Optimizing leaf area index estimation in mulched winter wheat: A hybrid model approach coupling PROSAIL

doi:10.1016/j.jia.2026.04.006

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Chunyu Wei^1,², Yadan Du^1,²^#, Yuxuan Liu^1,², Xiaotao Hu^1,², Zhikai Cheng^1,², Yang Xu^1,², Zhengtao Zhang^1,², Xiaobo Gu^1,², Kadambot H.M Siddique³, Yunhui Niu⁴

¹Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education/Northwest A&F University, Yangling 712100, China

²College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China

³The UWA Institute of Agriculture, The University of Western Australia, M082, Perth, WA 6009, Australia

⁴Jinghui Irrigation and Drainage Management Station in Lintong District, Xi'an 712100, China

Highlights

Quantitative assessment of plastic mulch's impact on PROSAIL-simulated reflectance.

Development of a linear spectral hybrid model integrating PROSAIL for plastic-mulched conditions.

Optimal leaf area index (LAI) retrieval accuracy achieved through RF-LASSO coupled with enhanced PROSAIL, demonstrated model robustness for LAI retrieval in plastic-mulched fields (R²>0.7).

Abstract
References

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摘要

冬小麦是中国主要的粮食作物，其叶面积指数（LAI）是精准农业中用于生长评估的重要指标。基于无人机的遥感技术和PROSAIL模型已被广泛应用于LAI估算，但在覆膜条件下的估算精度因地膜的光谱干扰而受限。基于陕西省杨陵区冬小麦田间实测数据，本研究通过将实测的土壤和地膜反射率融入PROSAIL模型，构建了一种线性光谱混合模型（LSHM），以改善覆膜冬小麦的LAI反演。研究采用了无人机获取的多时相影像和田间实测LAI数据，并将该模型与随机森林（RF）和LASSO变量筛选方法相结合以提高精度。与标准PROSAIL模型相比，LSHM通过融入实测土壤和地膜光谱，显著减小了PROSAIL模拟的反射率差异，在整个生育期内R²平均提高了16.5%。此外，RF-LASSO优化了波段反射率（BR）和植被指数（VI）组合，增强了模型的稳定性，在抽穗期（R²=0.83，RMSE=0.35，MSE=0.12）和灌浆期（R²=0.71，RMSE=0.50，MSE=0.25）均表现出优异性能。结果表明，混合PROSAIL模型在覆膜冬小麦LAI反演估算方面具有显著潜力，为垄沟覆膜种植系统中的作物性状表型分析提供了一种稳健的方法。

Abstract

Winter wheat is a key grain crop in China, with its leaf area index (LAI) serving as a vital indicator for growth assessment in precision agriculture. While UAV-based remote sensing and the PROSAIL model are widely used for LAI estimation, their accuracy under plastic mulch is limited due to spectral interference from mulch. Based on field measurements from winter wheat fields in Yangling, Shaanxi Province, this study developed a linear-spectral hybrid model (LSHM) by integrating measured soil and mulch reflectance into the PROSAIL model to improve LAI inversion for mulched winter wheat. UAV-collected multi-temporal images and field LAI measurements were used, and the model was coupled with random forest (RF) and LASSO variable selection to enhance accuracy. The LSHM significantly reduced reflectance differences in PROSAIL simulations by incorporating measured soil and mulch spectra, achieving R² improvements average of 16.5% across all growth stages compared to the standard PROSAIL model. Furthermore, the RF-LASSO optimized combination of band reflectance (BR) and vegetation indices (VIs) enhanced model stability, demonstrating superior performance (R²=0.83, RMSE=0.35, MSE=0.12 at heading and R²=0.71, RMSE=0.50, MSE=0.25 at filling). These results demonstrate that the hybrid PROSAIL model holds significant promise for estimating LAI inversion in mulched winter wheat, providing a robust approach for phenotyping crop traits in ridge-furrow mulched systems.

Keywords: mulched winter wheat PROSAIL model linear-spectral hybrid model LAI LASSO

Online: 08 April 2026

Fund:

This study was funded by the National Natural Science Foundation of China for Young Scholars (52109066，52579049), the China Postdoctoral Science Foundation (2023T160534 and 2022M712604), the Postdoctoral Science Foundation of Shaanxi Province, China (2023BSHTBZZ29), the National Key Research and Development Program (2023YFD190080504 and 2022YFD1900401), and the Key Research and Development Program of Shaanxi Province, China (2022NY-114).

About author: Chunyu Wei, E-mail: 2019012003@nwafu.edu.cn; #Correspondence Yadan Du, E-mail: dyd123027@163.com

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Chunyu Wei, Yadan Du, Yuxuan Liu, Xiaotao Hu, Zhikai Cheng, Yang Xu, Zhengtao Zhang, Xiaobo Gu, Kadambot H.M Siddique, Yunhui Niu. 2026. Optimizing leaf area index estimation in mulched winter wheat: A hybrid model approach coupling PROSAIL. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2026.04.006

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