Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF.

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Publication Details

Journal
Journal of Applied Remote Sensing
Published
2025-02-05
DOI
https://doi.org/10.1117/1.jrs.19.014514
Primary Topic
Water Quality Monitoring and Analysis
Type
article
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article

Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

Ya Guo, Junqing Chen, Hao Tang, Qian Xia et al.
Journal of Applied Remote Sensing
Water Quality Monitoring and Analysis
article

Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

Ya Guo, Junqing Chen, Hao Tang, Qian Xia, Jinglu Tan, Lijiang Fu
article en

Abstract

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF.

Journal of Applied Remote SensingVol. 19(01)
Jiangnan University (CN), University of Missouri (US)
Openalex Percentile: Top 97%
Water Quality Monitoring and Analysis
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Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale — Ya Guo, Junqing Chen, et al. · Journal of Applied Remote Sensing (2025) | TGRS Research Map | TGRS