Inversion estimation of soil organic matter content based on GF-5 hyperspectral remote sensing image
ZHAO Rui1, CUI Xi-min2, LIU Chao1
1. State Nuclear Electric Power Planning Design and Research Institute Company Limited, Beijing 100094, China; 2. College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing, 100083, China
Abstract：Based on the hyperspectral remote sensing images inversion, this paper estimated the soil organic matter content of the plain in the Harbin and Xing’an League boundary. Firstly, from the Gao-Fen 5(GF-5) hyperspectral remote sensing image, which has been pre-processing such as radiation and geometric correction, 100 groups of surface soil samples were collected by the five-point sampling method, and a series of physical and chemical analysis in the laboratory were performed to obtain the soil organic matter content. Then, the partial least squares regression method was applied to establish an estimation model of soil organic matter content by hyperspectral image soil desertification index, soil degradation index, normalized brightness index and soil salinity index. The prediction accuracy of the inversion model based on the original reflectance data, one differential reflectance data, and four soil indices were compared. From 65% modeling samples and 35% prediction samples it showed that the inversion model based on the soil index in the inversion model had the highest prediction accuracy. In the verification of the prediction group, ρ was 0.816 and the RMSE was 1.7287. Finally, the inversion model was applied to the inversion estimation of soil organic matter content by hyperspectral imagery. The actual measured SOM was consistent with the image inversion SOM content change trend, and the correlation reached 80.023%, which verified the accuracy of the model's inversion estimation.
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