Inversing chlorophyll contents in rice using radiation transport of leaf bilayer

Rice is one of the major grain crops in the world. It is of great significance to accurately inverse the chlorophyll content during growth and development in the field. However, there are some deficiencies using conventional interpretability that are driven by data inversion of the physical and chemical parameters. The leaf radiative transfer model can be expected to effectively simulate the spectral information of rice leaves, and then describe the effects of leaf parameters on spectral reflectance. A strong mechanism model can be used for the inversion of chlorophyll content of rice leaves, according to the physically driven method. The PIOSL (PROSPECT considering the internal optical structure of the leaves) model can be utilized to assume that the internal structure of the leaf blade is composed of the superposition of two layers of optical properties, which is much more in line with the actual growth of the plant. This study aims to verify the feasibility of retrieving rice leaf chlorophyll content by the PIOSL model. Firstly, the sensitivity analysis was carried out on each input parameter into the PIOSL model. The parameters C_ab and C_ab12 were determined as the high sensitivity in the 400-750 nm band. The inversion of chlorophyll content (C_ab) was then implemented. The projection (SPA) was used to extract the features of spectral data. Five characteristic bands were sensitive to chlorophyll: 490, 575, 645, 675, and 725 nm. A multiple regression model was constructed to realize the inversion of rice leaf chlorophyll. The inversion performance of the mechanism model was then evaluated using the PIOSL model. The chlorophyll content of rice leaves was also inverted to construct an initial lookup table. LSE (least squares estimate) was used to screen the simulated sample data in the initial lookup table closer to the measured spectra. A classification prediction model was constructed using a support vector machine (SVM), in order to determine whether the combination of the screened sample parameters was in line with the actual growth of the leaves. The improved look-up table was randomly split into the training and test sets in the ratio of 7:3. The hierarchical inversion of chlorophyll content of rice leaves was performed to construct the WOA-ELM (whale optimization, WOA; extreme learning machine, ELM) inversion model. A comparison was made on the commonly-used PROSPECT model with the same data. An improved lookup table was then obtained using the PROSPECT model. The WOA-ELM model was used for the chlorophyll inversion. The results showed that the R² and RMSE of the inversion model using PIOSL-WOA-ELM were 0.977 and 2.356 μg/cm², respectively, and the inversion accuracies of both models were above 0.9, compared with the PROSPECT-WOA-ELM model. According to the numerically driven inversion model and the physically-driven radiative transfer model, the inversion accuracy of the PIOSL-WOA-ELM model was higher than that of the multivariate regression with continuous projection. Therefore, it is feasible for a more effective inversion with the radiative transfer mechanism. The finding can provide new ideas for the accurate inversion of the distribution of rice chlorophyll in the leaf, further effectively retrieving the crop physicochemical parameters.