The present study focuses on the soil moisture estimation using dual polarimetric RISAT-1, C-band SAR data in HH and HV polarization. The semi empirical approach derived by Dubois and the same modified and proposed as modified Dubois model (MDM) is worked for winter wheat crop from initial vegetative to maturity stage. Surface roughness model derived roughness is replaced with ‘s’ parameter for retrieval of dielectric constant which is further used in Topp’s model to derive soil moisture. Model derived soil moisture and ground measured soil moisture demonstrates pragmatic correlation after performing linear regression. The behaviour of inversion model varies at different crop stages depending on the soil exposure and crop cover. Normalized difference vegetation index from moderate resolution imaging spectroradiometer has also been evaluated to monitor crop status. Performance of MDM is promising for early and maturity stages of crop for soil moisture estimation.

Soil moisture is the crucialparameter impacting the plant growth during its phenological cycle. The Water Cloud Model (WCM) is one of the most widely used semi-empirical models for retrieval of soil moisture of vegetated lands from synthetic aperture radar (SAR) images. The model considers the effect of soil as well as vegetation on the radar backscatter. The study assesses the applicability of Sentinel-1 SAR-derived vegetation descriptors in the WCM for soil moisture retrieval during the wheat growth cycle. Various combinations of vegetation descriptors (V₁and V₂), viz. VH polarized backscatter coefficient (σ⁰_VH), Radar Vegetation Index (RVI) and depolarization ratio (χ_v), were used in the model. The model performed better when different parameters are used as vegetation descriptors (V₁≠V₂) in the WCM rather than using the same parameter for both the vegetation descriptors (V₁= V₂). The best results were observed when σ⁰_VHwas considered as one of the vegetation descriptors (V₁) while either χ_vor RVI were utilized as the other vegetation descriptor (V₂) giving a Pearson correlation coefficient (R) of 0.959 and 0.958 and a root mean square error (RMSE) of 0.499 dB and 0.516 dB respectively. The validation of the model-retrieved soil moisture against the in-situ measured values gave an R value of 0.72 and a RMSE of 0.096m³/m³. The plant height was also predicted by the WCM in which the retrieved soil moisture from SAR data was used as a parameter. The predicted plant height was compared to in-situ measured plant height and an R value of 0.76 and RMSE of 0.214 was obtained as the best result. The study demonstrates the capability of SAR-derived parameters as vegetation descriptors in the WCM for soil moisture retrieval.

Morphological parameters like cotton height, branches, Leaf Area Index and biomass are mainly affected by the vegetation water content (VWC). Periodical assessment of the VWC and crop parameters is required for timely management of the crop for maximizing yield. The study aimed at using both optical and microwave remotely sensed data to assess cotton crop condition based on the above mentioned traits. Vegetation indices (VI) derived from ground based measurements (5 narrow band and 2 broad band VIs) as well as satellite derived reflectance (2 broad band VIs) were assessed. Regression models were derived for estimating LAI, biomass and plant water content using the ground based indices and applied to the satellite derived spectral index (from LISS-III) map to estimate the respective parameters. HH and HV polarization from RISAT-1 were used to derive Radar Vegetation Index (RVI). The coefficient of determination of the model for estimating LAI, biomass and vegetation water content of cotton with optical vegetation index as input parameter were found to be 0.42, 0.51 and 0.52, respectively. The correlation between RVI and plant height, date of planting in terms of the age of the crop and vegetation water content were found to range between 0.4 to 0.6. The fresh biomass from RVI showed spatial variability from 100 gm-2 to 4000 gm-2 while the dry biomass map derived from NDVI showed spatial variability of 50 to 950 g m-2 for the study area. Plant water content in the district varied from 65 to 85%. The correlation between optical vegetation index and RVI was not significant. Hence a multiple linear regression model using both optical index (NDVI and LSWI) and SAR index (RVI) was developed to assess the LAI, biomass and plant water content. The model showed a R2 of 0.5 for LAI estimation but not significant for biomass and water content. This study show cased the use of combined optical and microwave (C band) remote sensing for cotton condition assessment.