干旱区生态环境知识资源中心(Arid): Integrating Latent Heat Flux Products from MODIS and Landsat Data Using Multi-Resolution Kalman Filter Method in the Midstream of Heihe River Basin of Northwest China

Arid

DOI	10.3390/rs11151787
	Integrating Latent Heat Flux Products from MODIS and Landsat Data Using Multi-Resolution Kalman Filter Method in the Midstream of Heihe River Basin of Northwest China
	Xu, Jia 1; Yao, Yunjun 1; Tan, Kanran 2; Li, Yufu 3; Liu, Shaomin 4; Shang, Ke 1; Jia, Kun 1; Zhang, Xiaotong 1; Chen, Xiaowei 1; Bei, Xiangyi 1
通讯作者	Yao, Yunjun
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:15
英文摘要	An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is crucial to the management and planning of water resources for arid and semi-arid areas, for which LE estimations from different satellite sensors unfortunately often contain data gaps and are inconsistent. Many integration approaches have been implemented to overcome these limitations; however, most suffer from either the persistent bias of relying on datasets at only one resolution or the spatiotemporal inconsistency of LE products. In this study, we exhibit an integration case in the midstream of the Heihe River Basin of northwest China by using a multi-resolution Kalman filter (MKF) method to develop continuous and consistent LE maps from satellite LE datasets across different resolutions. The Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16), the Landsat-based LE product derived from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor, and ground observations of eddy covariance flux tower from June to September 2012 are used. The integrated results illustrate that data gaps of MOD16 dropped to less than 0.4% from the original 27-52%, and the root-mean-square error (RMSE) between the LE products decreased by 50.7% on average. Our findings indicate that the MKF method has excellent capacity to fill data gaps, reduce uncertainty, and improve the consistency of multiple LE datasets at different resolutions.
英文关键词	latent heat flux data integration multi-resolution Heihe River Basin
类型	Article
语种	英语
国家	Peoples R China ; USA
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000482442800051
WOS关键词	EDDY-COVARIANCE ; TERRESTRIAL EVAPOTRANSPIRATION ; ENERGY FLUXES ; WATER ; ALGORITHM ; MODEL ; SCALE ; FIELD ; VALIDATION ; MANAGEMENT
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	北京师范大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218398
作者单位	1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China; 2.Johns Hopkins Univ, Whiting Sch Engn, Dept Comp Sci, Baltimore, MD 21218 USA; 3.Jincheng Meteorol Adm, Jincheng 048026, Peoples R China; 4.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China
推荐引用方式 GB/T 7714	Xu, Jia,Yao, Yunjun,Tan, Kanran,et al. Integrating Latent Heat Flux Products from MODIS and Landsat Data Using Multi-Resolution Kalman Filter Method in the Midstream of Heihe River Basin of Northwest China[J]. 北京师范大学,2019,11(15).
APA	Xu, Jia.,Yao, Yunjun.,Tan, Kanran.,Li, Yufu.,Liu, Shaomin.,...&Bei, Xiangyi.(2019).Integrating Latent Heat Flux Products from MODIS and Landsat Data Using Multi-Resolution Kalman Filter Method in the Midstream of Heihe River Basin of Northwest China.REMOTE SENSING,11(15).
MLA	Xu, Jia,et al."Integrating Latent Heat Flux Products from MODIS and Landsat Data Using Multi-Resolution Kalman Filter Method in the Midstream of Heihe River Basin of Northwest China".REMOTE SENSING 11.15(2019).