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Article

Validation of Multi-Scale LAI Products in Heterogeneous Terrain-Based UAV Images

1
Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Science, Southwest University, No. 2 Tiansheng Road Beibei District, Chongqing 400715, China
2
Yibin Academy of Southwest University, Yibin 644000, China
3
Southwest University Library, Southwest University, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(19), 3393; https://doi.org/10.3390/rs17193393
Submission received: 8 July 2025 / Revised: 7 September 2025 / Accepted: 9 September 2025 / Published: 9 October 2025

Abstract

Significant uncertainties persist across different Leaf Area Index (LAI) products due to multiple factors; therefore, the accuracy assessment of the global LAI products is an indispensable step before their application. In this study, comprehensive validation of multi-scale LAI products including Sentinel-2, Landsat-8/9, and MCD15A3H was implemented utilizing fine-resolution LAI maps which were based on UAV images and field-measured LAI data. The validation results demonstrated a consistent, systematic underestimation across all the LAI products within the study area, the RMSE of these products ranged from 0.56 to 1.63, and the coarse-resolution MCD15A3H LAI product demonstrated highest accuracy (RMSE = 0.56, R2 = 0.69). The Sentinel-2 products exhibited intermediate accuracy among all those products (RMSE: 1.16–1.36). The Landsat-8/9 LAI product showed markedly lower accuracy relative to Sentinel-2; its RMSE (1.63) exceeded that of Sentinel-2 10 m LAI and 20 m LAI by 40.52% and 21.64%, respectively. In addition, all these LAI products showed consistent seasonal variation patterns with the reference LAI maps. Moreover, Sentinel-2 10 m LAI products showed serious underestimation for all vegetation types, with forests providing the highest RMSE = 0.89. This study serves as a valuable reference for the application of multi-scale LAI products in heterogeneous terrain and provides directions for the improvement of fine-resolution LAI retrieval algorithms.
Keywords: leaf area index; validation; multi-scale; UAV images leaf area index; validation; multi-scale; UAV images

Share and Cite

MDPI and ACS Style

Liu, M.; Yu, W.; Li, D.; Shang, F.; Zhang, L.; Wang, S.; Yang, W.; Zhao, R.; Wang, X. Validation of Multi-Scale LAI Products in Heterogeneous Terrain-Based UAV Images. Remote Sens. 2025, 17, 3393. https://doi.org/10.3390/rs17193393

AMA Style

Liu M, Yu W, Li D, Shang F, Zhang L, Wang S, Yang W, Zhao R, Wang X. Validation of Multi-Scale LAI Products in Heterogeneous Terrain-Based UAV Images. Remote Sensing. 2025; 17(19):3393. https://doi.org/10.3390/rs17193393

Chicago/Turabian Style

Liu, Meng, Wenping Yu, Dandan Li, Fangfang Shang, Longlong Zhang, Shuangjie Wang, Wen Yang, Ruoyi Zhao, and Xuemei Wang. 2025. "Validation of Multi-Scale LAI Products in Heterogeneous Terrain-Based UAV Images" Remote Sensing 17, no. 19: 3393. https://doi.org/10.3390/rs17193393

APA Style

Liu, M., Yu, W., Li, D., Shang, F., Zhang, L., Wang, S., Yang, W., Zhao, R., & Wang, X. (2025). Validation of Multi-Scale LAI Products in Heterogeneous Terrain-Based UAV Images. Remote Sensing, 17(19), 3393. https://doi.org/10.3390/rs17193393

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