干旱区生态环境知识资源中心(Arid): Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform

Arid

DOI	10.3390/rs12203430
	Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform
	Wang, Wei; Samat, Alim; Ge, Yongxiao; Ma, Long; Tuheti, Abula; Zou, Shan; Abuduwaili, Jilili
通讯作者	Abuduwaili, J
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:20
英文摘要	A lack of long-term soil wind erosion data impedes sustainable land management in developing regions, especially in Central Asia (CA). Compared with large-scale field measurements, wind erosion modeling based on geospatial data is an efficient and effective method for quantitative soil wind erosion mapping. However, conventional local-based wind erosion modeling is time-consuming and labor-intensive, especially when processing large amounts of geospatial data. To address this issue, we developed a Google Earth Engine-based Revised Wind Erosion Equation (RWEQ) model, named GEE-RWEQ, to delineate the Soil Wind Erosion Potential (SWEP). Based on the GEE-RWEQ model, terabytes of Remote Sensing (RS) data, climate assimilation data, and some other geospatial data were applied to produce monthly SWEP with a high spatial resolution (500 m) across CA between 2000 and 2019. The results show that the mean SWEP is in good agreement with the ground observation-based dust storm index (DSI), satellite-based Aerosol Optical Depth (AOD), and Absorbing Aerosol Index (AAI), confirming that GEE-RWEQ is a robust wind erosion prediction model. Wind speed factors primarily determined the wind erosion in CA (r = 0.7, p < 0.001), and the SWEP has significantly increased since 2011 because of the reversal of global terrestrial stilling in recent years. The Aral Sea Dry Lakebed (ASDLB), formed by shrinkage of the Aral Sea, is the most severe wind erosion area in CA (47.29 kg/m(2)/y). Temporally, the wind erosion dominated by wind speed has the largest spatial extent of wind erosion in Spring (MAM). Meanwhile, affected by the spatial difference of the snowmelt period in CA, the wind erosion hazard center moved from the southwest (Karakum Desert) to the middle of CA (Kyzylkum Desert and Muyunkum Desert) during spring. According to the impacts of land cover change on the spatial dynamic of wind erosion, the SWEP of bareland was the highest, while that of forestland was the lowest.
英文关键词	wind erosion modeling RWEQ GEE central Asia spatial-temporal variation land degradation
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000585660100001
WOS关键词	LAND DEGRADATION ; CLIMATE-CHANGE ; SPEED VARIABILITY ; INNER-MONGOLIA ; CHINA ; PREDICTION ; MODEL ; DUST ; REGION ; DESERTIFICATION
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	中国科学院新疆生态与地理研究所 ; 新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/327302
作者单位	[Wang, Wei; Samat, Alim; Ge, Yongxiao; Ma, Long; Tuheti, Abula; Zou, Shan; Abuduwaili, Jilili] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China; [Wang, Wei; Samat, Alim; Ge, Yongxiao; Ma, Long; Zou, Shan; Abuduwaili, Jilili] Chinese Acad Sci, Res Ctr Ecol & Environm Cent Asia, Urumqi 830011, Peoples R China; [Wang, Wei; Samat, Alim; Ma, Long; Zou, Shan; Abuduwaili, Jilili] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Tuheti, Abula] Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China
推荐引用方式 GB/T 7714	Wang, Wei,Samat, Alim,Ge, Yongxiao,et al. Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform[J]. 中国科学院新疆生态与地理研究所, 新疆大学,2020,12(20).
APA	Wang, Wei.,Samat, Alim.,Ge, Yongxiao.,Ma, Long.,Tuheti, Abula.,...&Abuduwaili, Jilili.(2020).Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform.REMOTE SENSING,12(20).
MLA	Wang, Wei,et al."Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform".REMOTE SENSING 12.20(2020).