The India–France SARAL/AltiKa mission is the first Ka-band altimetric mission dedicated primarily to oceanography. The mission objectives were firstly the observation of the oceanic mesoscales but also global and regional sea level monitoring, including the coastal zone, data assimilation, and operational oceanography. SARAL/AltiKa proved also to be a great opportunity for inland waters applications, for observing ice sheet or icebergs, as well as for geodetic investigations. The mission ended its nominal phase after three years in orbit and began a new phase (drifting orbit) in July 2016. The objective of this paper is to highlight some of the most remarkable achievements of the SARAL/AltiKa mission in terms of scientific applications. Compared to the standard Ku-band altimetry measurements, the Ka-band provides substantial improvements in terms of spatial resolution and data accuracy. We show here that this leads to remarkable advances in terms of observation of the mesoscale and coastal ocean, waves, river water levels, ice sheets, icebergs, fine scale bathymetry features as well as for the many related applications.

In this study, a distributed hydrological model (MIKE SHE) was employed in an integrated framework to use remote sensing data, and its advantages over using conventional data were analyzed. The command of 6 Main Canal of the Damodar Irrigation Project, West Bengal, India was chosen as the study area. IRS LISS III and RADARSAT SAR data were processed for generating soil and land use/cover maps, respectively. Thematic layers for topography, groundwater table, Thiessen polygons for rainfall and evapotranspiration, settlement area, and soil were prepared in GIS (ARC-INFO). An application program was developed to transform GIS processed ASCII data into the model input format (matrix data) and also model output to GIS. The model was subsequently calibrated and validated for monsoon season (June to October) of 1999 and 2000, respectively, considering the goodness of fit criteria between observed and simulated groundwater depths. Simulations were done with different combinations of remote sensing (RS) and conventional data setups to study the impact of using RS data on the model calibration and simulation results. The introduction of remotely sensed data shows improvements in the model calibration and eventually in its performance. Statistical results for the RS data integrated model show high Nash–Sutcliffe coefficient (0.85), lower root mean square error (0.31), and closer agreement between the mean simulated $\left(8.37\mathrm{m}\right)$ and observed $\left(8.32\mathrm{m}\right)$ groundwater depths as compared to the conventional data. Model results show little increase in the total irrigation demand by 7.7% using RS data in comparison to conventional data. In contrast, spatial distribution of irrigation demands was found to vary considerably among distributaries using RS data. The large impact of remotely sensed versus conventional data is that the RS data integrated distributed model gives more reliable and accurate results and, hence, could play a major role in irrigation studies.