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. Doi: 10.28991/CEJ-2023-09-06-08 Full Text: PDF

Estimation of soil moisture using Synthetic Aperture Radar (SAR) backscatter values, over agricultural area, is still difficult. SAR backscatter is sensitive to the surface properties like roughness, crop cover, and soil type, along with its strong sensitivity to soil moisture. Hence, to develop a methodology for agricultural area soil moisture estimation using SAR, it is necessary to incorporate the effects of crop cover and soil texture in the soil moisture retrieval model. A field experiment was conducted by the authors and used along with Sentinel 1A SAR data to estimate the soil moisture in the paddy agricultural fields. Generally, the water used for irrigation in the study region was obtained from ground water. As in the hot climate conditions ground water level would be reduced, and the water for irrigation must be supplied optimally. Hence, available soil moisture in the field was estimated from SAR data on the day of satellite passing the crop fields and utilized for deciding the amount of water to be supplied. The soil moisture values of soil samples that are collected from the agricultural field are calculated with the laboratory experiments. A soil moisture retrieval model is derived and proposed in this paper after a comparative analysis of experimental soil moisture values and satellite values. The feasibility of above model for paddy agricultural fields is validated using the field measurements.