Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA

doi:10.1016/j.jhydrol.2020.125406

	Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA
	Liu, Yangxiaoyue 3,4; Yao, Ling 2; Jing, Wenlong 1,3,4; Di, Liping 1; Yang, Ji 3,4; Li, Yong 3,4
刊名	JOURNAL OF HYDROLOGY
	2020-11-01
卷号	590 页码:16
关键词	Satellite-based soil moisture Reconstruction Triangular feature space-based model Random forest
ISSN号	0022-1694
DOI	10.1016/j.jhydrol.2020.125406
通讯作者	Jing, Wenlong(jingwl@lreis.ac.cn)
英文摘要	Soil moisture plays a critical role in regional water cycles and is one of the key indicators for agricultural droughts. Satellite-based microwave radiometers have been the main instrument for mapping surface soil moisture at regional to global scales. However, known issues still exist that lead to data gaps in microwave-based remotely sensed soil moisture products. Multiple gap-filling approaches have been recently developed to generate spatially continuous satellite-based soil moisture. However, there are few inter-comparisons of such approaches despite their importance. In this study, we present a comparison between a triangular feature space-based (Tri) model and a machine learning (ML) based random forest (RF) model for seamless reconstruction in the European Space Agency's Essential Climate Variables Soil Moisture product (ECV SM) over Oklahoma, USA. Five variables are implemented in the models: the normalized difference vegetation index (NDVI), daytime land surface temperature (LST), nighttime LST, daily average LST, and diurnal LST (.LST), and different combinations of these five variables are examined. The reconstructed soil moisture is validated against the original ECV SM and local in-situ measurements. The RF model achieved precise and outstanding performance (R-2 = 0.95, RMSE = 0.02 m(3)/m(3), bias = 0.07%) in comparison to the original ECV SM. Comparatively, the Tri and RF models revealed equivalent performance in fitting the in-situ measurements, and both effectively alleviated the bias from the ECV SM. Specifically, Tri, which used daytime LST and NDVI to establish a soil moisture estimation model, displayed the highest correlation coefficient as well as the smallest error (R= 0.620, RMSE = 0.080 m(3)/m(3), ubRMSE = 0.038 m(3)/m(3)) for the in-situ measurements. Moreover, the assessments for sub-regions proved the consistency and robustness of both the Tri and RF models. Missing sub-regional training samples are unlikely to degrade performance of the models as long as there are sufficient valid samples in the whole region. The results of this study highlight the promising potential of Tri and RF models in efficiently filling the gaps in the ECV SM and can provide a reference for future studies focused on satellite-based soil moisture gap-filling algorithm selections.
资助项目	National Postdoctoral Program for Innovative Talents, China[BX20200100] ; National Natural Science Foundation of China[41801362] ; National Natural Science Foundation of China[41771380] ; National Natural Science Foundation of China[41976190] ; GDAS' Project of Science and Technology Development[2020GDASYL20200103006] ; GDAS' Project of Science and Technology Development[2020GDASYL-20200104003] ; GDAS' Project of Science and Technology Development[2018GDASCX-0905] ; GDAS' Project of Science and Technology Development[2016GDASRC-0211] ; GDAS' Project of Science and Technology Development[2017GDASCX-0601] ; GDAS' Project of Science and Technology Development[2017GDASCX-0801] ; GDAS' Project of Science and Technology Development[2018GDASCX-0403] ; GDAS' Project of Science and Technology Development[2019GDASYL-0301001] ; GDAS' Project of Science and Technology Development[2019GDASYL-0501001] ; GDAS' Project of Science and Technology Development[2019GDASYL-0502001] ; Guangzhou Science and Technology Project[201902010033] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), China[GML2019ZD0301]
WOS关键词	LAND-SURFACE TEMPERATURE ; ECOSYSTEM RESPIRATION ; TRIANGLE METHOD ; WATER CONTENT ; INDEX SPACE ; VALIDATION ; MODIS ; SMOS ; VARIABILITY ; PRODUCT
WOS研究方向	Engineering ; Geology ; Water Resources
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000599754500133
资助机构	National Postdoctoral Program for Innovative Talents, China ; National Natural Science Foundation of China ; GDAS' Project of Science and Technology Development ; Guangzhou Science and Technology Project ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/137570]
专题	中国科学院地理科学与资源研究所
通讯作者	Jing, Wenlong
作者单位	1.George Mason Univ, Ctr Spatial Informat Sci & Syst, 4087 Univ Dr, Fairfax, VA 22030 USA 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Southern Marine Sci & Engn Guangdong Lab Guangzho, Guangzhou 511458, Peoples R China 4.Guangzhou Inst Geog, Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangdong Open Lab Geospatial Informat Technol &, Guangzhou 510070, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yangxiaoyue,Yao, Ling,Jing, Wenlong,et al. Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA[J]. JOURNAL OF HYDROLOGY,2020,590:16.
APA	Liu, Yangxiaoyue,Yao, Ling,Jing, Wenlong,Di, Liping,Yang, Ji,&Li, Yong.(2020).Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA.JOURNAL OF HYDROLOGY,590,16.
MLA	Liu, Yangxiaoyue,et al."Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA".JOURNAL OF HYDROLOGY 590(2020):16.