In this paper, we assess the flood mapping capabilities of the X-band Synthetic Aperture Radar (SAR) imagery acquired by the bistatic pair TanDEM-X/TerraSAR-X (TDX/TSX). The main objective is to investigate the added value of the bistatic TDX/TSX Interferometric Synthetic Aperture Radar (InSAR) coherence in addition to the SAR backscatter in the context of inundation mapping. As a classifier, we consider a Random Forest (RF) classification scheme using TDX/TSX SAR intensities and their bistatic InSAR coherence to extract the flood extent map. To evaluate the classification results and as no “ground truth” was available at the SAR data acquisition time, we set up a LISFLOOD-FP hydraulic model for simulating the temporal evolution of the flood water. The flood map simulated by the model shows good performances with an Overall Accuracy (OA) of $97.92\%$ and a Critical Success Index (CSI) of $94.01\%$ . The SAR-derived flood map is then compared to the LISFLOOD-FP extent map simulated at the SAR data acquisition time. As a test case, we consider the flooding event of the Richelieu River that occurred in the Montérégie region of Quebec (Canada) from April to June 2011. Experimental results highlight the potential of the bistatic InSAR coherence for more accurate flood mapping in a complex landscape with urban and vegetation areas. The classification results of the SAR-derived flood map with respect to the LISFLOOD-FP flood map reach an OA of $78.65\%$ and a Precision of $82.08\%$ when integrating the bistatic InSAR coherence. These classification OA and Precision values are $69.63\%$ and $64.52\%$ , respectively, using only the TDX/TSX SAR intensity.

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R²) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m³/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

Due to the climate change situation, water deficit stress is becoming one of the main factors that threatens the agricultural sector in semi-arid zones. Thus, it is extremely important to provide efficient tools of water deficit monitoring and early detection. To do so, a set of Synthetic Aperture Radar (SAR) backscattering and interferometric SAR (InSAR) Sentinel-1 data, covering the period from January to June 2016, are considered over a durum wheat field in Tunisia. We first studied the temporal variation of the InSAR coherence data and the SAR backscattering coefficient as a function of the phenological stage of the wheat. Subsequently, the parameters of the SAR and InSAR coherence images were analyzed with regard to the water stress coefficient and the wheat height variations. The main findings of this study highlight the high correlation (r = $0.88$) that exists between the InSAR coherence and the water stress coefficient, on the one hand, and between the backscattering coefficient, the interferometric coherence, and the water deficit coefficient ($R^2$ = $0.95$ and $RMSE$ = $14\%$), on the other hand. When a water deficit occurs, the water stress coefficient increases, the crop growth decreases, and the height variation becomes low, and this leads to the increase of the InSAR coherence value. In summary, the reliability of Sentinel-1 SAR and InSAR coherence data to monitor the biophysical parameters of the durum wheat was validated in the context of water deficits in semi-arid regions.