Combining UAV Multispectral Imaging and PROSAIL Model to Estimate LAI of Potato at Plot Scale

Shuang Li, Yongxin Lin, Ping Zhu, Liping Jin, Chunsong Bian () and Jiangang Liu ()
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Shuang Li: State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Yongxin Lin: State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Ping Zhu: State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Liping Jin: State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Chunsong Bian: State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Jiangang Liu: State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Agriculture, 2024, vol. 14, issue 12, 1-20

Abstract: Accurate and rapid estimation of the leaf area index (LAI) is essential for assessing crop growth and nutritional status, guiding farm management, and providing valuable phenotyping data for plant breeding. Compared to the tedious and time-consuming manual measurements of the LAI, remote sensing has emerged as a valuable tool for rapid and accurate estimation of the LAI; however, the empirical inversion modeling methods face challenges of low efficiency for actual LAI measurements and poor model interpretability. The integration of radiative transfer models (RTMs) can overcome these problems to some extent. The aim of this study was to explore the potential of combining the PROSAIL model with high-resolution unmanned aerial vehicle (UAV) multispectral imaging to estimate the LAI across different growth stages at the plot scale. In this study, four inversion strategies for estimating the LAI were tested. Firstly, two types of lookup tables (LUTs) were built to estimate potato LAI of different varieties across different growth stages. Specifically, LUT1 was based on band reflectance, and LUT2 was based on vegetation index. Secondly, the hybrid models combining LUTs generated by PROSAIL and two machine learning algorithms (random forest (RF), Partial Least Squares Regression (PLSR)) are built to estimate potato LAI. The determination of coefficient (R 2 ) of models for estimating LAI by LUTs ranged from 0.24 to 0.64. The hybrid method that integrates UAV multispectral, PROSAIL, and machine learning significantly improved the accuracy of LAI estimation. Compared to the results based on LUT2, the hybrid model achieved higher accuracy with the R 2 of the inversion model improved by 30% to 263%. The LAI retrieval model using the PROSAIL model and PLSR achieved an R 2 as high as 0.87, while the R 2 using the RF algorithm ranged from 0.33 to 0.81. The proposed hybrid model, integrated with UAV multispectral data, PROSAIL, and PLSR can achieve approximate accuracy compared with the empirical inversion models, which can alleviate the labor-intensive process of handheld LAI measurements for building inversion models. Thus, the hybrid approach provides a feasible and efficient strategy for estimating the LAI of potato varieties across different growth stages at the plot scale.

Keywords: potato; leaf area index; UAV multispectral imaging; PROSAIL; phenotyping (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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