Soil moisture estimation over an arid environment in Mongolia from passive microwave remote sensing based on a simplified parameterization method

Abstract

Satellite passive microwave remote sensing provides a major basis for the analysis of temporal and spatial variation of top soil moisture at large scales. A global soil moisture data product is currently released by the National Aeronautics and Space Administration (NASA) of USA based on AMSR-E (the Advanced Microwave Scanning Radiometer for the Earth Observing System onboard the NASA EOS Aqua satellite) global dual-polarization brightness temperature observations of the Earth. But the accuracy of this soil moisture product is not satisfactory in arid Mongolian regions. Based on a simplified parameterization method for characterizing the effects of the sparse vegetation cover and surface roughness on surface microwave emission, the estimation of daytime (local noon) and nighttime (local midnight) top soil moisture during a near two-year period from October 1, 2005 to September 20, 2007 over the arid CEOP(The Coordinated Energy and Water Cycle Observations Project)-Mongolia experiment area was conducted based on a multifrequency algorithm by mainly using AMSR-E 6.9 GHz, 10.7 GHz and 18.7 GHz vertical-polarized brightness temperature data. In the inversion model, the only model parameter asking for experimental calibration, i. e., the vegetation single scattering albedo, could be achieved through optimization technique by using in-situ soil moisture measurements. The results showed that the root mean square error (RMSE) between satellite estimated soil moisture and ground-measured soil moisture was close to 0.030 cm(3)/cm(3). Without other supplementary data else, the retrieval algorithm proposed in this paper may be applied to near real-time monitoring of the dynamic variation in top soil moisture at large scales, in an all-weather working manner, over arid and or semiarid environments characterized by natural sparse vegetation conditions. Therefore, it will be promising to provide sustaining and reliable initial near-surface soil moisture information for the long term study of climate change and water cycle over arid and or semiarid regions.