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Article

Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors

1
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3
Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
4
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
5
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
6
University of Chinese Academy of Sciences, Beijing 100049, China
7
Department of Meteorology, Department of Geography & Environmental Science, National Centre for Atmospheric Science, University of Reading, Reading RG6 7BE, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(3), 698; https://doi.org/10.3390/rs15030698
Received: 21 November 2022 / Revised: 19 January 2023 / Accepted: 20 January 2023 / Published: 24 January 2023
(This article belongs to the Special Issue Remote Sensing for Mapping Global Land Surface Parameters)

Abstract

The terrestrial gross primary productivity (GPP) plays a crucial role in regional or global ecological environment monitoring and carbon cycle research. Many previous studies have produced multiple products using different models, but there are still significant differences between these products. This study generated a global GPP dataset (NI-LUE GPP) with 0.05° spatial resolution and at 8 day-intervals from 2001 to 2018 based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen (N) index. To simulate the global GPP, we mapped the global optimal ecosystem temperatures (Topteco) using satellite-retrieved solar-induced chlorophyll fluorescence (SIF) and applied it to calculate temperature stress. In addition, green chlorophyll index (CIgreen), which had a strong correlation with the measured canopy N concentrations (r = 0.82), was selected as the vegetation index to characterize the canopy N concentrations to calculate the spatiotemporal dynamic maximum light use efficiency (εmax). Multiple existing global GPP datasets were used for comparison. Verified by FLUXNET GPP, our product performed well on daily and yearly scales. NI-LUE GPP indicated that the mean global annual GPP is 129.69 ± 3.11 Pg C with an increasing trend of 0.53 Pg C/yr from 2001 to 2018. By calculating the SPAtial Efficiency (SPAEF) with other products, we found that NI-LUE GPP has good spatial consistency, which indicated that our product has a reasonable spatial pattern. This product provides a reliable and alternative dataset for large-scale carbon cycle research and monitoring long-term GPP variations.
Keywords: gross primary production (GPP); nitrogen (N); carbon dioxide (CO2); environmental factors; light use efficiency (LUE) gross primary production (GPP); nitrogen (N); carbon dioxide (CO2); environmental factors; light use efficiency (LUE)

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MDPI and ACS Style

Zhang, H.; Bai, J.; Sun, R.; Wang, Y.; Pan, Y.; McGuire, P.C.; Xiao, Z. Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors. Remote Sens. 2023, 15, 698. https://doi.org/10.3390/rs15030698

AMA Style

Zhang H, Bai J, Sun R, Wang Y, Pan Y, McGuire PC, Xiao Z. Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors. Remote Sensing. 2023; 15(3):698. https://doi.org/10.3390/rs15030698

Chicago/Turabian Style

Zhang, Helin, Jia Bai, Rui Sun, Yan Wang, Yuhao Pan, Patrick C. McGuire, and Zhiqiang Xiao. 2023. "Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors" Remote Sensing 15, no. 3: 698. https://doi.org/10.3390/rs15030698

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