Estimation of Lake Particulate Phosphorus by Remote Sensing Based on Hyperspectral Data from the HJ-2A/B Satellite
Taking Chaohu Lake as the research area,using HJ-2A/B satellite HSI hyperspectral remote sensing data,combined with ground measured sample data,Pearson correlation analysis and CARS algorithm were used to screen the sensitive spectral bands of par-ticle state.Combined with some machine learning algorithms,such as random forest(RF),support vector machine(SVM),extreme learning machine(ELM),convolutional neural network(CNN),the remote sensing estimation model of lake particulate phosphorus was constructed and inversion was performed.The results showed that the Pearson-CNN model had good predictive ability,with R2,RMSE and RPD of 0.765,32.49 μg/L and 1.97 respectively.This shows that the model can quickly capture the spectral characteristics of particulate phosphorus and has strong nonlinear learning ability,conducive to improving the estimation accuracy of the remote sensing model of particulate phosphorus,providing good support for monitoring and evaluation of lake phosphorus sources.
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