Search Results (3,541)

Between 1 July and 30 November 2022, four spatially adjacent shallow M_W ≥ 5.7 earthquakes successively struck the Hormozgan province in southern Iran. This earthquake sequence offers a vital opportunity to clarify the subsurface seismogenic structure and rupture evolution in the eastern segment of the Zagros Fold-and-Thrust Belt (ZFTB). In the paper, we apply multi-temporal archived SAR images from the Sentinel-1 satellite to extract the high-precision coseismic surface deformation covering the July and November earthquake events, respectively, and further investigate the related seismogenic fault structure and slip distribution. Geodetic inversion results reveal that the cumulative coseismic slip of the three M_W ≥ 5.7 earthquakes in July is distributed at a downdip depth of 5.5 to 8 km on a SW-dipping thrust seismogenic fault plane, while the coseismic slip of the November M_W 5.7 earthquake is concentrated in the shallow downdip range of 1.5 to 6 km on the same fault, finely characterizing a partially overlapping depth-segmented rupture. According to a joint analysis of the regional topography and geomorphology, active fault distribution, and coseismic inversions, we conclude that this earthquake sequence nucleated on a secondary blind back-thrust fault of the Zagros Frontal Fault (ZFF). Coseismic Coulomb stress changes reveal that the July earthquake sequence triggered the occurrence of the November earthquake and that the shallow eastern segment of the Mountain Frontal Fault (MFF) and the eastern segment of the ZFF exhibit significant stress loading, indicating a high risk of future rupture. Full article

Cassava is a critical staple crop for food security and rural livelihoods in Sub-Saharan Africa, yet high-resolution maps of its distribution remain scarce, particularly for smallholder systems. In this study, we generated a 10 m resolution cassava presence map for Uganda (CM24) by fine-tuning a Random Forest classifier on TESSERA foundation model embeddings derived from Sentinel-1 and Sentinel-2 time series. Using field survey data from the Copernicus4GEOGLAM campaign for training and validation, the model achieved excellent discriminative ability (validation AUC = 0.9532, test AUC = 0.9524). Visual validation against high-resolution satellite imagery confirmed good spatial agreement, capturing both large contiguous fields and small fragmented plots. Comparison with two existing global products (CassavaMap and SPAM2020) and two seasons of national survey data conducted by the Uganda Bureau of Statistics showed that CM24 produced national harvested area estimates that fell between the two survey totals, whereas CassavaMap and SPAM2020 systematically overestimated harvested area by factors of two to three. Our results demonstrate that foundation-model embeddings offer a robust and scalable approach for mapping cassava in heterogeneous smallholder landscapes. The resulting CM24 map provides a spatially explicit tool to support disease surveillance, agricultural monitoring, and food security planning in Uganda and beyond. Full article

Selective logging is an important component of forest degradation in the Brazilian Amazon. The detection and mapping of selective logging via satellite imagery remains challenging because spatial features associated with selective logging are generally small-scale, spatially heterogeneous, and short-lived disturbances in the forest. This study evaluated the potential of Sentinel-2 MSI imagery at 10 m and 20 m, and Landsat-8 OLI imagery at 30 m and pansharpened 15 m, to discriminate land-cover classes associated with selective logging in the state of Mato Grosso in the Brazilian Amazon for 2017 using the Random Forest algorithm. The resulting maps were used to characterize selective logging alerts from the Deter system and areas under Sustainable Forest Management Plans (SFMP). Sentinel-2 at 10 m achieved the highest overall accuracy, while Landsat-based products tended to estimate larger areas of exposed soil and, in some cases, regeneration. Deter polygons showed higher proportions of exposed soil and degradation and lower remaining forest cover than SFMP areas, suggesting that Deter alerts tend to capture more advanced stages of visible forest disturbance. Overall, the results indicate that differences in overall accuracy among the evaluated products were small, but class-specific performance and spatial representation patterns remain important for interpreting selective logging-related disturbance in the Amazon. Full article

The emerging potential of low-Earth-orbit (LEO) satellite-based Positioning, Navigation, and Timing services has increased the need for real-time, stable, and accurate orbit determination techniques. Here, we propose a method for estimating sub-meter-level LEO satellite orbits using Global Navigation Satellite System (GNSS) code pseudorange observations. To mitigate ionospheric delay, a dual-frequency ionosphere-free combination was applied, while code-carrier smoothing was employed to reduce code observation noise. A satellite weighting model based on Signal-in-Space Range Error was developed to reflect the orbit and clock error characteristics of different GNSS, and a robust weighting scheme was applied to alleviate the impact of observation outliers. Further, Galileo High Accuracy Service corrections compensated for orbit, clock and code bias errors. The algorithm was validated using the GNSS observation data collected from the Sentinel-6A satellite on 10 August 2023. Each successively applied technique gradually improved orbit determination accuracy, achieving up to a 51% reduction in 3D root mean square error (RMSE). The final RMSE values in the radial, along-track, cross-track, and 3D components were 39.4, 18.8, 23.5, and 49.6 cm, respectively. Temporal analysis showed no distinct periodicity in orbit errors and no significant correlation with satellite visibility or ground track. Full article

Climate change and soil degradation pose a challenge to grape quality, motivating the development of integrated monitoring approaches combining soil analysis with remote sensing techniques. However, harmonized information addressing this multidisciplinary challenge remains scarce. Therefore, this systematic review synthesizes the scientific literature published since 2020 with the aim of (i) identifying key soil properties and techniques applied, (ii) evaluating remote sensing approaches and their integration with soil data, and (iii) highlighting knowledge gaps and challenges for sustainable precision viticulture. A search in Scopus yielded 197 full-text articles classified into three thematic groups and analyzed using a standardized extraction protocol. Our synthesis reveals that pH, electrical conductivity, soil organic matter, and cation exchange capacity are the most consistently reported physicochemical parameters across the reviewed studies, while next-generation sequencing and multi-omics approaches are increasingly adopted in microbiological research to characterize rhizosphere communities and their links to terroir expression. In remote sensing, multispectral UAV platforms and satellite missions (Sentinel-2, Landsat) combined with vegetation indices, principally NDVI, dominate the toolset for monitoring vine vigor and water status. Nevertheless, genuine integration of remote-sensing outputs with root-zone soil measurements remains uncommon, with most studies treating both data streams independently. The principal knowledge gaps identified concern the absence of standardized sustainability assessment frameworks, limited cross-terroir transferability of predictive models, and insufficient long-term multi-site datasets to underpin climate change adaptation in vineyard management. Full article

This study presents a multi-indicator remote sensing framework for assessing satellite-derived water-quality-related and trophic-state-related dynamics across four freshwater systems in Türkiye Egirdir Lake, Sapanca Lake, Catalan Dam, and Yuvacik Dam between the baseline (2015–2018) and recent (2023–2025) periods. Rather than providing a regulatory or use-specific satellite-based assessment of water-quality-related indicators, the study evaluates optically and thermally detectable surface water indicators derived from Sentinel-2 MSI and Landsat 8/9 imagery processed in Google Earth Engine. The Normalized Difference Chlorophyll Index (NDCI), the Normalized Difference Turbidity Index (NDTI), and land surface temperature (LST, applied to water surfaces) were used to detect change patterns through period-mean difference mapping (Δ-mask) and interannual time series analysis. Results reveal distinct spatial and temporal dynamics broadly consistent with the interplay of climatic, hydrological, and anthropogenic drivers. In the southern Mediterranean systems, positive ΔNDCI anomalies in littoral and inflow zones were associated with increasing summer LST, with Egirdir Lake exhibiting a statistically significant warming trend of +0.170 °C yr⁻¹ (Mann–Kendall τ = 0.53, p = 0.029), interpreted cautiously as a physically plausible signal consistent with regional climate trends, suggesting elevated thermally mediated eutrophication-related optical risk. In the northern Marmara systems, satellite-observed patterns were more strongly associated with anthropogenic nutrient loading and morphological constraints, with turbidity-related optical increases concentrated in western and marginal zones despite relatively stable thermal conditions. As concurrent in situ measurements were unavailable, cross-sensor consistency checks and literature-based benchmarking were applied as alternative validation strategies. Across all four systems, positive ΔNDCI anomalies were systematically concentrated in shallow marginal and inflow zones, while ΔNDTI patterns varied by system, underscoring the role of littoral dynamics as early indicators of optically detectable water-quality deterioration and trophic-state-related change. The proposed framework offers a scalable, cost-effective approach for freshwater quality surveillance in data-scarce environments and provides direct support for integrated water resource management under Türkiye’s National Water Plan (2026–2036). Full article

Modeling crop development under different agrotechnologies is important not only for assessing the factors that affect their yields but also because of the role of vegetation in regulation of the hydrology regime. For this reason, interest in the plant module in the semi-distributed hydrological model SWAT is increasing. The model has to be supplied with a lot of information for running and testing, which can be achieved with ground-based, statistical and satellite data. The aim of the study is to determine the accuracy of the SWAT model to predict crop development by using ground-based and satellite data for LAI in the case of a 5-year field experiment. Two staple crops in rotation were monitored—winter wheat and maize—under different erosion control technologies (up-and-down conventional tillage, conventional contour tillage, and minimum contour tillage with inclusion of cover crop before maize) on sloping terrain on moderately eroded Epicalcic Chernozem in the region of Ruse, north Bulgaria. The remote sensing data from the Copernicus Sentinel-2 mission were used for estimation of LAI of both crops and verified against ground-based data in two ways—via a custom LAI script available through the Sentinel Hub cloud platform and as input to a machine learning quantile regression forests (QRF) model. The calibrated satellite-derived LAI, ground-based soil moisture and yields data were used to calibrate several SWAT model parameters (EPCO, ESCO, CN2, LAImax, HU, HI) and assess the model performance regarding these variables. Although a good temporal fit of the SWAT-modeled LAI data with the satellite data was achieved, the accuracy of predicted LAI is moderately high only in the last two years of the rotation (R² = 60.4%). The accuracy of calibrated yields (R² = 55.5%) is acceptable in four of the years. On average for the period, the applied erosion control agrotechnologies did not cause significantly different yields, but they are 14% higher compared to the up-and-down conventional tillage. The most sensitive SWAT parameters accounting for this effect are EPCO and ESCO. Full article

Estimation of rice crop yield at the village level is essential because village is the Insurance Unit (IU) for rice crop in many regions in India, and timely and accurate yield information at this scale supports timely and transparent claim settlements for farmers and supports local agricultural planning. To achieve this, a multi-source satellite data-based machine learning approach was used to estimate rice yield at the village level using optical and SAR data, climatic data and land surface model-derived parameters in Kakinada of Andhra Pradesh, India. The predictor dataset included seasonal cumulative rainfall, seasonal Normalized Difference Vegetation Index (NDVI)-Max, seasonal NDVI-Mean, seasonal Land Surface Water Index (LSWI)-Max, seasonal LSWI-Mean, season total Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and season total Root Zone Soil Moisture (RZSM), and season total backscatter of the Sentinel-1 VH polarization were used to represent crop greenness, moisture status, photosynthetic activity, soil water availability, canopy structure, and seasonal water supply. For model development and validation, village-level rice yield data from 2017 to 2023 was used, which was collected through Crop Cutting Experiment (CCE) at the maturity stage of Kharif season. In this study, four machine learning models such as Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) were evaluated. The multi-source satellite data and yield data for the period 2017–2021 were used to train the models, which were independently tested on 2022 data and then applied to predict the rice yield in 2023. Leave-One-Year-Out (LOYO) cross-validation was also conducted on the 2017–2022 data to assess temporal robustness and generalization capability across years. Among the evaluated models, Random Forest exhibited the best overall performance. For the independent test year 2022, RF achieved an R² of 0.465, RMSE of 415.34 kg ha⁻¹, MAE of 322.22 kg ha⁻¹, and MAPE of 10.36%. For the prediction year 2023, RF achieved improved accuracy with an R² of 0.838, RMSE of 325.75 kg ha⁻¹, MAE of 262.21 kg ha⁻¹, and MAPE of 7.68%. Further, LOYO cross-validation also showed the robustness of RF, achieving the highest mean R² of 0.702 and mean RMSE of 384.73 kg ha⁻¹. The results illustrate that multi-source satellite data combined with machine learning can be a reliable and operationally useful tool in predicting village-level rice yield, which can be used for crop insurance claim settlement. Full article

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

Context: Accurate citrus crop mapping is essential for agricultural monitoring, production planning, and supply-chain management, particularly in Brazil, one of the world’s leading orange producers and the leading orange-juice exporter. Satellite image time series from Sentinel-2 provide rich spectral and temporal information for crop identification. However, citrus mapping remains challenging due to fragmented agricultural landscapes, cloud contamination, class imbalance, and spectral overlap with other vegetation classes. Problem: Conventional machine learning models often depend on handcrafted vegetation indices, while attention-based deep learning models may require larger datasets and can become unstable under geographically constrained conditions. Therefore, there is a need for a compact and robust deep learning architecture capable of extracting citrus phenological signatures directly from multispectral time-series data. Methods: This study evaluates a Spatio-Temporal Pixel-Set Encoder Convolutional Neural Network (PSE-CNN) for citrus crop classification in the immediate geographic regions of São João da Boa Vista and Mogi Guaçu, São Paulo, Brazil. MapBiomas Collection 10.1 data from 2019 to 2024 were used to derive reference polygons, and Sentinel-2 imagery was processed into cloud-masked, 15-day temporal composites using ten spectral bands. The proposed PSE-CNN was benchmarked against PSE-TAE, PSE-Transformer, Random Forest, and XGBoost using spatially grouped data partitioning and temporal test years. Results: The proposed PSE-CNN achieved the highest Unified F1-Score of $0.704$ and the lowest coefficient of variation of $3.03\%$, indicating stronger inter-annual stability across test years and random seeds among the evaluated models. It also outperformed classical models that relied on handcrafted vegetation indices and demonstrated greater overall stability than attention-based deep learning alternatives. Conclusions: The results indicate that combining pixel-set encoding with temporal convolution provides a resource-aware and stable framework for retrospective citrus crop mapping from Sentinel-2 satellite image time series. These findings suggest that PSE-CNN can support scalable agricultural monitoring, contributing to sustainable crop inventory systems in regions where labeled data and computational infrastructure are limited. Full article

High-precision topography of tidal flats is essential for coastal monitoring, geomorphic change analysis, and ecological assessment. Although satellite remote sensing supports repeated and large-area observation, topographic inversion over small-scale tidal flats—here defined as localized intertidal patches with limited areal extent, represented in this study by a 1.11 km² tidal flat near Dafeng Port—remains challenging, because ICESat-2 laser altimetry tracks across such areas are typically sparse and spatially clustered within narrow sub-regions, leaving extensive observation-blind zones without direct elevation labels. This label-clustering problem constrains the applicability of traditional empirical models and tends to cause deep learning models to generalize poorly beyond the spatial distribution of training samples. To address this issue, this study proposes a Residual Attention Physical-constraint Semi-supervised U-Net (RAPS-UNet) that fuses ICESat-2 ATL03/ATL08 elevation labels with Sentinel-1 SAR and Sentinel-2 optical features. The preprocessing pipeline comprises refined ICESat-2 photon filtering, adaptive inundation-frequency extraction, multi-source feature selection, and baseline DEM construction. RAPS-UNet integrates residual learning, attention-based multi-source fusion, physics-constrained loss, and confidence-weighted pseudo-label augmentation to improve extrapolation under clustered-label conditions. A four-level validation protocol—in-distribution validation, spatial holdout testing, and field-based assessment over both interpolation and extrapolation zones—was designed to evaluate spatial generalization. Against a field-surveyed DEM, RAPS-UNet achieved an overall RMSE of 0.20 m, an MAE of 0.16 m, and an R² of 0.91; the field-based interpolation and extrapolation zones yielded RMSEs of 0.17 m and 0.22 m, respectively, while the spatial holdout test reached an RMSE of 0.23 m and an R² of 0.81. Relative to the traditional inundation frequency–elevation linear model (RMSE = 0.35 m), RAPS-UNet reduced the field-validation RMSE by approximately 43%. The proposed framework therefore offers a practical approach for fine-scale coastal-zone topographic mapping under sparse and spatially clustered altimetry conditions. Full article

Seagrass meadows support critical coastal ecosystems, but corresponding species-level remote sensing data remain limited, particularly in the Arabian Gulf, where field spectral data for dominant taxa are extremely limited. We present the first multi-species spectral characterisation of three dominant seagrass species in the Kingdom of Bahrain—Halophila stipulacea (n = 46 spectra, 25 stations), Halodule uninervis (n = 34, 19 stations), and Halophila ovalis (n = 17, 8 stations)—measured with an ASD FieldSpec^® 4 Hi-Res spectroradiometer (Malvern Panalytical, Malvern, UK; 350–2500 nm) from samples collected across 29 geographic stations (52 species–station sampling units). All sample counts reported here underwent quality control. Kruskal–Wallis tests with Benjamini–Hochberg (BH) correction, Jeffries–Matusita (JM) distance, Hedges’ g, and linear discriminant analysis (LDA) were used to characterise inter-species differences. H. ovalis was clearly distinguished from both co-occurring species: the Hd. uninervis–H. ovalis pair showed a discriminating window of 692–1394 nm (mean |g| = 1.31, BH q = 0.000046), and that for the H. stipulacea–H. ovalis pair was 700–1376 nm (mean |g| = 1.21, BH q = 0.000285); the JM distances were 1.60–1.67. A secondary shortwave-infrared discriminating window (1607–1755 nm; mean |g| = 0.90, BH q = 0.006) was also identified for the Hd. uninervis–H. ovalis pair. The H. stipulacea–Hd. uninervis pair showed meaningful geometric separation (JM = 0.994) but no individually significant wavelengths at the available sample size. ASentinel-2-proxy LDA achieved 85.6% overall accuracy (balanced accuracy = 87.3%; macro area under the curve = 0.917), outperforming a Landsat-proxy model by 20 percentage points. For each species, both a best-overall index and a visible-range alternative optimised for submerged satellite remote sensing are reported. The primary indices achieved balanced accuracies of 0.877–0.924; the visible-range alternatives achieved 0.818–0.907. Performance degraded substantially under noise (σ ≥ 0.002: −7.5 percentage points [pp]) and wavelength misregistration (±2–3 nm shifts caused losses of 5.5–15.7 pp), calling for stringent calibration requirements. These results constitute the first multi-species spectral library for Kingdom of Bahrain seagrasses, supporting Sentinel-2-based species mapping in the Arabian Gulf. Full article

Rurbanization and peri-urbanization are among the most dynamic territorial processes affecting metropolitan regions in Morocco, particularly within the Casablanca–Settat region. These transformations, driven by rapid urban growth, demographic pressure, and socio-economic change, generate complex transitional spaces between rural and urban environments. In this context, the present study proposes a hybrid methodology for detecting, classifying, and analyzing the rural–urban continuum by using remote sensing data and artificial intelligence techniques. The approach integrates Sentinel-2 satellite imagery, spectral indices, Global Human Settlement Layer datasets, and socio-demographic indicators derived from the Moroccan census. Two models, Self-Organizing Maps (SOM) and Graph Neural Networks (GNN), were applied to classify territories into four categories: urban, peri-urban, rurban, and rural. Model outputs were combined with expert-based decision rules to improve classification robustness and interpretability. The SOM model achieved up to $89.3\%$ agreement with expert classifications and a Cohen’s Kappa coefficient of $0.842$, demonstrating strong interpretability and consistency, while the GNN model reached $53\%$ agreement and effectively modeled spatial dependencies and neighborhood interactions. Diachronic analysis between 2014 and 2024 revealed a $54\%$ increase in peri-urban municipalities, a $24\%$ decrease in rurban territories, and a decline in rural municipalities, highlighting intensified urban sprawl and fragmentation of agricultural landscapes. Beyond its scientific contribution, this study provides a valuable decision-support framework for urban planners, environmental agencies, and policy makers involved in territorial governance and sustainable development. It can support land-use planning, monitoring of urban sprawl, protection of agricultural lands, and the implementation of adaptive territorial policies aimed at improving the resilience and sustainability of rurban environments. Full article

Sustainable small hydropower (SHP) is central to the clean-energy transition of mountainous Central Asia, yet its long-term reliability depends on a rapidly changing cryosphere. Winter river-ice dynamics—an underappreciated control on run-of-river generation—remain poorly characterized owing to the collapse of in situ hydrometeorological networks since 1991. We use a 112-month Sentinel-1 C-band SAR time series (February 2017–May 2026) over a 5320 km² headwater catchment of the Chu River basin, northern Tien Shan, Kyrgyzstan, to quantify river-ice phenology at 20 m resolution using a per-pixel summer-baseline anomaly approach. Mid-winter (December–February) ice cover declined significantly at −0.51%·yr⁻¹ (p = 0.013; Mann–Kendall p = 0.029), with the 2026 winter recording an unprecedented 2.6–2.8 σ departure from the 2017–2025 climatology. Contrasting the cold 2022 and warm 2026 winters revealed bidirectional climate sensitivity—early breakup versus persistent thin ice—posing distinct SHP hazards. ERA5-Land reanalysis (1992–2026) showed significant winter warming with no precipitation or snowfall trend, indicating thermally forced ice decline. Interpreted within a conceptual Peak Water scenario, this signals a closing window of opportunity for SHP generation, with direct relevance to sustainable water–energy management and the UN Sustainable Development Goals (SDG 7; SDG 13). Our results provide the first decadal, satellite-based evidence of river-ice loss for Central Asian mountain rivers and a transferable monitoring framework to support climate-resilient, sustainable hydropower planning in ungauged basins. Full article

Vegetation change in cryosphere-affected mountain basins reflects interacting climate and human pressures but their relative influence remains uncertain in the Upper Indus Basin. The novelty of this study is the integration of satellite vegetation, climate variables, human pressure indicators, residual attribution and diagnostic validation in a data-scarce high-mountain basin. We evaluated growing-season Normalized Difference Vegetation Index dynamics and associated drivers from 2001 to 2023 using trend analysis, correlation, Random Forest diagnostics, Sentinel 2 validation, and residual trend analysis. The results showed widespread greening across 96.59% of the basin, with stronger improvement in the lower and central areas. Significant greening covered 69.94% of the basin, while only 1.55% showed significant browning. Precipitation and temperature were predominantly positive drivers of vegetation change, whereas potential evapotranspiration and solar radiation were mostly negative. Soil moisture played a strong regulatory role along elevation gradients. Residual trend analysis provided approximate and method-dependent estimates of the possible anthropogenic influence on vegetation change at 73.09% and climatic drivers at 26.91% rather than direct causal decomposition. These values are approximate and method-dependent estimates, not direct causal decomposition. The findings highlight human-related greening in lower valleys and climate-controlled vegetation responses in high-mountain areas. Full article