Mr. Yansong Bao Profile

Yansong Bao

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Proceedings Article | 14 November 2007 Paper

Surface soil moisture estimation using multi-incidence angle ENVISAT ASAR images

Yansong Bao, Shumin Tang, Li Liu

Proceedings Volume 6790, 67900J (2007) https://doi.org/10.1117/12.743164

KEYWORDS: Soil science, Backscatter, Data modeling, Atmospheric modeling, Surface roughness, Synthetic aperture radar, Remote sensing, Polarization, Agriculture, Speckle

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Soil moisture was very important for agricultural, meteorological and hydrological research. This paper focused on monitoring soil surface moisture using multi-incidence angle ENVISAT-ASAR data, by adapting a semi-empirical polynomial relationship between backscattering coefficient and soil moisture. As SAR signals at low and high incidence angles differently responded to soil moisture and surface roughness, soil moisture could be estimated with a higher accuracy by using two incidence angle SAR data to remove the effect of surface roughness. The AIEM (Advance Integral Equation Model) model was employed to analyze the effect of soil moisture and roughness on backscattering coefficient at low and high incidence angles. Based on low and high incidence angle data simulated by AIEM, firstly, backscattering parameter model was built. Secondly, a roughness inversion model was built based on two incidence angle data. Thirdly, the roughness inversion model was inputted into backscattering parameter model, and Taylor approximation was employed to develop a polynomial inversion model for soil moisture. In the soil moisture estimation model, the formula coefficients were obtained by least square method. Finally, the inversion model was used to derive soil moisture from the simulated data, results showed that there was a significant correlation (R=0.97) between the estimated and inputted soil moisture, and the RSM error was 0.051. The inversion methodology was also applied to the ASAR data, a good agreement was observed between the estimated and measured volumetric soil moisture. The correlation was 0.556, and the root mean square error was 0.0757. Compared with an experience model, the semi-experience model improved soil moisture estimation accuracy.

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