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New Global MuSyQ GPP/NPP Remote Sensing Products From 1981 to 2018
Open accessAbstract
Long time series of vegetation productivity products are significant for the research of global carbon cycle and climate change. In this article, the 0.05° global gross primary productivity (GPP) and net primary productivity (NPP) products from 1981 to 2018 were estimated by using the improved multisource data synergized quantitative (MuSyQ) NPP algorithm. The model was based on the fraction of absorbed photosynthetically active radiation (FPAR) and leaf area index (LAI) data from the global land surface satellite (GLASS) dataset, the light use efficiency (LUE) from the parameterization approach with the clearness index (CI), the ERA-Interim meteorological data, and other environmental factors. The results suggested that the accuracy of the MuSyQ GPP product was slightly higher than that of the MOD17 GPP product when compared with the FLUXNET GPP, especially for the evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), wetland (WET), cropland (CRO), woody savanna (WSAV), and closed shrubland (CSH) land types. MuSyQ NPP product also has higher accuracy [R2= 0.81, RMSE = 214.6 gC/(m2year)] than MOD17 NPP [R2= 0.55, RMSE = 214.7 gC/(m2year)] when compared with the BigFoot NPP, which indicated the reliability of the improved MuSyQ-NPP algorithm in estimating global NPP. Our results showed a significant upward trend in global NPP, which was most affected by FPAR, followed by LUE, temperature, and PAR. The average NPP declined significantly in Asia and Amazon tropical…
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“We evaluate the present-day GPP estimates against FLUXCOM-X-BASE GPP product 31 , which uses machine learning trained on eddy covariance data to generate global estimates, and MUltiscale Satellite remotE Sensing (MUSES) GPP product 32 .”
From Amazon dieback beyond the 21st century under high-emission scenarios by Earth System models (2025) · cites this paper as [32]
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Amazon dieback beyond the 21st century under high-emission scenarios by Earth System models
Melnikova, I., Hajima, T., Shiogama, H., Hayashi, M., Ito, A., Nishina, K., Tachiiri, K., & Yokohata, T. (2025) · Communications Earth & Environment · doi:10.1038/s43247-025-02606-5 · Cited by 5
Metadata
- Authors
- Wang, J., Sun, R., Zhang, H., Xiao, Z., Zhu, A., Wang, M., Yu, T., & Xiang, K.
- Affiliations
- Beijing Normal University (CN)
- Guangdong Ecological Meteorology Center, Guangzhou, China
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2021
- Volume
- 14
- Pages
- 5596-5612
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W3160159609
- Cited by
- 76
- References
- 0
- Citations by year
- 2026: 18 · 2025: 14 · 2024: 16 · 2023: 16 · 2022: 10 · 2021: 2
- FWCI
- 5.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key R&D Program of China (2017YFA0603002, 2016YFB0501502)
- National Natural Science Foundation of China (41531174)
- Topics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
- Life Sciences › Agricultural and Biological Sciences › Forestry › Remote Sensing and LiDAR Applications
- Keywords
- gross primary productivity, net primary productivity, long-term time series, light use efficiency, clearness index, fraction of absorbed photosynthetically active radiation, leaf area index, FLUXNET validation, forest expansion, carbon cycle
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