Highlights What are the main findings? A normalization-calibration approach based on the Beer-Lambert law and regression models effectively reduced biases and improved consistency among multi-source FPAR observations. FPARLAI-NOS is highly correlated and consistent with FPARFPARnet. FPARLAI-2200 and FPARDHP also correlate strongly with FPARLAI-NOS. In contrast, FPARLAINet shows a much lower correlation with FPARFPARnet. What is the implication of the main finding? This study established a normalization-calibration model for multi-source ground-based FPAR observations, which overcomes the limited representativeness of single instruments and enhances spatial sampling. It thereby provides a more reliable ground reference for validating large-scale FPAR remote sensing products. The findings offer practical guidance for field experiments, including the selection of sensor types, optimization of deployment locations, and design of measurement protocols for different forest ecosystems.Highlights What are the main findings? A normalization-calibration approach based on the Beer-Lambert law and regression models effectively reduced biases and improved consistency among multi-source FPAR observations. FPARLAI-NOS is highly correlated and consistent with FPARFPARnet. FPARLAI-2200 and FPARDHP also correlate strongly with FPARLAI-NOS. In contrast, FPARLAINet shows a much lower correlation with FPARFPARnet. What is the implication of the main finding? This study established a normalization-calibration model for multi-source ground-based FPAR observations, which overcomes the limited representativeness of single instruments and enhances spatial sampling. It thereby provides a more reliable ground reference for validating large-scale FPAR remote sensing products. The findings offer practical guidance for field experiments, including the selection of sensor types, optimization of deployment locations, and design of measurement protocols for different forest ecosystems.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.