Topographic corrections of synthetic aperture radar (SAR) images over hilly regions are vital for retrieval of correct backscatter values associated with natural targets. The coarse resolution external digital elevation models (DEM) available for topographic corrections of high resolution SAR images often result into degradation of spatial resolution or improper estimation of backscatter values in SAR images. Also, many a times the external DEMs do not spatially co-register well with the SAR data. The present study showcases the methodology and results of topographic correction of ALOS-PALSAR image using high resolution DEM generated from the same data. High resolution DEMs of Jaipur region, India were generated using multiple pair SAR images acquired from ALOS-PALSAR using interferometric (InSAR) techniques. The DEMs were validated using differential global positioning system measured elevation values as ground control points and were compared with photogrammetric DEM (advanced spaceborne thermal emission and reflection radiometer – ASTER) and SRTM (Shuttle Radar Topography Mission) DEM. It was observed that ALOS-PALSAR images with optimum baseline parameters produced high resolution DEM with better height accuracy. Finally, the validated DEM was used for topographic correction of ALOS-PALSAR images of the same region and were found to produce better result as compared with ASTER and SRTM-DEM.

In this paper, we present an unsupervised model-based decomposition technique for the analysis of hybrid-pol datasets. The proposed approach develops a hybrid-pol version of full-pol three-component scattering model by deriving all decomposition parameters in terms of the hybrid-pol Stokes parameters. By using these decomposition parameters, the contribution of three basic scattering mechanisms, namely surface-scattering from Bragg surface region, dihedral-scattering from urban region and volume-scattering from forest region, are estimated. The decomposition parameters take surface dielectric properties under consideration that makes the proposed method suitable to provide more realistic representation of the Earth's surface. To estimate the efficacy of the proposed technique, the hybrid-pol data acquired over Mumbai city using ISRO's RISAT-1 satellite is used. For comparative analysis, the proposed technique is compared with the conventional decomposition techniques, such as $m-\delta$, $m-\dot{\alpha }$, and modified $m-\chi$ .

An optimal model was developed for accounting forest carbon stock from synergistic use of optical data from Landsat TM and synthetic aperture radar (SAR) data from COSMO-Skymed (X-band), Radarsat-2 (C-band) and ALOS PALSAR (L-band) sensors over a tropical deciduous heterogeneous forest of India. The best-fit integrated multiple linear regression model had a model accuracy of 83%, r² = 0.96, root mean square error = 10.02 Mg/ha and Willmott’s index of agreement of 0.98. The model further validated using chi-squared and t-test. Results of models for calculating the aboveground biomass (AGB) were converted to C and CO₂ using conversion factors. Average AGB, C and CO₂ were 70.5, 35.26 and 130.89 Mg/ha, respectively. The synergistic use of optical and multi-frequency SAR data enhanced the AGB saturation threshold to about 150 Mg/ha for tropical deciduous mixed forests. Hence, the synergistic use of this data is suggested for large-scale AGB and C estimations for tropical forests. Optical remote sensing sensors are extensively used due to greater data availability despite their poor sensitivity toward forest parameters. In contrast, SAR signals are highly sensitive toward forest biophysical and structural parameters, providing a better alternative. This unique integrated approach provides valuable information regarding the spatial distribution and quantification of forest biomass and carbon.

A new dielectric constant estimation approach for the lunar surface using Mini-RF synthetic aperture radar (SAR) data and Chandrayaan-2 SAR data is presented in this work. Both the SAR systems are based on a hybrid-polarimetry (hybrid-pol) configuration that transmits a circularly polarized wave and coherently measures the backscattered wave by dual orthogonal linearly polarized channels. From the three-component hybrid-pol SAR decomposition technique, the ratio of the Fresnel reflection coefficients for horizontal and vertical polarization transmission can be estimated. This ratio, which is referred to as the co-polarization ratio, is used by the proposed methodology to find the real value of the dielectric constant. For performance validation, the proposed method is implemented on the Mini-RF hybrid-pol SAR data acquired over Apollo 17 landing sites. The values of the real part of dielectric constant are estimated for six different regions covering the collection sites of six Apollo 17 samples: 72 441, 73 241, 74 241, 75 081, 76 001, and 79 135. The results obtained using the proposed method are found to be in good agreement with the laboratory-measured results of the corresponding samples. Furthermore, the proposed methodology is also implemented on the Chandrayaan-2 hybrid-pol SAR data acquired over the Biot crater region. Various small areas possessing different possible surface characteristics, situated inside and outside the Biot crater , are being analyzed for validation.

The study aimed to determine the above-ground biomass (AGB) of tropical deciduous forests in Central India using field-based techniques and spaceborne quad-pol ALOS PALSAR-2 L-band and dual-pol Sentinel 1A C-band synthetic aperture radar (SAR). Field sampling based on 73 forested grids exhibited a diverse forest composition in the Satpura Tiger Reserve (STR) with a total of 54 tree species of which 31.48% species were IUCN Red-Listed. The forests were highly diverse (H′=3.22) with a low concentration of dominance (CD = 0.06) and a quite high species evenness (E = 0.81). The sensitivity and correlation between field-based AGB and SAR backscatter in STR were highest in cross-polarization due to its higher sensitivity to the vegetation structure in both L- and C-band SAR, where L-VH based AGB estimates were found more accurate (RMSE:5.17%). The study provided vital information for setting up strategies for biodiversity conservation, enhancing forest productivity, and restoring wildlife habitats.