The seasonal snow cover and permanent ice in form of Himalayan glaciers provide fresh water to many perineal rivers of Himalayas. The melt water from seasonal snow and glaciers, especially during of 15 March to 15 June acts as important source of water for drinking, hydropower and irrigation requirements of many areas in North India. This work has highlights the use of C-band Synthetic Aperture Radar (SAR) data from RISAT-1, Sentinel-1A and 1B satellites and ALOS-PALSAR-2 PolInSAR data for snow cover and glacier dynamics study for parts of North West Himalaya. Glacier velocity was derived using InSAR based method using 6 day temporal interval images from Sentinel-1 satellites and 14 day interval for PALSAR-2 satellite. High coherence was obtained for main glacier in both the data sets, which resulted accurate line of site (LOS) glacier velocity estimates for test glaciers. These InSAR data glacier velocity results are obtained after a gap of 21 years. Glacier facies was estimated using multi-temporal SAR image composition based classification. All these maps were verified by extensive ground surveys done at these sites during 2014–2017. The time series data of C-band SAR in VV/VH polarisation was also used to map snow cover in test basins of Bhagirathi and Beas River. The VV/VH data clearly shows difference between dry and wet snow, thus helping in improved snow cover mapping using SAR data. This study will help in refining algorithms to be used for such studies using upcoming NASA-ISRO SAR (NISAR) mission.

Satellite altimetry has revolutionized river monitoring, particularly for hydrologists working on river flow monitoring in sparsely or ungauged areas. Despite this, there's a lack of a comprehensive evaluation of radar and lidar altimeters with varying sensor specifications for river water level retrieval, seasonal change characterization, and water surface slope (WSS) using gauged long-term water level and global navigation satellite system (GNSS) data. This study addresses this gap by combined evaluation of radar (ENVISAT to Sentinel-3) and lidar (ICESat-1, ICESat-2) altimeters along the Ganga River, from Prayagraj to Varanasi. We found that, all the radar altimetry missions showed better accuracy for water level retrievals (R2 > = 0.8; RMSE 0.11 to 1.16 m) and water level change quantification (RMSE 0.59 m). However, Sentinel-3 with SAR acquisition mode outperformed (RMSE 0.11 to 0.14 m) all the radar missions having low resolution mode. Despite lidar missions' high vertical accuracy, they show relatively lower accuracy in water level time series generation due to non-repeating characteristics. In contrary, ICESat-2 demonstrates potential in capturing spatial and seasonal variability of WSS, enhancing the accuracy of SWOT discharge products when combined with SWOT River database (SWORD). This study provides a comprehensive baseline for end-users interested in utilizing radar and lidar missions for various hydrological applications, including river discharge estimation. Moreover, the studied river reach shares the SWOT calibration orbit, allowing the utilization of generated satellite and in-situ databases for the effective evaluation of SWOT measurements.

The Surface Water and Ocean Topography (SWOT) mission offers novel global observations of river hydrodynamics, yet its performance across varied river morphologies remains understudied. We evaluate SWOT node and raster products over a ∼210 km stretch of the Ganga River, India, during the fast‐sampling phase, using GNSS‐based continuous measurements, two in situ stations, and four altimetry virtual stations. Node products show slightly better water surface elevation (WSE) accuracy than raster products, with RMSEs of 0.19 m (GNSS), 0.09 m (in situ station, Varanasi), and 0.88 m (virtual stations), though quality filters reduce temporal resolution. Raster data, however, captures 2D WSE variability, enhancing spatial sampling in wide river cross‐sections. SWOT‐derived water surface slopes (WSS) yield mixed results across SWORD reaches (RMSE: 2.54 cm/km). Strong backscatter from main river channel waters (mean +13.34 dB) aids width retrieval. Our analysis highlights SWOT's potential for river hydrodynamic applications while underscoring the need of evaluation in complex riverine environments.