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Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset Wang, Shujian; Zhang, Xunhe; Hou, Lili; Sun, Jiejie; Xu, Ming

Abstract

Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on Vmax and Jmax, leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO₂ concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgCyear−¹ to 133.94 PgCyear−¹ from 2000 to 2022, with an average of 125.93 PgCyear−¹. We also found that the global GPP showed an increasing trend of 0.548 PgCyear−¹ during the study period. Further analyses using the structure equation model showed that atmospheric CO2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP.

Item Metadata

Title
Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
Creator
Wang, Shujian; Zhang, Xunhe; Hou, Lili; Sun, Jiejie; Xu, Ming
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2024-10-08
Description
Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on Vmax and Jmax, leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO₂ concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgCyear−¹ to 133.94 PgCyear−¹ from 2000 to 2022, with an average of 125.93 PgCyear−¹. We also found that the global GPP showed an increasing trend of 0.548 PgCyear−¹ during the study period. Further analyses using the structure equation model showed that atmospheric CO2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP.
Subject
ecological process model; photosynthesis; remote sensing data; carbon sink; SEM model; GPP; MODIS; LAI
Genre
Article
Type
Text
Language
eng
Date Available
2024-10-18
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0445605
URI
http://hdl.handle.net/2429/89441
Affiliation
Other UBC; Non UBC
Citation
Remote Sensing 16 (19): 3731 (2024)
Publisher DOI
10.3390/rs16193731
Peer Review Status
Reviewed
Scholarly Level
Faculty; Researcher
Rights URI
https://creativecommons.org/licenses/by/4.0/
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CC BY 4.0