Inversion of Soil Moisture Content Based on Drone Hvperspectral Data
Soil moisture content is one of the important factors for the development of agricul-tural production.The use of unmanned aerial vehicles equipped with spectrometers can serve as a new scientific and technological means for quickly and accurately predicting soil moisture con-tent,which is of great significance for the development of agriculture in China.In order to e-liminate the background noise,redundancy and collinearity problems in UAV hyperspectral da-ta,this paper proposes to transform the spectral data and conduct principal component analysis.The principal component variables are used as model input variables to establish BP neural net-work inversion model(PCA-BPNN)and random forest model(PCA-RF)to predict soil water content,and R2,RMSE The comprehensive evaluation index of RPD verifies and compares the accuracy of two inversion results.The results show that the spectral reciprocal logarithmic trans-formation processing can effectively improve model accuracy and prediction ability,and the PCA-RF model based on reciprocal logarithmic spectroscopy has the highest accuracy(R2M=0.892,RPDv=1.474).
HyperspectralSoil water contentPrincipal component analysis
NETL
NSTL
万方数据
