<div style="text-align: justify;">One of the most crucial environmental problems affecting developing countries in arid and semi-arid regions is soil salinity. Its detection using radar imaging systems is one of promising domains of remote sensing research. Due to the components of the mixture and their corresponding dielectric properties, backscattering of these types of soils can be modeled and monitored. Ulaby developed an empirical model that established a non-linear relation between soil moisture and backscattering coefficient. The dielectric properties of the soil depend on soil moisture content along with its salinity, texture and frequency. The radar backscattering from wet soil surface depends upon dielectric properties besides angle of incidence of the Synthetic Aperture Radar. Most previous studies neglected the effect of the presence of salts combined with the other parameters on the backscattering coefficient. In this paper, we present the salinity effect on the calculation of the backscattering coefficient using Modified Ulaby (MU) model. To validate the modeling approach, the secondary data of RADARSAT-1 satellite images in standard modes (C-band) were collected for the area of Wadi El-Natrun, Egypt, where fieldwork was conducted simultaneously with some of the RADARSAT-1 image acquisitions. The results show that the simulated backscattering using MU gives a better fit with measured SAR images. Moreover, the comparison of simulated backscattering using MU gives better correlation with widely accepted Advanced Integral Equation Model (AIEM) and Dubois Model (DM). It is suggested that the proposed model will be of interest to agricultural scientists, and applicable to remote sensing of salt-affected areas.</div>

<div style="text-align: justify;">Modified Ulaby Model on Backscattering as a Function of Salinity, Frequency and Soil Moisture</div>

Background: Groundnut, commonly known as peanut, is a significant oil, food and feed legume crop grown in all seasons in Tamil Nadu, including kharif, rabi and summer and it is cultivated both under irrigated and rainfed conditions in all the seasons at Thiruvannamlai district. One of the most important applications of remote sensing in agriculture is a Crop Acreage and Production Estimation (CAPE). The CAPE&rsquo;s main goal is to estimate crop acreage and production of important crops, so that advanced food production, distribution and supply data were achieved.&nbsp; 
 
Methods: Multi-temporal Sentinel 1A SAR IW- GRD data with 20 m spatial resolution and 12 days temporal resolution of Vertical - Horizontal (V-H) polarization were downloaded for the period of 4th October 2020 to 8th January 2021 to have the full coverage during the crop growth period in the study area used for this work. Crop backscattering and multi-temporal features were extracted from MAPscape 5.2 automated pre-processing tool and its classified using supervised maximum likelihood classification for groundnut acreage extraction for Thiruvannamalai district. 
 
Result: The rabi groundnut area of Thiruvannamalai district of Tamil Nadu was estimated using SAR Sentinel-1A data as 32298 ha with a higher accuracy percentage of 87.4 and kappa coefficient of 0.75.

Modified Ulaby Model on Backscattering as a Function of Salinity, Frequency and Soil Moisture

Rabi Groundnut Area Estimation using Synthetic Aperture Radar (SAR) in Thiruvannamalai District of Tamil Nadu