Estimation of soil moisture is essential for research of climatology, hydrology, and ecology. The commonly used remotely sensed approach is LST-NDVI (land-surface temperature-normalized difference vegetation index). In this study, the apparent thermal inertia (ATI) is used instead of
surface temperature to develop an ATI-NDVI space for estimation of soil moisture. Comparison with ground-based measurements shows a root mean square error (RMSE) of 0.0378 m3 m−
3 between retrieved and measured soil moistures. Validation with
time series in situ data indicates the RMSE as 0.0162, 0.0285, 0.0368, and 0.0093 m3 m−
3 for forest, shrub, cropland, and grassland, respectively, which is comparable to or even better than the results of previous studies. The proposed
method in this study is a remote-sensing approach without elaborate ancillary data except for the percentage of sand in the soil, and it is practical and convenient to be applied to regions with surfaces from bare soil to full vegetation and the entire range of surface moisture contents from
wet to dry.
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Document Type: Research Article
Department of Geographical Science, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610041, China
Publication date: May 19, 2014
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