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Remote Sensing
Volume 17
Issue 23
10.3390/rs17233797
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Article

A Normalization-Calibration Model for Multi-Source Ground-Based FPAR Observations in Mountainous Forests

by
Yongxin Cai
1,2,3,
Ainong Li
1,2,3,*,
Jinhu Bian
1,2,3,
Zhengjian Zhang
1,3,
Limin Chen
1,2,3,
Xiaohan Lin
1,2,3,
Yi Deng
1,2,3,
Xi Nan
1,3,
Guangbin Lei
1,2,3 and
Amin Naboureh
1,3
1
Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610213, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
3
Wanglang Mountain Remote Sensing Field Observation and Research Station of Sichuan Province, Mianyang 621000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(23), 3797; https://doi.org/10.3390/rs17233797 (registering DOI)
Submission received: 22 September 2025 / Revised: 11 November 2025 / Accepted: 20 November 2025 / Published: 22 November 2025
Versions Notes

Abstract

The fraction of absorbed photosynthetically active radiation (FPAR) is a key physiological variable for characterizing vegetation structure and associated matter and energy exchange processes. Accurate and effective monitoring of FPAR is essential for understanding ecosystem functioning. However, systematic biases among existing ground-based observation techniques hinder the effective integration of FPAR data, limiting its potential for spatial scaling. This study selected five ground-based observation techniques, FPARnet, LAI-NOS, LAINet, LAI-2200, and digital hemispherical photography (DHP), based on the existing FPAR and LAI observation techniques at Wanglang Station, to develop a PAIe-LAI-FPAR conversion model using the Beer–Lambert law. The correlation and consistency of FPAR derived from different observation techniques were comparatively analyzed. On this basis, a normalization-calibration model based on regression was developed for FPARLAI-NOS, FPARLAI-2200, and FPARDHP, using FPARFPARnet as the reference. Comparative analysis results show that FPARLAI-NOS and FPARFPARnet, as well as FPARLAI-2200 and FPARDHP with FPARLAI-NOS, exhibit good correlation and consistency (R ≥ 0.9, RMSEobs ≤ 0.08). However, FPARLAINet shows a relatively weak correlation with FPARFPARnet (R = 0.12). After normalization-calibration, the consistency among multi-source FPAR observations was significantly improved (R remains unchanged, and the average RMSEobs decreases by approximately 7.8%. The sample points are more closely aligned along the y = x line after calibration). This study provides a practical reference for the normalization-calibration of FPAR observations in mountainous forests based on multi-source ground-based observation techniques.
Keywords: mountain regions; forest ecosystems; FPAR; LAI; Beer–Lambert law; ground-based observations mountain regions; forest ecosystems; FPAR; LAI; Beer–Lambert law; ground-based observations

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MDPI and ACS Style

Cai, Y.; Li, A.; Bian, J.; Zhang, Z.; Chen, L.; Lin, X.; Deng, Y.; Nan, X.; Lei, G.; Naboureh, A. A Normalization-Calibration Model for Multi-Source Ground-Based FPAR Observations in Mountainous Forests. Remote Sens. 2025, 17, 3797. https://doi.org/10.3390/rs17233797

AMA Style

Cai Y, Li A, Bian J, Zhang Z, Chen L, Lin X, Deng Y, Nan X, Lei G, Naboureh A. A Normalization-Calibration Model for Multi-Source Ground-Based FPAR Observations in Mountainous Forests. Remote Sensing. 2025; 17(23):3797. https://doi.org/10.3390/rs17233797

Chicago/Turabian Style

Cai, Yongxin, Ainong Li, Jinhu Bian, Zhengjian Zhang, Limin Chen, Xiaohan Lin, Yi Deng, Xi Nan, Guangbin Lei, and Amin Naboureh. 2025. "A Normalization-Calibration Model for Multi-Source Ground-Based FPAR Observations in Mountainous Forests" Remote Sensing 17, no. 23: 3797. https://doi.org/10.3390/rs17233797

APA Style

Cai, Y., Li, A., Bian, J., Zhang, Z., Chen, L., Lin, X., Deng, Y., Nan, X., Lei, G., & Naboureh, A. (2025). A Normalization-Calibration Model for Multi-Source Ground-Based FPAR Observations in Mountainous Forests. Remote Sensing, 17(23), 3797. https://doi.org/10.3390/rs17233797

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