气候变化领域集成服务门户(CCPortal): A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001-2020 based on a two-step downscaling method

CCPortal

DOI	10.1080/15481603.2023.2290337
	A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001-2020 based on a two-step downscaling method
	Shangguan, Yulin; Min, Xiaoxiao; Wang, Nan; Tong, Cheng; Shi, Zhou
发表日期	2024
ISSN	1548-1603
EISSN	1943-7226
卷号	61 期号:1
英文摘要	Long-term, high-resolution soil moisture (SM) is a vital variable for understanding the water-energy cycle and the impacts of climate change on the Qinghai-Tibet Plateau (QTP). However, most existing satellite SM data are only available at coarse scale (+/- 25 km) and suffer a lot from data gaps due to satellite orbit coverage and snow cover, especially on the QTP. Although substantial efforts have been devoted to downscale SM utilizing multiple soil moisture indices (SMIs) or diverse machine learning (ML) methods, the potentials of different SMIs and ML approaches in SM downscaling on the complex plateau remain unclear, and there is still a necessity to obtain an accurate, long-term, high-resolution and seamless SM data over the QTP. To address this issue, this study generated the long-term, high-accuracy and seamless soil moisture dataset (LHS-SM) over the QTP during 2001-2020 using a two-step downscaling method (first downscaling then merging). Firstly, the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1 km utilizing five ML approaches. Then, a dynamic data merging method that considers spatiotemporal nonstationary error was applied to derive the final LHS-SM data. The performance of fifteen SMIs was also assessed and the optimal indexes for downscaling were identified. Results indicated that the shortwave infrared band-based indices had better performance than the near infrared band-based and energy-based indices. The generated LHS-SM data exhibited satisfying accuracy (mean R = 0.52, ubRMSE = 0.047 m(3)/m(3)) and certain improvement to the ESA CCI SM data both at station and network scales. Compared with existing 1 km SM datasets, the LHS-SM data also showed the best performance (mean R = 0.62, ubRMSE = 0.047 m(3)/m(3)), while existing datasets either failed to fully characterize the spatial details or had some data gaps and unreasonable distributions. Strong spatial heterogeneity was observed in the SM dynamics during 2001-2020 with the southwest and northeast showing a dry gets wetter scheme and the southeast presenting a wet gets drier trend. Overall, the LHS-SM dataset gained its added values by compensating the drawbacks of existing 1 km SM products over the QTP and was much valuable for many regional applications.
关键词	Soil moisture Long term high accuracy downscaling the Qinghai-Tibet Plateau
英文关键词	LAND-SURFACE TEMPERATURES; ALL-WEATHER CONDITIONS; HIGH-RESOLUTION; VEGETATION; MODIS; INDEX; SMAP; CHINA; WATER; SATELLITE
WOS研究方向	Geography, Physical ; Remote Sensing
WOS记录号	WOS:001146407300001
来源期刊	GISCIENCE & REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283010
作者单位	Zhejiang University; Ministry of Agriculture & Rural Affairs
推荐引用方式 GB/T 7714	Shangguan, Yulin,Min, Xiaoxiao,Wang, Nan,et al. A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001-2020 based on a two-step downscaling method[J],2024,61(1).
APA	Shangguan, Yulin,Min, Xiaoxiao,Wang, Nan,Tong, Cheng,&Shi, Zhou.(2024).A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001-2020 based on a two-step downscaling method.GISCIENCE & REMOTE SENSING,61(1).
MLA	Shangguan, Yulin,et al."A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001-2020 based on a two-step downscaling method".GISCIENCE & REMOTE SENSING 61.1(2024).