Abstract
The study discusses the soil moisture estimation using dual polarimetric RISAT-1. The semi-empirical approach of Modified Dubois Model (MDM) derived by (SrinivasaRao 2013) is worked out using (σ ̊HH) and (σ ̊HV) for soil moisture estimation using dual polarimetric backscattering image. IRS LISS IV data have been used to analyze the site suitability of different land use/cover types. The retrieval of backscattering coefficient values (σ ̊) from SAR is the common principle factor for soil moisture estimation. The surface roughness was measured in the selected sample location, for which the same backscattering values derived from the SAR is linearly correlated showing r2 = 0.93. The estimated surface roughness is used for retrieval of dielectric constant using MDM. The dielectric constant derived from MDM in combination with the Topps model proposed by (Topp 1980), is used to derive satellite-based soil moisture estimation. Linear regression analysis was performed, and the soil moisture derived from SAR are well correlated with the volumetric soil moisture showing the value of r2 = 0.63.
Highlights
In hydrological studies, soil moisture is a critical component that controls the infiltration and runoff rates
Only a small percentage (~ 1%) of the total fresh water budget is contained in the soil layers of the earth surface, it acts as a key parameter in the exchange of mass and energy at the soil-atmospheric layer and in hydrological processes
Since the microwave radiation can penetrate through clouds, it aids the acquisition of images in all weather conditions, and penetrates into the soil for at least 1-5 cm and often upto 15 cm of the root zone level of soil moisture for volumetric analysis
Summary
Soil moisture is a critical component that controls the infiltration and runoff rates. The soil surface conditions, soil moisture content, and roughness are important parameters in agriculture and vegetation growth monitoring. Remote sensing is an advanced technology that provides accurate and repetitive spatial data; microwave remote sensing synthetic aperture radar (SAR) is useful as they have a tendency to measure the soil parameters under any weather condition (Mirmazloumi 2016; Radar 2016). The large variation in the dielectric constant, which depends on the radar backscattering coefficient, has a linear relation with the soil moisture if the roughness condition is absent (Terrain 2007). RISAT-1 measures the volumetric soil moisture content in all weather conditions. The Soil Moisture content was derived using Modified Dubois Model for RISAT-1 data. The overall aim of the study explains the RISAT SAR retrieval of soil moisture both spatially and temporally to improve our understanding of the hydrological behavior of soil
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