Estimation of chlorophyll content in rice canopy leaves using 3D radiative transfer modeling and unmanned aerial hyperspectral images.

Abstract

Background: The chlorophyll content has a strong influence on plant photosynthesis and crop growth and is a key factor for understanding the functioning of farming systems. Therefore, the accurate estimation of chlorophyll content (Cab) is important in precision agriculture. In this study, the three-dimensional radiative transfer model (3DRTM) was used to calculate the radiative transfer and simulate the canopy hyperspectral image of a rice field. Then, a physically based joint inversion model was developed using an iterative optimization approach with penalty function and a priori information constraints to estimate chlorophyll content efficiently and accurately from the hyperspectral curve of a rice canopy.

Results: The inversion model demonstrates that the sparrow search algorithm (SSA) can estimate rice Cab, providing relatively satisfactory Cab estimation outcomes. In addition, the inversion of the SSA method with or without carotenoids content (Car) constraints was compared, and compared to the inversion of Cab without Car constraints [coefficient of determination (R²) = 0.690, root mean square error (RMSE) = 7.677 µg/cm²)], the SSA with constraints was more accurate (R² = 0.812, RMSE = 5.413 µg/cm²).

Conclusions: The Large-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes (LESS) exhibited higher accuracy in estimating the rice Cab compared to the 1DRTM PROSAIL model, which is constituted by coupling the Leaf Optical Properties Spectra (PROSPECT) model and the Scattering by Arbitrarily Inclined Leaves (SAIL) model. The 3DRTM is conducive to precisely estimating Cab from the hyperspectral data of the rice canopy, thereby holding great potential for precise nutrient management in rice cultivation.