A deadly cascade: A catastrophic landslide in Uttarakhand state in India on February 2021 damaged two hydropower plants, and more than 200 people were killed or are missing. Shugar et al. describe the cascade of events that led to this disaster. A massive rock and ice avalanche roared down a Himalayan valley, turning into a deadly debris flow upstream from the first of the two hydropower plants. The sequence of events highlights the increasing risk in the Himalayas caused by increased warming and development. Science , abh4455, this issue p. 300

Forest cover is a crucial factor that influences the performance of optical satellite-based snow cover monitoring algorithms. However, evaluation of such algorithms in forested landscapes is rare due to lack of reliable in situ data in such regions. In this investigation, we assessed the performance of the operational snow detection (SCA) and fractional snow cover estimation (FSC) algorithms employed by the Copernicus Land Monitoring Service for High-Resolution Snow & Ice Monitoring (HRSI) with a combination of Sentinel-2 and Landsat-7/8 satellite scenes, lidar-based, and in situ datasets. These algorithms were evaluated over test sites located in the forested mountainous landscape of the Pyrenees in Spain and the Sierra Nevada in the USA. Over the Pyrenees site, the effectiveness of snow cover detection was evaluated with respect to a time-series of in situ snow depth measurements logged over test plots with different aspects, canopy cover, and solar irradiance. Over the Sierra Nevada site, the impact of ground vegetation was assessed over the under canopy fractional snow cover retrievals using airborne lidar-derived fractional vegetation cover information. The analyses over the Pyrenees indicated a good accuracy of snow detection with the exception of plots with either dense canopy cover or insufficient solar exposure (shaded forested slope), or both. The operational HRSI algorithm yielded similar performances (25-30% RMSE) as the computationally intensive spectral unmixing approach while retrieving the subcanopy ground FSC over the Sierra Nevada site. It was observed that a more accurate lidar-derived tree cover density map did not improve the subcanopy FSC retrievals.

Model-based decompositions have gained considerable attention after the initial work of Freeman and Durden. This decomposition, which assumes the target to be reflectionsymmetric, was later relaxed in the Yamaguchi et al. decomposition with the addition of the helix parameter. Since then, many decomposition have been proposed where either the scattering model was modified to fit the data or the coherency matrix representing the second-order statistics of the full polarimetric data is rotated to fit the scattering model. In this paper, we propose to modify the Yamaguchi four-component decomposition (Y4O) scattering powers using the concept of statistical information theory for matrices. In order to achieve this modification, we propose a method to estimate the polarization orientation angle (OA) from full-polarimetric SAR images using the Hellinger distance. In this method, the OA is estimated by maximizing the Hellinger distance between the unrotated and the rotated T ₃₃ and the T ₂₂ components of the coherency matrix [T]. Then, the powers of the Yamaguchi four-component model-based decomposition (Y4O) are modified using the maximum relative stochastic distance between the T ₃₃ and the T ₂₂ components of the coherency matrix at the estimated OA. The results show that the overall double-bounce powers over rotated urban areas have significantly improved with the reduction of volume powers. The percentage of pixels with negative powers have also decreased from the Y4O decomposition. The proposed method is both qualitatively and quantitatively compared with the results obtained from the Y4O and the Y4R decompositions for a Radarsat-2 C-band San-Francisco dataset and an UAVSAR L-band Hayward dataset.

Remote sensing is an indispensable tool for Earth observation over large areal extent. Snow cover extent monitoring has been one such application where microwave sensors have been a popular choice due to their sensitivity toward the dielectric property. Snow is a dynamic matter since its dielectric state is dependent on climatic factors prevailing locally around it. In the literature, the polarization fraction (PF) has been used for landcover characterization. In this paper, we utilize the seasonal variation of the PF for mapping snow cover over the Himalayan terrain. Temporal variation of the strength in the polarized return due to change in landcover and season is the prime motivation behind this approach. In addition, the effect of SAR data acquisition time on mapping algorithms is considered in this work which is seldom discussed in the literature. Furthermore, a study is conducted to analyze the seasonal variation in the entropy ($H$) and the scattering type ($\alpha$) parameters over the bare snow-covered ground and the forested areas. The seasonal response of the terrain in the $H/\alpha$ plane corresponding to specific scattering characteristics is analyzed. The applicability of the approach is tested with RADARSAT-2 (FQ28) C-band full polarimetric image pairs over the Manali–Dhundi region, Himachal Pradesh, India, located in the western Hindu-Kush Himalayas. The snow cover map is explicitly validated with in situ observatory measurements and optical satellite imagery.

Satellite SAR-based soil moisture retrieval over agricultural fields, under crop overlain conditions, is a challenging exercise. This is so since the overlying crop volume interacts with both the incoming and the backscattered radar signal. Therefore, the soil moisture linked solely to the top layer ( $0-5$ $cm$ ) of the soil cannot be reliably retrieved under such conditions without avoiding the obscuring effect of growing crop volume. In this investigation, we demonstrated a proof of concept for a time-series approach to retrieve soil moisture during crop growth cycle. Contrary to the use of the single-scene approach, the novelty of the proposed approach lies in exploiting the satellite SAR time-series acquired during a cropping cycle. The proposed time-series approach is effective for capturing the nuances in the crop phenological stages while calibrating the Dubois-WCM soil moisture retrieval model. By employing this approach, we achieved the 0.04 $m^{3} m^{-3}$ soil moisture retrieval RMSE benchmark at a high spatial resolution and addressed the issue of solving for the Dubois-WCM model constants under data constrained conditions. Furthermore, we observed that combination of temporally non-overlapping vegetation descriptors (optical and SAR) resulted in degradation in the performance of the retrievals and under such circumstances single polarimetric descriptors performed better.

Change over the Himalayan terrain in the form of seasonal snow precipitation is an inevitable phenomenon. Mapping of snow cover in this region is a critical task, since meltwater emanating from the snowfields and glaciers serves as a source to several major Asian river systems. In this regard, a snow cover mapping technique is proposed in this letter by exploiting the ratio of the seasonal variation of copolarized $(hh{-}vv)$ correlation coefficient and the total scattering power. This ratio provides a very efficient index for snow characterization. The difference image is obtained by temporal (winter–summer) ratioing of this index. The snow cover map is obtained by thresholding the difference image using the standard method of Otsu. The proposed algorithm is validated using the temporal RADARSAT-2 (FQ-28) C-band full-polarimetric synthetic aperture radar data sets acquired over the Manali–Dhundi region of Himachal Pradesh, India. The results are explicitly validated with in situ observatory measurements and compared with the normalized difference snow index-based snow cover maps derived from the LANDSAT-8 optical satellite images.