Abstract: ObjectiveLeaf area index (LAI) is a core parameter characterizing forest canopy structure. Its accurate monitoring can effectively reflect the growth status of shelterbelts, correlate with the assessment of their soil and water conservation functions such as runoff regulation and sediment interception, and is of great significance for the monitoring of shelterbelt ecosystems and the optimization of soil and water conservation functions in plain areas. A large number of linear farmland shelterbelts are distributed in plain shelterbelts, and due to their narrow belt structure, it is quite difficult to accurately extract their LAI. MethodsTo address this issue, this study selected Yuanyang county, Xinxiang city, Henan province as the research area, utilizing Sentinel-2A remote sensing image with a 10m spatial resolution. The PROSAIL model was employed, with its parameters optimized through local and global sensitivity analysis. Combined with the random forest algorithm, a mapping relationship between vegetation canopy reflectance and LAI characteristic variables was established to determine the optimal LAI estimation model. Finally, ground-measured data were used to verify the accuracy of the inversion results.Results1) The inversion method based on Sentinel-2A and the PROSAIL model achieved an R² of 0.87 and an RMSE of 0.39, indicating a high degree of fit between the inversion results and measured values.2)Compared with the inversion results from Landsat-9 satellite imagery with a 30m spatial resolution, Sentinel-2A showed a 7.4% increase in R² and a 2.5% decrease in RMSE, demonstrating its advantages in LAI inversion for plain shelterbelts, especially agricultural shelterbelts.ConclusionsThis study constructs an inversion strategy integrating Sentinel-2A data with the PROSAIL model, which addresses the challenge of accurate LAI retrieval under the structural condition of narrow forest belts. The proposed approach provides critical data support for farmland soil and water conservation practices and the optimal configuration of shelterbelt ecosystems.