Search Results (1,720)

The Amazon Basin and its rivers play a vital role in regional biodiversity, the carbon cycle, and socio-economic security. Through erosion and deposition, river planforms change over time, affecting local infrastructure, food security, and changes to ecosystems. Long-term monitoring is essential for observing these dynamics. Synthetic Aperture Radar (SAR) provides a method to consistently map river planform dynamics across large areas because it is largely independent of atmospheric conditions. This study presents an approach for deriving river planform metrics across the entire Amazon Basin using Sentinel-1 C-band SAR data. This approach followed three main steps: water mask generation, validation of the data and river metrics extraction. Sentinel-1 imagery from 2017 to 2025 was composited into quarterly mean images, after which Otsu thresholding was applied to derive water classifications. Additional post-processing steps were applied to reduce terrain- and seasonal effects. The final water masks were divided into water-change classes, validated using stratified sampling and achieved an overall accuracy of 98.5%. Quarterly river planform metrics, including sinuosity, mean channel width and migration rate, were derived using channel centerline extraction, but due to a lack of in situ validation data the river metric values have not been validated. The resulting time series provide insights into how river planform changes across all Amazon sub-basins from 2017 to 2025 can be monitored using SAR-based methods. The results reveal spatial differences in river dynamics between tributaries, mostly depending on flow pattern, up- or downstream path and location in the upper, middle or lower Amazon Basin. These findings demonstrate the potential of SAR time series for monitoring large-scale river planform dynamics. 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

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

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

The spatial distribution of flammable tree species directly influences forest fuel structure and fire risk patterns. However, mixed pixels limit the ability of conventional classification methods to characterize continuous within-pixel variation in species composition, thereby constraining fine-scale forest mapping. To address this issue, this study developed a subpixel mapping framework for flammable tree species in Yajiang County, Sichuan Province, by integrating Sentinel-2 time-series data with a spectral mixing–unmixing strategy. Using 2019 Sentinel-2 time-series data and National Forest Inventory (NFI) data, temporal mixed samples with known abundance fractions were generated using a linear spectral mixing model. An XGBoost-based collaborative multi-regression framework was then applied to estimate the proportions of different tree-species endmembers within complex forest pixels. Quantitative evaluation using synthetic mixed samples showed that the model achieved stable unmixing performance across different random mixing scenarios. The best performance was obtained under the Mixed 2 scenario with a sample size of 250 K, reaching an R² of 0.821. The resulting maps revealed continuous spatial variation in the abundance and composition of flammable tree species. Mountain pine was the most widespread and dominant species, followed by spruce and mountain oak, whereas birch and fir mainly exhibited localized patchy distributions. An additional NFI-based categorical evaluation assessed the consistency of the final maps with real forest inventory records. The identification accuracies were 93.95% for pure stands and 91.22% for mixed stands, while the species classification accuracies were 87.28% for pure stands and 84.41% for dominant species in mixed stands. The proposed framework provides useful spatial information for regional forest fuel assessment and fire risk management. Full article

Remote-sensing land surface deformation (LSD) is a powerful and effective approach for investigating regional alpine permafrost variations. However, alpine permafrost is often distributed in areas characterized by earthquakes, and the LSD of alpine permafrost is potentially contaminated or diminished by earthquake-related LSD. Therefore, this study aimed to derive the effective LSD in the alpine permafrost of the Source Area Yellow River (SAYR) by removing LSD originating from the M_w 7.4 Maduo earthquake in 2021-05-22 and analyzing the spatiotemporal variations in LSD during 2017–2024. Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) was used to obtain the initial LSD time series from Sentinel-1 images acquired during 2017–2024. The LSD of the M_w 7.4 Maduo earthquake, its aftershocks and the post-seismic relaxation in SAYR was simulated separately by considering its temporal process and removed from the LSD time series in SAYR. The final LSD was validated against in situ Global Navigation Satellite System (GNSS) measurements, and the spatiotemporal variations in LSD in SAYAR were subsequently analyzed. The study found the following: (1) the removal of the earthquake-related LSD was successful both spatially and temporally and the final LSD has mean absolute error (MAE) of 3.22 mm and root mean squared error (RMSE) of 3.92 mm; (2) during 2017–2024, the vertical LSD in SAYR was mostly −8–8 mm/y; (3) soil moisture determined the spatial distribution of the LSD direction in SAYR as a result of local drainage conditions, air temperature, precipitation and snow melt. This study demonstrated the necessity of removing the earthquake-related LSD when investigating the alpine permafrost LSD in tectonically active areas. The strategy adopted in this study serves as a technical reference for future investigations of this kind. The findings in this study provide insight for a thorough understanding of permafrost evolution on the Tibetan Plateau in the context of climate change. Full article

River ecological restoration in lowland plain basins is often constrained by fragmented river networks, degraded riparian zones, eutrophication risk, and intensive human disturbance. Conventional monitoring approaches rarely connect watershed-scale dynamics with responses from typical restoration units, limiting quantitative evaluation and the separation of direct project outcomes from broader environmental variability. To address this gap, this study developed a collaborative satellite–unmanned aerial vehicle (UAV)–unmanned surface vehicle (USV) monitoring framework and applied it to the Nihe River Basin, China, a lowland plain river undergoing systematic restoration under the Shan-shui Initiative. The framework combines Sentinel-2 time-series imagery, high-resolution Gaofen-1, Gaofen-2, and Jilin-1 imagery, UAV orthophotos, USV observations, and auxiliary environmental datasets. Unlike single-scale monitoring approaches, it links watershed-scale indicators, including water-body dynamics, chlorophyll-related eutrophication risk, riparian ecological background, and soil-water conservation capacity, with unit-scale diagnosis of riparian buffer and riverine wetland restoration. Results showed that river water-body area increased from 37.78 km² to 40.59 km² during 2021–2024, while normalized difference chlorophyll index (NDCI)-based eutrophication risk improved in 9.12% of the monitored river area and degraded in only 0.47%. Riparian vegetation cover remained high, whereas regional soil-water conservation capacity declined due to climatic factors, revealing asynchronous responses between local recovery and regional background conditions. At the unit scale, riparian buffer restoration enhanced buffer continuity and near-bank water quality, as reflected by decreased chemical oxygen demand (COD), increased dissolved oxygen (DO), and limited ammonia nitrogen (NH₃-N) improvement. Riverine wetland restoration promoted land-use adjustment and ecological spatial reorganization. This cross-scale evidence chain supports adaptive management of inland river and wetland restoration projects. Full article

Soil water retention capacity (SWRC) is vital for agriculture and watersheds, but traditional measurements are hindered by destructive sampling and spatial discontinuity. This study selected Youyi and Heshan farm in Heilongjiang Province as the study area, using the time-series Normalized Difference Water Index (NDWI) from Sentinel-2 during the snowmelt-to-bare-soil window as a soil water retention signature (SWRS) for monitoring SWRC. The exponential decay fitting model (EDFM) was used to construct a Soil Moisture Decay Index (SMDI) to analyze the spatial patterns of the SWRC. Results showed that: (1) time-series NDWI exhibited distinct exponential decay signatures varying with soil textures and degradation gradients; (2) the EDFM effectively fitted the time-series NDWI (R² = 0.84–0.99), extracting decay rate and stable level to quantify SWRC; (3) SMDI showed high consistency with in situ soil moisture (R = 0.82–0.88) and measured field capacity (Youyi Farm: R² = 0.56; Heshan Farm: R² = 0.59), and correlated significantly with soil organic matter (R² = 0.61–0.71) and texture (R² =0.50–0.64), confirming the physical controls on water retention; and (4) SMDI spatial distribution revealed distinct degradation patterns across varying topographic and soil conditions. This study innovatively transformed point-scale static SWRC measurements into spatially continuous monitoring, offering new tools for precision water management and degraded-soil restoration, with strong theoretical and practical value. Full article

Tailings storage facility (TSF) failures have caused severe casualties and economic losses. This study used Enhanced Small Baseline Subset InSAR (E-SBAS-InSAR) and 88 Sentinel-1A images to retrieve the 2022–2024 surface deformation time series of the Shiguilong TSF, located in the Fe–Cu polymetallic metallogenic belt of the middle–lower Yangtze River. The reliability of the results was assessed through consistency comparisons with Small Baseline Subset InSAR (SBAS-InSAR) and Persistent Scatterer InSAR (PS-InSAR). A time-series decomposition model was applied to extract seasonal deformation components and analyze their lagged responses to temperature and intense rainfall events. The results show that: (1) E-SBAS-InSAR achieved a monitoring-point density nearly 7 times higher than SBAS-InSAR, enabling dense and long-term deformation characterization; (2) subsidence at Shiguilong continued to increase, with cumulative subsidence reaching −76.8 mm and a maximum annual mean subsidence rate of −22.78 mm/yr; (3) deformation was mainly controlled by long-term consolidation of loose tailings and creep of dam–tailings materials, while seasonal factors induced stage-dependent fluctuations; (4) seasonal deformation showed lagged responses of 6 days to temperature variations and 2 days to intense rainfall events, with rainfall exerting a more pronounced influence. This work is significant for TSFs monitoring under complex surface conditions. Full article

Marine-terminating glaciers are major contributors to sea-level rise, yet their frontal ablation—the combined loss from ice discharge and terminus retreat—remains poorly constrained. This study presents a monthly time series of ice discharge for 40 marine-terminating glaciers in Alaska from 2015 to 2021, derived from Sentinel-1 velocity data, and reconstructed ice thickness information. Frontal ablation was calculated as the sum of ice discharge and terminus mass loss, from manually delineated terminus positions between 2015 and 2020. The mean annual ice discharge was 11.81 ± 5.35 Gt a⁻¹, dominated by Hubbard, Columbia and Yahtse glaciers, which together accounted for ~70% of Alaska’s total ice discharge. Terminus retreat contributed an additional 1.30 ± 0.07 Gt a⁻¹, resulting in a total frontal ablation of 13.11 ± 5.35 Gt a⁻¹. Most glaciers exhibited late-summer velocity minima indicating seasonal changes in subglacial drainage efficiency, while the strongest relationship was found with regional ocean temperature. These findings confirm that Alaska’s marine-terminating glaciers currently lose relatively little mass through frontal retreat compared to their regional mass balance. Our observations are consistent with previous studies suggesting that many Alaskan marine-terminating glaciers have passed their phase of rapid retreat. The presented analysis also provides fundamental information for refining sea-level rise projections. Full article

Accurate time series forecasting is fundamental to geohazard early warning, yet remains a major challenge. Conventional in situ geotechnical monitoring remains costly and spatially constrained, whereas deep learning applied to remote sensing data has become increasingly prevalent but often suffers from opacity of model decision-making. To address this issue, we propose a Transformer-based forecasting framework, namely PatchTST-Fusion, adapted for multivariate Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) time series. The framework integrates model interpretability analysis through TimeSHAP, providing temporal and feature-level attributions across the input sequence. Landslide deformation time series are first derived from Copernicus Sentinel-1 SAR data. Variational Mode Decomposition is then applied to decompose the non-linear signals into trend, seasonal, and noise components. The denoised displacement series are modeled and forecast using the proposed PatchTST-Fusion, which incorporates rainfall and reservoir water level fluctuations as feature-level drivers. Application to the Daping landslide cluster in the Three Gorges Reservoir Area in China demonstrates that our method captures both the long-term and transient non-linear coupling between deformation and its triggers, surpassing state-of-the-art models including CNN-BiGRU-Attention, Informer and original PatchTST with 7–55% improvements in MAE and 10–52% improvements in RMSE. Beyond predictive gains, feature attribution of environmental triggers via TimeSHAP reveals that rainfall and reservoir regulation exert temporally distinct influences on slope kinematics, with high relative importance concentrated in specific periods and characteristic lagged responses. This interpretable framework provides both enhanced forecasting accuracy and process-based insights, offering a broadly applicable tool for landslide early warning in reservoir regions. Full article

Surface deformation induced by underground coal mining shows a clear time-lag effect, with persistent residual deformation in old goafs under strip pillar mining conditions, drawing significant research attention. This study focuses on the Gucheng mining area, where 210 Sentinel-1A SAR images (May 2017–January 2025) were processed using SBAS-InSAR to derive 7.5 years of time-series surface deformation. Based on these results, five strip pillar mining panels with different cessation times were selected. Through comparative analysis, a time-progressive sequence was constructed to identify post-mining residual deformation and stage-wise stabilization characteristics, and to reveal long-term deformation responses occurring years after cessation, thereby reconstructing the long-term evolution of surface deformation in old goafs. Furthermore, a stacking ensemble prediction model was developed to predict subsidence trends at representative feature points. The results indicate that subsidence mainly ranges from −20 to −10 mm/a, with a maximum of approximately −64 mm/a and cumulative subsidence of about −515 mm. Surface deformation follows a stage-wise evolution pattern of “residual subsidence—stage-wise stabilization—secondary subsidence—deformation stabilization”, with durations of approximately 2, 2, and 14 years, respectively, and overall stabilization occurring after approximately 18 years. The predicted results from the stacking model are highly consistent with the SBAS-InSAR monitoring data and can reliably describe the evolution trend of surface subsidence. The findings provide important evidence for understanding long-term surface deformation in old goafs of strip pillar mining areas. Full article

Accurate mapping of crop planting structures is critical for precision agriculture, yet it remains challenging in rugged terrain with fragmented fields, frequent cloud contamination, and limited high-quality training samples. This study evaluates an integrated framework combining recursive feature elimination (RFE) and the pretrained Tabular Prior-Data Fitted Network (TabPFN) for small-sample crop classification using Sentinel-2 time-series data in Yuxi City, located on the western margin of the Yunnan–Guizhou Plateau. A multidimensional feature set integrating spectral and temporal vegetation indices and textural and geospatial information was constructed and optimized via RFE. The TabPFN model achieved an overall accuracy (OA) of 96.27%, a kappa coefficient of 0.9558, and a macro-F1 score of 0.956 in the main validation. In repeated small-sample experiments, TabPFN maintained a mean OA of 90.60% at a 30% training-sample ratio and 82.89% at a 10% ratio. RF-guided feature ranking and ablation analyses suggested that temporal vegetation indices were important predictors, followed by early-season spectral characteristics, textural features, and supplementary geospatial information. Overall, these findings indicate that RFE-TabPFN is a feasible option for 10 m crop mapping in Yuxi under limited training samples, while its broader applicability still requires further testing across additional years, regions, and cropping systems. Full article

In alpine mountain regions, significant seasonal surface changes reduce InSAR coherence over long time spans, hindering geohazard identification. This study proposes a method for geohazard detection based on InSAR seasonal coherence variation. First, time-series interferograms and coherence maps are generated from Sentinel-1 imagery. Each year is then partitioned into summer, transition, and winter seasons by analyzing the spatial migration of high-coherence zones. Interferometric pairs from the transition season are further screened and reassigned to summer or winter groups according to their coherence characteristics. Stacking-InSAR is applied separately to the summer and winter datasets to derive seasonal deformation rates; long-temporal-baseline pairs (60–120 days) that maintain sufficient coherence are selectively incorporated to improve the detectability of slow-moving slopes. Finally, geohazards are identified by combining the summer and winter deformation results. Applied in the eastern Himalayan syntaxis, the method showed that less than 19% of geohazards were detectable in both seasons, indicating seasonal variations in geohazard activity. Moreover, it identified approximately 29% more geohazards on average than traditional Stacking-InSAR using all interferograms. Thus, the proposed approach enables more accurate and effective geohazard detection in cold mountains, supporting disaster prevention and mitigation. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article