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NovaSAR-1 is a joint technology initiative of SSTL (Surrey Satellite Technology Ltd.), UK, and Airbus DS (former EADS Astrium Ltd, Stevenage, UK). The NovaSAR-1 mini-satellite was launched on 16 September 2018 and it is operating on the S-band frequency range, which is less common in Spaceborne Synthetic Aperture Radar (SAR) systems. Both higher and lower SAR frequency bands (L-band & X-band SAR) have their advantages as well as limitations in different kinds of applications. High frequency (X-band) SAR systems are useful for top surface information extraction such as the DSM generation. However, at the same time, more noise and lesser coherence issues are associated with high-frequency SAR systems. Low-frequency SAR (L-band) systems exhibit better ground penetration, high coherence, and low noise, but less precise scatterer level information. The S-band comes approximately in the middle of the X and L-band SAR frequency range and may be used as a trade-off between high and low-frequency SAR systems to have some advantages. In the presented study, the separability analysis of the radar backscattering coefficient of HH polarization (Stripmap and ScanSAR) of NovaSAR-1 S-band datasets corresponding to different land use and land covers (LULCs) has been done to analyze the potential of NovaSAR-1 S-band SAR data. The analysis was carried out for datasets acquired between 9th July 2019 to 15th July 2019 at 5 experimental sites in parts of six different Indian states (West Bengal, Maharashtra, Jharkhand, Odisha, Chhattisgarh, and Uttar Pradesh). The statistical analysis of σ० for five different sites of India for different LULCs, such as bare soil, forest, water, urban, cropland, and road features has been carried out. The range for minimum and maximum mean σ० values for urban, sub-urban, cropland, bare soil, barren land, forest, turbid water, road features, water with a smooth surface (calm water), and road features (airplane runway) were found to be -5.45 to 4.76, -11.14 to -5.21, -17.25 to -5.99, -18.15 to -13.44, -17.64 to -9.34, -17.17 to -14.34, -18.2 to -14.05, -27.29 to -23.76 and − 26.64 to -14.98 respectively. The range of σ० pixel values of calibrated datasets corresponding to different LULCs depicted that the data quality is good for the identification of various land covers. The separability analysis of the different land cover classes depicted that classes have good separability except for a few pairs of LULC. With the availability of fully polarimetric and InSAR data in the planned NISAR mission, the polarimetric scattering behaviour with phase information for InSAR will further be utilized.

Mangroves forests serve as an ecosystem stabilizer since they play an important role in providing habitats for many terrestrial and aquatic species along with a huge capability of carbon sequestration and absorbing greenhouse gases. The process of conversion of carbon dioxide into biomass is very rapid in mangrove forests. Mangroves play a crucial role in protecting the human settlement and arresting shoreline erosion by reducing wave height up to a great extent as they form a natural barricade against high sea tides and windstorms. In most cases, human settlement in the vicinity of mangrove forests has affected the eco-system of the forest and placed them in environmental pressure. Since, a continuous mapping, monitoring, and preservation of coastal mangroves may help in climate resilience, therefore a mangrove land cover extraction method using remotely sensed L-band full-pol UAVSAR data (acquired on 25-Feb-2016) based on Entropy (H) and Anisotropy (A) concept has been proposed in this study. The k-Mean clustering has been applied to the subsetted (1-Entropy)*(Anisotropy) image generated by PolSARpro_v5.0 software's H/A/Alpha Decomposition. The mangrove land cover of the study area was extracted to be 116.07 Km 2 using k-Mean clustering and validated with the mangrove land cover area provided by Global Mangrove Watch (GMW) data.