干旱区生态环境知识资源中心(Arid): Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia

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DOI	10.3390/rs15040984
	Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia
	Ejaz, Nuaman; Bahrawi, Jarbou; Alghamdi, Khalid Mohammed; Rahman, Khalil Ur; Shang, Songhao
通讯作者	Shang, SH
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:4
英文摘要	Precise assessment of drought and its impact on the natural ecosystem is an arduous task in regions with limited climatic observations due to sparsely distributed in situ stations, especially in the hyper-arid region of Kingdom of Saudi Arabia (KSA). Therefore, this study investigates the application of remote sensing techniques to monitor drought and compare the remote sensing-retrieved drought indices (RSDIs) with the standardized meteorological drought index (Standardized Precipitation Evapotranspiration Index, SPEI) during 2001-2020. The computed RSDIs include Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI), which are derived using multi-temporal Landsat 7 ETM+, Landsat 8 OLI/TIRS satellites, and the Google Earth Engine (GEE) platform. Pearson correlation coefficient (CC) is used to find the extent of agreement between the SPEI and RSDIs. The comparison showed CC values of 0.74, 0.67, 0.57, and 0.47 observed for VHI/SPEI-12, VHI/SPEI-6, VHI/SPEI-3, and VHI/SPEI-1, respectively. Comparatively low agreement was observed between TCI and SPEI with CC values of 0.60, 0.61, 0.42, and 0.37 observed for TCI/SPEI-12, TCI/SPEI-6, TCI/SPEI-3, and TCI/SPEI-1. A lower correlation with CC values of 0.53, 0.45, 0.33 and 0.24 was observed for VCI/SPEI-12, VCI/SPEI-6, VCI/SPEI-3, and VCI/SPEI-1, respectively. Overall, the results suggest that VHI and SPEI are better correlated drought indices and are suitable for drought monitoring in the data-scarce hyper-arid regions. This research will help to improve our understanding of the relationships between meteorological and remote sensing drought indices.
英文关键词	drought assessment meteorological drought remote sensing drought indices standardized drought indices Landsat Google Earth Engine
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:000941837200001
WOS关键词	AGRICULTURAL DROUGHT ; VEGETATION HEALTH ; TEMPERATURE ; RAINFALL ; MODEL ; CLIMATOLOGY ; CALIBRATION ; RETRIEVAL ; REGION ; COVER
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
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398241
推荐引用方式 GB/T 7714	Ejaz, Nuaman,Bahrawi, Jarbou,Alghamdi, Khalid Mohammed,et al. Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia[J],2023,15(4).
APA	Ejaz, Nuaman,Bahrawi, Jarbou,Alghamdi, Khalid Mohammed,Rahman, Khalil Ur,&Shang, Songhao.(2023).Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia.REMOTE SENSING,15(4).
MLA	Ejaz, Nuaman,et al."Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia".REMOTE SENSING 15.4(2023).