a high relative growth rate but remained very small in absolute area. (2) The regional ecological environmental quality remained at a lower-to-medium level, with mean RSEI values ranging from 0.27 to 0.47. RSEI showed a phased pattern of initial improvement, subsequent decline, and partial recovery; the marked decline around 2015 was associated with the combined effects of drought stress and land use degradation rather than a single driving factor. RSEI exhibited significant positive spatial autocorrelation, with Moran’s I values ranging from 0.898 to 0.993. High-value clusters were mainly distributed in the southern region, whereas low-value clusters were concentrated in the central and northern regions. (3) Different land use transitions produced differentiated ecological effects. The conversion of unused land to grassland contributed positively to ecological restoration, while grassland degradation and construction land expansion exerted negative effects. The positive RSEI response of some grassland-to-farmland transitions should be interpreted cautiously in relation to local irrigation and intensive farmland management. (4) GeoDetector results indicated that land use type and DEM were the dominant factors controlling the spatial differentiation of RSEI, with average q values of 0.7188 and 0.6178, respectively. The interaction between DEM and land use type showed the strongest explanatory power, indicating that ecological quality was jointly shaped by land use structure and natural background conditions. This study provides a scientific basis for grassland protection, unused-land restoration, farmland management, and spatially differentiated ecological restoration in Siziwang Banner and similar ecologically fragile arid and semi-arid grassland regions.

Earth doi: 10.3390/earth7030100

Authors: Md Monzer Hossain Sarker Nusrat Jahan Bipa

The journal retracts the article titled “Spectral Analysis of Ocean Variability at Helgoland Roads, North Sea: A Time Series Study” [...]

Earth doi: 10.3390/earth7030099

Authors: Chiara Graziani Francesca Matrone Andrea Maria Lingua

Mountain ecosystems are highly sensitive to climate change and require spatially explicit monitoring tools to support adaptive management. Within the framework of the Interreg-ALCOTRA “ACLIMO” project, this study investigates land cover dynamics in the Gesso Valley (Maritime Alps, Italy) over the period 2010–2021 using deep learning–based classification of high-resolution aerial orthophotos integrated with climate data analysis. Multi-temporal RGB and NIR imagery (2010, 2018, 2021) was classified using convolutional neural networks (U-Net and MMSegmentation) in ArcGIS Pro, with CORINE Land Cover datasets used for training. The best-performing model, based on CLC + Backbone 2018, achieved an overall accuracy of 82%, increasing to 87% after fine-tuning. Change detection revealed a general shift towards increased vegetation cover, while climate analysis based on regional weather stations (1990–2021) identified a warming trend of +0.4 °C/decade and recent drier conditions. Logistic regression highlighted significant associations between land cover transitions and climate anomalies, with temperature positively influencing change probability (OR = 1.40). The study demonstrates the potential of operational GIS-integrated deep learning workflows for climate change monitoring in complex alpine environments under real-world data constraints.

Earth doi: 10.3390/earth7030098

Authors: Sarthak Sahoo Kshyana Prava Samal Prabhash K. Mishra Muthukrishnavellaisamy Kumarasamy Aradhana Thakur Dwarika Mohan Das Dinagarapandi Pandi

Understanding spatio-temporal rainfall variability is critical for water resource management, especially for climate-sensitive river basins. This study examines rainfall trends and variability in the Baitarani River Basin (eastern India) using high-resolution gridded data for 1979–2020. Rainfall trends were investigated using non-parametric Mann–Kendall test (MK test) and Sen’s slope estimator (SSE). The shift point was detected using multiple homogeneity tests [Pettitt test, Standard Normal Homogeneity Test (SNHT), and Buishand test], while rainfall variability was quantified using an entropy-based Marginal Disorder Index (MDI). The analyses were performed at annual and seasonal scales. MK Z-statistic indicates the increasing or decreasing nature of a series, whereas Sen’s β slope provides the rate of change in that particular series. The MK test and SSE were applied again to examine trends before and after the identified change point. Finally, maps illustrating spatial trends and percentage changes were produced using ArcGIS 10.6. Over the 42-year period, the MK test revealed significant increasing annual trends in both districts, Keonjhar (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +21.8%, and Mayurbhunj (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +19.2%. During 1979–2020 post-monsoon rainfall showed the highest increase (62–70%) while, post 2001, monsoon rainfall declined substantially (1.7–3.3 mm/year) across all districts, with Balasore showing the largest decrease (−3.3 mm/year). The earlier period (1979–2001) had stable monsoon rainfall but greater variability in retreating monsoon, especially in northern regions. Entropy-based variability analysis indicated the Bhadrak and Balasore districts as having maximum variability with an MDI value of 1.44 and 1.35, respectively, for monsoon and annual rainfall series. These findings underscore the importance of incorporating changing seasonal dynamics into water-resource planning and flood-risk management for the Baitarani River Basin in the context of climate change.

Earth doi: 10.3390/earth7030097

Authors: Maged El Osta Milad Masoud Nassir Al-Amri Abdulaziz Alqarawy Riyadh Halawani Mohamed Rashed Mohamed S. Abd El-baki Salah Elsayed

Groundwater resources in the Kingdom of Saudi Arabia (KSA) are important for meeting the needs of human communities, agriculture, and industry. In Western KSA, groundwater from coastal aquifers is an essential resource that complements desalinated seawater. Therefore, ensuring the quality and contamination of groundwater has emerged as a critical priority for preserving water security. The aim of this research is to evaluate the groundwater quality and its vulnerability to contamination within the Wadi Marawani Basin. To achieve this aim, water quality indices (WQIs), the DRASTIC model, and machine learning (ML) algorithms were employed alongside a Geographic Information System (GIS). The results of the chemical analysis of 64 water samples were used in these assessments. Furthermore, several input parameters were evaluated using the DRASTIC model to estimate the DRASTIC index (DI) and generate a groundwater vulnerability map. Three ML algorithms—specifically, a Multilayer Perceptron (MLP), a Random Forest (RF), and a Decision Tree (DT)—were utilized to forecast WQIs such as the total dissolved solids (TDS) and sodium adsorption ratio (SAR), in addition to the DRASTIC index (DI). The results revealed that around 36% of the samples were classified as fresh water (<1000 mg/L). The SAR ranged from 1.10 to 32.50, indicating that most samples were suitable for irrigation. Approximately 22% of the basin was classified as demonstrating high vulnerability, whereas about 78% demonstrated low-to-moderate vulnerability. Assessment of the ML models showed high predictive accuracy for the TDS, SAR, and DI. The MLP-Vul. model attained an R2 value of 1.00 and RMSE value of 0.01, the RF-Vul. model achieved an R2 of 0.94 and RMSE of 3.17, and the DT-Vul. model attained an R2 of 0.92 and RMSE of 3.57. Although there was a minor increase in RMSE across all models during the testing phase, their predictive performance remained clear.

Earth doi: 10.3390/earth7030096

Authors: Somchith Phetmany Bounmy Keohavong Bounlue Douangdy Xaythavone Bounyasone Xuewei Hu

In situ leaching is increasingly used for rare earth element (REE) extraction because of its operational efficiency; however, acidic and chemically reactive leaching solutions may generate substantial environmental risks in riverine systems. This study evaluated water contamination and screening-level ecological risk following a cyanide leakage incident associated with a pilot REE mining operation in Houaphanh Province, northern Lao PDR. Surface water samples were collected from 12 downstream monitoring locations between February and April 2024. Physicochemical parameters, free cyanide (CN−), and dissolved metals, including arsenic (As), lead (Pb), copper (Cu), manganese (Mn), aluminum (Al), zinc (Zn), and iron (Fe), were analyzed using portable multiparameter probes, colorimetric cyanide determination, and ICP-OES. Contamination severity was interpreted using Pollution Index (PI) and Hazard Quotient (HQ) indicators based on Lao national standards and international guideline values. Results showed severe downstream contamination, with free cyanide and several dissolved metals substantially exceeding permissible thresholds. Observed elevated concentrations of As (30.29 mg/L), Pb (10.38 mg/L), Cu (14.97 mg/L), and CN− (0.51 mg/L) indicated elevated ecological risk conditions, while acidic pH conditions may have enhanced metal mobilization and downstream transport. Descriptive spatial observations indicated apparent downstream contaminant dispersion within affected downstream river communities reliant on river water for domestic use, irrigation, and fisheries. Field observations additionally documented fish mortality, reduced irrigation usability, and deterioration of river water quality conditions in affected downstream communities. The findings suggest the potential vulnerability of Mekong-connected river systems to chemically intensive REE extraction activities and highlight the importance of preventive environmental governance, continuous monitoring, and operational risk management in emerging rare earth mining regions.

Earth doi: 10.3390/earth7030095

Authors: Abusiswe Rigala Mbulisi Sibanda Timothy Dube

Wetlands are among the planet’s most productive ecosystems, yet they are increasingly imperiled by intersecting global challenges, particularly agricultural expansion, food security demands, and climate change. 1 This study investigated the spatial extent of floodplain wetlands and assesses Land Use/Land Cover (LULC) dynamics in the uMngeni catchment using multi-temporal Landsat imagery for the years 2000, 2010, 2020, and 2024. 2 Seven key land cover classes were classified, which included agriculture, bare land, built-up areas, forest, grassland, wetlands, and water bodies, using the Random Forest (RF) classification incorporating spectral indices (NDVI, NDWI) and topographic variables (slope and aspect) on Google Earth Engine (GEE). The overall accuracies for the respective years were 88.98% (2000), 91.23% (2010), 84.21% (2020), and 86.55% (2024), with corresponding Kappa coefficients of 0.82, 0.84, 0.78 and 0.80. 3 The findings show a significant 37% decline in wetland area from 2000 (2978 ha) to 2024 (1874 ha), with the most pronounced loss (46%) occurring between 2000 and 2010. Built-up areas increased by 38% over the same period, while agriculture peaked in 2010 (9312 ha) before declining to 7632 ha by 2024. The dominant transitions involved wetlands and grasslands being replaced by urban land and bare surfaces, particularly along the floodplain edges. 4 These patterns reflect intensifying human pressure on wetland ecosystems. Targeted interventions, such as enforcing buffer zones, regulating land use near water bodies, and restoring degraded wetlands, are critical to conserving ecosystem services and achieving sustainability outcomes aligned with the Sustainable Development Goals.

Earth doi: 10.3390/earth7030094

Authors: Sanaz Monavvar Sabegh Davoud Zarehaghi Saeed Samadianfard Mohammad Taghi Sattari Sajjad Ahmad

Soil water retention curves (SWRCs) are fundamental inputs for simulating vadose-zone processes, yet their direct measurement is labor-intensive and often impractical across large spatial domains. Pedotransfer functions (PTFs), therefore, provide an essential alternative for estimating SWRCs from readily measured soil properties. This study developed machine learning-based PTFs to estimate SWRCs using the UNSODA 2.0 database. An extreme gradient boosting (XGB) model was implemented and optimized using two Bayesian hyperparameter tuning frameworks, Hyperopt and Optuna, across eleven input scenarios incorporating combinations of textural, structural, and compositional soil attributes. Model performance was assessed using RMSE, R2, and Kling–Gupta efficiency (KGE). To prevent data leakage from the hierarchical structure of the UNSODA 2.0 database, a nested grouped cross-validation framework was employed, ensuring an unbiased assessment of model generalization performance across independent soil samples. The Optuna-tuned XGB model trained on the full feature set achieved the highest accuracy, with a test RMSE of 0.0183, R2 of 0.9815, and KGE of 0.9825, outperforming both the baseline and Hyperopt-optimized models. Feature importance and SHAP analyses indicated that soil texture dominated the estimations, while porosity, bulk density, and organic matter provided complementary improvements and particle density contributed marginally. These findings demonstrate that advanced hyperparameter optimization enhances the accuracy and interpretability of XGB-based PTFs, offering a robust framework for improved estimation of SWRCs in hydrological and soil-management applications.

Earth doi: 10.3390/earth7030093

Authors: Pedro S. Santos Anabela Veiga Sandra Mourato

The mineral resources sector has faced intense social and environmental scrutiny in recent years, often driven by a perceived lack of information and transparency regarding projects. Due to a perceived lack of information and transparency regarding projects, local communities have increasingly opposed them, leading to the revision of the Portuguese Decree-Law Nº. 30/2021, which now mandates public clarification sessions for communities in affected territories. This study reviews the state of the art concerning socio-environmental conflicts and analyses the role of social awareness within the context of Corporate Social Responsibility (CSR). A survey was conducted in the parish of Alqueidão da Serra (Central Portugal), a community historically exposed to stone extraction, to assess perceptions of sustainability and the sector’s impact. The methodology combined the literature review with a statistical analysis of the population’s views. Results indicate that the community recognises both the economic relevance and necessity of the sector, while simultaneously expressing concerns regarding local impacts. In this context, an exploratory Not In My Back Yard (NIMBY) tendency is identified in 45 ± 6% of the population, with women showing a greater propensity. The study concludes that socio-environmental issues are the primary drivers of conflict. These findings support recommendations for enhanced population sensitivity studies and structured public clarification sessions to mitigate conflict.

Earth doi: 10.3390/earth7030091

Authors: Kiran Jalem Gouranga Pal Sagar Kumar Swain K. K. Basheer Ahammed

Urban flooding in coastal megacities remains a critical challenge, with recurrent inundation driven by extreme rainfall, inadequate drainage, and topographic vulnerability. This study investigated the spatio-temporal dynamics of flooding in Mumbai between 2018 and 2025 using Sentinel-1 SAR data (VV and VH polarizations) along with automated thresholding and unsupervised classification techniques. The VV polarization consistently detected a larger flood extent than VH, with maximum inundation reaching 152 km2 in 2024, compared to 67 km2 with VH, highlighting VV’s superior sensitivity to surface water. Ward-wise analysis revealed that Chembur West (16.47 km2), Matunga (12.33 km2), and Ghatkopar (5.43 km2) were the most flood-prone areas, while Colaba and Marine Lines experienced lower exposure due to higher elevation and better drainage infrastructure. Annual flood variation corresponded with intense rainfall events, particularly those exceeding 300 mm/day in 2020, 2023, and 2024. Validation with Brihanmumbai Municipal Corporation (BMC) reported flood data confirmed a strong spatial agreement with SAR-derived flood zones, supporting the reliability of the geospatial model. The integration of remote sensing, rainfall data, and ward-level analysis offers a scalable framework for urban flood risk mapping. These findings emphasize the need for resilient drainage planning, green infrastructure, and real-time flood monitoring systems.

Earth doi: 10.3390/earth7030092

Authors: Biré Kemedou Pélagie Kolou Koko Zébéto Houédakor Kossi Komi Vidjinnagni Vinasse Ametooyona Azagoun Kossiwa Zinsou-Klassou Jérôme Chenal

Togolese agriculture is vulnerable to climate variability. In this context, this study aims to analyze the seasonal variability of climatic parameters and its effects on food production in the western Plateaux region. To achieve this, climatic data (from stations in Agou-Gare, Adéta, Amou, Badou, Kouma Konda, and Atakpamé) and agricultural data (yields, production, and areas of maize, rice, cowpea, cassava, and yam) from 1991 to 2020 were processed using RStudio 4.4.0. A methodology integrating both daily rainfall and changes in available soil water (ASW) was used to determine the rainy seasons and their durations. Seasonal rainfall totals were used to analyze spatial variability. Finally, an ordinary least squares (OLS) regression model with a threshold of 10% was used to assess the effect of climate parameters on food production. The results reveal a transition from a bimodal rainfall regime to a monomodal regime, characterised by a dry season of 4–5 months and a rainy season of 7–8 months. This transition is accompanied by an increase in temperatures ranging from 24.69 °C to 34.7 °C. The results also reveal an uncertain start to the long rainy season (early or late), an extension of the short season and dry spells lasting between 11 and 34 days that affect crops. Finally, spatial variability in precipitation remains significant during the long rainy season. Agroclimatic analysis reveals that maximum temperature positively influences cowpea yields (p = 0.0079) but negatively influences cassava (p = 0.00013) and rice (p = 0.050) yields. These results could inform the development of effective adaptation strategies tailored to this environment, helping to maintain and increase food production in the context of climate change.

Earth doi: 10.3390/earth7030090

Authors: Pattharaporn Thongnim Sueppong Mueanchamnong

Vapor Pressure Deficit (VPD) is a critical determinant of atmospheric evaporative demand and plant water stress in tropical agricultural systems. This study applied a Gaussian Mixture Model (GMM) and K-Means clustering to 36,528 hourly meteorological observations collected from Eastern Thailand between August 2021 and September 2025, with the objective of identifying distinct atmospheric moisture regimes relevant to precision irrigation management in durian cultivation. Two input configurations were evaluated: a multivariate feature space comprising air temperature, relative humidity, wind speed, solar radiation, and VPD; and a univariate input consisting of VPD alone. Model selection for GMM was guided by the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), while K-Means performance was assessed using the Elbow method, Silhouette Coefficient, Calinski–Harabasz Index, and Davies–Bouldin Index. For the multivariate input, GMM identified K = 7 as the optimal number of clusters, supported by the largest single-step reduction in both AIC and BIC at this transition point. For the univariate VPD input, K = 5 was selected as the most parsimonious and agriculturally interpretable solution. The seven clusters derived from the multivariate GMM were organized into four atmospheric moisture regimes, such as very low, moderate, high, and very high evaporative demand, capturing the full spectrum of diurnal and seasonal VPD variability characteristic of Eastern Thailand. The results demonstrate that GMM-based probabilistic clustering applied to multivariate meteorological inputs provides a more comprehensive characterization of atmospheric moisture dynamics than univariate or geometric clustering approaches, offering a practical framework for tiered irrigation scheduling and drought stress early warning systems in tropical fruit cultivation.

Earth doi: 10.3390/earth7030089

Authors: Mohammed Achite Pandurang Choudhari Abderrezak Kamel Toubal Priyanshu Nathawat Nehal Elshaboury Nikola M. Milentijević Tommaso Caloiero

Water erosion is the main driver of soil loss in semi-arid mountainous regions, particularly in Algeria. Identifying the spatial distribution of erosion is a crucial first step, providing decision-makers with essential information to develop effective mitigation strategies. The main objective of this study is to apply the Revised Universal Soil Loss Equation (RUSLE) to estimate soil loss and rank the sub-basins of the Wadi Cheliff Basin (43,750 km2). Different geographical and non-spatial data sets have been employed to develop different thematic layers of the RUSLE factors, such as rainfall erosivity factor (R), soil erodibility factor (K), topographic factor (LS), crop management factor (C), and support practice factor (P). The RUSLE empirical model indicated strong spatial variability of soil loss across the Wadi Cheliff Basin, with estimated values ranging from 0 to 50 t ha−1 yr−1 during October 2017–May 2018. Higher erosion rates (20–50 t ha−1 yr−1) were concentrated in the northern part of the basin near the Mediterranean coast, primarily due to high rainfall erosivity (800–977 MJ mm ha−1 h−1 yr−1) and steep slopes (LS up to 29.48). In contrast, the southern part of the basin exhibited lower soil loss (0–10 t ha−1 yr−1), associated with lower rainfall and gentler slopes. Areas affected by extreme erosion (>50 t ha−1 yr−1) were very limited, representing only 0.02% in October 2017 and 0.40% in May 2018. Maximum soil loss values (224.00 t ha−1 yr−1 in October 2017 and 204.10 t ha−1 yr−1 in May 2018) indicate that high-intensity erosion is limited to specific localized hotspots, rather than being broadly distributed across the basin. Information on soil erosion patterns at the sub-basin level can guide the planning of effective conservation practices. Such information is helpful for the implementation of erosion control practices and improving overall environmental management in the basin.

Earth doi: 10.3390/earth7030088

Authors: Milica Stajić Dejan Mirčetić Atila Bezdan Radovan Savić Sanja Antić Nikola Santrač Andrea Salvai Milena Lakićević Boško Blagojević

Reference evapotranspiration (ET0) is most commonly estimated using the FAO-56 Penman–Monteith (PM) equation. However, its application is often limited by the lack of required meteorological parameters. Due to their flexibility, ability to operate with limited input, and high accuracy in estimating ET0, machine learning models have become increasingly relevant in scientific research, offering a practical alternative under limited data conditions. In this study, artificial neural networks (ANNs) were applied to estimate daily ET0 using meteorological data from the Novi Sad station in Vojvodina (Serbia). The dataset consisted of eight meteorological variables relevant to evapotranspiration processes. Analysis showed that some variables had a stronger influence on ET0 prediction than others. To evaluate their combined effect, a series of ANN models with different input combinations were developed and tested. The random forests, gradient boosting and k-nearest neighbors models were used as a benchmark, and model performance was evaluated using R2, NSE, RMSE, and MAE. The highest accuracy was achieved when all variables were included, providing the model with maximum information. The best performance was obtained using a two-hidden-layer architecture with 32 and 16 neurons, resulting in R2 = 0.97, NSE = 97.07%, RMSE = 0.23 mm/day, and MAE = 0.21 mm/day. The results showed that a limited number of input variables can be used to estimate ET0 with high accuracy, achieving an R2 value of 0.95 using only three input variables. Therefore, the findings of this study may contribute to more accurate and cost-effective irrigation scheduling and water balance estimation, providing practical benefits for agricultural water management and farmers in Serbia.

Earth doi: 10.3390/earth7030087

Authors: Ahmet Kaçmaz Ugur Alganci

Floods are among the most catastrophic natural disasters globally, causing significant damage to both life and infrastructure. Consequently, immediate and accurate assessment of inundated areas is critical for effective emergency response. While optical remote sensing is typically used for flood assessment, it is often ineffective during active flood events due to persistent cloud cover and precipitation. To address this, this research develops a deep learning method utilizing Synthetic Aperture Radar (SAR), which offers all-weather, 24 h imaging capabilities. Specifically, an attention-based differential Siamese U-Net was developed to detect temporal changes in bi-temporal SAR imagery (e.g., Sentinel-1) acquired before and after flood events. The method was evaluated on the S1GFloods dataset, comprising 5360 bi-temporal Sentinel-1 SAR image pairs across 46 flood incidents on six continents. Experimental results demonstrate a flood Intersection over Union (IoU) of 92.43%, an F1 score of 96.07%, and a recall of 97.64%. These metrics rank the proposed approach third overall among top-performing methods on this dataset. Notably, the high recall rate indicates the model is particularly beneficial for emergency response, as it minimizes the number of undetected flooded areas. Despite utilizing a CNN-based architecture that is less complex than Vision Transformer models, this method achieves results comparable to the state-of-the-art DAM-Net, with a performance difference of only 0.77%.

Earth doi: 10.3390/earth7030086

Authors: Shun Chen Ping Jiang

Reducing carbon emissions while improving air quality is a central challenge for rapidly urbanizing regions. Focusing on 31 prefecture-level cities in the Middle Reaches of the Yangtze River Urban Agglomeration, this study examines carbon–pollution synergy (CPS), spatial dynamics, and the driving factors of CO2 and representative air pollutants from 2013 to 2023. Spatial autocorrelation analysis, a revised four-factor Logarithmic Mean Divisia Index (LMDI) decomposition, and a factor-based CPS assessment were used to identify spatial clustering, compare driver heterogeneity, and evaluate coordination between CO2 and primary pollutants. To improve methodological consistency, the LMDI decomposition and CPS assessment focus on the primary pollutants SO2, CO, and NO2, whereas PM2.5 and O3 are retained in the spatial analysis and discussion because they are strongly affected by secondary formation, atmospheric transport, and meteorological conditions. The results show that CO2 and the selected pollutants exhibit significant but pollutant-specific spatial clustering. High CO2 values remain concentrated in the core cities of Wuhan, Changsha, and Nanchang, PM2.5 shows a persistent north–south gradient, and SO2 hotspots shift from traditional industrial cores toward peripheral areas receiving industrial relocation. The revised LMDI results show that economic development is the most stable positive driver of CO2 and the primary pollutants, whereas the energy-consumption factor generally suppresses emissions. The recalculated population-scale factor fluctuates around 1, indicating a comparatively limited and stage-dependent contribution once the other factors are controlled for. CPS analysis further indicates that coordinated reduction is most robust under the energy-consumption factor and, for conventional combustion-related pollutants, also under the energy-structure factor. Overall, the region has a clear basis for CPS governance, but effective implementation requires pollutant-specific and region-specific control strategies rather than a uniform co-mitigation pathway.

Earth doi: 10.3390/earth7030085

Authors: Maxim Arseni Valentina-Andreea Calmuc Madalina Calmuc Laureana Odajiu Silvius Stanciu Puiu Lucian Georgescu

Intensive agriculture, deforestation, and frequent land-use changes contribute to increased soil erosion and sediment transport from both arable and non-arable lands into minor river channels. These factors directly and indirectly influence riverbank erosion and, in turn, sediment transport in rivers. Evidence on anthropogenic land-use/land-cover (LU-LC) change impact remains limited in both quantitative and spatial terms within the Danube River Basin. The study area includes research results from 17 locations concerning satellite-derived LU-LC changes along the Romanian sector of the Danube River, as well as validation results with particular highlighting on the Corabia area, Romania. According to results derived from combining LU-LC products based on Copernicus satellite data (comparing the years 2000 and 2018) and validated in the field through UAV flights conducted in 2025, the conversion of riparian vegetation into cultivated or uncultivated land accelerates bank failure. This is particularly evident where agricultural areas are located in the immediate vicinity of riverbanks. Such bank failures can be attributed to a reduction in root cohesion and a decrease in soil–bank structural stability. As a consequence, sediment delivery to the river channel increases via overland flow. The workflow proposed in this study offers a transferable and adaptable solution for areas with similar characteristics for a multitemporal approach regarding the influence of agricultural lands especially on sediment transport and riverbank erosion.

Earth doi: 10.3390/earth7030084

Authors: Zhen Zhao Xianghui Cao Guangxiong Qin Yuejun Zheng Kifayatullah Khan Wenpeng Li

Groundwater is one of the key factors affecting the changes and evolution of surface processes in arid regions, determining the direction and scope of the evolution of surface eco-hydrological processes. To achieve sustainable water resource management in arid areas, this study aims to systematically explore the dynamic changes in groundwater level and their ecological effects on the basis of multi-source remote sensing data by multivariate statistical methods. The results show that groundwater levels in the Bayin River Basin increased from 2895.35 m in 2005 to 2906.75 m in 2022 at a rate of 6.7 m/decade, driven by increased runoff and irrigation. Conversely, groundwater levels in urbanized areas near Delingha City slightly decreased by approximately 0.3 m/decade, with a general west-to-east declining spatial gradient. These changes have generated cascading ecological effects. Overall, rising groundwater has coincided with increased vegetation index, wetland extent, and soil moisture. Annual average NDVI rose from 0.18 in 2000 to 0.23 in 2022, an increase of 27.7%, and wetland area expanded from 349.25 km2 in 2005 to 355.25 km2 in 2022. Soil moisture content showed an insignificant upward trend form 0.14% in 2003 to 0.15% in 2022, with the slope of 0.01%/yr. However, soil salinization has exhibited an aggravating trend, with salinization index (SI) values of 0.25, 0.26, and 0.31 in 2000, 2010, and 2020, respectively. Affected by human activities and geological constraints, the ecological effects associated with groundwater level changes display pronounced regional heterogeneity. This study provides a solid basis for regional water resource regulation and further quantification of water conveyance benefits.

Earth doi: 10.3390/earth7030083

Authors: Vera Surtia Bachtiar Purnawan Purnawan Reri Afrianita Yega Serlina Haldi Reivan Thamrin Zulva Shabri Assyifa Raudina

The correction concerns Figure 1 of the published article [...]

Earth doi: 10.3390/earth7030082

Authors: Carla Iruri-Ramos Karla Vilca-Campana Lorenzo Carrasco-Valencia Andrea Chanove-Manrique María Rosa Cervera Sardá Berly Cárdenas-Pillco

Green and blue-green infrastructures are key for reducing the effects of urban heat islands driven by rapid city expansion. However, the spatial relationship between land-cover patterns and air-temperature distribution, plus the combined cooling effects of green and blue spaces, remains insufficiently explored. This study applies the InVEST Urban Cooling Model to analyze the spatiotemporal changes in land use and their impact on the heat-mitigation service provided by green and blue spaces in the city of Arequipa, Peru, between 2006 and 2024. Furthermore, land-use change is projected for 2030 using the CA-Markov model and the InVEST Scenario Generator tool. These projections enabled the evaluation of two heat-mitigation scenarios by modifying the spatial distribution of green, blue-green, and urbanized areas. The findings indicate that urbanized areas doubled over the measurement period. The greatest loss of agricultural land and tree-covered areas occurred between 2020 and 2024, with a decline of up to 5%. Correspondingly, the percentage of low heat mitigation index areas (0.1–0.2 and ≤0.1) increased by 3.8%, reaching a total increase of up to 6.7%. Scenario simulations showed that reducing both green and blue-green infrastructure had similar impacts on the heat-mitigation index, providing valuable insights for urban planning and environmental management.

Earth doi: 10.3390/earth7030081

Authors: Christos A. Karavitis Constantina Vasilakou Dimitrios E. Tsesmelis Nikolaos A. Skondras Panagiotis D. Oikonomou Kleomenis Kalogeropoulos Panagiotis A. Balabanis Rodrigo Maia Enrique Playán Nery Zapata Jorge Gironás Luiz Gabriel Azevedo Monica Porto Manuel Vanegas Santiago Maria Reyna Dionysis Assimacopoulos João Pedro Pêgo Andreas Tsatsaris Garyfalia Economou Stavros Alexandris Vassilia Fassouli Constantinos Chatzithomas Iordanis Moustakidis Pantelis E. Barouchas

Integrated water resources management uses decision-making and planning techniques in developing long-term strategies to ensure the sustainability of water resources and the resulting water security of future generations. Policy formulation through such integrated planning interlinks with indicators serving as an information channel to decision-makers. The present effort aims to develop a specific methodology using technical, environmental, and social indicators, formulating composite indices to identify vulnerability to changing water conditions. Thus, a set of indices developed through a multiyear research effort in Latin America, namely Drought Vulnerability Index (DVI), Water Stress Vulnerability Index (WSTVI), Water Scarcity Vulnerability Index (WSCVI), and Water Changing Conditions Vulnerability Index (WCCVI). Time series analysis covered the years 1991–2020, whereas the reference period was 1961–2020. Climate and water resources information is mainly obtained from ERA5-Land reanalysis; social, economic, infrastructure, and institutional data derived from harmonized sources (COROADO Project-EU, FAO, The World Bank, WHO/UNICEF JMP). Statistical tests and Principal Component Analysis (PCA) identified the indicators included in the equations for each index. Expert knowledge played an important role in the development as data were collected according to known local specificities and global trends, as well as scientific criteria and methodological rigor regarding the proposed new indices. Finally, application of such a framework for spatially explicit analysis indicated higher levels of vulnerability to changing water conditions in the northern part of Mexico, the Andes, Bolivia, Paraguay, and Central America, and lower levels in Chile, Brazil, Uruguay, and Argentina. This application demonstrates that the produced composite indices may be implemented with matching success all over Latin America and, therefore, in diversified natural, technical, environmental, social and economic conditions.

Earth doi: 10.3390/earth7030080

Authors: Ling Nan Qiaorui Ba Chengyong Wu Xiangxiang Hu

Satellite-observed greening in arid regions is often interpreted as ecological restoration success, yet this assessment may conflate natural recovery with agricultural expansion. We developed an Arid Remote Sensing Ecological Index (ARSEI) incorporating a Comprehensive Salinity Index (CSI) to address systematic biases in the traditional RSEI when applied to irrigated drylands. ARSEI scores were validated against MODIS Net Primary Production (NPP) (R2>0.75 at the regional scale), confirming its reliability in capturing ecosystem productivity, while CSI effectively maps the upper-bound of surface salinization potential dictated by intrinsic soil properties. Applied to China’s Mu Us Sandy Land (2000–2024), the ARSEI reveals that 2327 km2 of sandy land—54% of current cropland—was converted to agriculture, creating “assessment-induced false greening” signals. While the traditional RSEI increased monotonically (+135%), the ARSEI shows a nuanced pattern with plateau (2010–2015) and decline (2015–2020) phases, reflecting salinization risks masked by high crop NDVI. Optimal Parameters-Based Geographical Detector analysis demonstrates that Land Cover × Precipitation interactions (q = 0.28) drive spatial heterogeneity through irrigation-mediated water redistribution. The ARSEI provides a dialectical evaluation framework: acknowledging agricultural greening’s economic benefits while monitoring subsurface degradation risks. This study offers a critical methodological advance for sustainable land assessment in global drylands undergoing agricultural intensification.

Earth doi: 10.3390/earth7030079

Authors: Emilio Megías Manuel García-Román

Following the completion of the installation and commissioning of a solar sludge drying system serving the largest wastewater treatment plant on the island of Tenerife, a study has been carried out on the possibilities of implementing this type of infrastructure in other important plants in the Canary Archipelago. To this end and given the favorable climatic conditions found in the Canary Islands for this type of facility, the availability of land and possible impacts on surrounding areas have been studied. There are potential implementations on the islands. Thanks to these facilities, the volume of sludge to be transported to disposal or reuse areas is drastically reduced. The major drawback of these systems is the significant amount of land required, which is not always available on densely populated islands with rugged terrain.

Earth doi: 10.3390/earth7030078

Authors: Armando Aguilar-Meléndez Josep De la Puente Marisol Monterrubio-Velasco Alejandro García-Elías Jesús Huerta-Chua Armando Aguilar-Campos

Seismic Shake-e is a free app that provides valuable data and tools related to earthquakes, covering the stages before, during, and after seismic events. In this text, we describe the main features of the Seismic Shake-e 2.1 (SSe) app, the considerations that guided its development, examples of its use, and the challenges for future versions. Version 1.0 of this app was awarded as one of the winners of EOVALUE: Call for Innovative Apps in environmental and social fields, a project by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. SSe recognizes two user levels: basic and intermediate/advanced. There are six modules for each level. The main topics of these modules for both user types are: (1) Accelerometer Networks (AN), (2) Seismograms Analyzer-e (SAe), (3) Seismic Design of Buildings (SDB), (4) Earthquake Preparedness (EP), (5) Earthquake Early Warning Systems (EEWS) & Tsunami Warning Systems (TWS), and (6) Earthquake Emergency Response & Recovery. The two key modules are AN and SAe: the first explains how to obtain seismic records, and the second provides tools for their analysis. We include some applications of SSe, along with their results and discussion. We also list the advantages of the main modules and discuss potential future developments and improvements. The uniqueness of this work is that we highlight the software’s essential features and demonstrate its applications.

Earth doi: 10.3390/earth7030077

Authors: Raúl Arasa Agudo Matilde García-Valdecasas Ojeda Miquel Picanyol Sadurní Bernat Codina Sánchez

The Artificial Intelligence Forecasting System (AIFS), recently released by the European Centre for Medium-Range Weather Forecasts (ECMWF), represents a major shift in global weather prediction by replacing traditional physically based approaches with machine-learning methods. This study evaluates the impact of using AIFS as initial and lateral boundary conditions for the Weather Research and Forecasting (WRF) model, in contrast to the well-established physically based GFS. The aim of this work is to analyze the sensitivity of these different modelling configurations during three high-impact storms that affected Spain in 2025 and the effects of replacing GFS for AIFS as lateral and boundary conditions for WRF over the accuracy of operational forecasts. The analysis focuses on maximum wind gusts, accumulated precipitation, and the generation of meteorological warnings. Results show that AIFS substantially underestimates wind gusts with mean bias values between −13 and −25 km/h, and its forecasts differ markedly from those of GFS. When coupled with WRF, however, both AIFS-WRF and GFS-WRF produce similar results, with a general tendency to overestimate gusts, with mean bias values between 4 and 15 km/h. In all cases, WRF adds value, improving the representation of wind-related variables compared with the raw global model outputs. For accumulated precipitation, both WRF configurations reproduce the main rainfall patterns associated with the storms. AIFS-WRF shows a stronger tendency to overestimate precipitation, with RMSE values of 64, 23, and 12 mm for the different high-impact storms considered, although it also achieves the highest correlations. Finally, the analysis of meteorological warnings indicates that AIFS alone generates almost no wind gusts alerts. Once coupled with WRF, both configurations generate warnings in the regions where the most severe conditions occurred. Overall, while the added value of mesoscale models such as WRF is well established and confirmed here, the AI-based AIFS does not show clear advantages in comparison with traditional global models for these high-impact events being analyzed.

Earth doi: 10.3390/earth7030076

Authors: Sitthisak Moukomla Phurith Meeprom Kritchayan Intarat

Rapid urbanization in tropical Southeast Asia is transforming pervious land into impervious surfaces, intensifying the surface urban heat island (SUHI) effect and increasing the need for consistent urban thermal monitoring. This study assesses how impervious surface area (ISA) expansion relates to the urban thermal environment across five tropical megacities (Bangkok, Jakarta, Manila, Kuala Lumpur, and Ho Chi Minh City). AlphaEarth geospatial foundation model embeddings were used to reduce observation gaps caused by persistent cloud-cover, while MODIS land surface temperature (LST) was used to quantify the thermal response. We compared AlphaEarth classification against conventional Sentinel-2/NDVI approaches and an additional fairer annual Sentinel-2 full-band-plus-index Random Forest baseline, quantified ISA expansion for 2017–2024, and related ISA fraction to dry-season LST at 1 km resolution. Repeated random-holdout tests based on Google Earth Engine samples showed AlphaEarth mean IoU = 0.866 (95% CI: 0.857–0.875), compared with 0.758 (0.749–0.767) for the annual Sentinel-2 full-band-plus-index baseline and 0.686 (0.674–0.698) for the best single-date 5-index baseline. Spatial-block holdout tests gave similar but slightly lower values (AlphaEarth IoU = 0.859; annual Sentinel-2 baseline = 0.747; best single-date baseline = 0.673). Ho Chi Minh City experienced the fastest ISA expansion (+11.0 percentage points; slope = 1.48 pp yr−1, 95% CI: 1.06–1.91), whereas Bangkok reached the highest ISA fraction (65.1%). ISA fraction and LST were consistently and positively associated across cities and years (Pearson r = 0.748–0.900), and mean SUHI intensity during 2017–2024 ranged from 4.01 °C in Bangkok to 8.51 °C in Manila. These results indicate that foundation model embeddings can support cloud-resilient mapping of impervious surface change and thereby improve assessment of tropical urban thermal environments, while also highlighting the need for independent ground-truth validation.

Earth doi: 10.3390/earth7030075

Authors: Kangdong Wang Songbao Feng Hao Yu

Rapid urbanization imposes significant pressure on riverine water environments, yet the evolution of hydrochemical characteristics and dissolved organic matter (DOM) in rivers across urbanization gradients within developing regions, such as the Huaibei Plain, remains inadequately understood. Thus, this study investigates the hydrochemical and DOM characteristics of rivers across distinct urbanization gradients (suburban, peri-urban, and urban) in this area. Using an excitation–emission matrix coupled with a parallel factor analysis (EEM-PARAFAC) and hydrochemical analyses, we found that while rock weathering is the primary major ion source, human activities distinctly alter water profiles. Agriculturally dominated suburban rivers had significantly higher nitrate (NO3−) concentrations than those in urban and peri-urban rivers. Their DOM was predominantly humic-like (C1, C3) with a high humification index (HIX), indicating a substantial input of soil-derived humic substances driven by runoff from the agricultural catchment. Conversely, urban and peri-urban rivers exhibited higher chloride (Cl−) concentrations due to domestic sewage. Their DOM was dominated by protein-like components (C2 and C4, averaging 65–68%), with high biological indices (BIX) reflecting autochthonous origins. Correlation analysis confirmed these anthropogenic impacts: NO3− positively correlated with humic-like components and HIX, while Cl− strongly correlated with protein-like components. These findings confirm that DOM components and spectral indices are effective tracers of anthropogenic disturbance and hold promise for monitoring and predicting water quality, thus providing a scientific basis for improved water resource management and restoration strategies.

Earth doi: 10.3390/earth7030074

Authors: Salis Deris Artikanur Widiatmaka Widiatmaka Wiwin Ambarwulan Irmadi Nahib Wikanti Asriningrum Ety Parwati

Carbon is an essential component in the regulation of climate systems through the global biogeochemical cycle. However, changes in land use/land cover (LULC) have reduced the capacity of terrestrial ecosystems like watershed to store carbon. This shows the need for a policy framework that balances conservative objectives with agricultural demands, as watersheds are required to support carbon storage and food production. Previous studies have generally assessed carbon dynamics or LULC change separately, with limited integration of policy-driven scenarios. Therefore, this study aimed to conduct multi-scenario carbon storage modeling to evaluate LULC protection strategies in the Cimanuk Watershed, Indonesia, an area experiencing significant LULC pressures. The method used consisted of Support Vector Machine (SVM)–Markov, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Geodetector, and Getis-Ord Gi*. A total of four scenarios were used to project LULC and carbon storage in 2042, which included Business as Usual (BAU), Paddy Field Protection (PFP), Forest Protection (FOP), and Paddy Field and Forest Protection (PFFOP). The results showed that forest area declined by 39,400 ha between 2015 and 2025, thereby reducing carbon storage. The PFFOP scenario was identified as the most viable, combining the protection of paddy fields and forests to balance agricultural production and carbon sequestration. Among the factors analyzed, slope exerted the greatest influence on carbon storage. Spatial cluster analysis showed that carbon hotspots were predominantly located in the upper Cimanuk sub-watershed. These results offered valuable insights into scenario-based sustainable watershed management to optimize carbon storage and maintain agricultural function. Furthermore, the proposed framework showed promising potential for application in other tropical watersheds, serving as a reference for decision-makers in sustainable watershed management.

Earth doi: 10.3390/earth7030073

Authors: Aleksandar Valjarević Ljiljana Gulan Uroš Durlević

This study investigates the spatial distribution of ambient dose equivalent rates (ADER) on Avala and Kosmaj mountains, two protected landscapes located within the territory of the City of Belgrade, Serbia. Both sites, characterized by rich biodiversity and cultural heritage, were analyzed to assess their radiological safety and suitability for outdoor recreation. In mid-October 2025, in situ measurements were conducted at 42 sampling points using the Radex RD1503+ GM counter. The recorded values ranged from 0.085 to 0.2 µSv/h, remaining below the recommended safety threshold of 0.2 µSv/h. To visualize the gamma dose spatial variability, all field data were georeferenced and processed in QGIS 3.28.10 using the Inverse Distance Weighting (IDW) interpolation method. Integration of GIS and Remote Sensing techniques enabled the correlation between gamma radiation patterns, land cover, and elevation gradients derived from digital elevation models (DEMs). The comprehensive GIS-based approach confirms that Avala and Kosmaj maintain low natural background radiation levels comparable to global averages for similar geomorphological settings, and therefore are safe and suitable for sports, tourism and recreation. The applied combination of field dosimetry, Remote Sensing, and geostatistical modeling provides a valuable framework for continuous environmental monitoring and sustainable landscape management in protected mountainous landscapes in Central Serbia.

Earth doi: 10.3390/earth7030072

Authors: Dino Bečić Mateo Gašparović

Urban heat risk is intensifying globally, yet the relative contributions of climate warming and demographic restructuring to spatiotemporal risk change remain poorly understood, particularly in post-socialist cities experiencing simultaneous thermal intensification and population aging. This study develops a Heat Risk Population Index (HRPI) integrating satellite-derived land surface temperature, CERRA reanalysis air temperature, and census-based demographic sensitivity for 218 Zagreb neighborhood councils (2001–2024). A multi-scale analytical framework combining additive decomposition, enhanced partial correlations, and geographically weighted regression (GWR) was applied to disentangle the drivers of heat risk change. HRPI increased significantly across all neighborhood councils (mean ΔHRPI = 0.197, p < 0.001), with strong positive spatial autocorrelation (Moran’s I = 0.416). While air temperature change dominated the city-wide mean increase (72.1%), demographic sensitivity change explained the largest share of spatial variance across neighborhood councils (partial r = 0.677 vs. 0.524 for air temperature), driven by spatially heterogeneous demographic transitions—youth out-migration, aging-in-place in southeastern post-socialist estates, and gentrification in central districts. GWR substantially outperformed global OLS (ΔAICc = 60.1; Adj. R2: 0.649 → 0.816), with local demographic effect sizes varying fivefold across the city. These results demonstrate that heat risk drivers operate at distinct spatial scales: climate dominates city-wide magnitude while demographics determine spatial differentiation. Effective adaptation requires universal thermal interventions combined with spatially targeted demographic strategies in identified hotspot neighborhoods. The multi-scale framework is applicable to other post-socialist cities undergoing concurrent climate and demographic change.

Earth doi: 10.3390/earth7030071

Authors: Dang Thi Hong Ngoc Ngo Thi Hieu Tran Van Ty Nguyen Anh Hung Pankaj Kumar Nigel K. Downes Huynh Vuong Thu Minh

Drought is a recurrent hazard in the Vietnamese Mekong Delta (VMD), with major implications for agriculture, water resources, and rural livelihoods. This study assesses drought variability in Kien Giang Province, Vietnam, from 1992 to 2024 using two multiscale indicators: the Multivariate Standardized Precipitation Index (MSPI) and the Multivariate Standardized Precipitation Evapotranspiration Index (MSPEI). Principal Component Analysis (PCA) was applied to Standardized Precipitation Index (SPI)- and Precipitation Evapotranspiration Index (SPEI)-based time series spanning multiple accumulation periods (3–48 months) to derive integrated drought signals and to reduce redundancy across timescales. The results show that the first principal component (PC1) captured a high proportion of total variance across stations, indicating strong coherence in drought dynamics across the province. Both MSPI and MSPEI successfully identified major historical drought episodes, particularly the severe events of 2004–2005 and 2015–2016. However, the two indices differed in their temporal behaviour: MSPI responded more directly to precipitation deficits, whereas MSPEI showed slower post-drought recovery in recent years, suggesting greater sensitivity to evaporative demand and climatic water-balance stress. These differences indicate that evapotranspiration-sensitive indices may provide added analytical value in warming coastal environments. Overall, the combined multiscale framework offers a robust basis for drought monitoring, comparative assessment, and water-resource planning in Kien Giang and other drought-prone coastal delta settings.

Earth doi: 10.3390/earth7020070

Authors: Karen G. Añaños Wendi A. Gonzales Asto Alina D. Corpodean José A. Rodríguez Martín

The Horn of Africa (Kenya, Djibouti, Uganda, Eritrea, Somalia, Ethiopia, South Sudan, and Sudan) faces the highest rates of hunger and malnutrition in the world, exacerbated by conflict and adverse weather conditions. These factors have serious health, educational, social, and economic consequences, especially for children under five and pregnant women. In this context, we analyze each country’s progress toward Sustainable Development Goals (SDGs) 1, 2, 3, and 4, which are closely linked to the eradication of hunger, improved health, and access to quality education. Using comparable data from the United Nations 2030 Agenda up to 2019, the achievement of the SDGs is assessed through a multidimensional approach based on Pena’s P2 distance method, constructing a composite indicator that allows for robust cross-country comparisons. This method helps identify the key measures needed to prevent future humanitarian crises in the Horn of Africa, including providing urgent assistance to these countries in vital areas such as water, nutrition, education, sanitation, and child and maternal immunization. Factors related to the work of qualified healthcare personnel in treating diseases and improving maternal and neonatal health, as well as facilitating access to basic services such as clean drinking water and sanitation and ensuring girls’ access to primary education, top the rankings in terms of their correlation with greater progress by these countries in achieving these four SDGs, which are crucial for improving the well-being of their populations.

Earth doi: 10.3390/earth7020069

Authors: Thuruthiyil Bahuleyan Subhamgi Brema Jayanarayanan Jibu Thomas Priya Krishnamoorthy Lakshmi Ammal

Microplastic (MP) contamination has become a widespread environmental concern in coastal and freshwater wetlands, ecosystems that play a crucial role in hydrological regulation, nutrient cycling, and biodiversity conservation. Despite their ecological importance, research on MPs in wetlands remains fragmented and comparatively underexplored. This study presents a comprehensive bibliometric and visualization analysis of global research on MPs in coastal wetlands. A total of 17,523 publications were retrieved from the Web of Science Core Collection (2002–2025) using predefined search strings and screening criteria. Analytical tools, including VOSviewer version 1.6.20, were employed to examine co-authorship networks, country contributions, and keyword co-occurrence patterns. The results indicate a significant increase in MP-related publications after 2016, with China, the United States, and India emerging as leading contributors. However, wetland-specific studies constitute only a small fraction compared to marine-focused MP research, highlighting a substantial research gap. Key research themes identified include MP sources, transport pathways, sediment–water interactions, and ecotoxicological impacts. Additionally, there is growing attention to remediation approaches, particularly those involving TiO2, ZnO, Fe3O4, and graphene derivatives, employing photocatalytic, magnetic, and adsorptive mechanisms. Overall, the findings underscore the limited focus on wetland ecosystems in MP research and emphasize the urgent need for integrated research efforts and management strategies to address MP contamination in these vulnerable ecosystems.

Earth doi: 10.3390/earth7020068

Authors: Christina N. Tsaimou Maria Intzeler Vasiliki K. Tsoukala

Ports are increasingly recognized as actors that influence the sustainability of urban environments due to their spatial footprint, operational intensity, and close interaction with surrounding cities. As digital technologies become more embedded in infrastructure management, Digital Twins (DTs) are emerging in port systems as tools that can support more integrated and sustainable port–city development. This paper investigates how DT technologies applied in ports can contribute to broader urban sustainability objectives within port–city systems. The analysis is based on a synthesis of documented DT practices from selected European ports. Geographic Information System (GIS) visualization is used to illustrate the spatial relationship between port infrastructure and the surrounding urban environment, as well as to map the connections between DT application fields and relevant Sustainable Development Goals (SDGs). A comparative interpretation of the extent to which DT applications align with urban sustainability goals across the examined ports is achieved through the development of an SDG contribution scale. Insights derived from the European cases are subsequently contextualized for the Port of Piraeus, exploring how similar DT approaches could support both operational efficiency and the long-term climate resilience of the port–city environment. Overall, the findings provide practical insights for port authorities, urban planners, and policymakers seeking to align digital transformation strategies with sustainable and climate-responsive infrastructure development in port–city systems.

Earth doi: 10.3390/earth7020067

Authors: Mirjana Miletić Milena Lakićević Ana Firanj Sremac

Urban districts along major rivers are undergoing rapid transformation, yet long-term evidence on how redevelopment reshapes land cover and vegetation structure remains limited in post-socialist cities. This study examines the spatio-temporal evolution of land use and land cover (LULC) and vegetation dynamics along the Sava River corridor in Belgrade from 1990 to 2024. CORINE Land Cover (CLC) datasets were combined with Landsat-derived NDVI and MSAVI time series, while high-resolution Esri Wayback imagery was used for visual interpretation and qualitative corroboration of the detected land-cover and vegetation patterns. Beyond conventional NDVI/LULC assessments, the study integrates multi-decadal spectral trends with functional vegetation structure classification to evaluate canopy continuity and ecological configuration under contrasting redevelopment models. Results reveal a pronounced divergence between the two riverbanks. The left bank (New Belgrade) maintains stable land-cover composition and consistently higher NDVI and MSAVI values, indicating preserved green infrastructure and sustained canopy continuity. In contrast, the right bank (Belgrade Waterfront) experienced substantial land-cover conversion after 2006, with a statistically significant decline in vegetation greenness (NDVI −0.020 dec−1, p < 0.001) and a marked increase in impervious surfaces. MSAVI-based functional classes indicate a shift from mixed low vegetation to predominantly sealed land, while tree canopy remained persistently low throughout redevelopment. The findings demonstrate measurable ecological simplification and canopy loss, even where nominal green areas remain present. By providing a rare multi-decadal, spatially explicit comparison of two contrasting planning paradigms within the same river corridor, the study contributes new empirical evidence on how governance and redevelopment models shape riparian ecological trajectories and sustainable urbanism in post-socialist cities. Strengthening blue-green infrastructure and restoring native riparian vegetation are essential for enhancing climate resilience and ensuring long-term riverfront sustainability.

Earth doi: 10.3390/earth7020066

Authors: María A. Pérez-Fernández Irene Ariadna De Lara-Del Rey Anathi Magadlela

Mediterranean-type ecosystems (METs) occur on five continents and represent some of the most climatically constrained yet biologically rich regions on Earth. In these environments, legumes and their nitrogen-fixing rhizobial symbionts—including widely distributed genera such as Rhizobium, Bradyrhizobium, and Ensifer—play a pivotal role in sustaining plant productivity, nutrient cycling, and ecosystem resilience. This review synthesizes current knowledge on the environmental regulation of legume–Rhizobium symbiosis specifically within Mediterranean-type ecosystems, focusing on how nitrogen (N) and phosphorus (P) availability, light conditions, and carbon allocation trade-offs shape symbiotic performance across the five Mediterranean-type regions of the world (California, central Chile, the Cape Region of South Africa, southwestern Australia, and the Mediterranean Basin). By integrating physiological, ecological, and biogeochemical perspectives, we highlight how the shared features of these regions—strong seasonal drought, chronic nutrient limitation (particularly P in southwestern Australia and the Cape Region), recurrent fires, and exceptionally high plant diversity—constrain and, at the same time, favor the ecological success of symbiotic legumes. Throughout the review, we use case studies from key legume genera such as Lupinus in Chile and southwestern Australia, Virgilia and other Cape legumes in South Africa, Acacia in Australian kwongan and woodlands, and Medicago and Cytisus in the Mediterranean Basin and California to illustrate how general principles of legume–Rhizobium ecology manifest under Mediterranean-type climatic and edaphic constraints. Beyond summarizing established mechanisms, we critically examine the limitations of current metagenomic approaches, which often provide descriptive inventories of soil microbial communities without linking microbial composition to functional outcomes. We argue that advancing the field requires integrated, hypothesis-driven research that combines multi-omic tools with plant eco-physiology, soil nutrient dynamics, and temporal replication. Finally, we outline key priorities for future research, including the integration of functional ‘omics’, the study of microbiome interactions beyond rhizobia, the development of predictive models for Mediterranean-type ecosystems under climate change, and the application of symbiotic principles to restoration and agroecological management. By bridging molecular, physiological, and ecosystem perspectives, this review provides a conceptual framework for understanding and enhancing legume–Rhizobium symbiosis across five continents in a rapidly changing world.

Earth doi: 10.3390/earth7020065

Authors: Nenad Maric Mila Ilic Jelena Avdalovic Gordana Devic Jelena Milic

Hydrocarbon-contaminated sites are among the most common challenges for environmental professionals worldwide. Although bioremediation strategies have emerged, their efficiency in cleaning hydrocarbon-contaminated soil depends considerably on local conditions. This study presents a science-based framework to assess the potential for soil bioremediation based on site-specific conditions. At multiple depths, soil samples were collected from four locations (S1, S7, S13, and S16) within a historically contaminated heating plant site. Using a three-step framework based on the content of total petroleum hydrocarbons (TPH), hydrocarbon pollutant fractions, ecotoxicity, and microbial population density, the study quantitatively (using a scoring matrix) revealed considerable variability across locations regarding the potential for bioremediation. Thus, due to balanced parameter contributions, S16 has the most promising bioremediation potential. Location S1 may require additional effort to enhance microbial populations. Locations S7 and S13 have low scores, with S13 being the least suitable, requiring extensive efforts to improve site-specific conditions for bioremediation. By integrating chemical, biological, and ecological factors, this science-based framework emphasizes the importance of site pre-characterization, thus providing an evaluation tool for bioremediation applications at hydrocarbon-contaminated sites with similar data availability. Moreover, the pre-remediation matrix scoring evaluation results align with the in situ bioremediation efficiency observed at the site.

Earth doi: 10.3390/earth7020064

Authors: Konstantinos Tsimnadis Konstantinos Merakos Vanias Elena Kallikantzarou Christos Karavitis Panagiotis Trivellas

Mountainous Mediterranean rivers provide essential ecosystem services but are increasingly affected by land-use change, hydraulic works, and inadequate wastewater management. This study investigates the links between geomorphological transformation and river water quality in the Central Eurytania drainage basin (Greece) over the past two decades, within the institutional framework of European and Greek environmental legislation, with emphasis on the protection and restoration of aquatic ecosystems. Georeferenced satellite imagery from 2003/2010 and 2023, Google Earth Engine (GEE, Python Earth Engine API: 1.7.20)-based spatial analysis, high-resolution UAV orthomosaics, and seasonal spectrophotometric analyses were integrated to assess spatial and temporal dynamics. Results indicate that land-use changes, including the construction of solar parks, expansion of tourism infrastructure, and partial agricultural abandonment, reflect ongoing socio-economic shifts influencing fluvial processes. Water-quality analyses further showed that channel alteration and wastewater inputs jointly degrade ecological conditions. The findings highlight the need for integrated watershed management focused on riparian buffer restoration, improved wastewater control, and systematic monitoring of hydromorphological change. The proposed interdisciplinary framework contributes to the assessment of environmental resilience in Mediterranean mountainous watersheds, which are increasingly vulnerable to climatic and socio-economic pressures.

Earth doi: 10.3390/earth7020063

Authors: Carlos Diego Rodríguez-Yparraguirre Abel José Rodríguez-Yparraguirre Cesar Moreno-Rojo Wendy Akemmy Castañeda-Rodríguez Janet Verónica Saavedra-Vera Atilio Ruben Lopez-Carranza Iván Martin Olivares-Espino Andrés David Epifania-Huerta Elías Guarniz-Vásquez Wilson Arcenio Maco-Vasquez

The agricultural sector is undergoing a profound digital transformation driven by artificial intelligence, the Internet of Things, remote sensing, robotics, blockchain, and edge computing, which are being integrated into crop monitoring, irrigation management, disease detection, and supply chain transparency systems. This study employs systematic evidence mapping to characterize the applications of emerging digital technologies in sustainable agriculture; it delineates technological trajectories, areas of application, implementation gaps, and opportunities for improvement. Adhering to the PRISMA 2020 reporting protocol, 101 peer-reviewed articles indexed in Scopus and Web of Science (2020–2025) were identified, screened, and subjected to integrated thematic and bibliometric synthesis, using RStudio Version: 2026.01.1+403 and VOSviewer 1.6.20 for data mining on keywords and technological evolution patterns. Results show that deep learning and computer vision models achieved diagnostic accuracies of 90–99%, smart irrigation systems reduced water consumption by 10–30%, predictive yield models frequently reported R2 values above 0.80, and greenhouse automation reduced energy consumption by approximately 20–30%. Blockchain-based architectures improved traceability and secure data transmission by 15–20%, while remote sensing integration enhanced spatial estimation accuracy up to R2 = 0.92. The findings demonstrate a measurable transition toward data-driven, resource-efficient agricultural ecosystems supported by validated digital architectures. However, interoperability limitations, lack of standardized performance metrics, scalability challenges, and uneven geographical implementation—identified in nearly 40% of studies—highlight the need for harmonized evaluation frameworks, cross-platform integration standards, and long-term field validation to ensure sustainable and scalable digital transformation.

Earth doi: 10.3390/earth7020062

Authors: Alex McBratney Sandra Evangelista Nicolas Francos Anilkumar Hunakunti Ho Jun Jang Wartini Ng Thomas O’Donoghue Julio Cesar Pachón Maldonado Minhyung Park Amin Sharififar Quentin Styc Yijia Tang

The long-term sustainability of Australian agriculture is fundamentally constrained by the capacity, condition, availability, and governance of soil resources. Australian soils are among the oldest and most weathered globally, highly heterogeneous, and often slow or effectively irreversible to recover once degraded. Traditional approaches centred on soil health, while valuable at paddock scale, are insufficient to address national-scale challenges related to spatial variability, data continuity, economic valuation, and policy integration. This paper examines soil security as a policy-relevant framework for supporting more sustainable Australian agriculture. Building on the dimensions of soil security (capacity, condition, capital, connectivity, and codification), we synthesise recent Australian case studies to show how soil security extends beyond soil health to integrate biophysical properties, digital soil infrastructure, socio-economic value, and governance mechanisms. Drawing on recent Australian case studies, this review identifies advances in digital soil mapping, national soil assessments, economic valuation of soil capital, stakeholder connectivity, and emerging policy frameworks, while also identifying persistent gaps in regulation, data standardisation, and institutional coordination. The paper argues that soil security can help operationalise 3-N agriculture—Net-Zero, Nature-Positive, and Nutrient-Balanced systems—by translating sustainability goals into spatially explicit, place-based decisions grounded in soil realities. By explicitly accounting for soil capacity limits, condition trajectories, capital value, information flows, and codified rules, soil security can support more realistic climate mitigation strategies, targeted nature-positive interventions, and durable nutrient security outcomes. We conclude that embedding soil security more explicitly within Australian agricultural research, policy, and governance would strengthen efforts to deliver productive, resilient, and socially legitimate food and fibre systems. Without soil security, sustainability frameworks may remain difficult to operationalise consistently; with soil security, they can be translated more effectively into measurable, place-based, and durable decisions.

Earth doi: 10.3390/earth7020061

Authors: Nikolaos-Fivos Galatoulas Dimitrios E. Tsesmelis Angeliki Kavga Kleomenis Kalogeropoulos Pantelis E. Barouchas

Climate variability and rising water scarcity are major challenges to agricultural sustainability, particularly in Mediterranean climates with high spatial heterogeneity. Agroclimatic zoning is a fundamental analytical tool for digital agriculture and climate-resilient agriculture. The current effort proposes an integrated agroclimatic and micro-agroclimatic zoning approach for Greece, based on the Aridity Index (AI), CORINE Land Cover 2018 land-use data, and topographic factors. Daily precipitation and reference evapotranspiration data from 139 meteorological stations and 382 rain gauges were spatially interpolated using Empirical Bayesian Kriging, identifying eight agroclimatic classes adapted to the country’s specific conditions. The results indicate a high degree of variability in space, with most agricultural areas being classified as dry to sub-humid, suggesting higher irrigation requirements and sensitivity to drought. Micro-agroclimatic zones have been identified by combining agroclimatic classes, land use, and elevation. Consequently, the derived zones can be used as groundwork for designing methodologies towards more efficient agrometeorological monitoring through the improved localization of IoT agrometeorological stations. Validation with the Köppen–Geiger climate classification reveals high spatial and statistical agreement (χ2 = 248,454.09, df = 49, p < 0.001), proving the climatic validity of the proposed approach and its higher sensitivity to local water balance conditions.

Earth doi: 10.3390/earth7020060

Authors: Sitthisak Moukomla Supaporn Manajitprasert Nichaphat Petchkaew Phurith Meeprom

Nocturnal urban heat presents significant but understudied risks within tropical megacities, where high humidity and heat storage in built-up areas prevent nighttime thermal recovery and intensify chronic heat stress. This study investigates the nocturnal surface urban heat island (SUHI) dynamics in the Bangkok Metropolitan Region (BMR) over two decades (2003–2023) with a daytime SUHI comparative baseline. We examined long-term thermal variations using MODIS land surface temperature data and Landsat urban–rural classification. The results demonstrate an increase in nighttime land surface temperature (LST) of 0.109, with nocturnal SUHI proving more persistent than its daytime counterpart with a temperature difference as high as 2.0 °C between urban and rural areas during the night. While daytime SUHI peaked at 6.3 °C in April 2011, with the strongest effects during April–May, nocturnal SUHI exhibited less seasonal variability but sustained elevated values throughout the year. Heat-retaining nocturnal hotspots have expanded from central Bangkok to newly developed urban areas. Cross-correlation analysis suggests that El Niño–Southern Oscillation (ENSO) strongly modulates SUHI anomalies, with maximum cross-correlations for a time lag of 3 months. These results suggest the need for urban adaptation strategies that specifically address nocturnal heat, as well as design strategies such as improved ventilation, high-emissivity materials, green infrastructure allowing evapotranspiration, and cooling centers for vulnerable populations to enhance thermal resilience across the BMR.

Earth doi: 10.3390/earth7020059

Authors: Nazila Alaei Fatemeh Saeedi Nazarlu Hassan Khavarian Nehzak Raoof Mostafazadeh

The spatial assessment of soil erosion drivers provides essential information for prioritizing soil conservation areas. This study aims to compare the performance of the Ordinary Least Squares (OLS) regression model and the Geographically Weighted Regression (GWR) model in explaining and analyzing the spatial variations of soil erosion in the Qara-Su watershed (Ardabil Province, Iran) and identifying the relative roles of the driving factors affecting erosion. To determine the relative importance of factors influencing soil erosion in the Qara-Su watershed, potential soil erosion (A) data and RUSLE model factors, including R, K, LS, C, and P, were collected at 13,845 points within the watershed. Initially, general relationships between erosion and contributing factors were examined using the OLS regression model. Subsequently, to analyze the spatial variability of relationships and identify the relative importance of factors at different locations within the watershed, the GWR model with an adaptive kernel and optimal bandwidth selection based on AICc was employed. The performance of the OLS and GWR models was compared based on fit indices such as R2 and Akaike Information Criterion corrected (AICc), and the relative importance of erosion factors was determined based on the mean local GWR coefficients. Results from the RUSLE model indicated an average annual soil erosion of approximately 7.64 tons per hectare, suggesting that the watershed falls into the moderate erosion risk category. According to the GWR model, significant improvements in explaining variations and reducing errors were observed, with higher R2 and adjusted R2 values (0.62 vs. 0.50) and lower AICc values (3687 vs. 97,848) compared to the OLS model. The local GWR coefficients confirmed spatial non-stationarity and revealed that LS (topography) has the highest importance in mountainous areas. The C factor showed a stronger protective effect in agricultural land-use areas. These results provide a basis for developing targeted strategies to mitigate and manage erosion drivers with higher relative importance and facilitate a better understanding of the causes and mechanisms of soil erosion across the watershed.

Earth doi: 10.3390/earth7020058

Authors: Dragana Milijašević Joksimović Ana M. Petrović Valentina Nikolova Jan Babej Ivan Novković

This study evaluates long-term (2014–2023) and seasonal water quality in the Jerma River, a transboundary watershed between Serbia and Bulgaria, using annual mean physicochemical and microbiological parameters at two monitoring sites as well as Water Pollution Index (WPI) classifications. Seasonal datasets (spring, summer, autumn and winter) were applied to assess intra-annual variability. The water quality reflects the influence of geomorphology, hydrological seasonality, and anthropogenic pressures. The river reflects the influence of geomorphology, hydrological seasonality, and anthropogenic pressures typical of mountain catchments. Upstream conditions were favorable, characterized by stable oxygen regimes, low suspended solids, and low nutrient levels, with occasional phosphorus enrichment. Downstream sections showed stronger deviations, including elevated nutrients, departures from reference conditions, and variability in microbiological indicators. Seasonal analyses indicated stable oxygen conditions at both sites, while nutrient and microbial parameters varied during specific periods. Dissolved metals remained within Class I thresholds throughout the study. WPI values confirmed spatial and seasonal differences. Upstream water quality mainly corresponded to Class II, with occasional shifts toward Class III, whereas downstream values fluctuated between Classes II and IV, with short-term deterioration during seasons. Higher variability occurred in spring and summer, while winter conditions were more stable. Overall, the results indicate better upstream status and increased nutrient and microbial pressures downstream, highlighting the need for continued monitoring and management.

Earth doi: 10.3390/earth7020057

Authors: Mueletshedzi Mukhaninga Caston Sigauke Thakhani Ravele

Accurate forecasting of local weather patterns is essential for climate resilience and sustainable planning. This study analysed 35 years (1990–2025) of hourly temperature and precipitation data from Thohoyandou, South Africa, to assess the impacts of climate change and improve anomaly prediction. Exploratory analysis and Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) decomposition revealed accelerated warming trends of 0.025 °C per year in temperature anomalies, alongside highly irregular rainfall patterns characterised by extreme events rather than systematic changes. Three models, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and a Generalised Additive Model (GAM), were evaluated for anomaly forecasting, with feature selection guided by LASSO regression. For temperature, the LSTM performed better than the ANN and GAM, with MSE = 0.458, MAE = 0.457, MBE = 0.087, and MASE = 0.510. For temperature anomalies, the LSTM model performed best, followed by the GAM and ANN models. For precipitation anomalies, the LSTM model also achieved the lowest prediction error, with MSE = 0.187, MAE = 0.111, MBE = −0.009, and MASE = 1.873; however, MASE values above 1 indicate that rainfall forecasting remains challenging. These results show the LSTM model’s ability to handle temperature anomalies and the difficulty of modelling rainfall. GAM performed less accurately but steadily in modelling precipitation.

Earth doi: 10.3390/earth7020056

Authors: Matteo Gentilucci Hamed Younes Rihab Hadji Gilberto Pambianchi

The increasing frequency of disasters caused by landslides, mainly due to climate change leading to more intense extreme events, requires reliable predictive models for risk mitigation. Italy, in particular, is a country at high risk of landslides, but the lack of an updated catalogue of landslide activation dates poses a significant challenge for defining reliable activation thresholds. This study develops a methodology for mapping landslide susceptibility based on events in a pilot area of central Italy, integrating a database of landslides with known activation dates with predisposing and triggering parameters. Two statistical techniques were compared to assess their predictive performance in discriminating landslide from non-landslide conditions during extreme precipitation events. A comparison between binary logistic regression (BLR) and decision trees (QUEST) revealed the clear superiority of the BLR model, which achieved excellent predictive accuracy (AUC = 0.913). The model identified clay-rich lithology, gentle slopes (0–16°) and maximum daily precipitation as the most significant controlling factors. This result led to the generation of three derivative products: a susceptibility map, a hazard map for an extreme precipitation scenario with a 100-year return period, and a spatially distributed map of activation thresholds. This threshold map quantifies the intensity of precipitation required to exceed a critical probability of landslide initiation (p > 0.7) at any point in the territory. The susceptibility map highlights critical areas within the study area, while the hazard map also includes the return period of the event. The threshold map is a direct and operational tool for early warning systems, transforming a statistical model into a guide for real-time risk management. The study area serves as a pilot area that could allow this methodology to be replicated. With the integration of real-time meteorological data, it could function as a real-time warning system. The proposed framework therefore provides a directly actionable tool for civil protection agencies, land-use planning authorities, and emergency managers, enabling location-specific rainfall alert thresholds to be issued rather than a single regional value, with the potential to reduce both false alarms and missed warnings.

Earth doi: 10.3390/earth7020055

Authors: Milica Vidak Vasić Tea Spasojević-Šantić Zagorka Radojević

This study presents a comparative evaluation of Environmental Product Declarations (EPDs) within the traditional ceramic industry, emphasizing how differences in data structures, reporting formats, and background databases influence the interpretation of environmental performance. Four product categories—ceramic tiles, sanitary ware, clay bricks, and clay roof tiles—were analyzed using datasets from One Click LCA and the International EPD System. Environmental indicators assessed include fossil-based and total Global Warming Potential (GWP), freshwater consumption, and energy demand, standardized per 1 kg of product. The analysis reveals that discrepancies between platforms arise primarily from the limited level of process-specific information required by current EPD formats, rather than from the platforms themselves. Missing details on raw material composition, firing conditions, and energy sources restrict comparability and hinder the development of robust benchmarks. Furthermore, the study highlights the need for harmonized databases, more transparent PCR requirements, and consistent reporting rules to support meaningful cross-platform comparisons. As the first study to examine EPD data structures for ceramic products across two major reporting systems, it highlights the need to expand product-specific benchmarks and enhance disclosure practices to strengthen the role of EPDs in sustainable market design and climate policy.

Earth doi: 10.3390/earth7020054

Authors: Nzuzo Nxumalo Ntombifuthi Precious Nzimande Sifiso Xulu

In response to land-use and land-cover (LULC) changes in South Africa, which have varied effects on biodiversity, several studies have characterized LULC changes using remote sensing data due to its cost-effectiveness, repetitiveness, spatial coverage and flexibility. However, the geotemporal and methodological characteristics of these studies remain relatively unknown. In this regard, we review remote sensing-based studies conducted in South Africa using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From the 343 articles retrieved from Web of Science, Google Scholar, and Scopus databases, 103 studies were eligible for analysis. The analysis showed that (a) various remote sensing datasets were increasingly and effectively used to characterize LULC in South Africa over the period 2001–2024, primarily Landsat data with integration of various advanced classification algorithms; (b) most studies were conducted in the eastern seaboard, particularly in the Maputaland–Pondoland–Albany hotspot and highveld to the north, and (c) much research dealt with issues pertaining to “pristine class” conversion to urban area and other human-induced activities, mainly in biodiversity-rich landscapes. Overall, LULC studies achieved consistently reliable accuracies, largely using publicly available geospatial datasets, thereby creating an accessible foundation for all researchers. LULC research is expected to increase as conservation efforts strengthen amid ongoing developments in South Africa.

Earth doi: 10.3390/earth7020053

Authors: Yves Hategekimana Valentine Mukanyandwi Georges Kwizera Fidele Karamage Emmanuel Ntawukuriryayo Fabrice Manzi Gaspard Rwanyiziri Moise Busogi

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda.

Earth doi: 10.3390/earth7020052

Authors: Claudiana Mesquita de Alvarenga Lívia Alves Alvarenga Pâmela Aparecida Melo Javier Tomasella Pâmela Rafanele França Pinto Carlos Rogério de Mello Jorge M. G. P. Isidoro

Climate change significantly affects hydrological responses, yet studies addressing future water availability in the Paraopeba River Basin (PRB), an important tributary of the São Francisco River Basin in Brazil, remain limited, particularly under CMIP6 scenarios and using distributed hydrological modeling approaches. In this context, this study evaluated the hydrological responses of the PRB, under climate change using the MHD-INPE. Future projections were based on an ensemble of seven climate models from the NEX-GDDP-CMIP6 collection, considering a baseline period (1992–2014), three future periods 17(2040–2060, 2061–2080 and 2081–2100) and two socioeconomic scenarios (SSP245 and SSP585). The model satisfactorily reproduced observed streamflow during the baseline period. Under the SSP585 scenario, the projections indicate stronger alterations in water availability, with a potential intensification of flood and drought events, as reflected by reductions in minimum streamflows (Q90) and increases in maximum streamflows (Q10), particularly in sub-basins 4 and 5, where Q90 reductions approach 30% and Q10 increases reach 11.7%. Additionally, a decrease in Q7,10 values was observed, which enabled the analysis of the Conflict Index (Icg), indicating that water withdrawals currently granted may exceed the limits established by existing legislation in future scenarios (Igc > 1).

Earth doi: 10.3390/earth7020051

Authors: Md Abdul Mueed Choudhury Giuseppe Modica Salvatore Praticò Ernesto Marcheggiani

The Copernicus Sentinel-2 (S2) data are a crucial resource for urban policymakers in land-cover classification, offering a freely accessible alternative to expensive commercial data sources. While medium spatial resolution often limits the applicability of data-intensive machine learning approaches, the Geographic Object-Based Image Analysis (GEOBIA) framework could be an effective, operational alternative for urban land-cover classification using S2 data. This study applies the Geographic Object-Based Image Analysis (GEOBIA) approach to classify land cover in Hue, Vietnam, using Sentinel-2 data processed through the eCognition interface. The study’s findings emphasize the potential of GEOBIA and S2 data in enhancing decision-making processes for city authorities, ensuring better resource allocation, environmental protection, and infrastructure development. The results indicate that the method performs reliably for mesoscale and spatially continuous classes, such as vegetation and built-up surfaces, while accuracy is lower for small or spectrally heterogeneous features, particularly shallow water bodies and fragmented rice paddies, due to mixed-pixel effects inherent in 10–20 m resolution imagery. The results demonstrate an Overall Accuracy (OA) of 91%, highlighting the method’s effectiveness in extracting and classifying urban land-cover classes. This study demonstrates a replicable model for urban land monitoring that can be adapted across various geographic contexts. Furthermore, this approach fosters a more data-driven governance model, where urban expansion and land-use changes can be monitored in real time, allowing for proactive interventions. With urbanization accelerating worldwide, particularly in rapidly developing regions, such a cost-effective and accessible classification method can significantly aid in achieving long-term urban sustainability. The findings illustrate the relevance of GEOBIA as a feasible tool for supporting data-driven urban governance, enabling systematic tracking of land-use change, informed infrastructure planning, and sustainable urban management in both developed and rapidly urbanizing regions.

Earth doi: 10.3390/earth7020050

Authors: Vera Surtia Bachtiar Purnawan Purnawan Reri Afrianita Yega Serlina Haldi Reivan Thamrin Zulva Shabri Assyifa Raudina

Urban transport is a major contributor to particulate matter (PM) pollution, yet information on the spatial distribution of fine and ultrafine particle fractions remains limited in medium-sized tropical cities. This study examines the spatial variability of transport-related particulate matter across eleven urban districts in Padang, Indonesia, using Nano Sampler-based measurements. Size-segregated PM concentrations (PM10, PM2.5, PM1, and PM0.5) were obtained from 24 h sampling campaigns conducted between June and July 2025 at locations selected based on urban density, proximity to major roadways, and land-use characteristics. Descriptive statistics, correlation analysis, and principal component analysis were applied to evaluate spatial patterns and traffic-related influences. The results show pronounced spatial heterogeneity in PM concentrations. Traffic-intensive and mixed-use districts exhibited higher PM levels, particularly for coarse and ultrafine fractions, whereas coastal districts showed lower concentrations due to enhanced atmospheric ventilation. Strong correlations were observed between traffic volume and coarse PM fractions, with moderate associations for fine and ultrafine particles, indicating combined exhaust and non-exhaust emissions. These findings highlight the importance of district-specific mitigation strategies and size-resolved monitoring to support effective urban air-quality management.

Earth doi: 10.3390/earth7020049

Authors: Miles Richardson

References [...]

Earth doi: 10.3390/earth7020047

Authors: Zhenxin Xu Yu Ye

Agricultural carbon emissions, a key part of terrestrial carbon emissions, affect global carbon accounting, with historical data scarcity adding to calculation difficulty. Exploring agricultural planting structure evolution can supplement historical data and improve accounting accuracy. Based on local chronicles and statistics, this study reconstructs Northeast China’s planting structure of six major crops during the 1950s–1980s via threshold classification and transfer matrix methods. Results show high-carbon crops (corn in particular) expanded notably, while low-carbon crops (especially sorghum) declined. Planting patterns varied regionally, with the most complex structural changes occurring in the 1960s–1970s. Agricultural carbon emissions fluctuated in phases; the planting scale effect dominated emission growth, the intensity effect inhibited it, and the structural effect played a heterogeneous auxiliary role. This study provides a historical basis for low-carbon agricultural planning and differentiated carbon reduction policies.

Earth doi: 10.3390/earth7020048

Authors: Essam Sharaf El Din Ahmed Shaker

Monitoring surface water quality over large river systems remains challenging due to sparse in situ sampling and the need for decision-ready indicators. This study aims to address this problem by developing and evaluating an integrated Landsat 8-based backpropagation neural network and Canadian Council of Ministers of the Environment Water Quality Index (L8-BPNN-CCME-WQI) for precise surface water quality assessment over the Saint John River (SJR), New Brunswick, Canada. The proposed approach combines atmospherically corrected Landsat 8 imagery, BPNN for estimating multiple surface water quality parameters (SWQPs), and CCME-WQI to translate SWQP fields into transparent water quality levels. The L8-BPNN-CCME-WQI models were trained using in situ measurements of turbidity, total suspended solids (TSS), total solids (TS), total dissolved solids (TDS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, electrical conductivity (EC), and temperature collected during our five field campaigns (from June 2015 to August 2016) and surface reflectance from five Landsat 8 scenes. The developed models achieved high performance during internal calibration and testing (R2 ≥ 0.80 for all SWQPs) and demonstrated robust performance (R2 ≈ 0.75–0.88) when applied to two independent surface water quality datasets from additional rivers across New Brunswick. Pixel-wise SWQP predictions were then input to the CCME-WQI formulation to derive reach-scale water quality levels, revealing that the lower Saint John River basin (below the Mactaquac Dam) is generally classified as “Fair” (CCME-WQI ≈ 67), whereas the middle basin upstream (above the Mactaquac Dam) is “Marginal” (CCME-WQI ≈ 59), reflecting stronger industrial and agricultural pressures. Overall, the L8-BPNN-CCME-WQI framework provides a scalable methodology for converting multi-parameter satellite-derived water quality information into spatially exhaustive CCME-WQI classes, supporting targeted regulation, prioritization of mitigation in critical reaches, and evaluation of management actions in large river systems.

Earth doi: 10.3390/earth7020046

Authors: Yoji Natori Mayuko Taketa Elias Akiko Enomoto

Socio-ecological production landscapes (SEPLs) are places that support and are supported by biodiversity and that play important roles in maintaining biodiversity as well as providing the goods and services that society demands. SEPLs exist with, by and for people. The lack of sustainable value chains that give advantage to goods and services produced in SEPLs threatens the continuation of sustainable practices, many of which are manifestations of traditional ecological knowledge, in an increasingly globalized and efficiency-driven economy. Sustainable management of SEPLs will be an important contribution to biodiversity conservation and sustainable development. To determine the approaches for creating sustainable value chains for goods and services from SEPLs, we gathered information from practitioners and experts on SEPLs using (1) an online questionnaire to experts, (2) an online knowledge-sharing session, and (3) focus group discussions, under the framework of the International Partnership for the Satoyama Initiative (IPSI). Through the questionnaire (13 respondents) and the online session, we identified key issues and obstacles, as well as solutions to them in practice. Focus group discussions were held on value chains specific to cacao and rice to gain a deeper understanding of value chain issues from these vastly different commodities. As a way to create value chains that contribute to sustainability of SEPLs, we present a theory of change consisting of five approaches that producers can implement to shorten value chains. The proposed evaluation framework of this theory will use case studies produced by IPSI members and beyond on diversity of commodities from SEPLs. This is a call to action on an important element of biodiversity conservation and sustainable development.

Earth doi: 10.3390/earth7020045

Authors: Moleboheng Molefe Lerato Shikwambana Sifiso Xulu

The sugarcane industry in South Africa is ranked among the top 15 producers worldwide and plays a significant role in supporting the nation’s socioeconomic development, producing approximately 2.3 million tons annually. Harvesting is largely labour-intensive and commonly involves the pre-harvest burning of sugarcane. This widespread practice is associated with (a) local air quality deterioration driven by pollutants such as carbon monoxide (CO), black carbon (BC), and sulphur dioxide (SO2) and (b) adverse public health outcomes, including respiratory and cardiovascular diseases. This study aims to assess the air quality across KwaZulu-Natal and compare inland and coastal sugarcane-growing regions during the May–August 2023 harvest season. The CO and SO2 concentrations are obtained from Sentinel-5P, while the BC data are sourced from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The Air Quality Index (AQI) is calculated using the CO, SO2, PM2.5, and NO2 data from the Copernicus Atmosphere Monitoring Service (CAMS). The findings consistently indicate higher pollutant concentrations in inland regions, suggesting more concentrated burning activities and lower atmospheric dispersion relative to coastal areas. Overall, the results highlight the greater prevalence of poor air quality in inland sugarcane regions compared with coastal zones.

Earth doi: 10.3390/earth7020044

Authors: Abinash Silwal Anil Subedi Rajee Tamrakar Kshitij Dahal Dewasis Dahal Kenneth Okechukwu Ekpetere Mohamed Zhran

Flood inundation mapping (FIM) is essential in disaster risk management, infrastructure planning, and climate adaptation. Traditional hydrodynamic models, such as the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) and LISFLOOD-Floodplain (LISFLOOD-FP), provide physically interpretable flood simulations but are often data- and computation-intensive and difficult to scale across regions. In recent years, machine learning (ML) and deep learning (DL) approaches have emerged as data-driven alternatives that leverage remote sensing observations, digital elevation models (DEMs), and hydro-climatic datasets to enable scalable and near-real-time flood mapping. Our review synthesizes recent advances in ML-based flood inundation mapping, categorizing methods into traditional machine learning techniques (e.g., Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB)), deep learning architectures (e.g., Convolutional Neural Networks (CNNs), U-Net, Long Short-Term Memory networks (LSTM)), and emerging hybrid and physics-informed frameworks. We evaluate model performance across flood extent and flood depth estimation tasks, highlighting strengths, limitations, and common benchmarking practices reported in the literature. The review identifies key challenges related to model interpretability, data bias, transferability, and regulatory acceptance, and highlights recent progress in explainable artificial intelligence (XAI), uncertainty-aware modeling, and physics-informed learning as pathways toward operational adoption. By unifying terminology, performance metrics, and methodological comparisons, this review provides a coherent framework for advancing trustworthy, scalable, and decision-relevant flood inundation mapping under increasing climate-driven flood risk.

Earth doi: 10.3390/earth7020043

Authors: Liladhar Sapkota Qiao Liu Narendra Raj Khanal Bishal Gurung Yunyi Luo

Assessment of landslide risk is crucial given the substantial related economic losses and infrastructure damage in mountain areas every year. Particularly, the Sagarmatha National Park (SNP), a key destination for Himalayan glacier tourism, remains relatively understudied in this context. Existing studies primarily focus on regional inventories or simply inventory landslides and lack tourism-specific hazard assessment. This study evaluates landslide distribution, its controlling factors, and the exposure of infrastructure to varying degrees of landslide susceptibility in SNP. A blind inventory of 680 landslides and twelve conditioning factors, including six topographic and six non-topographic variables, were analyzed using Frequency Ratio (FR), Logistic Regression (LR), and Random Forest (RF) models. In addition, spatial overlay analysis was employed to assess the degree of infrastructure exposure. Results indicate that Land Surface Temperature (LST) is the most dominant factor influencing landslides occurrence, followed by rainfall, elevation, and slope, along with specific aspects like south and west and, land cover class like Barren land and Alpine meadows. Random Forest achieved the highest predictive accuracy (91%), outperforming both Logistic Regression (87%) and Frequency Ratio (84%). Exposure assessment of key tourism infrastructure indicates that trekking routes, helipads, buildings, campsites, and bridges are subject to varying levels of landslide risk. Although only 2.73 km (0.52%) of trekking routes intersect active landslide scars, 147 km (28%) lie within high-exposure zones. Consequently, both typical and paraglacial landslides threaten access to glacier tourism destinations, highlighting significant implications for Nepal’s tourism.

Earth doi: 10.3390/earth7020042

Authors: Lerato Shikwambana Mahlatse Kganyago Xiang Zhang

Southern Africa is highly sensitive to climate variability associated with the El Niño Southern Oscillation (ENSO), which strongly influences hydroclimate, vegetation dynamics, and atmospheric composition. This study examined the impacts of the 2015/16 El Niño on vegetation, meteorological conditions, and atmospheric emissions over Southern Africa using satellite observations and reanalysis data. Time-lagged cross-correlation analysis of seasonally adjusted time-series was applied to characterize synchronous and delayed interactions among vegetation indices, hydrological variables, meteorological drivers, and air-quality parameters. Bayesian causal impact analysis was further used to quantify El Niño-induced anomalies by comparing observed conditions with counterfactual scenarios representing the absence of the event. The results showed that vegetation greenness responds primarily to concurrent moisture availability, with strong positive associations between NDVI, precipitation, soil moisture, and canopy water. Moisture-related variables exert delayed influences on atmospheric composition, highlighting the role of wet scavenging and dilution. Carbonaceous aerosols (black carbon [BC] and organic carbon [OC]), particulate matter [PM2.5], and aerosol optical depth exhibit strong synchronous coupling, indicating a dominant biomass-burning source. The causal impact analysis reveals statistically significant and sustained post-2015 increases in fire-related emissions (carbon monoxide [CO], BC, OC, PM2.5, and aerosol optical depth [AOD]), particularly during austral winter and dry seasons. In contrast, precipitation, soil moisture, evapotranspiration, and vegetation greenness show persistent negative anomalies, reflecting widespread drought stress under elevated temperatures. Overall, the findings demonstrate that the 2015/16 El Niño amplified fire emissions while suppressing ecosystem functioning across Southern Africa, underscoring strong climate–fire–vegetation feedback with important air-quality and environmental implications.