Leveraging PolSAR Features and Machine Learning for Improved Land Cover Discrimination with ALOS-2 PALSAR-2: A Comprehensive Evaluation over the Istanbul Metropolitan Region

Deep learning-based landslide detection using Sentinel-1 SAR imagery in the Chilean Patagonia

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.

Multi-Sensor Mapping of Vulnerable Urban Settlements Using SAR, Multispectral, and Hyperspectral Imagery: A Case Study in C\'ordoba, Argentina

Informal settlements represent a major urban challenge in rapidly expanding cities, yet their identification from Earth Observation (EO) data remains difficult because of their heterogeneous appearance and incomplete official inventories. This work presents a multi-sensor deep learning (DL) framework for slum-likelihood mapping in C\'ordoba, Argentina, integrating high-resolution PlanetScope multispectral (MS) imagery, COSMO-SkyMed (CSK) Synthetic Aperture Radar (SAR) data, and medium-resolution PRISMA hyperspectral (HS) observations. The problem is formulated as a patch-level classification task using the official Registro Nacional de Barrios Populares (ReNaBaP) inventory as reference, and the models are evaluated through four geographically partitioned folds. SAR-only and MS-only baselines, their configurations with PRISMA HS support, and early fusion (EF), middle fusion (MF), and late fusion (LF) strategies are systematically compared. Results show that LF+HS provides the best overall balance between classification performance and spatial selectivity, while PRISMA contributes complementary spectral information alongside the higher-resolution MS and SAR representations. Beyond the standard evaluation against ReNaBaP, an external municipal vulnerability layer is used to interpret detections outside the official polygons, showing that several apparent false positives overlap broader vulnerable urban areas. Thermal analysis further shows that ReNaBaP settlements exhibit significantly higher surface temperatures than their immediate surroundings during a heatwave event, indicating localised surface-heat amplification. Taken together, these results suggest that multi-sensor EO fusion can support both the mapping of ReNaBaP settlements and the interpretation of broader urban vulnerability patterns.

Wildfire Scar Detection in Mediterranean Chile Using Sentinel-1 InSAR Coherence and Machine Learning: The 2017 “Las Máquinas” Megafire

Wildfire monitoring using synthetic aperture radar (SAR) provides critical capabilities under challenging atmospheric conditions where optical sensors are limited by smoke and cloud cover. We evaluated Sentinel-1 C-band SAR interferometric coherence for Burned-area detection of the 2017 “Las Máquinas” megafire (Maule, Chile), comparing Ascending (Asc) and Descending (Dsc) orbital geometries processed with the AMSTer InSAR software. Multi-temporal RGB Coherent Change Detection composites were constructed using two interferometric pairs per orbit: the Normalised Differential Activity Index (NDAI, R channel), pre-fire coherence (G channel), and co-event coherence (B channel), clearly delineating the fire scar through red and orange signatures reflecting fire-induced vegetation loss and soil exposure. Seven machine-learning classifiers (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and XGBoost) were trained on the three-band coherence feature space. For the Ascending orbit, XGBoost achieved the highest performance (OA = 0.9328; F1 = 0.9195) and mapped 143,950 ha (76.2%) as Burned. For the Descending orbit, XGBoost also performed best (OA = 0.9221; F1 = 0.9055) and mapped 144,475 ha (76.5%) as Burned. In this case study, the Ascending geometry performed marginally better than the Descending one; however, the leading classifiers were statistically indistinguishable, indicating that the Burned and Unburned classes are close to linearly separable in the coherence feature space. These results confirm the effectiveness of coherence-based SAR analysis for large-scale wildfire mapping under adverse atmospheric conditions.

Spectral–Texture Feature Fusion With Spatial Group Cross-Validation for Sea Ice Mapping Using HY-1C Satellite Imagery

Accurate extraction of sea ice extent is of great importance for climate research, maritime navigation safety, and marine environmental monitoring. Traditional optical remote sensing methods are often limited by insufficient feature representation, while conventional machine-learning evaluations may overestimate classification accuracy due to spatial autocorrelation among samples. To address these issues, this article proposes a sea ice classification framework that integrates spectral and textural information through a novel spectral–texture fusion index (STFI) and adopts a spatial group cross-validation strategy. The STFI nonlinearly combines spectral and texture features to enhance the separability between sea ice and seawater, while spatial group cross-validation reduces the overestimation caused by spatial autocorrelation. A multifeature dataset is constructed, and three machine-learning models—random forest, XGBoost, and LightGBM—are trained and evaluated under this validation scheme. Results show that LightGBM achieves the best performance, with an F1-score, overall accuracy, and Matthews correlation coefficient of 92.99%, 89.73%, and 70.40%, respectively. Feature separability analysis, SHapley Additive exPlanations interpretation, and ablation experiments consistently confirm STFI as the most influential feature, and using only the spectral index, texture feature, and STFI already yields excellent classification. Comparisons with threshold segmentation, maximum likelihood estimation, support vector machine, and U-Net further verify the superiority of the proposed method. Cross-sensor experiments demonstrate high consistency when the model is directly transferred to MODIS imagery, and cross-regional validation in the Tatar Strait and the Yellow Sea shows good generalization capability. Moreover, a daily sea ice extent time series for Liaodong Bay during the 2024–2025 winter is generated from HY-1C and MODIS data, exhibiting good agreement with operational ice charts from the Liaoning Maritime Safety Administration (R2 = 0.96, r = 0.98, p < 0.001). The proposed framework offers a reliable solution for operational sea ice monitoring.

Rainfall-Induced Landslide Detection in the Central–Southern Andes of Chile Using Integrated SAR, Optical, and Machine-Learning Approaches in Google Earth Engine

This article presents a framework for postevent detection of rainfall-induced landslides in the south-central Andes of Chile by combining Sentinel-1 C-band synthetic aperture radar backscatter changes (∼10-m resolution), Sentinel-2 multispectral variables (10-m resolution), and advanced land observing satellite PALSAR-derived topographic attributes within a machine-learning workflow implemented in Google Earth Engine. The framework comprises three stages: 1) multisource feature extraction; 2) classification using random forest (RF), gradient tree boosting (GTB), support vector machine (SVM), classification and regression trees (CART), and Naïve Bayes (NB), evaluated through nested leave-one-area of interest-out spatial cross-validation; and 3) probability mapping. The approach was applied to landslides triggered by an extreme rainfall event between 20 and 25 June 2023, in the Andean sector of the Biobío Region. Under the reference configuration, RF achieved the best performance (F1 = 0.883, Precision = 0.947, Recall = 0.829), followed by GTB (F1 = 0.869); CART, SVM, and NB reached F1-scores of 0.825, 0.822, and 0.753, respectively. Sensitivity analyses showed strong effects of temporal sampling and optical data quality. The 7-day postevent scenario yielded spatially unreliable results (F1 = 0.463), whereas a 50-day period improved performance (F1 = 0.650). Restricting Sentinel-2 composites to the dry season improved results relative to longer periods affected by cloud and snow contamination (F1 = 0.883 versus 0.818). Overall, normalized difference vegetation index percentage change combined with RF provided the most robust performance for operational landslide probability mapping.