干旱区生态环境知识资源中心(Arid): Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis

Arid

DOI	10.1016/j.rse.2018.10.026
	Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis
	Liu, Yongwei 1,2; Liu, Yuanbo 1,2; Wang, Wen 3
通讯作者	Liu, Yuanbo
来源期刊	REMOTE SENSING OF ENVIRONMENT
ISSN	0034-4257
EISSN	1879-0704
出版年	2019
卷号	220 页码:1-18
英文摘要	The multi-satellite-retrieved (ESA CCI SM) and the Global Land Data Assimilation System-Noah-simulated (GLDAS-Noah) surface soil moisture (SM) datasets are compared for global drought analysis over a multi-decadal time period (1991-2015). Global drought events and their duration, frequency and severity are assessed on a grid basis with soil moisture anomaly percentage index (SMAPI). The results show that the ESA CCI SM and the GLDAS-Noah based SMAPI values are significantly (p < 0.05) correlated over most (83%) of the study region, of seasonally dependent. Both datasets show similar global patterns in drought duration, drought frequency and drought severity. The droughts present generally longer duration, higher frequency and more severity in arid and semi-arid regions than humid and sub-humid regions. The ESA CCI SM droughts are relatively higher in frequency and more intense in severity than the GLDAS-Noah SM droughts in many regions of the globe, while the two datasets show considerable differences in drought duration over arid, semi-arid and highly vegetated regions. For long-term trend detection, both datasets show high consistency in spatial pattern of SMAPI, with major significant drying trends in arid and semi-arid regions. Part (similar to 20%) trends are confirmed by the Global Precipitation Climatology Centre (GPCC) precipitation dataset using the Standard Precipitation Index (SPI). The two SM datasets exhibit large disparity in trending drought duration, drought frequency and drought severity. Despite that, both show major significant increasing trends in arid and semi-arid regions. Both soil moisture datasets are capable of identifying extreme drought events reported in southern China, North America, Europe and southern Africa. The ESA CCI SM dataset is more effective in determining the severity and spatial pattern of drought excluding densely vegetated regions, while the GLDAS-Noah dataset is more powerful in detecting drought occurrence, even over densely vegetated regions. Overall, the ESA CCI SM and GLDAS-Noah SM show potential in global drought analysis, yet cautions should be paid to arid and semi-arid regions where drying trends are prevalent and large discrepancy presents between two datasets.
英文关键词	Global droughts Soil moisture Satellite datasets GLDAS-Noah GPCC
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000451936700001
WOS关键词	SURFACE MODELS ; PRODUCTS ; REANALYSIS ; TRENDS ; SMOS ; ANOMALIES ; REGIONS ; EUROPE ; ASCAT ; PART
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	河海大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218421
作者单位	1.Chinese Acad Sci, Key Lab Watershed Geog Sci, Nanjing 210008, Peoples R China; 2.Chinese Acad Sci, Nanjing Inst Geog & Limnol, Nanjing 210008, Peoples R China; 3.Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yongwei,Liu, Yuanbo,Wang, Wen. Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis[J]. 河海大学,2019,220:1-18.
APA	Liu, Yongwei,Liu, Yuanbo,&Wang, Wen.(2019).Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis.REMOTE SENSING OF ENVIRONMENT,220,1-18.
MLA	Liu, Yongwei,et al."Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis".REMOTE SENSING OF ENVIRONMENT 220(2019):1-18.