Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data

Abstract

Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 m3/m3, and root-mean-square-error (RMSE) = 0.047 m3/m3, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 m3/m3, and RMSE = 0.152 and 0.103 m3/m3, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 m3/m3, and RMSE = 0.051 m3/m3 excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products.