This paper presents the potential for soil moisture (SM) retrieval using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide Swath (IW) mode along with Land Surface Temperature (LST) estimated from analysis of LANDSAT-8 digital thermal data. In this study Sentinel-1 data acquired on 27 February 2020 was downloaded from Copernicus website and LANDSAT-8 OLI data acquired on 24 February 2020 from the website https://earthexplorer.usgs.gov/.The soil samples were collected from 70 test fields in different villages of three talukas for estimating soil moisture content using the gravimetric method. The Sentinel-1 SAR microwave data was analysed using open source tools of Sentinel Application Platform (SNAP) software for estimation of backscattering coefficient. Land surface temperature estimated using Landsat-8 thermal data. The Landsat-8, Thermal infrared sensor Band-10 data and operational land imager Band-4 and Band-5 data were used in estimating LST. The Soil Moisture Index (SMI) for all field test sites was computed using the LST values. The regression analysis using σ0VV and σ0VH polarization with soil moisture indicated that σ0VV polarization was more sensitive to soil moisture content as compared to σ0VH polarization. The multiple regression analysis using field measured soil moisture (MS %) as dependent variable, and σ0VV and SMI as independent variable was carried which resulted in the coefficient of determination (R2) of 0.788, 0.777 and 0.778 for Godhra, Goghamba and Kalol talukas, respectively. These linear regression equations were used to compute the predicted soil moisture in three talukas.

Estimation of reference evapotranspiration (ET0) and actual evapotranspiration (ETc) is a key factor for estimation of crop water requirement, water balance and irrigation scheduling. The FAO-56 Penman–Monteith equation has been accepted universally for estimating of reference evapotranspiration (ET0). Considering the high spatial variation of meteorological phenomenon and limited availability of such dense network for data collection, application of remote sensing and GIS has gained momentum for estimation of ET0 and ETc over the larger area with accurately and efficiently. For estimation of ET0 and ETc, the most widely applied MOD16 remote sensing images as well as Landsat 8 remote sensing images are applied in this study of Panam canal command area which is located in the semi-arid middle Gujarat region. Initially, FAO-56 PM method is used to estimate ET0 and MOD16 data is used to estimate ET0, whereas Landsat 8 is used to estimate land surface temperature and then by using the regression equation to estimate maximum and minimum temperature to find out ET0 for the study area. Based on result obtained, it was found that Landsat 8 remote sensing-based data have better capacity to estimate actual evapotranspiration compared to the MOD16 remote sensing data. The better performance of Landsat 8 data compared to MOD16 data is due to the reason that it has better spatial resolution(30m) compared to MOD16 (1 km) remote sensing image and can represent the actual field conditions of farm fields which are generally smaller.

Land surface temperature (LST) can be described as the temperature of the earth's surface and it is most important parameters in climate change, evapotranspiration, urban climate, vegetation monitoring and environmental studies. LST, calculated from remote sensing data, is used in many areas of science such as; hydrology, agriculture, forestry, oceanography etc. The main objective of this study was to develop a model making the LST retrieval process quite simple and automated. This model developed using the ArcGIS Desktop 10.3.1 with the Model Building. Without the model, the process of retrieving LST is very long, and it is susceptible to many mistakes. In this model when user inputs required bands (4,5 and 10) of Landsat-8 data then the model calculate automatically LST and display output. The model first makes the conversions to top of atmosphere (TOA) spectral radiance. Then NDVI is calculated based on band 4 and 5 (NIR and RED) reflectance. Then using the TOA and NDVI model calculates brightness temperature (BT) and Proportion of Vegetation respectively. After that it calculate Land Surface Emissivity with the help of NDVI and Proportion of Vegetation and finally, the model calculates land surface temperatures in degrees Celsius. The findings highlight the capabilities of GIS modelers for such spatial estimation. The developed model can be helpful to field engineers and researchers for using Landsat-8 images for direct estimation of LST, to be used for different other studies to derive LST based products.