Interpretable LAI Fine Inversion of Maize by Fusing Satellite, UAV Multispectral, and Thermal Infrared Images

Yu Yao, Hengbin Wang, Xiao Yang, Xiang Gao, Shuai Yang, Yuanyuan Zhao, Shaoming Li, Xiaodong Zhang and Zhe Liu ()
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Yu Yao: College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Hengbin Wang: College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Xiao Yang: College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Xiang Gao: College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Shuai Yang: College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Yuanyuan Zhao: College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Shaoming Li: College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Xiaodong Zhang: College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Zhe Liu: College of Land Science and Technology, China Agricultural University, Beijing 100193, China

Agriculture, 2025, vol. 15, issue 3, 1-23

Abstract: Leaf area index (LAI) serves as a crucial indicator for characterizing the growth and development process of maize. However, the LAI inversion of maize based on unmanned aerial vehicles (UAVs) is highly susceptible to various factors such as weather conditions, light intensity, and sensor performance. In contrast to satellites, the spectral stability of UAV-based data is relatively inferior, and the phenomenon of “spectral fragmentation” is prone to occur during large-scale monitoring. This study was designed to solve the problem that maize LAI inversion based on UAVs is difficult to achieve both high spatial resolution and spectral consistency. A two-stage remote sensing data fusion method integrating coarse and fine fusion was proposed. The SHapley Additive exPlanations (SHAP) model was introduced to investigate the contributions of 20 features in 7 categories to LAI inversion of maize, and canopy temperature extracted from thermal infrared images was one of them. Additionally, the most suitable feature sampling window was determined through multi-scale sampling experiments. The grid search method was used to optimize the hyperparameters of models such as Gradient Boosting, XGBoost, and Random Forest, and their accuracy was compared. The results showed that, by utilizing a 3 × 3 feature sampling window and 9 features with the highest contributions, the LAI inversion accuracy of the whole growth stage based on Random Forest could reach R 2 = 0.90 and RMSE = 0.38 m 2 /m 2 . Compared with the single UAV data source mode, the inversion accuracy was enhanced by nearly 25%. The R 2 in the jointing, tasseling, and filling stages were 0.87, 0.86, and 0.62, respectively. Moreover, this study verified the significant role of thermal infrared data in LAI inversion, providing a new method for fine LAI inversion of maize.

Keywords: LAI; interpretable machine learning; data fusion; Sentinel-2; UAV; maize (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: 2025
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