干旱区生态环境知识资源中心(Arid): Applicability Assessment of Passive Microwave LST Downscaling over Semi-Homogeneous Desert Underlying Surface Based on Machine Learning

Arid

DOI	10.3390/rs15102626
	Applicability Assessment of Passive Microwave LST Downscaling over Semi-Homogeneous Desert Underlying Surface Based on Machine Learning
	Li, Yongkang; Liu, Yongqiang; Huang, Wenjiang; Yan, Yang; Tan, Jiao; He, Qing
通讯作者	He, Q
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:10
英文摘要	The spatial and temporal resolution of remote sensing products in land surface temperature (LST) studies can be improved using the downscaling method. This is a crucial area of research as it provides basic data for the study of climate change. However, there have been few studies evaluating the applicability of downscaling methods using underlying surfaces of varying complexities. In this study, we focused on the semi-homogeneous underlying surface of Gurbantunggut Desert and evaluated the applicability of five classical, passive microwave, downscaling methods based on the machine learning of Catboost, using 365 days of AMSR-2 and MODIS data in 2019, which can be scanned once during the day and night. Our results showed four main points: (1) The correlation coefficients between feature vectors and the LST of the semi-homogeneous underlying surface were clearly different from those of the surrounding oases. The correlation coefficient of the semi-homogeneous underlying surface was high, and that of the surrounding oases was low. (2) At the same frequency, the correlation coefficient between vertically polarized BT and LST was greater than that between horizontally polarized BT and LST. Considering the semi-heterogeneous underlying surface, 23.8 GHz and 36.5 GHz may be more suitable for passive microwave LST retrieval than 89 GHz according to physical mechanisms. (3) The fine-scale LST downscaling accuracy achieved with all BT channels of AMSR-2 was higher than that achieved with the other four classical models. The day and night RMSE values verified with MYD11A1 data were 2.82 K and 1.38 K, respectively. (4) The correlation coefficients between downscaled LST and the soil temperature of the top layer of the site were the highest, with daytime-nighttime R-2 values of 0.978 and 0.970, and RMSE values of 3.42 and 4.99 K, respectively. The all-channel-based LST downscaling method is very effective and can provide a theoretical foundation for the acquisition of all-weather, multi-layer soil temperature.
英文关键词	land surface temperature AMSR-2 Catboost downscale Gurbantunggut Desert
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:001033213200001
WOS关键词	VEGETATION INDEX ; TEMPERATURE ; RETRIEVAL ; EMISSIVITY ; PREDICTION ; PRODUCTS ; AREA ; VALIDATION ; FRACTION
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/2XILL650/398275
推荐引用方式 GB/T 7714	Li, Yongkang,Liu, Yongqiang,Huang, Wenjiang,et al. Applicability Assessment of Passive Microwave LST Downscaling over Semi-Homogeneous Desert Underlying Surface Based on Machine Learning[J],2023,15(10).
APA	Li, Yongkang,Liu, Yongqiang,Huang, Wenjiang,Yan, Yang,Tan, Jiao,&He, Qing.(2023).Applicability Assessment of Passive Microwave LST Downscaling over Semi-Homogeneous Desert Underlying Surface Based on Machine Learning.REMOTE SENSING,15(10).
MLA	Li, Yongkang,et al."Applicability Assessment of Passive Microwave LST Downscaling over Semi-Homogeneous Desert Underlying Surface Based on Machine Learning".REMOTE SENSING 15.10(2023).