Search Results (6,605)

Passive microwave remote sensing provides indispensable observations for constructing long-term snow depth records, which are critical for climatology, hydrology, and operational applications. Nevertheless, despite decades of snow depth monitoring, systematic evaluations of how inter-sensor brightness temperature differences (TBDs) propagate into retrieval uncertainties are still lacking. In this study, TBDs between DMSP-F18/SSMIS, FY-3D/MWRI, and AMSR2 sensors were quantified, and the sensitivity of seven snow depth retrieval algorithms to these discrepancies was systematically assessed. The results indicate that TBDs between SSMIS and AMSR2 are larger than those between MWRI and AMSR2, likely reflecting variations in sensor specifications such as frequency, observation angle, and overpass time. In terms of algorithm sensitivity, SPD, WESTDC, FY-3B, and FY-3D demonstrate less sensitivity across sensors, with standard deviations of snow depth differences generally below 2 cm. In contrast, the Foster algorithm exhibits pronounced sensitivity to TBDs, with standard deviations exceeding 11 cm and snow depth differences reaching over 20 cm in heavily forested regions (forest fracion >90%). This study provides guidance for SWE virtual constellation design and algorithm selection, supporting long-term, seamless, and consistent snow depth retrievals. Full article

Atmospheric sulfur (S) deposition, a key component of acid deposition, poses risks to ecosystems, human health, and sustainable development. In China, decades of coal-dominated energy use caused severe S pollution, but recent emission-control policies and energy restructuring have sought to reverse this trend. However, the effectiveness and regional differences in these measures remain insufficiently quantified. Here, we combined continuous observations from 43 monitoring sites (2013–2023), satellite-derived SO₂ vertical column density, and multi-source environmental datasets to construct a high-resolution record of wet S deposition. A random forest model, validated with R² = 0.52 and RMSE = 1.2 kg ha⁻¹ yr⁻¹, was used to estimate fluxes and spatial patterns, while ridge regression and SHAP analysis quantified the relative contributions of emissions, precipitation, and socioeconomic factors. This framework allows us to assess both the environmental and health-related sustainability implications of sulfur deposition. Results show a nationwide decline of more than 50% in wet S deposition during 2013–2023, with two-thirds of sites and 95% of grids showing significant decreases. Historical hotspots such as the North China Plain and Sichuan Basin improved markedly, while some southern provinces (e.g., Guizhou, Hunan, Jiangxi) still exhibited high deposition (>20 kg ha⁻¹ yr⁻¹). Over 90% of the reduction was attributable to emission declines, confirming the dominant effect of sustained policy-driven measures. This study extends sulfur deposition records to 2023, demonstrates the value of integrating ground monitoring with remote sensing and machine learning, and provides robust evidence that China’s emission reduction policies have delivered significant environmental and sustainability benefits. The findings offer insights for region-specific governance and for developing countries balancing economic growth with ecological protection. Full article

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected from a high-precision remote sensing platform located in a representative mountainous valley (Xinyang city) in central China, spanning December 2024 to February 2025. Our findings indicate that both horizontal and vertical wind speeds from the ERA5 dataset exhibit diminishing deviations as altitude increases. Significant biases are observed below 500 m, with horizontal mean wind speed deviations ranging from −4 to −3 m/s and vertical mean wind speed deviations falling between 0.1 and 0.2 m/s. Conversely, minimal biases are noted near the top of the boundary layer. Both ERA5 and observations reveal a dominance of northeasterly and southwesterly winds at near-surface levels, which aligns with the valley orientation. This underscores the substantial impact of heterogeneous mountainous terrain on the low-level dynamic field. At an altitude of 1000 m, both datasets present similar frequency patterns, with peak frequencies of approximately 15%; however, notable discrepancies in peak wind directions are evident (north–northeast for observations and north–northwest for ERA5). In contrast to dynamic variables, ERA5 temperature deviations are centered around 0 K within the lower layers (0–500 m) but show a slight increase, varying from around 0 K to 6.8 K, indicating an upward trend in deviation with altitude. Similarly, relative humidity (RH) demonstrates an increasing bias with altitude, although its representation of moisture variability remains insufficient. During a typical cold event, substantial deviations in multiple ERA5 variables highlight the needs for further improvements. The integration of machine learning techniques and mathematical correction algorithms is strongly recommended as a means to enhance the accuracy of ERA5 data under such extreme conditions. These findings contribute to a deeper understanding of the use of ERA5 datasets in the mountainous areas of central China and offer reliable scientific references for weather forecasting and climate modelings in these areas. Full article

Acid mine drainage (AMD) is an environmental concern that needs to be addressed by some mining industries because of its high concentrations of metals and acidity that destroy affected ecosystems. Its formation typically persists beyond the operating life of a mine site. Its management is even more challenging for sites that are abandoned without rehabilitation. In this study, a legacy copper–gold mine located in Sto. Niño, Tublay, Benguet, Philippines, generating a copper- and manganese-rich AMD (Cu, maximum 17.2 mg/L; Mn, maximum 2.90 mg/L) at pH 4.59 (minimum) was investigated. With its remote location inhabited by the indigenous people local community (IPLC), a novel limestone-based hybrid passive treatment system that combines a limestone leach bed (LLB) and a controlled modular packed bed reactor (CMPB) has been developed from the laboratory and successfully deployed in the field while investigating the effective hydraulic retention time (HRT), particle size, and redox conditions (oxic and anoxic) in removing Cu and Mn and increasing pH. Laboratory-scale and pilot-scale systems using simulated and actual AMD, respectively, revealed that a 15 h HRT and both oxic and anoxic conditions were effective in treating the AMD. Considering these results and unsteady conditions of the stream in the legacy mine, a hybrid multi-stage limestone leach bed and packed bed were deployed having variable particle size (5 mm to 100 mm) and HRT. Regular monitoring of the system showed the effective removal of Cu (88.5%) and Mn (66.83%) as well as the increase of pH (6.26), addressing the threat of AMD in the area. Improvement of the lifespan of the system needs to be addressed, as issues of Cu-armoring were observed, resulting in reduced performance over time. Nonetheless, the study presents a novel technique in implementing passive treatment systems beyond the typical treatment trains reported in the literature. Full article

Remote sensing provides a viable alternative for understanding landscape modifications attributed to beaver activity. The central objective of this study is to integrate multi-source remote sensing observations in tandem with a deep learning (DL) (convolutional neural net or transformer) model to automatically map beaver-influenced floodplain inundations (BIFI) over large geographical extents. We trained, validated, and tested eleven different model configurations in three architectures using five ResNet and five B-Finetuned encoders. The training dataset consisted of >25,000 manually annotated aerial image tiles of BIFIs in Connecticut. The YOLOv8 architecture outperformed competing configurations and achieved an F1 score of 80.59% and pixel-based map accuracy of 98.95%. SegFormer and U-Net++’s highest-performing models had F1 scores of 68.98% and 78.86%, respectively. The YOLOv8l-seg model was deployed at a statewide scale based on 1 m resolution multi-temporal aerial imagery acquired from 1990 to 2019 under leaf-on and leaf-off conditions. Our results suggest a variety of inferences when comparing leaf-on and leaf-off conditions of the same year. The model exhibits limitations in identifying BIFIs in panchromatic imagery in occluded environments. Study findings demonstrate the potential of harnessing historical and modern aerial image datasets with state-of-the-art DL models to increase our understanding of beaver activity across space and time. Full article

Agricultural drought, increasingly intensified by climate change, poses a significant threat to food security and water resources in semi-arid regions, including Türkiye’s Konya Closed Basin. This study evaluates six satellite-derived indices—Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI), Evapotranspiration Condition Index (ETCI), and Soil Moisture Condition Index (SMCI)—to monitor agricultural drought (2001–2024) and proposes a drought vulnerability map using a novel Drought Vulnerability Index (DVI). Integrating Moderate Resolution Imaging Spectroradiometer (MODIS), Climate Hazards Center InfraRed Precipitation with Station (CHIRPS), and Land Data Assimilation System (FLDAS) datasets, the DVI combines these indices with weighted contributions (VHI: 0.27, ETCI: 0.25, SMCI: 0.22, PCI: 0.26) to spatially classify vulnerability. The results highlight severe drought episodes in 2001, 2007, 2008, 2014, 2016, and 2020, with extreme vulnerability concentrated in the southern and central basin, driven by prolonged vegetation stress and soil moisture deficits. The DVI reveals that 38% of the agricultural area in the basin is classified as moderately vulnerable, while 29% is critically vulnerable—comprising 22% under high vulnerability and 7% under extreme vulnerability. The proposed drought vulnerability map offers an actionable framework to support targeted water management strategies and policy interventions in drought-prone agricultural systems. Full article

Investigating atmospheric volatile organic compounds (VOCs) is critical for understanding their sources, chemical reactivity, and impacts on air quality, climate, and human health, especially in remote regions like the Tibetan Plateau where baseline data remains scarce. In this study, ambient VOCs species were simultaneously measured at four remote background sites on the Tibetan Plateau (Nyingchi, Namtso, Ngari, and Mount Everest) from 2012 to 2014 to investigate their concentration, composition, sources, and chemical reactivity. Weekly integrated samples were collected and analyzed using a Gas Chromatograph-Mass Spectrometer/Flame Ionization Detector (GC-MS/FID) system. The total VOC mixing ratios exhibited site-dependent variability, with the highest levels observed in Nyingchi, followed by Mount Everest, Ngari and Namtso. The VOC composition in those remote sites was dominated by alkanes (25.7–48.5%) and aromatics (11.4–34.7%), followed by halocarbons (19.1–28.1%) and alkenes (11.5–18.5%). A distinct seasonal trend was observed, with higher VOC concentrations in summer and lower levels in spring and autumn. Source analysis based on correlations between specific VOC species suggests that combustion emissions (e.g., biomass burning or residential heating) were a major contributor during winter and spring, while traffic-related emissions influenced summer VOC levels. In addition, long-range transport of pollutants from South Asia also significantly impacted VOC concentrations across the plateau. Furthermore, reactivity assessments indicated that alkenes were the dominant contributors to OH radical loss rates, whereas aromatics were the largest drivers of ozone formation potential (OFP). These findings highlight the complex interplay of local emissions and regional transport in shaping VOC chemistry in this high-altitude background environment, with implications for atmospheric oxidation capacity and secondary pollutant formation. Full article

Remote sensing technologies are increasingly integrated with autonomous robotic platforms to enhance data-driven decision-making in precision agriculture. Rather than replacing conventional platforms such as satellites or UAVs, autonomous ground robots complement them by enabling high-resolution, site-specific observations in real time, especially at the plant level. This review analyzes how remote sensing sensors—including multispectral, hyperspectral, LiDAR, and thermal—are deployed via robotic systems for specific agricultural tasks such as canopy mapping, weed identification, soil moisture monitoring, and precision spraying. Key benefits include higher spatial and temporal resolution, improved monitoring of under-canopy conditions, and enhanced task automation. However, the practical deployment of such systems is constrained by terrain complexity, power demands, and sensor calibration. The integration of artificial intelligence and IoT connectivity emerges as a critical enabler for responsive, scalable solutions. By focusing on how autonomous robots function as mobile sensor platforms, this article contributes to the understanding of their role within modern precision agriculture workflows. The findings support future development pathways aimed at increasing operational efficiency and sustainability across diverse crop systems. Full article

Leveraging multi-source remote sensing datasets and dynamic groundwater monitoring well observations, this study explores the multiscale spatiotemporal linkages of groundwater storage changes and land deformation in North China Plain (NCP) after the South-to-North Water Diversion Project (SNWDP). Firstly, we employed Gravity Recovery and Climate Experiment (GRACE) and interferometric synthetic aperture radar (InSAR) technology to estimate groundwater storage (GWS) and land deformation. Secondly and significantly, we proposed a novel GRACE statistical downscaling algorithm that integrates a weight allocation strategy and GWS estimation applied with InSAR technology. Finally, the downscaled results were employed to analyze spatial differences in land deformation across typical ground fissure areas. The results indicate that (1) between 2018 and 2021, groundwater storage in the NCP exhibited a declining trend, with an average reduction of −3.81 ± 0.53 km³/a and a maximum land deformation rate of −177 mm/a; (2) the downscaled groundwater storage anomalies (GWSA) showed high correlation with in situ measurements (R = 0.75, RMSE = 2.91 cm); and (3) in the Shunyi fissure area, groundwater storage on the northern side increased continuously, with a maximum growth rate of 28 mm/a, resulting in surface uplift exceeding 70 mm. Full article

Coasts are critical ecological, economic and social interfaces between terrestrial and marine systems. The current upsurge in the acquisition and availability of remote sensing datasets, such as Landsat remote sensing data series, provides new opportunities for analyzing multi-decadal coastal changes and other components of coastal risk. The emergence of machine learning-based techniques represents a new trend that can support large-scale coastal monitoring and modeling using remote sensing big data. This study presents a comprehensive multi-decadal analysis of coastal changes for the period from 1990 to 2024 using Landsat remote sensing data series along the eastern and southern coasts of Peninsular Malaysia. These coastal regions include the states of Kelantan, Terengganu, Pahang, and Johor. An innovative approach combining deep learning-based shoreline extraction with the Digital Shoreline Analysis System (DSAS) was meticulously applied to the Landsat datasets. Two semantic segmentation models, U-Net and DeepLabV3+, were evaluated for automated shoreline delineation from the Landsat imagery, with U-Net demonstrating superior boundary precision and generalizability. The DSAS framework quantified shoreline change metrics—including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), and Linear Regression Rate (LRR)—across the states of Kelantan, Terengganu, Pahang, and Johor. The results reveal distinct spatial–temporal patterns: Kelantan exhibited the highest rates of shoreline change with erosion of −64.9 m/year and accretion of up to +47.6 m/year; Terengganu showed a moderated change partly due to recent coastal protection structures; Pahang displayed both significant erosion, particularly south of the Pahang River with rates of over −50 m/year, and accretion near river mouths; Johor’s coastline predominantly exhibited accretion, with NSM values of over +1900 m, linked to extensive land reclamation activities and natural sediment deposition, although local erosion was observed along the west coast. This research highlights emerging erosion hotspots and, in some regions, the impact of engineered coastal interventions, providing critical insights for sustainable coastal zone management in Malaysia’s monsoon-influenced tropical coastal environment. The integrated deep learning and DSAS approach applied to Landsat remote sensing data series provides a scalable and reproducible framework for long-term coastal monitoring and climate adaptation planning around the world. Full article

Drought-induced forest mortality threatens biodiversity globally, particularly in arid, and semi-arid woodlands. The continual development of remote sensing approaches enables enhanced monitoring of forest health. Herein, we investigate the ability of a limited ground-truthed canopy dieback dataset and satellite image derived Normalised Difference Vegetation Index (NDVI) to make inferences about forest health as temporal and spatial extent from its collection increases. We used ground-truthed observations of relative canopy mortality from the Pinus edulis-Juniperus osteosperma woodlands of southeastern Utah, United States of America, collected after the 2017–2018 drought, and PlanetScope satellite imagery. Through assessing different modelling approaches, we found that NDVI is significantly associated with sitewide mean canopy dieback, with beta regression being the most optimal modelling framework due to the bounded nature of the variable relative canopy dieback. Model performance was further improved by incorporating the proportion of J. osteosperma as an interaction term, matching the reports of species-specific differential dieback. A time-series analysis revealed that NDVI retained its predictive power for our whole testing period; four years after the initial ground-truthing, thus enabling retrospective inference of defoliation and regreening. A spatial random forest model trained on our ground-truthed observations accurately predicted dieback across the broader landscape. These findings demonstrate that modest field campaigns combined with high-resolution satellite data can generate reliable, scalable insights into forest health, offering a cost-effective method for monitoring drought-impacted ecosystems under climate change. Full article

Currently, ozone (O₃) has become one of the primary air pollutants in China, underscoring the importance of analyzing ozone formation sensitivity (OFS) for effective pollution control. Ozone sensitivity indices serve as effective tools for OFS identification. Among them, the ratio of volatile organic compounds (VOCs) to nitrogen oxides (NO_x)—such as the formaldehyde-to-nitrogen dioxide ratio (FNR, defined as HCHO/NO₂, where HCHO represents VOCs and NO₂ represents NO_x)—is one of the most widely used satellite-based indicators. Recent studies have highlighted glyoxal (CHOCHO) as another critical ozone precursor, prompting the proposal of the glyoxal-to-nitrogen dioxide ratio (GNR, CHOCHO/NO₂) as an alternative metric. This study systematically compares the performance of FNR and GNR across four major urban agglomerations in China: Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Chengdu–Chongqing (CY) region, by integrating satellite remote sensing with ground-based observations. Results reveal that both indices exhibit consistent spatial trends in OFS distribution, transitioning from VOC-limited regimes in urban centers to NO_x-limited regimes in surrounding suburban areas. However, differences emerge in threshold values and classification outcomes. During summer, FNR identifies urban areas as transitional regimes (or VOC-limited in regions such as YRD and PRD), while suburban areas are classified as NO_x-limited. In contrast, GNR, which shows heightened sensitive to anthropogenic VOCs (AVOCs), exhibits a more restricted spatial extent in the transition regimes. By autumn, most urban areas shift toward VOC-limited regimes, while suburban regions remain NO_x-limited. Thresholds for both VOCs and NO_x increase during this period, with GNR demonstrating stronger sensitivity to NO_x. These findings underscore that the choice between FNR and GNR directly influences OFS determination, as their differing responses to biogenic and anthropogenic emissions lead to different conclusions. Future research should focus on integrating the complementary strengths of both indices to develop a more robust OFS identification method, thereby providing a theoretical basis for formulating effective ozone control strategies. Full article

Clouds cover nearly two-thirds of Earth’s surface, making reliable cloud mask data essential for remote sensing applications and atmospheric research. This study develops a TrAdaBoost transfer learning framework that integrates active CALIOP and passive MODIS observations to enable unified, high-accuracy cloud detection across FY-4A/AGRI, FY-4B/AGRI, and Himawari-8/9 AHI sensors. The proposed TrAdaBoost Cloud Mask algorithm (TCM) achieves robust performance in dual validations with CALIPSO VFM and MOD35/MYD35, attaining a hit rate (HR) above 0.85 and a cloudy probability of detection (${POD}_{cld})$ exceeding 0.89. Relative to official products, TCM consistently delivers higher accuracy, with the most pronounced gains on FY-4A/AGRI. SHAP interpretability analysis highlights that 0.47 μm albedo, 10.8/10.4 μm and 12.0/12.4 μm brightness temperatures and geometric factors such as solar zenith angles (SZA) and satellite zenith angles (VZA) are key contributors influencing cloud detection. Multidimensional consistency assessments further indicate strong inter-sensor agreement under diverse SZA and land cover conditions, underscoring the stability and generalizability of TCM. These results provide a robust foundation for the advancement of multi-source satellite cloud mask algorithms and the development of cloud data products integrated. Full article

Xiamen Bay (XMB), a representative semi-enclosed bay, demonstrates hydrodynamic conditions and water exchange characteristics that are significantly influenced by alterations in the coastline. The three-dimensional hydrodynamic model and remote sensing interpretation techniques were utilized to examine coastline changes and evaluated the spatio-temporal variations in water residence time in XMB from 1955 to 2021. The results indicate that the coastline of the XMB has been considerably modified by extensive reclamation activities. The total reclaimed area reached up to 188.08 km² during the period of 1955–2021, resulting in a 17.8% reduction in the total bay area. The average residence time increased from 13.28 days in 1955 to 16.94 days in 2003 and then decreased to 16.12 days because of ecological restoration initiatives. Spatially, water residence time increased from the outer sea towards the inner bay, with the high value observed in the northwest part of XMB while the low value was observed in the southeastern region. Among the various sub-regions, Tong’an Bay experienced the most significant change in residence time, followed by the West Sea. Conversely, the Dadeng Waters and Jiulong River Estuary showed relatively minor increases in residence time. The primary factors influencing variations in water residence time are large-scale reclamation projects and ecological restoration measures. These findings provide a significant scientific foundation and technical support for the integrated management of the coastal zone and ecological restoration construction in XMB. Full article

Wildfire events pose significant threats to global ecosystems, with Greece experiencing substantial economic losses exceeding EUR 1.7 billion in 2023 alone, generating immediate financial burdens while contributing to atmospheric carbon dioxide emissions and accelerating climate change effects. This study presents a group of classification models for Greece wildfires utilizing historical datasets spanning 2017 to 2021, incorporating satellite-derived remote sensing data, topographical characteristics, and meteorological observations through a multimodal methodology that integrates satellite imagery processing with traditional numerical data analysis techniques. The framework encompasses multiple deep learning architectures, specifically implementing four standalone models comprising two convolutional neural networks optimized for spatial image processing and long short-term memory networks designed for temporal pattern recognition, extending classification approaches by incorporating visual satellite data alongside established numerical datasets to enable the system to leverage both spatial visual patterns and temporal numerical trends. The implementation employs an ensemble methodology that combines individual model classifications through systematic voting mechanisms, harnessing the complementary strengths of each architectural approach to deliver enhanced predictive capabilities and demonstrate the substantial benefits achieved through multimodal data integration for comprehensive wildfire risk assessment applications. Full article