India's radar imaging satellite RISAT-1 was the first spaceborne SAR platform operated in the compact polarimetric (CP) mode, which posed several advantages compared to the commonly used quad-pol system architecture in terms of design complexity and data quality. But, to make use of the advantages offered by the compact polarimetry mode, the acquired datasets should be free from the system induced polarimetric distortions, which include the channel imbalances, phase bias, and crosstalk between the channels. In addition to these system induced errors, the ionosphere also induces polarimetric distortion to the datasets in terms of Faraday rotation. For accurate ground target characterization using the CP datasets, the polarimetric distortions should be estimated and minimized. This article focuses on the CP calibration of the RISAT-1 dataset using trihedral and dihedral corner reflectors deployed at the study area. The Cloude CP decomposition technique is used to assess the ground target characterization quality of the dataset before and after polarimetric calibration.

LS factor plays a key role in soil erosion risk assessment using widely adopted empirical models such as RUSLE (Revised Universal Soil Loss Equation). The study was carried out to estimate the impact of varying DEM resolutions on LS factor values in a small hilly watershed near Dehradun, India. The impact of various computational algorithms as well as varying slope exponents on LS factor values were also estimated. DEMs of different resolutions such as ASTER (30 m), CARTOSAT (10 m and 30 m), SRTM (30 m) and UAV generated DEM (18 cm) were used for comparison. On comparison, LS factor generated using varying resolution DEMs including UAV generated DEM yielded similar results indicated by similar mean values, even though the maximum values in the study area varied slightly. Use of spatially distributed slope exponent values resulted in higher mean LS factor values from all the DEMs, except UAV generated one, when compared to the use of a constant, slope exponent value. All DEMs except UAV generated DEM, yielded lower LS factor values when multi-flow direction (MFD) algorithm was used for computation instead of single flow direction (SFD). The variations in results when using UAV DEM may be due to its ability to capture the micro topographical variations on the ground, which affects the various computational procedures. The LS factor results obtained using different computational procedures and algorithms needs to be validated using ground collected information of LS factor, for wider acceptability and use by soil erosion and geospatial modelling communities.