Estimating soil moisture using Sentinel-1 and Sentinel-2 sensors for dryland and palustrine wetland areas

Ridhwannah Gangat; Heidi van Deventer; Laven Naidoo; Elhadi Adam

doi:10.17159/sajs.2020/6535

Authors

Ridhwannah Gangat Archaeology and Environmental Studies, Department of Geography, University of the Witwatersrand, Johannesburg, South Africa https://orcid.org/0000-0003-3334-5956
Heidi van Deventer 1. Archaeology and Environmental Studies, Department of Geography, University of the Witwatersrand, Johannesburg, South Africa; 2.Council for Scientific and Industrial Research, Pretoria, South Africa https://orcid.org/0000-0002-7854-2155
Laven Naidoo Council for Scientific and Industrial Research, Pretoria, South Africa https://orcid.org/0000-0002-7091-0566
Elhadi Adam Archaeology and Environmental Studies, Department of Geography, University of the Witwatersrand, Johannesburg, South Africa https://orcid.org/0000-0003-3626-5839

DOI:

https://doi.org/10.17159/sajs.2020/6535

Keywords:

soil moisture content, volumetric water content, hydroperiod, random forest regression, machine learning regression models

Abstract

Soil moisture content (SMC) plays an important role in the hydrological functioning of wetlands. Remote sensing shows potential for the quantification and monitoring of the SMC of palustrine wetlands; however, this technique remains to be assessed across a wetland–terrestrial gradient in South Africa. The ability of the Sentinel Synthetic Aperture Radar (SAR) and optical sensors, which are freely available from the European Space Agency, were evaluated to predict SMC for a palustrine wetland and surrounding terrestrial areas in the grassland biome of South Africa. The percentage of volumetric water content (%VWC) was measured across the wetland and terrestrial areas of the Colbyn Wetland Nature Reserve, located in the City of Tshwane Metropolitan Municipality of the Gauteng Province, using a handheld SMT-100 soil moisture meter at a depth of 5 cm during the peak and end of the hydroperiod in 2018. The %VWC was regressed against the Sentinel imagery, using random forest, simple linear and support vector machine regression models. Random forest yielded the highest prediction accuracies in comparison to the other models. The results indicate that the Sentinel images have the potential to be used to predict SMC with a high coefficient of determination (Sentinel-1 SAR = R²>0.9; Sentinel-2 optical = R²>0.9) and a relatively low root mean square error (Sentinel-1 RMSE =<17%; Sentinel-2 optical = RMSE <21%). Predicted maps show higher ranges of SMC for wetlands (> 50%VWC; p<0.05) compared to terrestrial areas, and therefore SMC monitoring may benefit the inventorying of wetlands, as well as monitoring of their extent and ecological condition.

Significance:

The freely available and space-borne Sentinel sensors show potential for the quantification of surface soil moisture across a wetland–terrestrial gradient.
Significant differences between the surface soil moisture of palustrine wetlands and terrestrial areas, imply that inventorying and monitoring of the extent and hydroperiod of palustrine wetlands can potentially be done.