干旱区生态环境知识资源中心(Arid): Accessible Remote Sensing Data Mining Based Dew Estimation

Arid

DOI	10.3390/rs14225653
	Accessible Remote Sensing Data Mining Based Dew Estimation
	Suo, Ying; Wang, Zhongjing; Zhang, Zixiong; Fassnacht, Steven R.
通讯作者	Wang, ZJ
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:22
英文摘要	Dew has been considered a supplementary water resource as it constitutes an important water supply in many ecosystems, especially in arid and semiarid areas. Remote sensing allows large-scale surface observations, offering the possibility to estimate dew in such arid and semiarid regions. In this study, by screening and combining different remote sensing variables, we obtained a well-performing monthly scale dew yield estimation model based on the support vector machine (SVM) learning method. Using daytime and nighttime land surface temperatures (LST), the normalized difference vegetation index (NDVI), and three emissivity bands (3.929-3.989 mu m, 10.780-11.280 mu m, and 11.770-12.270 mu m) as the model inputs, the simulated site-scale monthly dew yield achieved a correlation coefficient (CC) of 0.89 and a root mean square error (RMSE) of 0.30 (mm) for the training set, and CC = 0.59 and RMSE = 0.55 (mm) for the test set. Applying the model to the Heihe River Basin (HRB), the results showed that the annual dew yield ranged from 8.83 to 20.28 mm/year, accounting for 2.12 to 66.88% of the total precipitation, with 74.81% of the area having an annual dew amount of 16 to 19 mm/year. We expanded the model application to Northwest China and obtained a dew yield of 5 similar to 30 mm/year from 2011 to 2020, indicating that dew is a non-negligible part of the water balance in this arid area. As a non-negligible part of the water cycle, the use of remote sensing to estimate dew can provide better support for future water resource assessment and analysis.
英文关键词	dew estimation machine learning remote sensing Northwest China
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:000887783600001
WOS关键词	WATER-VAPOR ; EDDY-COVARIANCE ; RIVER-BASIN ; DESERT ; ECOSYSTEM ; BALANCE ; EVAPORATION ; EVAPOTRANSPIRATION ; CONDENSATION ; REFLECTANCE
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/2XILL650/394237
推荐引用方式 GB/T 7714	Suo, Ying,Wang, Zhongjing,Zhang, Zixiong,et al. Accessible Remote Sensing Data Mining Based Dew Estimation[J],2022,14(22).
APA	Suo, Ying,Wang, Zhongjing,Zhang, Zixiong,&Fassnacht, Steven R..(2022).Accessible Remote Sensing Data Mining Based Dew Estimation.REMOTE SENSING,14(22).
MLA	Suo, Ying,et al."Accessible Remote Sensing Data Mining Based Dew Estimation".REMOTE SENSING 14.22(2022).