Search Results (1,173)

Thick-cloud contamination severely limits the usability of optical remote sensing imagery because cloud-covered regions may suffer from complete loss of surface information. Synthetic aperture radar (SAR) imagery provides complementary structural cues due to its cloud-penetrating capability, but the substantial cross-modal discrepancy between SAR and optical images makes high-fidelity SAR–optical fusion challenging. Existing methods usually either directly fuse heterogeneous SAR and optical features or use SAR-to-optical translation with insufficient spectral and structural constraints, which may lead to spectral distortion, structural artifacts, or degradation of cloud-free regions. To address these issues, we propose TFCRNet, a two-stage translation-and-fusion network for SAR–optical thick-cloud removal. In the translation stage, a Multi-Scale Feature Fusion Generator (MSFFG) transforms SAR imagery into optical-like images, while a Spectral Discriminator (SpeD) and a Structural Discriminator (StrD) separately constrain spectral fidelity and structural integrity. In the fusion stage, a Region-Gated Cross-Attention Fusion (RGCAF) module performs cloud-aware feature interaction between the translated optical image and the cloudy optical image. Using an externally supplied cloud mask, RGCAF emphasizes translated SAR-derived cues in cloud-covered regions while retaining reliable optical information in cloud-free regions. TFCRNet therefore requires a cloud mask during inference. Experiments on the SEN12MS-CR and SMILE-CR datasets show that TFCRNet achieves the best overall performance among the baseline methods reproduced under the unified experimental protocol adopted in this study. TFCRNet obtains 33.01/30.53 dB PSNR and 0.91/0.88 SSIM on SEN12MS-CR and SMILE-CR, respectively. These controlled results should be distinguished from literature-reported values obtained under different experimental settings, several of which are higher on selected metrics. Fine-grained ablations demonstrate that SpeD and StrD provide differentiated spectral and structural supervision, while RGCAF improves multimodal reconstruction through cloud-mask-guided regional information routing rather than spatially uniform feature fusion. Full article

Reliable monitoring of reclaimed cropland is hindered when one optical acquisition is unavailable or degraded. We formulate missing-modality bi-temporal optical–SAR change detection and propose the Modality-Availability-Aware Robust Change Network (MARC-Net), a new architecture that combines explicit availability conditioning, condition-aware temporal proxy stabilization, a shared residual input adapter, multi-level signed temporal interaction, dilated context refinement, and hierarchical change decoding. A two-phase condition-balanced learning strategy jointly develops mixed-missing representations and optimizes Full, Missing-O, and Missing-S behavior without reconstructing the unavailable image. On four reclaimed-cropland scenes, the final model obtains IoUs of 0.7826, 0.7039, and 0.7780, respectively, with a three-condition mean of 0.7548. It exceeds the strongest evaluated external baseline mean (0.7381) while using one checkpoint and a fixed argmax decision rule. LOSO and controlled optical-degradation experiments further characterize robustness under geographic shift and progressive observation-quality degradation. These results demonstrate that availability-aware temporal stabilization and condition-balanced optimization provide an effective operating point for incomplete-input reclaimed-cropland monitoring while preserving complete-input performance. Full article

Rapid and reliable assessment of structural damage following disasters is critical for prioritizing rescue operations. In this study, we present a unified deep learning framework for building damage assessment from satellite imagery. The proposed approach integrates segmentation-driven feature construction, morphological processing, cost-sensitive learning, and cross-disaster evaluation to support robust performance under limited and imbalanced data conditions. The framework combines an adapted U-Net for building localization with a hybrid convolutional neural network (CNN)-deep neural network (DNN) classifier for damage-level prediction and evaluates transferability across disaster events, geographic regions, and sensing conditions. The proposed method is evaluated on selected events from the xView2 Building Damage Assessment (xBD) and BRIGHT datasets, using optical imagery from xBD and pre-disaster optical and post-disaster Synthetic Aperture Radar (SAR) imagery from BRIGHT. Despite the limited and highly imbalanced event-specific samples, the framework achieves a mean cross-validation macro-F1 score of 70% and a maximum fold-level score of 77% on the Mexico earthquake subset of xBD and up to 98% on earthquake-related events in BRIGHT. Cross-validation characterizes performance variability across source-image-grouped data partitions, while cross-disaster evaluation reveals event-dependent transferability and provides a preliminary indication that structural domain similarity may be related to transfer performance. Although the evaluation is constrained by data availability, the results indicate that lightweight, cost-sensitive deep learning frameworks may support auxiliary post-disaster screening and decision support in resource-constrained scenarios. This study highlights both the potential and the remaining challenges of deploying artificial intelligence (AI) for rapid post-disaster assessment. Full article

Land subsidence threatens the operational safety of railways in soft-soil plains. This study investigates the spatiotemporal evolution and mechanisms of subsidence along the Beijing–Shanghai Conventional Railway (BSR) and High-Speed Railway (HSR) in Shanghai using 2015–2025 Sentinel-1 imagery. To explicitly decouple macroscopic environmental background subsidence from localized engineering disturbances, we propose a novel framework integrating SBAS-InSAR monitoring, RF-SHAP multi-source attribution, and LightGBM baseline prediction. Results reveal significant deformation heterogeneity governed by foundation designs: the shallow-subgrade BSR experienced a mean subsidence rate of −2.03 mm/yr (with 4.69% extreme pixels), whereas the deep-anchored HSR remained highly stable at −0.81 mm/yr. Attribution analysis demonstrates that anthropogenic factors primarily drive regional deformation, contributing 70.9% to the variance, with distance to the BSR, groundwater levels, and building density identified as core nonlinear predictors. Furthermore, by analyzing dynamic prediction residuals, the framework accurately traced high-risk structural anomalies, successfully isolating −17.9 mm/yr of acute settlement induced by short-term construction and 100–120 mm of cumulative consolidation triggered by long-term static loads. This approach provides a robust, data-driven diagnostic tool to assist in targeted track-bed maintenance for railway safety management. Full article

Accurate mapping of commercial durian plantations is essential for agricultural inventory, precision agriculture, and sustainable land management but remains challenging because of spectral similarity with other evergreen vegetation. This study developed a multisource remote sensing approach integrating multi-temporal Sentinel-2 imagery, Sentinel-1 Synthetic Aperture Radar (SAR), DEM-derived terrain variables, recursive feature selection, and machine learning for binary durian plantation classification in Sisaket Province, Thailand. A total of 2430 field reference samples were used to evaluate Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART). RF achieved the strongest overall performance using the integrated Sentinel-2, Sentinel-1, and DEM dataset, with an Overall Accuracy of 90.38%, an F1-score of 90.59%, a Kappa coefficient of 0.81, and an AUC of 0.991. Recursive feature selection reduced the predictor set from 53 to 12 variables (77.4%) while retaining high classification performance. The retained 12-predictor subset comprised nine Sentinel-2-derived variables (five seasonal EVI and four NDBI variables), two Sentinel-1 VV/VH ratio variables, and one DEM-derived elevation variable, corresponding to 75.0%, 16.7%, and 8.3% of the retained predictors, respectively. The proposed approach provides an accurate and computationally efficient method for durian plantation mapping within the study area, with broader applicability requiring further validation. Full article

Sedimentation is eroding the water security of reservoirs in hydrologically active basins: the Poechos reservoir (Peru) has lost 62% of its original 887.7 hm³ capacity in 48 years, yet its Elevation–Area–Volume (EAV) curve is refreshed only by bathymetric surveys at decade-plus intervals, compromising flood regulation and the water supply for over 100,000 ha of farmland. To close this gap, we propose an integrated, low-cost, fully reproducible framework that reconstructs the EAV curve from freely available satellite data: Sentinel-1 SAR (287 acquisitions, 2021–2026), PlanetScope imagery as ground truth (23 dates), and Surface Water and Ocean Topography (SWOT) altimetry (53 validated passes, 2023–2026). Water surfaces were delineated with a deep learning segmentation model (Feature Pyramid Network with an InceptionV4 encoder), selected among nine architecture–encoder combinations and calibrated to a 0.64 decision threshold, achieving a 90.66% Intersection over Union (IoU) and a 95.10% F1 score; a stochastic quantile mapping algorithm then asynchronously coupled the area and elevation series. The resulting EAV curve matched daily operational records from Peru’s National Water Authority (ANA) with high precision (NSE = 0.94, R² = 0.96, and RMSE = 25.93 hm³); the residual bias (BIAS = −11.23 hm³) reflects active sedimentation unaccounted for in the official curve. This bias peaked at an accumulated deficit of 24.5 hm³ during the 2023–2024 hydrological year (3.5 hm³/year), of which up to 19.6 hm³ is attributed to the 2023 Yaku cyclone as a phenomenologically scaled upper-bound estimate (9.8–19.6 hm³ across 40–80% attribution fractions), since SWOT was not yet operational during the event. Updating every 21 days under any weather and requiring no new field campaigns beyond the baseline bathymetric anchor, the trained ensemble was further transferred zero-shot to three additional reservoirs (San Lorenzo, Tinajones, and Gallito Ciego), demonstrating a scalable path from infrequent static assessments to near-continuous, dynamic monitoring of water storage. Full article

Oil palm plantations play an important role in agricultural production and carbon storage in tropical regions. The accurate estimation of aboveground carbon (AGC) is essential for sustainable plantation management, climate change mitigation, and carbon monitoring. Although field measurements provide reliable estimates, they are often time-consuming, labor-intensive, and costly, particularly over large plantation areas. Recent advances in remote sensing and machine learning offer efficient alternatives for AGC estimation using satellite imagery. In this study, we developed a machine learning framework for AGC estimation in oil palm plantations using Sentinel-2 multispectral imagery and Sentinel-1 synthetic aperture radar (SAR) data. Field measurements were integrated with spectral variables, vegetation indices, and SAR-derived parameters extracted from satellite data. A hybrid feature selection approach combining Pearson correlation, mutual information and mRMR was used to identify the most relevant variables. Six machine learning algorithms were evaluated, including Linear Regression, Random Forest, XGBoost, Gradient Boosting, LightGBM, and Extra Trees. Because the 160 observations comprise sixteen 10 m × 10 m grid cells nested within ten 40 m × 40 m field plots, model performance was assessed with leave-one-plot-out cross-validation: all sixteen cells of a plot were held out together, and the hybrid feature selection was repeated inside every fold using only that fold’s training plots. Performance was measured on pooled out-of-fold predictions using R², root mean squared error (RMSE), and average absolute relative error (AARE%). Under this spatially independent design the combined Sentinel-1 + Sentinel-2 dataset gave the highest accuracy (R² = 0.7950, RMSE = 4.14 t C ha⁻¹, AARE = 33.53%), followed by Sentinel-1 alone (R² = 0.7631, RMSE = 4.46 t C ha⁻¹) and Sentinel-2 alone (R² = 0.6108, RMSE = 5.71 t C ha⁻¹). Linear Regression and Extra Trees were the most robust models, whereas the boosted ensembles did not generalize to unseen plots. Repeating the evaluation with an ungrouped random split of the same data inflated R² by up to 0.70, showing that a large part of the accuracy obtainable under that design reflects within-plot spatial autocorrelation rather than predictive skill. These findings indicate that optical-SAR imagery combined with machine learning can provide useful AGC estimates in oil palm plantations, and that spatially independent validation is essential for reporting them honestly. The proposed framework can be used to support plantation-scale carbon mapping, monitoring, and carbon stock assessment, subject to further calibration and independent validation across additional plantations. Full article

Fine-scale mapping of forest types is a prerequisite for accurately assessing forest biomass, biodiversity, ecosystem service values, and carbon budgets. Shihe County, located within an ecotone transitioning from subtropical to temperate monsoon climates in China, was selected as the case study area. By integrating Sentinel-1 SAR and Sentinel-2 multispectral imagery, along with derived vegetation indices, texture features, and backscattering coefficients, as well as statistical features extracted from the 2022 NDVI time-series, we employed a hierarchical classification framework and a random forest algorithm to generate the forest type map. The results indicated the following: (1) With a single-date multi-feature dataset, late winter (3 March) was determined to be the optimal period for forest type classification in Shihe County. (2) Shortwave infrared bands, red-edge bands, the modified vegetation index, the normalized difference red-edge index, mean texture features, and VH-polarization data were the most influential variables. (3) Incorporating yearly NDVI time-series statistical features into the single-date winter subset significantly improved classification performance, yielding an overall accuracy of 86.39% and a Kappa of 0.781, which represents a 10.83% improvement over the baseline. Persistent misclassification between bamboo forests and tea plantations remained a primary constraint on further accuracy enhancement, while the classification accuracy for evergreen broadleaf forest and deciduous coniferous forest exhibited considerable uncertainty, likely attributable to limited reference sample sizes. (4) Deciduous broadleaf forests constituted the dominant land cover type in Shihe County, whereas evergreen coniferous forests, bamboo, and tea plantations were also widely distributed, collectively reflecting the region’s transitional ecological characteristics. Full article

Shoreline determination is fundamental to coastal research, spatial planning, and legal boundary delineation but remains challenging in low-relief coastal areas where small sea-level variations can produce substantial horizontal shoreline displacements. This study compares shoreline determination methods based on tide gauge (TG) observations, LiDAR data, Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical satellite imagery using the low-relief coast of Pärnu Bay, Estonia, as a case study. The comparison was based on shorelines derived from Sentinel-1 and Sentinel-2 imagery acquired on selected common acquisition dates within the 2015–2025 study period, rather than on a temporally continuous annual dataset, and compared with temporally matched LiDAR-derived shorelines extracted from a Digital Terrain Model (DTM) generated from a 2021 LiDAR survey. The LiDAR-derived shorelines were extracted using the mean sea level (MSL) observed at the Pärnu and Häädemeeste TGs at the satellite overpass time, while the satellite-derived shorelines were additionally validated against RTK GNSS measurements. The results demonstrate that the evaluated methods produce substantially different shoreline positions. Sentinel-2-derived shorelines generally corresponded more closely to the temporally matched LiDAR-derived shorelines than Sentinel-1-derived shorelines and most accurately represented the instantaneous land–water boundary during field validation. These findings demonstrate that different shoreline determination methods represent different shoreline definitions. Consequently, shoreline datasets should be interpreted according to their intended purpose rather than treated as directly interchangeable. Full article

Synthetic Aperture Radar (SAR) enables high-resolution sea-surface wind speed retrieval. However, the enhanced spatial resolution of SAR imagery introduces substantial challenges, from small-scale contamination sources that significantly degrade retrieval accuracy. Particularly in coastal regions, non-wind-related backscatter signals, such as ships and oil slicks, can severely bias wind speed estimates at sub-kilometer scales. In this study, the first-order moment (average, m₁) and second-order moment (variance, m₂) are computed from the normalized radar cross-section (NRCS) within sub-images of Sentinel-1 SAR data acquired in Interferometric Wide (IW) mode. Analysis reveals that clean-sea-surface signals in both VV and VH polarizations cluster around an approximately linear empirical trend, m₂ = 2m₁ + b, in the m₁-m₂ statistical feature space, whereas the examined contamination types deviate from this trend and occupy separable regions. Based on this characteristic, a quality-control framework is proposed for the systematic separation of clean sea surface from image noise (border noise and inter-swath stripe noise) and non-ocean targets (land contamination, bright targets, and dark spots). Validation using independent SAR data from the Taiwan Strait was conducted separately for native 10 m and height-adjusted 3 m buoy observations. For the native 10 m observations, the RMSE and MBE were essentially unchanged at 1.5 m/s and −0.3 m/s, respectively. For the height-adjusted nearshore observations, the RMSE decreased from 3.2 m/s to 2.1 m/s and the MBE changed from −1.5 m/s to −1.1 m/s. Full article

This study proposes a multi-sensor time-series framework for estimating sugarcane planting Days After Planting (DAP) using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery in Phu Khiao District, Chaiyaphum Province, Thailand. The framework integrates vegetation indices, SAR backscatter, Dynamic Time Warping Barycenter Averaging (DBA), Derivative Dynamic Time Warping (DDTW), and stage-specific Ordinary Least Squares (OLS) calibration to estimate planting DAP and crop age. Sugarcane fields were first identified using a Random Forest classifier trained on combined multispectral and SAR features, achieving an Overall Accuracy of 88.5% and a Kappa coefficient of 0.82 for the optimal feature configuration. Multi-temporal vegetation index and SAR backscatter time series were then smoothed using Locally Weighted Scatterplot Smoothing (LOWESS) and aligned with phenological reference prototypes generated by DBA using DDTW. Stage-specific OLS models were subsequently applied to reduce systematic prediction bias. The calibrated framework achieved a coefficient of determination (R²) of 0.9970 and a root mean square error (RMSE) of 5.21 days, representing a substantial improvement over the uncalibrated DDTW estimates (R² = 0.9953, RMSE = 7.00 days). DDTW alignment produced the highest accuracy during the grand growth stage (Stage 2), with normalized RMSE (NRMSE) ranging from 0.064 to 0.091 across individual features. Independent validation using 140 sugarcane plots from the 2024/2025 cropping season demonstrated the plausibility of the proposed framework, correctly identifying Stage 3 (sugar accumulation) growth for 98.6% of the plots and estimating a mean planting DAP of 267.06 ± 9.55 days. These findings demonstrate that the proposed framework provides an accurate and operational approach for estimating sugarcane planting dates from satellite time-series data, supporting crop age monitoring and harvest planning in tropical agricultural regions where field-based planting records are unavailable or incomplete. Full article

In karst regions, sugarcane mapping faces challenges from fragmented fields, undulating terrain, spectral confusion, and persistent cloud cover, which limit traditional optical remote sensing. To address these issues, we propose a fine-scale extraction framework that integrates Sentinel-2 optical and Sentinel-1 synthetic aperture radar (SAR) imagery through image-level fusion, and introduces a UNet-DFH network with a Multi-Scale Edge Fusion (MSEF) module and an Attention-Deformable Fusion Module (ADFM). This study makes three core contributions: (1) we construct a dedicated optical–SAR collaborative sugarcane extraction dataset for typical karst regions, alleviating the scarcity of multimodal labeled samples; (2) we propose the UNet-DFH network, where MSEF enhances boundary preservation and topological detail in shallow decoding stages, while ADFM improves robustness to geometric deformation and local misalignment in deep semantic stages; (3) we demonstrate that the joint mechanism of edge-preserving filtering and deformable adaptation yields a synergistic effect in addressing the precision–recall trade-off. Experiments in a typical karst area of Guangxi, China, demonstrate that optical–SAR fusion achieves an IoU of 80.08% and an OA of 92.09% during the sugar accumulation and maturity stage. During the more challenging tillering stage, UNet-DFH maintains relatively stable performance under optical-only conditions, with an IoU of 72.98%, Recall of 82.78%, and OA of 92.12%. Moreover, optical–SAR fusion improves Recall by 5.5 percentage points over optical-only inputs (from 83.54% to 89.04%), while Precision exhibits a moderate decrease from 91.89% to 88.84%, reflecting the expected trade-off associated with speckle noise. These results confirm the complementary value of multimodal data and the effectiveness of the proposed modules in preserving fragmented plot boundaries and improving segmentation performance in complex karst terrain. The framework offers a promising approach for high-precision crop mapping in the studied karst agricultural landscape. Full article

Accurate spatial information on soil pH is essential for soil management, agricultural decision-making, and ecosystem assessment. Although Earth observation (EO) data have played an increasingly important role in digital soil mapping (DSM), most studies have relied mainly on optical imagery, while synthetic aperture radar (SAR) information, especially interferometric coherence, remains underutilized for soil pH prediction. This study explored the value of Sentinel-1-derived interferometric coherence and backscatter images, Sentinel-2 optical imagery, and topographic–climatic variables for national-scale mapping of soil pH across Spain. Models were developed using random forest (RF) and boosted regression trees (BRT) with 3867 LUCAS 2018 topsoil samples under 11 prediction scenarios representing different radar configurations, radar-derived feature types, and multi-source data integration strategies. VH backscatter performed better than VV backscatter, while combining backscatter from both polarizations and both orbit directions further improved performance within the backscatter-only group. When different predictor groups were used separately, coherence images achieved R² values of 0.49–0.52, outperforming all other individual predictor groups. Under BRT, adding coherence to backscatter increased R² from 0.45 to 0.56 for pH in CaCl₂ and from 0.46 to 0.57 for pH in H₂O, and the further inclusion of Sentinel-2 optical imagery slightly improved performance. The best performance was achieved by integrating all satellite-derived variables with topographic and climatic predictors, with R² values of 0.62 for both pH in CaCl₂ and pH in H₂O under BRT. Variable importance analysis further identified coherence as the most influential predictor group within the evaluated predictor set, with short-temporal-baseline coherence features ranking highest. The predicted maps revealed clear spatial heterogeneity, with lower pH values mainly in northern and northwestern Spain and higher values in central and southeastern regions. These findings demonstrate the added value of Sentinel-1 interferometric coherence for national-scale soil pH mapping. Full article

Accurate aboveground biomass (AGB) estimation in subtropical forests is critical for regional carbon accounting and sustainable forest management. However, standardized multi-source feature screening and integrated machine learning–geostatistical analysis of AGB remain limited. This study integrated six heterogeneous datasets: Landsat-8 optical imagery, Sentinel-1 SAR, topographic, meteorological, soil data and the 2014 National Forest Inventory (NFI), and established 48 predictors in subtropical forests of Anhui Province, China. A two-stage variable selection framework was applied, with Pearson correlation screening reducing the initial 48 predictors to 36 less-correlated variables, followed by the recursive feature elimination (RFE) with 5-fold spatial block cross-validation for further predictor selection. Random Forest (RF), eXtreme Gradient Boosting (XGB), Empirical Bayesian Kriging Regression Prediction (EBKRP), hybrid RF_EBKRP and XGB_EBKRP models were evaluated. Stand age and stand density were dominant predictors in both RF and XGB, contributing 33.6% and 24.0% in RF and 36.5% and 17.3% in XGB, respectively. Elevation, precipitation, and canopy cover showed secondary importance, whereas vegetation indices contributed relatively little. RF_EBKRP achieved the highest prediction accuracy (R² = 0.77), reducing RMSE by 17.20% and 43.75% compared with RF and EBKRP, respectively. This study provides a reproducible RF–EBKRP workflow integrating nonlinear machine-learning prediction with geostatistical residual correction, supporting improved subtropical forest AGB mapping and management. Full article

Rock glaciers are critical indicators of periglacial environments and the spatial distribution of mountain permafrost. Given their complex deformation patterns and temporal variability, which may indicate progressive destabilization, a quantitative evaluation of their kinematic activity is critical from both climatological and geohazard perspectives. This study applies Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) to Sentinel-1 radar imagery on both ascending and descending orbits, in order to detect and map moving areas (MA) within the Pirin Mountains (Bulgaria). The primary objective of this study is to update the existing rock glacier inventory (RoGI) by integrating high-resolution Line-of-Sight (LOS) velocity data in accordance with the latest international standards established by the Rock Glacier Inventories and Kinematics (RGIK) standing committee. A secondary objective is to investigate the spatial relationships between the identified moving areas (MAs) and other surrounding geomorphological features (e.g., talus slopes), hence providing a wider context for slope dynamics and landform evolution. The results identified MAs with PSInSAR-derived Line-of-Sight (LOS) velocities reaching up to 10 cm yr⁻¹, which were subsequently classified according to RGIK kinematic categories. A substantial proportion of the detected moving areas occur outside mapped rock glacier boundaries and may reflect a range of geomorphological processes, including permafrost-related creep, talus creep, or other forms of slope deformation. The LOS velocity data were used to assess the activity status of 74 rock glacier units within the regional inventory, classifying 8 as transitional (velocity exceeding 1 cm yr⁻¹) and 66 as relict. Furthermore, we analyse the spatial distribution of these moving areas in relation to primary topographic variables, such as elevation, aspect, and slope. The results highlight the influence of topographic control factors and rock glacier dynamics and provide new insights into the distribution of active periglacial landforms and terrain potentially affected by permafrost in the Balkan Peninsula under changing climatic conditions. Full article