Available online at www.CivileJournal.org Civil Engineering Journal (E-ISSN: 2476-3055; ISSN: 2676-6957) Vol. 9, No. 06, June, 2023 1402 Estimation of Soil Moisture for Different Crops Using SAR Polarimetric Data K. Kanmani 1 , Vasanthi P. 1* , Packirisamy Pari 2 , N. S. Shafeer Ahamed 1 1 Department of Civil Engineering, School of Infrastructure, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamilnadu, 600 048, India. 3 Associate Consultant, Darashaw & Company Private Limited, Tamilnadu, India. Received 15 March 2023; Revised 19 May 2023; Accepted 26 May 2023; Published 01 June 2023 Abstract Soil moisture is an essential factor that influences agricultural productivity and hydrological processes. Soil moisture estimation using field detection methods takes time and is challenging. However, using Remote Sensing (RS) and Geographic Information System (GIS) technology, soil moisture parameters become easier to detect. In microwave remote sensing, synthetic aperture radar (SAR) data helps to retrieve soil moisture from more considerable depths because of its high penetration capability and the illumination power of its light source. This study aims to process the SAR Sentinel-1A data and estimate soil moisture using the Water Cloud Model (WCM). Many physical and empirical models have been developed to determine soil moisture from microwave remote sensing platforms. However, the Water Cloud Model gives more accurate results. In this study, the WCM model is used for mixed crop types. The experimental soil moisture was determined from in-situ soil samples collected from various agricultural areas. The soil backscattering values corresponding to the different soil sampling locations were derived from Sentinel SAR data. Using linear regression analysis, the laboratory's soil moisture results and soil backscattering values were correlated to arrive at a model. The model was validated using a secondary set of in-situ moisture content values taken during the same period. The R2 and RMSE of the model were observed to be 0.825 and 0.0274, respectively, proving a strong correlation between the experimental soil moisture and satellite-derived soil moisture for mixed crop field types. This paper explains the methodology for arriving at a model for soil moisture estimation. This model helps to recommend suitable crop types in large, complex areas based on predicted moisture content. Keywords: Water Cloud Model (WCM); Synthetic Aperture Radar (SAR); Soil Backscattering. 1. Introduction Soil moisture is the most influential factor causing surface runoff fluctuations and soil infiltration capacity [1, 2]. Soil moisture varies based on location and crops [3]. The in-situ measurements available to measure soil moisture are complex. However, with RS and GIS technology, we can estimate the soil moisture content for challenging areas such as highly vegetated areas, dense forests, and grasslands [4]. Active remote sensors such as RADAR transmit signals to the earth's surface by illuminating their own light source and receiving the signals that are reflected back to the sensor. SAR is an imaging radar that has its own energy source. The successive radar pulses from SAR are transmitted to the target, received back to the sensor as backscattered signals, and then recorded. The spatial resolution of this image depends on pulse length and beam width. * Corresponding author: vasanthi@crescent.education http://dx.doi.org/10.28991/CEJ-2023-09-06-08 © 2023 by the authors. Licensee C.E.J, Tehran, Iran. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).