Implementation of SMAP and sentinel microwave signal based algorithm for high resolution soil moisture

Abstract

In this study, we retrieve high-resolution soil moisture (SM) using an active-passive algorithm by merging Sentinel-1 C-band radar data with SMAP L-band radiometer measurements. Soil moisture is helpful in agricultural, hydrological, and meteorological applications, which requires soil moisture at high resolution. Soil moisture can be measured accurately using the in-situ technique, but it is not feasible at a large scale. Another approach can be using satellite data for a product at a very high resolution, which would be very costly. We present applications of using active-passive merging algorithms to merge two satellite data to retrieve the product at high resolution. We are doing disaggregation of coarse resolution (36 36 km) SMAP L-band radiometer brightness temperature at 1 1 km. For that, we are using Sentinel-1 C-band radar data. Then disaggregated brightness temperatures used in the microwave emission model to retrieve surface soil moisture. The microwave emission model based on a single channel retrieval algorithm is used in this study. High-resolution ancillary data (e.g., surface temperature, VWC, soil roughness, and soil texture) are required as inputs to the active-passive retrieval algorithm to generate high-resolution soil moisture. Also, for the classification of land parameters by IGBP Land Cover Classification is used in this algorithm. And then, confirming the results by comparing them with ground-based observations from insitu continuous monitoring stations or field campaigns or using airborne datasets. The reported study can be utilized for estimating high-resolution soil moisture using the available SAR dataset over India. This study will help in developing the algorithms for near future satellite missions carrying SAR instrument.