Abstract
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. Furthermore, the comparative analysis results of various datasets showed that prior knowledge could improve the precision of the retrieved LAI, and using this information to constrain parameters (e.g., chlorophyll content or LAI), which make important contributions to the spectra, is the key to this improvement. In addition, among the PLS, BP-ANN, LS-SVR, and RFR methods, RFR was the optimal modeling algorithm in the paper, as indicated by the high R2 and low NRMSE in various datasets.
Highlights
The leaf area index (LAI) is an essential indicator for assessing the nutrient level, photosynthetic capacity, and health status of vegetation [1,2,3,4,5,6]
We combine hyperspectral data dimension reduction and the hybrid inversion strategy for crops LAI inversion via the following steps: (1) generating the simulated database, which includes the simulated vegetation parameters generated using a truncated Gaussian distribution and simulated canopy spectra generated using the PROSAIL model; (2) extracting the LAI-related spectral feature information using PLS; (3) establishing a hybrid model to link the simulated LAI to the partial least squares principal components (PLS_PCs) with different regression algorithms; (4) comparing the LAI estimation models based on PLS and vegetation indexes (VIs) to optimize the modeling strategy; and (5) validating the LAI inversion model with data collected from the Sentinel-3 Experiment campaign
The combination of the results presented in Sections 3.1.1 and 3.1.2 indicates that the accuracy of the modelTbhaesceodmobninSaptioencifiofctdheatraesseutlt2s wpraessetnhteedhiinghSeescttioanmso3n.1g.1thanedth3r.1e.e2 dinadtaicsaetetss,tbhoatththwe hacecnurdaicffyerent VIs anodf twhehemnoddeiffl ebraesnedt sotnatSispteiccaiflicddimateasnestio2nwraesduthcetiohnighmesetthaomdosn(gPtChRe tohrrePeLdSa)twaseetrse, ubsoethd wtoheenxtract different VIs and when different statistical dimension reduction methods (PCR or PLS) were used to spectreaxltrfaecattuspreesc.trTahl efeaactucurersa.cTyhoef athcceumraocydeolfbtahseedmoodneSl pbeacsiefidc odnatSapseecti1ficwdaasttahseets1ecwonasd thhieghseecsot,nbdut its accurhaicgyhwesat,sbcultoistes atocctuhraactyofwtahsecmlosoedteoltbhaasteodf tohne mSpoedceilfibcadseadtaosnetSp2,ecwifhicildeatthasaetto2f, wthheilme tohdaet lobf athseed on the Gmenoedreicl bdaasteadsoetnwthaesGtheneelroicwdeasttasbeyt wa acsotnhseidloewraebstlebymaacrgoninsi.derable margin
Summary
The leaf area index (LAI) is an essential indicator for assessing the nutrient level, photosynthetic capacity, and health status of vegetation [1,2,3,4,5,6]. We combine hyperspectral data dimension reduction and the hybrid inversion strategy for crops LAI inversion via the following steps: (1) generating the simulated database, which includes the simulated vegetation parameters generated using a truncated Gaussian distribution and simulated canopy spectra generated using the PROSAIL model; (2) extracting the LAI-related spectral feature information using PLS; (3) establishing a hybrid model to link the simulated LAI to the partial least squares principal components (PLS_PCs) with different regression algorithms; (4) comparing the LAI estimation models based on PLS and VIs to optimize the modeling strategy; and (5) validating the LAI inversion model with data collected from the Sentinel-3 Experiment campaign (conducted in June 2009 in Barax, southern Spain)
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