干旱区生态环境知识资源中心(Arid): An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003-2018

Arid

DOI	10.5194/essd-13-1-2021
	An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003-2018
	Chen, Yongzhe; Feng, Xiaoming; Fu, Bojie
通讯作者	Feng, XM (corresponding author), Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China.
来源期刊	EARTH SYSTEM SCIENCE DATA
ISSN	1866-3508
EISSN	1866-3516
出版年	2021
卷号	13 期号:1 页码:1-31
英文摘要	Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R-2 = 0.95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003-2018 at 0.1 degrees resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1-10, 11-20, and from the 21st to the last day of that month. RSSSM is proven comparable to the in situ surface soil moisture measurements of the International Soil Moisture Network sites (overall R-2 and RMSE values of 0.42 and 0.087 m(3)m(-3)), while the overall R-2 and RMSE values for the existing popular similar products are usually within the ranges of 0.31-0.41 and 0.095-0.142 m(3)m(-3)), respectively. RSSSM generally presents advantages over other products in arid and relatively cold areas, which is probably because of the difficulty in simulating the impacts of thawing and transient precipitation on soil moisture, and during the growing seasons. Moreover, the persistent high quality during 2003-2018 as well as the complete spatial coverage ensure the applicability of RSSSM to studies on both the spatial and temporal patterns (e.g. long-term trend). RSSSM data suggest an increase in the global mean surface soil moisture. Moreover, without considering the deserts and rainforests, the surface soil moisture loss on consecutive rainless days is highest in summer over the low latitudes (30 degrees S-30 degrees N) but mostly in winter over the mid-latitudes (30-60 degrees N, 30-60 degrees S). Notably, the error propagation is well controlled with the extension of the simulation period to the past, indicating that the data fusion algorithm proposed here will be more meaningful in the future when more advanced microwave sensors become operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).
类型	Article
语种	英语
开放获取类型	Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000606792500001
WOS关键词	ESSENTIAL CLIMATE VARIABLES ; EFFECTIVE SCATTERING ALBEDO ; VEGETATION OPTICAL DEPTH ; TIME-SERIES ; AMSR-E ; MICROWAVE EMISSION ; LAND EVAPORATION ; GEOV1 LAI ; 4 DECADES ; DATA SETS
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/347807
作者单位	[Chen, Yongzhe; Feng, Xiaoming; Fu, Bojie] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China; [Chen, Yongzhe; Fu, Bojie] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Chen, Yongzhe,Feng, Xiaoming,Fu, Bojie. An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003-2018[J],2021,13(1):1-31.
APA	Chen, Yongzhe,Feng, Xiaoming,&Fu, Bojie.(2021).An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003-2018.EARTH SYSTEM SCIENCE DATA,13(1),1-31.
MLA	Chen, Yongzhe,et al."An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003-2018".EARTH SYSTEM SCIENCE DATA 13.1(2021):1-31.