Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images

Abstract

Rice is China’s main crop and its output accounts for 30% of the world’s total annual rice production. Rice growth status is closely related to chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values). The determination of a SPAD value is of great significance to the health status of rice, agricultural irrigation and regulated fertilization. The traditional SPAD value measurement method is not only time-consuming, laborious and expensive but also causes irreparable damage to vegetation. The main aim of the present study is to obtain a SPAD value through the inversion of hyperspectral remote sensing images. In order to achieve this purpose, the hyperspectral image of rice at different growth stages at the canopy scale was first acquired using a hyperspectral imaging instrument equipped with a drone; the spectral characteristics of the rice canopy at different growth stages were analyzed and combined with a ground-level measured SPAD value, the bands with high correlation between the SPAD values and the spectra of the rice canopy at different fertility stages were selected. Subsequently, we combined the spectral characteristics with the continuous projection algorithm to extract the characteristic band and used the PLS method in MATLAB software to analyze and calculate the weight of each type of spectral value and the corresponding canopy SPAD value; we then used the wavelength corresponding to the spectral value with the highest weight as the used band. Secondly, the four methods of univariate regression, partial least squares (PLS) regression, support vector machine (SVM) regression and back propagation (BP) neural network regression are integrated to establish the estimation model of the SPAD value of rice canopy. Finally, the models are used to map the SPAD values of the rice canopy. Research shows that the model with the highest decision coefficient among the four booting stage models is “booting stage-SVR” (R2 = 0.6258), and the model with the highest decision coefficient among the four dairy maturity models is “milk-ripe stage-BP” (R2 = 0.6716), all of which can meet the requirement of accurately retrieving the SPAD value of rice canopy. The above results can provide a technical reference for the accurate, rapid and non-destructive monitoring of chlorophyll content in rice leaves and provide a core band selection basis for large-scale hyperspectral remote sensing monitoring of rice.