Spatial Downscaling of Satellite Soil Moisture Data Using a Vegetation Temperature Condition Index

Abstract

Microwave remote sensing has been largely applied to retrieve soil moisture (SM) from active and passive sensors. The obvious advantage of microwave sensors is that SM can be obtained regardless of atmospheric conditions. However, existing global SM products only provide observations at coarse spatial resolutions, which often hamper their application in regional hydrological studies. On the other hand, the vegetation temperature condition index (VTCI) has been widely used to monitor the SM status. It is based on high-spatial-resolution visible and infrared satellite observations. The aim of this study is to develop a simple and efficient downscaling approach for estimating accurate SM at higher spatial resolution. The VTCI calculated from the Moderate Resolution Imaging Spectroradiometer is used to downscale the coarse-resolution SM product that has been developed under the framework of the European Space Agency's Climate Change Initiative (CCI) projects. The original and downscaled SM estimates are further validated against the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> SM observations collected in the Yunnan province (southwest China). It is found that the accuracy level of CCI SM is similar to the results from previously published validation studies. The downscaled SM can maintain the accuracy of CCI SM and, at the same time, present more spatial details, demonstrating the feasibility of the proposed method. Overall, the notable advantages of the proposed method are simplicity, limited data requirements and purely relying on satellite measurements, and comparable accuracy level to other complex downscaling schemes. It will facilitate local hydrological applications, particularly in data-scarce regions, where the above-listed characteristics are important and useful.