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Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach
Wang, HB (Wang, Haibo)1; Li, X (Li, Xin)2,3; Geng, LY (Geng, Liying)1
Source PublicationREMOTE SENSING
2019
Volume11Issue:3Pages:225
DOI10.3390/rs11030225
Abstract

Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R-2 = 0.80, RMSE = 2.66 gC/m(2)/day and R-2 = 0.53, RMSE = 2.12 gC/m(2)/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (epsilon(max) or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.

Subject AreaRemote Sensing
WOS IDWOS:000459944400013
Language英语
Indexed BySCI
KeywordEddy Covariance Measurements Use Efficiency Model Carbon Flux Model Modis-gpp Product Terrestrial Gross Alpine Meadow Semiarid Ecosystems Vegetation Cover Forest Exchange
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
Cooperation Status国内
ISSN2072-4292
Department三极观测与大数据
URL查看原文
PublisherMDPI
SubtypeArticle
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itpcas.ac.cn/handle/131C11/9410
Collection图书馆
Corresponding AuthorLi, X (Li, Xin)
Affiliation1.Chinese Acad Sci, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Northwest Inst Ecoenvironm & Resources, 320 Donggang West Rd, Lanzhou 730000, Peoples R China;
2.Chinese Acad Sci, Inst Tibetan Plateau Res, Beijing 100101, Peoples R China;
3.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China;
4.Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data Ap, Sch Geog Sci, Chongqing 400715, Peoples R China.
Recommended Citation
GB/T 7714
Wang, HB ,Li, X ,Geng, LY . Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach[J]. REMOTE SENSING,2019,11(3):225.
APA Wang, HB ,Li, X ,&Geng, LY .(2019).Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach.REMOTE SENSING,11(3),225.
MLA Wang, HB ,et al."Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach".REMOTE SENSING 11.3(2019):225.
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