气候变化领域集成服务门户(CCPortal): Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach

CCPortal

DOI	10.3390/rs11030225
	Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach
	Wang, Haibo; Li, Xin; Ma, Mingguo; Geng, Liying
通讯作者	Li, X (通讯作者)
发表日期	2019
ISSN	2072-4292
卷号	11 期号:3
英文摘要	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.
关键词	EDDY COVARIANCE MEASUREMENTS USE EFFICIENCY MODEL CARBON FLUX MODEL MODIS-GPP PRODUCT TERRESTRIAL GROSS ALPINE MEADOW SEMIARID ECOSYSTEMS VEGETATION COVER FOREST EXCHANGE
英文关键词	terrestrial ecosystem; MODIS GPP product; calibration; arid region; oasis-desert ecosystem
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000459944400013
来源期刊	REMOTE SENSING
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259405
推荐引用方式 GB/T 7714	Wang, Haibo,Li, Xin,Ma, Mingguo,et al. Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach[J]. 中国科学院青藏高原研究所,2019,11(3).
APA	Wang, Haibo,Li, Xin,Ma, Mingguo,&Geng, Liying.(2019).Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach.REMOTE SENSING,11(3).
MLA	Wang, Haibo,et al."Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach".REMOTE SENSING 11.3(2019).