5 February 2025 Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale
Lijiang Fu, Qian Xia, Hao Tang, Junqing Chen, Jinglu Tan, Ya Guo
Author Affiliations +
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.

© 2025 Society of Photo-Optical Instrumentation Engineers (SPIE)

Funding Statement

Lijiang Fu, Qian Xia, Hao Tang, Junqing Chen, Jinglu Tan, and Ya Guo "Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale," Journal of Applied Remote Sensing 19(1), 014514 (5 February 2025). https://doi.org/10.1117/1.JRS.19.014514
Received: 16 September 2024; Accepted: 17 January 2025; Published: 5 February 2025
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling

Vegetation

Atmospheric modeling

Linear regression

Environmental sensing

Fluorescence

Rain

Back to Top