Search Results (378)

To comprehensively explore the net carbon balance within cropland systems subject to diverse tillage practices (Down-slope cultivation (CK), Subsoiling tillage (SF), Ridge to district field (RF), Ridge to district field + subsoiling tillage (RF-S), Transverse slope planting (TP), Transverse slope planting + ridge to district field (TP-R), Transverse slope planting + subsoiling tillage (TP-S)), a series of well-designed field experiments were meticulously carried out. The CO₂ emission intensity of soil heterotrophic respiration, CH₄ emission intensity, carbon loss in runoff, carbon emissions from farmland materials, dry matter mass and carbon content of different crop organs after harvest were measured for the six different tillage practices. Moreover, the annual and seasonal variations in farmland soil carbon pools under different treatments were analyzed using the net carbon flux (NCF) of the cropland system. The results indicated that, under different tillage practices, the CO₂ emission intensity of soil heterotrophic respiration in each regime across different years generally exhibited a pattern of increasing initially and then decreasing, reaching its peak during the filling stage (pod-setting stage). The RF regime significantly reduced the CO₂ emissions from soil heterotrophic respiration (p < 0.05). The CH₄ emissions in each regime across different years also demonstrated an overall tendency of rising initially and subsequently declining, with an alternating positive–negative pattern, reaching its peak during the jointing stage (branching stage). The SF regime significantly decreased the CH₄ emissions (p < 0.05). The regimes with cross-slope tillage significantly reduced the carbon loss in runoff (p < 0.05). Throughout every year, the NPP of crops under the TP-S regime attained its peak value (p < 0.05). The RF regime effectively increased the NPP of crops, reduced the soil heterotrophic respiration CO₂ emissions and the carbon loss in runoff, and its NCF value reached the maximum level (p < 0.05), presenting a weak carbon “source”. Overall, ridged-field (RF) effectively curbs greenhouse gas emissions, boosts farmland carbon sequestration, and mitigates soil fertility decline. Full article

Winter wheat is a major staple crop worldwide, and real-time monitoring of soil moisture content (SMC) is critical for yield security. Targeting the monitoring needs under arid conditions in southern Xinjiang, this study proposes a UAV multispectral-based SMC estimation method that constructs novel three-dimensional (3-D) texture indices. Field experiments were conducted over two consecutive growing seasons in Kunyu City, southern Xinjiang, China, with four irrigation and four fertilization levels. High-resolution multispectral imagery was acquired at the jointing stage using a UAV-mounted camera. From the imagery, conventional texture features were extracted, and six two-dimensional (2-D) and four 3-D texture indices were constructed. A correlation matrix approach was used to screen feature combinations significantly associated with SMC. Random forest (RF), partial least squares regression (PLSR), and back-propagation neural networks (BPNN) were then used to develop SMC models for three soil depths (0–20, 20–40, and 40–60 cm). Results showed that estimation accuracy for the shallow layer (0–20 cm) was markedly higher than for the middle and deep layers. Under single-source input, using 3-D texture indices (Combination 3) with RF achieved the best shallow-layer performance (validation R² = 0.827, RMSE = 0.534, MRE = 2.686%). With multi-source fusion inputs (Combination 7: texture features + 2-D texture indices + 3-D texture indices) combined with RF, shallow-layer SMC estimation further improved (R² = 0.890, RMSE = 0.395, MRE = 1.91%). Relative to models using only conventional texture features, fusion increased R² by approximately 11.4%, 11.7%, and 18.1% for the shallow, middle, and deep layers, respectively. The findings indicate that 3-D texture indices (e.g., DTTI), which integrate multi-band texture information, more comprehensively capture canopy spatial structure and are more sensitive to shallow-layer moisture dynamics. Multi-source fusion provides complementary information and substantially enhances model accuracy. The proposed approach offers a new pathway for accurate SMC monitoring in arid croplands and is of practical significance for remote sensing-based moisture estimation and precision irrigation. Full article

Conversion of burned forestlands into rice croplands is often practised to increase food production. However, this practice can lead to a severe decline in soil quality and functioning. Unfortunately, no research has previously evaluated how and to what extent physico-chemical properties and overall quality of forest soils change when converted to rice paddy fields. This study has evaluated the changes in key soil properties and Soil Quality Index (SQI) when burned forests are converted to rice croplands in Guilan Province (Northern Iran). This conversion results in noticeable worsening of soil structure (shown by the decreases in size and stability of macro-aggregates, ~50%) and reductions in organic matter (−30%) and nutrient contents (−43% of TN and −49% of P) of soil in rice paddy fields in comparison to burned forest soils. In contrast, soil salinity increased by 180% and potassium by 12%, while pH remained unchanged between forestland and rice fields. The calculation of the SQI showed that the overall quality of the soil was severely affected by this change. The main message of this study is that replacement of forest ecosystems with rice croplands should be carefully controlled, in order to avoid noticeable impacts on soil properties and theiroverall quality. In sites where this conversion has occurred, sustainable land management practices, such as moderate supply of organic amendments and fertilisers, should be implemented to mitigate soil degradation. Full article

Understanding spatial variability in crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing negative environmental impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological conditions. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. A high-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation, with a relative root mean square error (RRMSE) of 23.7% (r² = 0.38). Adding hydrological variables reduced the error to 15.3% (r² = 0.73), and further combining with remote sensing data improved the explanatory power to an RRMSE of 10.0% (r² = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r² = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables, enabling the identification of long-term persistent yield regions (LTRs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency. Full article

This study was conducted to reveal the response relationship between soil preferential flow characteristics and soil pore structure of sloping cropland under contour antislope step measures in the alpine canyon area of Southwest China. In the sub-watershed of Nantangjing, Yunlong County, the upper and lower slopes of primary sloping cultivated land (PSC) and contour reverse-slope terraced rectified land (CR) were used for the study, and a field staining tracer test was used to compare the differences in preferential flow morphology between different slopes with and without measures. The maximum infiltration depth of preferential flow under the contour reverse-slope terrace land preparation reached 21 cm. The stained area ratio tended to decrease with increasing soil depth. Compared to the original slope farmland, the stable infiltration rate under land preparation increased from 0.017 to 0.244 cm³·s⁻¹, and the maximum macroporosity increased by up to 17.00%. Furthermore, land preparation measures significantly enhanced the correlation between macropore quantity and preferential flow characteristics, with the highest correlation coefficient reaching 0.98. And the soil factors in total porosity, total nitrogen and organic matter were particularly influential on preferential flow. Contour antislope terracing promotes the formation and development of preferential soil flow by remodeling soil structure and optimizing pore network distribution. Full article

Recent progress in geospatial foundation models (GFMs) has demonstrated strong generalization capabilities for remote sensing downstream tasks. However, existing GFMs still struggle with fine-grained cropland classification due to ambiguous field boundaries, insufficient and low-efficient temporal modeling, and limited cross-regional adaptability. In this paper, we propose CropSTS, a remote sensing foundation model designed with a decoupled temporal–spatial attention architecture, specifically tailored for the temporal dynamics of cropland remote sensing data. To efficiently pre-train the model under limited labeled data, we employ a hybrid framework combining joint-embedding predictive architecture with knowledge distillation from web-scale foundation models. Despite being trained on a small dataset and using a compact model, CropSTS achieves state-of-the-art performance on the PASTIS-R benchmark in terms of mIoU and F1-score. Our results validate that structural optimization for temporal encoding and cross-modal knowledge transfer constitute effective strategies for advancing GFM design in agricultural remote sensing. Full article

Rapid urbanization and industrial development have significantly altered land use and cover across the globe, intensifying urban thermal environments and exacerbating the urban heat island (UHI) effect. Gujranwala, Pakistan, represents an industrial growth that has driven substantial land use/land cover (LULC) changes and temperature increases; however, the directional and distance-based patterns of these changes remain unquantified. Therefore, this study is conducted to examine spatiotemporal changes in LULC and variations in the Urban Thermal Field Variation Index (UTFVI) between 2001 and 2021 and to project future scenarios for 2031 and 2041 using (1) Earth Observation Indices (EOIs) with machine learning (ML) classifiers (Random Forest) for precise LULC mapping through the Google Earth Engine (GEE) platform, (2) Cellular Automata–Artificial Neural Networks (CA-ANNs) for future scenario projection, and (3) Gradient Directional Analysis (GDA) to quantify directional (16-axis) and distance-based (concentric zones) patterns of urban expansion and thermal variation from 2001–2021. The study revealed significant LULC changes, with built-up areas expanding by 7.5% from 2001 to 2021, especially in the east, northeast, and southeast directions within a 20 km radius. Due to urban encroachment, vegetation and cropland decreased by 1.47% and 1.83%, respectively. The urban thermal environment worsened, with the highest land surface temperature (LST) rising from 41 °C in 2001 to 55 °C in 2021. Additionally, the UTFVI showed expanding areas under the ‘strong’ and ‘strongest’ categories, increasing from 30.58% in 2001 to 33.42% in 2041. Directional analysis highlighted severe thermal stress in the southern and southwestern areas linked to industrial activities and urban sprawl. This integrated approach provides a template for analyzing urban thermal environments in developing cities, supporting targeted mitigation strategies through direction- and distance-specific planning interventions to mitigate UHI impacts. Full article

Land subsidence significantly threatens vulnerable coastal environments. This study aims to explore how Semarang’s government, local communities, and researchers address land subsidence and its role in exacerbating flood risk, against the backdrop of ongoing efforts within flood risk governance. Employing an integrated mixed-methods approach, the research combined quantitative geospatial analysis (InSAR and land cover change detection) with qualitative socio-political and governance analysis (interviews, FGDs, field observations). Findings show high subsidence rates in Semarang. Line of sight displacement measurements revealed a continuous downward trend from late 2014 to mid-2023, with rates varying from −8.8 to −10.1 cm/year in Karangroto and Sembungharjo. Built-up areas concurrently expanded from 21,512 hectares in 2017 to 23,755 hectares in 2023, largely displacing cropland and tree cover. Groundwater extraction was identified as the dominant driver, alongside urbanization and geological factors. A critical disconnect emerged: community views focused on flooding, often overlooking subsidence’s fundamental role as an exacerbating factor. The study concluded that multi-level collaboration, improved risk communication, and sustainable land management are critical for enhancing urban coastal resilience against dual threats of subsidence and flooding. These insights offer guidance for similar rapidly developing coastal cities. Full article

The shifting irrigation reduction in the Hetao Irrigation District and the inability to effectively discharge salts from the system have led to significant changes in salt migration patterns. Based on the integration of long-term field observations (2017–2023) with soil hydrodynamics and solute transport models, this study explored the impact of irrigation reduction on water and salt migration in a cropland–wasteland–lake system. The results indicated that before and after the reduction in irrigation and decline in groundwater levels, the migration rates of groundwater from croplands to wastelands and from wastelands to lakes remained relatively stable, averaging 78% and 40%. During the crop growth period, after irrigation reduction and groundwater level decline, the volume of groundwater recharging lakes from wastelands decreased by 80–120 mm, causing a water deficit in the lakes of 679–789 mm. After irrigation reduction and groundwater level decline, during the crop growth period, 1402 kg/ha of salt remained in the wasteland groundwater, and 597–861 kg/ha of salt accumulated in the cropland groundwater, exceeding previous levels, leading to salinization in the cropland and wasteland groundwater. This study provides insights relevant to managing groundwater and soil salinity in irrigation areas. Full article

Ecuador, located in South America, ranks among the countries with the highest rates of pesticide use per unit of cropland. Pesticide exposure is linked to genotoxic effects and carcinogenicity. While most studies evaluating the effects of pesticides focus on the active ingredient, commercial formulations are complex mixtures of several components that may alter their toxicological profile. In this study, we analyzed four pesticides commonly used in corn cultivation, and their typical field mixtures, including the herbicides atrazine and pendimethalin, the insecticides chlorpyrifos and cypermethrin, and a fertilizer, to evaluate their genotoxic effects, oxidative status, and potential to induce cellular transformation. CHO-K1 cells were treated with subtoxic doses of these formulations. MTS, comet, micronucleus, H2AX expression, SOD and GPx activity, and wound healing assays were performed. The results showed these formulations induced genotoxicity, evidenced by the comet assay. Additionally, exposure activated cellular DNA repair mechanisms, evidenced by a 1.89- to 2.63-fold increase in H2AX expression across all treatments and mixtures after 10 h. Notably, pendimethalin was associated with signs of cellular transformation, as evidenced by a 1.4-times greater cell migration observed in the wound healing assay. These findings suggest that even at subtoxic concentrations, these pesticide formulations can cause genetic damage and potentially alter cellular control mechanisms. Full article

Prompt and precise cropland mapping is indispensable for safeguarding food security, enhancing land resource utilization, and advancing sustainable agricultural practices. Conventional approaches faced difficulties in complex terrain marked by fragmented plots, pronounced elevation differences, and non-uniform field borders. To address these challenges, we propose DAENet, a novel deep learning framework designed for accurate cropland extraction from high-resolution GaoFen-1 (GF-1) satellite imagery. DAENet employs a novel Geometric-Optimized and Boundary-Restrained (GOBR) Block, which combines channel attention, multi-scale spatial attention, and boundary supervision mechanisms to effectively mitigate challenges arising from disjointed cropland parcels, topography-cast shadows, and indistinct edges. We conducted comparative experiments using 8 mainstream semantic segmentation models. The results demonstrate that DAENet achieves superior performance, with an Intersection over Union (IoU) of 0.9636, representing a 4% improvement over the best-performing baseline, and an F1-score of 0.9811, marking a 2% increase. Ablation analysis further validated the indispensable contribution of GOBR modules in improving segmentation precision. Using our approach, we successfully extracted 25,556.98 hectares of cropland within the study area, encompassing a total of 67,850 individual blocks. Additionally, the proposed method exhibits robust generalization across varying spatial resolutions, underscoring its effectiveness as a high-accuracy solution for agricultural monitoring and sustainable land management in complex terrain. Full article

Dinder Biosphere Reserve (DBR), a UNESCO-recognized biodiversity hotspot in Sudan, faces escalating land-use pressure. We analyzed land cover changes from 2019 to 2024 using Sentinel-2 imagery processed in Google Earth Engine. A Random Forest classifier identified five land cover classes: water, built-up areas, vegetation, bare land, and crops. The transition matrix revealed significant changes over this period. About 1501 km² of vegetation and 1648 km² of cropland were converted to bare land. Built-up areas lost 95 km² to bare land. Bare land remained largely unchanged (4749 km²), while water bodies were the most stable (13,473 km² unchanged). Only minor transitions involved water (27.6 km² to vegetation, 15.2 km² to bare land). Notably, 411 km² of cropland and 1773 km² of bare land transitioned to vegetation, indicating some regrowth. These land cover changes reflect a dynamic interplay between degradation and recovery processes; however, the results should be interpreted with caution due to potential classification inaccuracies, seasonal variation in imagery, and absence of field validation. Continued satellite monitoring is essential to guide adaptive land management and safeguard ecosystem function in DBR. Full article

Sustainable cropland management is essential in maintaining national food security. In the black soil regions of China, which are key areas for commercial grain production, sustainable land use must be achieved urgently. To address the absence of integrated, large-scale, remote sensing-based sustainability frameworks in China’s black soil zones, we developed a comprehensive evaluation system with 13 indicators from four dimensions: the soil capacity, the natural capacity, the management level, and crop productivity. With this system and the entropy weight method, we systematically analyzed the spatiotemporal patterns of cropland sustainability in the selected black soil regions from 2010 to 2020. Additionally, a diagnostic model was applied to identify the key limiting factors constraining improvements in cropland sustainability. The results revealed that cropland sustainability in Heilongjiang Province has increased by 7% over the past decade, largely in the central and northeastern regions of the study area, with notable gains in soil capacity (+15.6%), crop productivity (+22.4%), and the management level (+4.8%). While the natural geographical characteristics show no obvious improvement in the overall score, they display significant spatial heterogeneity (with better conditions in the central/eastern regions than in the west). Sustainability increased the most in sloping dry farmland and paddy fields, followed by plain dry farmland and arid windy farmland areas. The soil organic carbon content and effective irrigation amount were the main obstacles affecting improvements in cropland sustainability in black soil regions. Promoting the implementation of technical models, strengthening investment in cropland infrastructure, and enhancing farmer engagement in black soil conservation are essential in ensuring long-term cropland sustainability. These findings provide a solid foundation for sustainable agricultural development, contributing to global food security and aligning with SDG 2 (zero hunger). Full article

The Requisition–Compensation Balance of Cropland (RCBC) policy is important for ensuring food security. Previous studies have mainly focused on the quantity and quality of cropland when assessing the impacts of this policy. In terms of morphology, previous studies have primarily relied on landscape indicators. Therefore, this study aims to thoroughly analyze the impacts of the RCBC policy on the quality and morphology of cropland (especially morphological spatial pattern analysis, MSPA) in the Pearl River Delta (PRD) during 1996–2021. To this end, we constructed a comprehensive evaluation index system by combining MSPA, landscape indicators, and field research. The results show that the cropland quality in the PRD has exhibited a consistent improvement trend. High-quality cropland is spreading from central cities to the periphery, and the spatial distribution is becoming more even. Nonetheless, MSPA reveals an increasing trend of cropland fragmentation. The results indicate a decline in the area of the “core”, an increase in the area of the “island”, and a decrease in the connectivity of the cropland. Our field research confirms that the RCBC policy has indirectly exacerbated cropland fragmentation. In many regions of the PRD, the fragmentation of cropland hinders the application of agricultural mechanization and increases the cost of cultivation, resulting in severe cropland abandonment. Therefore, local governments should implement rigorous planning and prioritize cropland morphology when compensating cropland. Our findings are expected to provide empirical evidence for improving the RCBC policy and protecting cropland. Full article

Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale variations in SMC, especially in landscapes with diverse land-cover types. Unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors offer a promising solution to overcome this limitation. This study compares the effectiveness of Sentinel-2, Landsat-8/9 multispectral data and UAV hyperspectral data (from 339.6 nm to 1028.8 nm with spectral bands) in estimating SMC in a research farm consisting of bare soil, cropland and grassland. A DJI Matrice 100 UAV equipped with a hyperspectral spectrometer collected data on 14 field campaigns, synchronized with satellite overflights. Five machine-learning algorithms including extreme learning machines (ELMs), Gaussian process regression (GPR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) were used to estimate SMC, focusing on the influence of land cover on the accuracy of SMC estimation. The findings indicated that GPR outperformed the other models when using Landsat-8/9 and hyperspectral photography data, demonstrating a tight correlation with the observed SMC (R² = 0.64 and 0.89, respectively). For Sentinel-2 data, ELM showed the highest correlation, with an R² value of 0.46. In addition, a comparative analysis showed that the UAV hyperspectral data outperformed both satellite sources due to better spatial and spectral resolution. In addition, the Landsat-8/9 data outperformed the Sentinel-2 data in terms of SMC estimation accuracy. For the different land-cover types, all types of remote-sensing data showed the highest accuracy for bare soil compared to cropland and grassland. This research highlights the potential of integrating UAV-based spectroscopy and machine-learning techniques as complementary tools to satellite platforms for precise SMC monitoring. The findings contribute to the further development of remote-sensing methods and improve the understanding of SMC dynamics in heterogeneous landscapes, with significant implications for precision agriculture. By enhancing the SMC estimation accuracy at high spatial resolution, this approach can optimize irrigation practices, improve cropping strategies and contribute to sustainable agricultural practices, ultimately enabling better decision-making for farmers and land managers. However, its broader applicability depends on factors such as scalability and performance under different conditions. Full article