On the Use of SAR Images for Predicting Vegetation Indices: Challenges and Limitations

Synergistic Use of Time-Series Multispectral and Synthetic Aperture Radar Data for Adaptive Glacier Surface-State Mapping

Monitoring glacier surface wetness and near-surface facies evolution with high spatiotemporal resolution is important for characterizing seasonal melt conditions and supporting downstream glaciological modeling. However, current remote sensing methods are hindered by cloud contamination in optical data and ambiguities in SAR backscatter interpretation. In this study, a novel framework for automated glacier surface-state mapping is proposed by integrating Sentinel-1 SAR and Sentinel-2 optical imagery. Pixel-wise wet snow probability maps are generated using a convolutional neural network trained on multitemporal optical data, which then guides an adaptive thresholding scheme for SAR-based wet snow detection. Finally, the wet snow maps are refined through a postprocessing scheme that leverages temporal consistency and spatial segmentation, and classification stability is significantly enhanced. The proposed algorithm is evaluated over three glaciers on the Tibetan Plateau using carefully constructed remote-sensing reference labels. The results show agreement with the reference labels, with mean F1-scores of 0.849 for Shenshe Glacier, 0.811 for Laohugou Glacier No.12, and 0.858 for Bayi Glacier, with peak values exceeding 0.93 during mid-season observations. The results also indicate relatively stable agreement under varying signal conditions. This study provides a flexible and transferable strategy for mapping wet-snow extent, wet-snow timing, and glacier surface facies evolution, which can provide useful constraints for subsequent mass-balance and runoff modelling in complex mountainous terrain.

National-Scale Digital Mapping of Soil pH Using Sentinel-2 Optical Data and Multi-Feature Sentinel-1 SAR Data

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.

SAMCE-CR: SAR-Guided Alignment and Multifrequency Collaborative Enhancement for Cloud Removal in Remote Sensing Images Fusion

In global Earth observation, multispectral imaging is frequently hindered by extensive cloud cover, leading to observation gaps. Introducing synthetic aperture radar (SAR) data with all-weather penetration capability for multimodal cloud removal has become a key approach to achieving global spatiotemporal seamless remote sensing monitoring. Existing SAR-optical fusion methods often lack effective decoupling and differentiated processing of heterogeneous data. This leads to texture distortion in reconstructed images, limiting the accuracy of downstream interpretation and target recognition. Therefore, a cloud removal method based on SAR-guided alignment and multifrequency collaborative enhancement is proposed in this article. First, the spatial geometric features of SAR images are mined by multireceptive field gating mechanism, and robust structural priors are extracted for under-cloud ground object reconstruction while effectively suppressing coherent speckle noise; Second, with the help of the deformable alignment module, the geometric alignment of SAR and optical images is realized with reference to optical images, alleviating the problem of scale and spatial misalignment; Finally, through the multifrequency collaborative enhancement module, the high and low frequency information are adaptively separated, and an improved attention mechanism is adopted to enhance the high and low frequency information, respectively, which effectively suppresses cloud interference and maintains surface details. Results on the M3R-CR and LuojiaSET-OSFCR datasets show that the proposed method comprehensively outperforms the other eight compared methods. Compared with the suboptimal method, the peak signal-to-noise ratios (PSNRs) achieved by the proposed method on the two datasets are improved by 0.5643 and 0.1964 dB, respectively. The source code of SAMCE-CR is shared at https://github.com/RSIDEA-ECUT/SAMCE-CR

Sensitivity of Spaceborne LiDAR, Optical, and SAR Features for Forest Biomass Modelling: A GEDI–Sentinel-2–SAOCOM Analysis

Abstract. This study evaluates the synergy of NASA’s Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR, Sentinel-2 multispectral imagery, and L-band Argentine Satellite System for Emergency Management (SAOCOM) 1A SAR data for aboveground biomass (AGB) estimation in the Belgrade Forest, Istanbul. Utilizing 1,356 GEDI L4A footprints as reference data, the research incorporates ten Sentinel-2 bands, five optical indices (NDVI, NDVIred, EVI, LSWI, CIre), SAR backscattering coefficients (σ°HH and σ°HV), polarimetric H/A/α polarimetric decomposition parameters and dual polarimetric radar vegetation indices, namely the Dual-Pol Radar Vegetation Index (DpRVI). High-dimensional feature spaces were optimized through ensemble-based, correlation-based, and hybrid RFECV selection strategies before evaluating four machine learning architectures: Multi-layer Perceptron (MLP), Kernel Ridge, Lasso, and Elastic Net. The MLP model achieved the highest predictive accuracy (R2 = 0.20, RMSE = 62.93 Mg/ha, MAE = 51.31 Mg/ha), outperforming linear regularization models, which exhibited R2 values between 0.15 and 0.16. Sensitivity analysis identified red-edge and SWIR bands, alongside indices such as NDVIred, LSWI, and CIre, as the most robust predictors, while the contribution of SAR-derived features remained comparatively limited. These findings underscore the efficacy of non-linear deep learning architectures and multi-source data fusion in resolving complex biophysical interactions within heterogeneous forest environments.

Improving Backscatter-Based Surface Water Classification in Arid Environments Through Interferometric Coherence

Synthetic Aperture Radar (SAR) backscatter serves as a key tool for tracking surface water dynamics; however, single-source data dependencies introduce systematic bias tied to the specific physical limitations of the signal. A primary challenge in SAR analysis is the backscatter ambiguity created by ‘water look-alike’ surfaces, which frequently result in false-positive water detections. We show that integrating interferometric repeat-pass coherence significantly enhances the robustness of hydrological mapping in environments where backscatter is prone to signal ambiguity. Using global-scale C-band Sentinel-1 (S1) VV-polarized one-year mosaics (December 2019 to November 2020), we first analyzed normalized backscatter and coherence signatures across major land cover and land use (LULC) classes. To benchmark the complementary value of these data streams, a tile-based minimum-error thresholding approach was applied to detect permanent water surfaces across five challenging global test sites. This evaluation was conducted without post-processing or masking to isolate the fundamental strengths of each dataset. The results indicate that coherence is an optimal complement to backscatter in arid and bare soil regions, where it vastly outperforms backscatter in mapping inland water surfaces. Crucially, since the spatial overlap of False Positives and False Negatives between datasets is minimal, the inherent complementarity of the datasets is proven here via a logical AND fusion rule, which significantly mitigates commission errors and yields substantial improvements in the aggregated F1-score and IoU performance. Analysis-ready L-band NISAR products could contribute to a more comprehensive approach for operational, large-scale surface water assessments.

Reconstructing Sentinel-2 Rice NDVI Time Series for Cloudy Seasons in India