Vegetation Index-Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity

Xin Chen, Tiexi Chen*, Shuci Liu, Yuanfang Chai, Renjie Guo, Jie Dai, Shengzhen Wang, Lele Zhang, Xueqiong Wei

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Abstract

Recently developed solar-induced chlorophyll fluorescence-related vegetation indices (e.g., near infrared reflectance of vegetation (NIRv) and kernel normalized difference vegetation index (kNDVI)) have been reported to be appropriate proxies for vegetation photosynthesis. These vegetation indices can be used to estimate gross primary productivity (GPP) without considering meteorological constraints. However, it is not clear whether such a statement holds true under various environmental conditions. In this study, we explored whether these vegetation indices require meteorological constraints to better characterize GPP under extreme drought conditions using three extreme drought cases in Europe in 2003, 2010, and 2018. According to the long-term series of observations, vegetation indices (NIRv and kNDVI) alone explained 60% and 57%, respectively, of the weekly GPP variation across the 66 flux sites. The explained variation increased to 69% and 64%, respectively, for the models that take into account radiative effects (NIRv and kNDVI multiplied by radiation). However, without considering meteorological constraints, these vegetation index-based estimations severely underestimated negative GPP anomalies under drought stress, especially in models that incorporate radiative effects. After incorporating vapor pressure deficit (VPD)-based meteorological constraints, the GPP estimations exhibited more pronounced negative anomalies during drought periods while maintaining model accuracy (at 70% and 65%, respectively). In addition, the GPP models based on site observations were applied at the regional scale (Europe). Our results indicated that the models without meteorological constraints again underestimated the impact of drought on GPP. This study emphasizes the importance of meteorological constraints in the estimation of GPP, especially under extreme drought conditions.

Original languageEnglish
Article numbere2023JG007499
Pages (from-to)1-15
Number of pages15
JournalJournal of Geophysical Research: Biogeosciences
Volume129
Issue number1
Early online date23 Jan 2024
DOIs
Publication statusPublished - Jan 2024

Bibliographical note

Funding Information:
We acknowledge the flux data provided by Integrated Carbon Observation System (ICOS). This study was supported by the Natural Science Foundation of Qinghai Province (Nos. 2023‐QLGKLYCZX‐10, 2023‐QLGKLYCZX‐4), the National Natural Science Foundation of China (Nos. 42161144003, 42130506, and 31570464) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Nos. KYCX22_1128, KYCX23_1322).

Publisher Copyright:
© 2024. American Geophysical Union. All Rights Reserved.

Funding

We acknowledge the flux data provided by Integrated Carbon Observation System (ICOS). This study was supported by the Natural Science Foundation of Qinghai Province (Nos. 2023‐QLGKLYCZX‐10, 2023‐QLGKLYCZX‐4), the National Natural Science Foundation of China (Nos. 42161144003, 42130506, and 31570464) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Nos. KYCX22_1128, KYCX23_1322).

FundersFunder number
Postgraduate Research & Practice Innovation Program of Jiangsu ProvinceKYCX23_1322, KYCX22_1128
National Natural Science Foundation of China31570464, 42161144003, 42130506
National Natural Science Foundation of China
Natural Science Foundation of Qinghai Province2023‐QLGKLYCZX‐10, 2023‐QLGKLYCZX‐4
Natural Science Foundation of Qinghai Province

    Keywords

    • drought
    • gross primary productivity
    • meteorological constraints
    • vegetation index

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