Detecting regional GPP variations with statistically downscaled solar-induced chlorophyll fluorescence (SIF) based on GOME-2 and MODIS data

doi:10.1080/01431161.2020.1798549

	Detecting regional GPP variations with statistically downscaled solar-induced chlorophyll fluorescence (SIF) based on GOME-2 and MODIS data
	Hu, Shi 1; Mo, Xingguo 1,2
刊名	INTERNATIONAL JOURNAL OF REMOTE SENSING
	2020-12-01
卷号	41 期号:23 页码:9206-9228
ISSN号	0143-1161
DOI	10.1080/01431161.2020.1798549
通讯作者	Mo, Xingguo(moxg@igsnrr.ac.cn)
英文摘要	Solar-Induced chlorophyll Fluorescence (SIF) is associated with vegetation canopy photosynthesis and is potentially used to retrieve Gross Primary Productivity (GPP). However, the coarse resolutions of the currently available SIF satellite data limit their applications. To expand the applicability of the SIF dataset, a framework was developed to disaggregate the Global Ozone Monitoring Experiment-2 (GOME-2) SIF dataset, which was based on statistical relationships between SIF and remotely sensed measurements of the Normalized Difference Vegetation Index (NDVI), the fraction of absorbed photosynthetically active radiation (f(PAR)), the soil moisture index and Land Surface Temperature (LST). The statistical relationships were established within a zone ofnx npixels (n is an element of[1, 25]) with a moving window technique. The regression function established withinnx npixels with the smallest Root Mean Square Error (RMSE) and highest coefficient of determination (R-2) was selected for downscaling regression. Compared with the fixed window technique (n= 5) and theglobal regression model, the moving window technique presented low residuals and highR(2)values. Validated with flux-tower eddy covariance measurements, the GPP retrieved within the downscaled SIF data shows the potential to improve vegetation GPP prediction, and the downscaled SIF could trace the seasonal phenology of evergreen forests.
资助项目	National Key R&D Program of China[2019YFA0607102] ; Natural Science Foundation of China[41971232] ; strategic Priority Research Program of the Chinese Academy of Sciences ; Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE)[XDA20040301] ; State Key Laboratory of Resources and Environmental Information System
WOS关键词	GROSS PRIMARY PRODUCTION ; LIGHT USE EFFICIENCY ; SATELLITE MEASUREMENTS ; SENTINEL-5 PRECURSOR ; SPACE ; REFLECTANCE ; QUALITY ; PHOTOSYNTHESIS ; SIMULATIONS ; RETRIEVAL
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000574934500001
资助机构	National Key R&D Program of China ; Natural Science Foundation of China ; strategic Priority Research Program of the Chinese Academy of Sciences ; Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) ; State Key Laboratory of Resources and Environmental Information System
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/157007]
专题	中国科学院地理科学与资源研究所
通讯作者	Mo, Xingguo
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, SDC Coll, Coll Resources & Environm, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Hu, Shi,Mo, Xingguo. Detecting regional GPP variations with statistically downscaled solar-induced chlorophyll fluorescence (SIF) based on GOME-2 and MODIS data[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2020,41(23):9206-9228.
APA	Hu, Shi,&Mo, Xingguo.(2020).Detecting regional GPP variations with statistically downscaled solar-induced chlorophyll fluorescence (SIF) based on GOME-2 and MODIS data.INTERNATIONAL JOURNAL OF REMOTE SENSING,41(23),9206-9228.
MLA	Hu, Shi,et al."Detecting regional GPP variations with statistically downscaled solar-induced chlorophyll fluorescence (SIF) based on GOME-2 and MODIS data".INTERNATIONAL JOURNAL OF REMOTE SENSING 41.23(2020):9206-9228.