Soil moisture estimation using RISAT-1 and SENTINEL-1 data using modified Dubois model in comparison with averaged NDVI

Abstract

In past studies, several researchers took potential use of multi-temporal optical data and dual-polarized SAR data to assess drought by estimating soil moisture. In this study, Modified Dubois Model (MDM) semi-empirical model with Topp's model is used for retrieval of soil moisture. It involves retrieving the backscattering coefficient from RISAT-1 and SENTINEL-1 datasets to derive the surface roughness and soil moisture conditions. The estimated soil moisture retrieved from microwave SAR parameters is validated with field measurements provides soil moisture spatial variability over different land use classes and bare soil condition. The RISAT-1 derived soil moisture has R² = 0.53, whereas SENTINEL-1 shows R² = 0.84. It also confirms the possibility of two different polarization σ°_HH and σ°_VV backscatter involving MDM. It observes that SENTINEL-1 was found well correlated with ground-measured soil moisture. Also, the averaged NDVI sounds reliable with soil moisture ratio, which helps to understand the impact of agricultural drought monitoring.

Keywords:

• RISAT-1
• SENTINEL-1A
• LISS IV
• NDVI
• semi-empirical model
• soil moisture

Acknowledgements

The authors gratefully acknowledge to the Director, National Centre for Coastal Research (NCCR), Chennai and to the Director, Institute of Remote Sensing, Anna University, Chennai. Also We would like to thank Dr. V. Kumar Professor & Head (Retd), Madurai Agriculture College (TNAU) for his continuous support during the time of field visit. The authors want to thank National Data Center (NDC) and National Remote Sensing Centre (NRSC), Hyderabad, for providing the RISAT satellite data and European Space Agency (ESA) for providing Sentinel satellite data used during the analysis.

Disclosure statement

No potential conflict of interest was reported by the author(s).