Estimating Global GPP From the Plant Functional Type Perspective Using a Machine Learning Approach

Author:

Guo Renjie1,Chen Tiexi123ORCID,Chen Xin1,Yuan Wenping4ORCID,Liu Shuci5ORCID,He Bin6ORCID,Li Lin78,Wang Shengzhen23ORCID,Hu Ting9,Yan Qingyun9,Wei Xueqiong1,Dai Jie1

Affiliation:

1. School of Geographical Sciences Nanjing University of Information Science and Technology Nanjing China

2. School of Geographical Sciences Qinghai Normal University Xining China

3. School of Ecology and Environmental Science Xining University Xining China

4. School of Atmospheric Sciences Sun Yat‐sen University Zhuhai China

5. Department of Environment and Science Queensland Government Brisbane Australia

6. College of Global Change and Earth System Science Beijing Normal University Beijing China

7. Computer Department Qinghai Normal University Xining China

8. Academy of Plateau Science and Sustainability Qinghai Normal University Xining China

9. School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing China

Abstract

AbstractThe long‐term monitoring of gross primary production (GPP) is crucial to the assessment of the carbon cycle of terrestrial ecosystems. In this study, a well‐known machine learning model (random forest, RF) is established to reconstruct the global GPP data set named ECGC_GPP. The model distinguished nine functional plant types, including C3 and C4 crops, using eddy fluxes, meteorological variables, and leaf area index (LAI) as training data of RF model. Based on ERA5_Land and the corrected GEOV2 data, global monthly GPP data set at a 0.05° resolution from 1999 to 2019 was estimated. The results showed that the RF model could explain 74.81% of the monthly variation of GPP in the testing data set, of which the average contribution of LAI reached 41.73%. The average annual and standard deviation of GPP during 1999–2019 were 117.14 ± 1.51 Pg C yr−1, with an upward trend of 0.21 Pg C yr−2 (p < 0.01). By using the plant functional type classification, the underestimation of cropland is improved. Therefore, ECGC_GPP provides reasonable global spatial pattern and long‐term trend of annual GPP.

Funder

National Natural Science Foundation of China

Postgraduate Research & Practice Innovation Program of Jiangsu Province

Publisher

American Geophysical Union (AGU)

Subject

Paleontology,Atmospheric Science,Soil Science,Water Science and Technology,Ecology,Aquatic Science,Forestry

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