Hyperspectral Remote Sensing Estimation of Rice Canopy LAI and LCC by UAV Coupled RTM and Machine Learning

Zhongyu Jin, Hongze Liu, Huini Cao, Shilong Li, Fenghua Yu () and Tongyu Xu
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Zhongyu Jin: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Hongze Liu: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Huini Cao: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Shilong Li: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Fenghua Yu: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Tongyu Xu: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China

Agriculture, 2024, vol. 15, issue 1, 1-27

Abstract: Leaf chlorophyll content (LCC) and leaf area index (LAI) are crucial for rice growth and development, serving as key parameters for assessing nutritional status, growth, water management, and yield prediction. This study introduces a novel canopy radiative transfer model (RTM) by coupling the radiation transfer model for rice leaves (RPIOSL) and unified BRDF model (UBM) models, comparing its simulated canopy hyperspectra with those from the PROSAIL model. Characteristic wavelengths were extracted using Sobol sensitivity analysis and competitive adaptive reweighted sampling methods. Using these wavelengths, rice phenotype estimation models were constructed with back propagation neural network (BPNN), extreme learning machine (ELM), and broad learning system (BLS) methods. The results indicate that the RPIOSL-UBM model’s hyperspectra closely match measured data in the 500–650 nm and 750–1000 nm ranges, reducing the root mean square error (RMSE) by 0.0359 compared to the PROSAIL model. The ELM-based models using the RPIOSL-UBM dataset proved most effective for estimating the LAI and LCC, with RMSE values of 0.6357 and 6.0101 μg · cm −2 , respectively. These values show significant improvements over the PROSAIL dataset models, with RMSE reductions of 0.1076 and 6.3297 μg · cm −2 , respectively. The findings demonstrate that the proposed model can effectively estimate rice phenotypic parameters from UAV-measured hyperspectral data, offering a new approach to assess rice nutritional status and enhance cultivation efficiency and yield. This study underscores the potential of advanced modeling techniques in precision agriculture.

Keywords: RPIOSL model; UBM model; LAI; LCC; UAV (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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