Estimating Crop LAI Using Spectral Feature Extraction and the Hybrid Inversion Method

作者：Liang Liang, Di Geng, Juan Yan, Siyi Qiu, Liping Di, Shuguo Wang, Lu Xu, Lijuan Wang, Jianrong Kang, Li Li · 发表于：Remote Sensing · 年份：2020 · DOI：10.3390/rs12213534 · 被引用次数：47 · 研究领域：Remote Sensing in Agriculture、Leaf Properties and Growth Measurement、Remote Sensing and Land Use

The leaf area index (LAI) is an essential indicator used in crop growth monitoring. In the study, a hybrid inversion method, which combined a physical model with a statistical method, was proposed to estimate the crop LAI. The simulated compact high-resolution imaging spectrometer (CHRIS) canopy spectral crop reflectance datasets were generated using the PROSAIL model (the coupling of PROSPECT leaf optical properties model and Scattering by Arbitrarily Inclined Leaves model) and the CHRIS band response function. Partial least squares (PLS) was then used to reduce the dimension of the simulated spectral data. Using the principal components (PCs) of PLS as the model inputs, the hybrid inversion models were built using various modeling algorithms, including the backpropagation artificial neural network (BP-ANN), least squares support vector regression (LS-SVR), and random forest regression (RFR). Finally, remote sensing mapping of the CHRIS data was achieved with the hybrid model to test the inversion accuracy of LAI estimates. The validation result yielded an accuracy of R2 = 0.939 and normalized root-mean-square error (NRMSE) = 6.474% for the PLS_RFR model, which indicated that the crops LAI could be estimated accurately by using spectral feature extraction and a hybrid inversion strategy. The results showed that the model based on principal components extracted by PLS had a good estimation accuracy and noise immunity and was the preferred method for LAI estimation. Furthermor...