IGSNRR OpenIR
Underestimates of Grassland Gross Primary Production in MODIS Standard Products
Zhu, Xiaoyan1,2; Pei, Yanyan1; Zheng, Zhaopei2; Dong, Jinwei1,3; Zhang, Yao4,5; Wang, Junbang1; Chen, Lajiao6; Doughty, Russell B.4,5; Zhang, Geli4,5; Xiao, Xiangming4,5,7
2018-11-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:11Pages:16
Corresponding AuthorZheng, Zhaopei(zzp999@163.com) ; Dong, Jinwei(dongjw@igsnrr.ac.cn)
AbstractAs the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPP(MOD)) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPP(EC)) from a global network of eddy covariance towers (FluxNet), we compared GPP(MOD) and GPP(EC) at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPP(MOD) in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R-2), and root mean square errors (RMSE). We found that GPP(MOD) generally underestimated GPP by about 34% across all biomes despite a significant relationship (R-2 = 0.66 (CI, 0.63-0.69), RMSE = 2.46 (2.33-2.58) g Cm-2 day(-1)) for the three grassland biomes. GPP(MOD) had varied performances with R-2 values of 0.72 (0.68-0.75) (temperate), 0.64 (0.59-0.68) (alpine), and 0.40 (0.27-0.52) (tropical). Thus, GPP(MOD) performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPP(MOD) underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (epsilon(max)) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in epsilon(max), FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes.
KeywordGPP MOD17 grassland ecosystem grassland types FluxNet
DOI10.3390/rs10111771
WOS KeywordLIGHT-USE EFFICIENCY ; NET PRIMARY PRODUCTION ; PRIMARY PRODUCTION GPP ; PHOTOSYNTHETICALLY ACTIVE RADIATION ; ENHANCED VEGETATION INDEX ; TERRESTRIAL GROSS ; ECOSYSTEM RESPIRATION ; ALPINE MEADOW ; WATER FLUXES ; RIVER-BASIN
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program[XDA19040301] ; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS)[QYZDB-SSW-DQC005] ; Thousand Youth Talents Plan
Funding OrganizationStrategic Priority Research Program ; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS) ; Thousand Youth Talents Plan
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000451733800102
PublisherMDPI
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/51382
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZheng, Zhaopei; Dong, Jinwei
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
2.Shandong Normal Univ, Coll Geog & Environm, Jinan 250358, Shandong, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA
5.Univ Oklahoma, Ctr Spatial Anal, Norman, OK 73019 USA
6.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China
7.Fudan Univ, Inst Biodivers Sci, Key Lab Biodivers Sci & Ecol Engn, Minist Educ, Shanghai 200438, Peoples R China
Recommended Citation
GB/T 7714
Zhu, Xiaoyan,Pei, Yanyan,Zheng, Zhaopei,et al. Underestimates of Grassland Gross Primary Production in MODIS Standard Products[J]. REMOTE SENSING,2018,10(11):16.
APA Zhu, Xiaoyan.,Pei, Yanyan.,Zheng, Zhaopei.,Dong, Jinwei.,Zhang, Yao.,...&Xiao, Xiangming.(2018).Underestimates of Grassland Gross Primary Production in MODIS Standard Products.REMOTE SENSING,10(11),16.
MLA Zhu, Xiaoyan,et al."Underestimates of Grassland Gross Primary Production in MODIS Standard Products".REMOTE SENSING 10.11(2018):16.
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