干旱区生态环境知识资源中心(Arid): Sensitivity of Remotely Sensed Vegetation to Hydrologic Predictors across the Colorado River Basin, 2001-2019

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DOI	10.1111/1752-1688.12965
	Sensitivity of Remotely Sensed Vegetation to Hydrologic Predictors across the Colorado River Basin, 2001-2019
	Saby, Linnea; McKee, Kevin L.; Kansara, Prakrut; Goodall, Jonathan L.; Band, Lawrence E.; Lakshmi, Venkataraman
通讯作者	Saby, L (corresponding author), Univ Virginia Charlottesville, Dept Engn Syst & Environm, Charlottesville, VA 22903 USA.
来源期刊	JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
ISSN	1093-474X
EISSN	1752-1688
出版年	2021-10
英文摘要	In water-limited regions, it is important to understand the response of vegetation to hydrologic predictors for fire management and water resource planning. We present a novel spatially distributed analysis of ecohydrological interactions in the semi-arid Colorado River Basin (CRB) over 18 years, 2001-2019. The hydrologic predictors used include precipitation from the Integrated Multi-satellitE Retrievals for GPM, 0- to 10-cm-depth surface soil moisture (SSM) estimations from the National Land Data Assimilation System, and newly available 0- to 40-cm depth Soil MERGE root zone soil moisture (RZSM) estimations. These are evaluated using time-lagged correlations with the Enhanced Vegetation Index (EVI) from MODerate Resolution Imaging Spectroradiometer (MODIS). We demonstrate that EVI response is strongest and most immediate to the hydrologic predictors in the hot and dry southwestern CRB, with lag times of 0-32 days. RZSM was expected to provide the best predictor of EVI, but we found SSM to be a superior predictor of EVI with interquartile range correlations of 0.20-0.37 across the CRB. RZSM had slightly lower interquartile range correlations of 0.15-0.35, and precipitation was the least effective predictor of EVI with interquartile range correlations of 0.11-0.26. Plotting these cross-correlations provides a spatially explicit overview of temporal dependence between SSM, RZSM, precipitation, and EVI with publicly available datasets.
英文关键词	vegetation response remote sensing ecohydrology soil moisture
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000712949200001
WOS关键词	VERTICAL MEASUREMENT DEPTH ; ZONE SOIL-MOISTURE ; INFORMATION-CONTENT ; ROOTING DEPTH ; NDVI ; CLIMATE ; PRODUCTIVITY ; RESPONSES ; RAINFALL ; IMPACT
WOS类目	Engineering, Environmental ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/367584
作者单位	[Saby, Linnea; Kansara, Prakrut; Goodall, Jonathan L.; Band, Lawrence E.; Lakshmi, Venkataraman] Univ Virginia Charlottesville, Dept Engn Syst & Environm, Charlottesville, VA 22903 USA; [McKee, Kevin L.] Virginia Polytech Inst & State Univ Blacksburg, Dept Stat, Blacksburg, VA USA; [Band, Lawrence E.] Univ Virginia Charlottesville, Dept Environm Sci, Charlottesville, VA USA
推荐引用方式 GB/T 7714	Saby, Linnea,McKee, Kevin L.,Kansara, Prakrut,et al. Sensitivity of Remotely Sensed Vegetation to Hydrologic Predictors across the Colorado River Basin, 2001-2019[J],2021.
APA	Saby, Linnea,McKee, Kevin L.,Kansara, Prakrut,Goodall, Jonathan L.,Band, Lawrence E.,&Lakshmi, Venkataraman.(2021).Sensitivity of Remotely Sensed Vegetation to Hydrologic Predictors across the Colorado River Basin, 2001-2019.JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION.
MLA	Saby, Linnea,et al."Sensitivity of Remotely Sensed Vegetation to Hydrologic Predictors across the Colorado River Basin, 2001-2019".JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION (2021).