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Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation

文献类型：期刊论文

作者Zhang, Yinghui1,2,3,4; Hu, Zhongwen1,2,3,4; Wang, Jingzhe5; Gao, Xing6; Yang, Cheng7; Yang, Fengshuo8; Wu, Guofeng1,2,3,4
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2023-07-01
卷号121页码:9
ISSN号1569-8432
关键词Temporal upscale FAPAR GPP Forest Fluxnet
DOI10.1016/j.jag.2023.103360
通讯作者Hu, Zhongwen(zwhoo@szu.edu.cn)
英文摘要Gross primary productivity (GPP) is a measure of carbon uptake by terrestrial ecosystems for carbon neutrality and serves as a key indicator for the Sustainable Development Goals. The instantaneous Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is applied in GPP estimation by light use efficiency (LUE) models. However, an obvious time scale mismatch exists between instantaneous FAPAR and daily accumulated GPP. In the study, we explored the potential of temporal upscaling instantaneous FAPAR in improving GPP simulation. The absorbed photosynthetically active radiation (APAR) derived from instantaneous and upscaled FAPARs were first compared. GPPs were estimated from instantaneous and upscaled FAPAR by MOD17 LUE model using default and optimized parameters. The optimized GPP was finally compared with three GPP products: GLASS, MOD17A2H and MYD17A2H GPP. The APARs and GPPs were evaluated with the Eddy Covariance (EC) GPP at 78 forest sites from the Fluxnet community. Results showed that when compared with EC GPP, the upscaled FAPAR-derived APAR held a higher R2 (0.26-0.73) than that from instantaneous FAPARs. The upscaled FAPARbased GPPs by MOD17 LUE model with default or optimized parameters had a larger R2 and smaller RMSE reducing uncertainties by 6.82-7.8% (maximum value: 27.9-49%). The optimized upscaled FAPAR-derived GPP was far superior to the GPP products with larger R2 (0.69-0.85) and smaller RMSE (12.8-14.6 g C/m2/8d). It is concluded that temporal upscaling of MODIS instantaneous FAPAR can well improve the estimation of forest GPP and the parameters of MOD17 LUE model should be further optimized. Our study is an effort toward reducing the uncertainties from FAPAR in the GPP estimation for better assessing the achievement of carbon neutrality and Sustainable Development Goals.
WOS关键词LIGHT-USE EFFICIENCY ; TERRESTRIAL GROSS ; CHINAFLUX ; MODELS ; INDEX ; FPAR
资助项目Shenzhen Science and Technology Program[JCYJ20220818101617037] ; National Natural Science Foundation of China[42201347] ; Chinese Academy of Sciences[XDA23090503] ; China Postdoctoral Science Foundation[2022M712163] ; Guangdong Basic and Applied Basic Research Foundation[2021A1515110910] ; Guangdong Basic and Applied Basic Research Foundation[2023A1515011273] ; Natural Science Foundation of Shandong Province[ZR2021QD128] ; Basic Research Program of Shenzhen[20220811173316001]
WOS研究方向Remote Sensing
语种英语
出版者ELSEVIER
WOS记录号WOS:001012529100001
资助机构Shenzhen Science and Technology Program ; National Natural Science Foundation of China ; Chinese Academy of Sciences ; China Postdoctoral Science Foundation ; Guangdong Basic and Applied Basic Research Foundation ; Natural Science Foundation of Shandong Province ; Basic Research Program of Shenzhen
源URL[http://ir.igsnrr.ac.cn/handle/311030/195132]  
专题中国科学院地理科学与资源研究所
通讯作者Hu, Zhongwen
作者单位1.Shenzhen Univ, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China
2.Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China
3.Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China
4.Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen 518060, Peoples R China
5.Shenzhen Polytech, Sch Artificial Intelligence, Shenzhen 518055, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
7.Hebei Normal Univ, Sch Geog Sci, Shijiazhuang 050024, Peoples R China
8.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yinghui,Hu, Zhongwen,Wang, Jingzhe,et al. Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2023,121:9.
APA Zhang, Yinghui.,Hu, Zhongwen.,Wang, Jingzhe.,Gao, Xing.,Yang, Cheng.,...&Wu, Guofeng.(2023).Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,121,9.
MLA Zhang, Yinghui,et al."Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 121(2023):9.

入库方式： OAI收割

来源：地理科学与资源研究所

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