Periodic crop condition monitoring is of prime importance in cotton belt of western India for water stress management. In this article, vegetation water content (VWC) is assessed using Radar Vegetation Index (RVI) derived from the RISAT-1 data during July to September, vegetative to first picking phase, for utilizing its potential for large area cotton condition assessment. The RVI estimation from dual-polarized data has been demonstrated for regional applications. Prediction models of VWC for cotton crop using RVI and in situ ground measurements depicts significant relationship, with R² varying from 0.5 to 0.6 and RMSE of 0.3–0.7 kg m⁻². High correlation exists between RVI with crop age and crop biomass with R² varying from 0.55 to 0.7, this proves useful for sowing date prediction. The results showed good validation (R² = 0.8) for operational applications. The estimated VWC was found with 30–35% error above 4 kg m⁻² biomasses as compared to 20–25% in lower ranges.

A hybrid-polarity architecture, consisting of transmitting circular polarisation and receiving two orthogonal linear polarisation and also their relative phase, was used to calculate four Stokes parameters. Different parameters like Degree of Polarisation, Alpha angle, Entropy, Anisotropy, Radar vegetation Index and decompositions like Raney decomposition (m-δ), Freeman-2 and 3 component decompositions were derived from these hybrid data. Crop biophysical parameters viz. plant height, plant age and plant biomass of cotton crops grown under two different environments, i.e., rainfed and irrigated in Guajrat, India were studied with respect to derived polarimetric parameters. Right circular transmitted and horizontally (RH) and vertically (RV) received backscatter values show good relation with the plant height, age and biomass. RH backscatter-13 dB to-7 dB and RV backscatter from-13 to-10 dB were observed for crop biophysical parameters. Volume component of all decomposition showed strong response to the increase in height, age and biomass of the plant. Radar Vegetation index (RVI) values have also shown significant increase from 0.6 to 0.7 with increasing age of the crop. The rate of growth was slow in the initial phase, but fast post mid-July for both early and late sown cases. The polarimetric parameters were found significantly correlated to the above plant biophysical parameters.

Synergistic use of Optical and Microwave remotely sensed data for cotton condition based on biophysical traits, indices from ground measurements and satellite derived reflectance were assessed. Regression models derived for estimating LAI, biomass and VWC using ground based indices were applied to satellite products. LAI, biomass and VWC of cotton with optical VIs as input (R²= 0.42, 0.51 and 0.52) respectively were estimated. Correlation between RVI and plant height, crop age and VWC were found between 0.4 and 0.6. Fresh biomass from RVI ranged 100 –4000 gm⁻², dry biomass from NDVI ranged 50–950 gm⁻² and VWC 65–85%. Correlation between VIs and RVI was found to be non- significant. A multiple linear regression model using NDVI vs. LSWI and RVI were developed to assess LAI, biomass and VWC. The model predicted LAI with R² of 0.5 but failed to estimate biomass and VWC.

Desertification and Land Degradation risk is one of the major environmental and socio-economic problem which constantly affects the global environment. Reduction or loss of land productivity caused by natural processes and human activities is land degradation which leads to desertification. The flaw in any system can be measured by basic concept of vulnerability. In the present study, desertification vulnerability assessment was carried out using geographic information system (GIS) for mapping sensitive areas using Mediterranean Desertification and Land Use (MEDALUS) approach, which identifies such sensitive areas on the basis of an index in which environmental quality as well as anthropogenic factors are included as layers for Surendranagar district of Gujarat state in India. Many aspects of vulnerability arising from natural and human factors were considered in this study. Soil, climate, land utilization, geography and vegetation contribute in the land degradation of any area. Indices related to these factors have been generated. Socio-economic factor is a one of the major input to assess desertification vulnerability as anthropogenic activities. As a result in this study MEDALUS model has been used for both natural and socioeconomic factors for finding the severity level of the desertification vulnerability in Surendranagar district.

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.