Integration method to estimate above-ground biomass in arid prairie regions using active and passive remote sensing data

Minfeng Xing; Binbin He; Xiaowen Li

doi:10.1117/1.JRS.8.083677

14 February 2014 Integration method to estimate above-ground biomass in arid prairie regions using active and passive remote sensing data

Minfeng Xing, Binbin He, Xiaowen Li

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083677 (February 2014). https://doi.org/10.1117/1.JRS.8.083677

Abstract

The use of microwave remote sensing for estimating vegetation biomass is limited in arid grassland regions because of the heterogeneous distribution of vegetation, sparse vegetation cover, and the strong influence from soil. To minimize the problem, a synergistic method of active and passive remote sensing data for retrieval of above-ground biomass (AGB) was developed in this paper. Vegetation coverage, which can be easily estimated from optical data, was combined in the scattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. Backscattering coefficients were simulated using the established scattering model. A look-up table was established using the relationship between the vegetation water content and the backscattering coefficient for water content retrieval. Then, AGB was estimated using the relationship between the vegetation water content and the AGB. The method was applied to estimate the AGB of the Wutumeiren prairie. Finally, the accuracy and sources of error in this innovative AGB retrieval method were evaluated. The results showed that the predicted AGB correlated with the measured AGB (R² =0.8414 , RMSE=0.1953 kg/m ² ). Thus, the method has operational potential for the estimation of the AGB of herbaceous vegetation in arid regions.

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Minfeng Xing, Binbin He, and Xiaowen Li "Integration method to estimate above-ground biomass in arid prairie regions using active and passive remote sensing data," Journal of Applied Remote Sensing 8(1), 083677 (14 February 2014). https://doi.org/10.1117/1.JRS.8.083677

Published: 14 February 2014

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