Conference Agenda

Flood mapping is one of the most important applications of Synthetic Aperture Radar (SAR) because it can monitor the earth's surface under all-weather, day-and-night conditions. While SAR intensity has been widely used for flood mapping, the potential and added value of interferometric coherence, especially its temporal behavior in different environments, remains mostly unexplored. In this study, we assess the potential and added value of interferometric coherence from Sentinel-1 time series for flood mapping in three contrasting regions: the urban area of Valencia (Spain), the arid region of Sistan and Baluchestan (Iran), and the agricultural area of Hannover (Germany). Our analysis of multi-temporal coherence shows that coherence provides clear flood indicators in arid regions through strong temporal decorrelation, but its performance is less reliable in vegetated and urban areas. In agricultural regions, pre-flood (baseline) coherence is inherently low due to vegetation phenology and temporal decorrelation, making any additional decrease due to flood inundation often indistinguishable. In urban areas, coherence generally remains stable, with only slight decreases observed in specific cases; therefore, the detectability of flooded areas using coherence-based approaches is limited in both agricultural and urban environments. In contrast, coherence in arid regions is high before flooding and drops significantly during flood events, making floods easy to detect in such regions. These findings demonstrate that, for flood mapping, interferometric coherence is a valuable but environment-dependent indicator, with the highest benefit seen in arid regions where intensity-based methods are limited.

Arctic sea ice thickness has been declining over recent decades due to climate change, making accurate prediction increasingly critical for environmental monitoring and climate modeling. Microwave remote sensing combined with machine learning has emerged as a promising approach for estimating sea ice thickness. This study investigates the prediction of lab-grown sea ice thickness, ranging from 27 to 47 cm, using time-series backscatter data collected from surface-based Ku- and C-band scatterometers in three polarizations (VV, HH, and HV). A Random Forest model was applied to the time series, incorporating Normalized Radar Cross-Section (NRCS) values and statistical features (mean and standard deviation) across various temporal variables (lead and lag times). The model achieved high prediction accuracy, with the lowest error recorded at RMSE = 0.03 cm. Feature importance analysis using the Permutation Importance method revealed that co-polarized C-band features (C-VV and C-HH) were the most influential in predicting sea ice thickness. These findings underscore the potential of integrating microwave remote sensing with Random Forest models to enhance sea ice thickness prediction and provide valuable insights for future research and real-time monitoring in Arctic regions.

Operational flood monitoring demands both accurate extent delineation and quantitative depth estimation, yet existing research addresses these objectives separately. This study presents an integrated SAR-to-depth framework combining state space model segmentation with DEM-based geometric depth estimation to deliver comprehensive flood intelligence from Sentinel-1 SAR imagery and digital elevation models. We propose CS-Mamba, a hierarchical U-Net architecture incorporating selective state space

mechanisms, achieving 79.79% mean IoU on 10 European flood events from the KuroSiwo benchmark while surpassing CNN baselines and outperforming RSMamba by 7.37 percentage points. Test performance exceeding validation confirms robust cross-event generalization to unseen disasters. Controlled experiments establish that deep learning predictions achieve sufficient accuracy for operational depth estimation, with CS-Mamba flood masks showing ±2% agreement with reference annotations across four global DEMs despite conservative extent delineation. This agreement enables integrated pipelines without manual annotation, while systematic DEM comparison identifies Copernicus and MERIT as optimal choices. The complete framework delivers three-class flood masks and pixel-wise depth maps at operational resolution, bridging the traditional gap between extent mapping and quantitative assessment for emergency response.

This contribution introduces TGSD-Net, a temporal variation-guided self-supervised network designed to improve despeckling of polarimetric SAR (PolSAR) imagery without the need for clean reference data. The method leverages consecutive multi-temporal observations to create pseudo training pairs and incorporates a lightweight temporal change detection prior, allowing the network to exploit temporal redundancy while remaining robust to land-cover variations. TGSD-Net further integrates auxiliary polarimetric decomposition features and a spatiotemporal information fusion module to enhance structural and scattering representations. The approach is tailored for multi-temporal SAR scenarios, where speckle, temporal variation, and heterogeneous land-cover types pose significant challenges. Experiments on real PolSAR datasets show that TGSD-Net achieves strong noise suppression while preserving edges, textures, and physical scattering properties. The results demonstrate the potential of self-supervised temporal learning to advance PolSAR image restoration and support downstream remote sensing applications.

Current Remote Sensing applications demand multi-source, multi-sensor data fusion. Multi-source, multi-sensor data fusion provides useful information integrated for quick and better interpretation, understanding and effective decision-making. Data fusion of Synthetic Aperture Radar (SAR) data of Earth Observation Satellite-04 (EOS-04) and Optical Multispectral (MX) data of Sentinel-2 are current topic of interest in this paper. SAR and Optical MX which includes active and passive remote sensing technologies belong to different mechanisms of wave interaction due to widely separated and non-overlapping regions of the electromagnetic spectrum. In this paper, a novel approach to the re-implementation of Wavelet, Brovey, Fast Intensity Hue Saturation (FIHS), Frequency filtering, and Pure pixel data fusion methods is presented. The presented novel approach emphasises modulation-based fusion technique with proper normalization and scaling of both the input datasets. Fusion results of presented fusion methods are evaluated visually as well as quantitatively with quality metrics. The quality metrics demonstrate the ability of the presented novel approach to fuse optical spectral information into SAR data effectively to generate improved high-resolution SAR-coloured fused products.