High spatio-temporal resolution mapping of soil moisture by integrating wireless sensor network observations and MODIS apparent thermal inertia in the Babao River Basin, China

doi:10.1016/j.rse.2017.01.027

	High spatio-temporal resolution mapping of soil moisture by integrating wireless sensor network observations and MODIS apparent thermal inertia in the Babao River Basin, China
	Zhang, Y (Zhang, Yang)1; Kang, J (Kang, Jian)1; Jin, R (Jin, Rui)1,2; Li, X (Li, Xin)1,2; Ma, CF (Ma, Chunfeng)1; Qin, J (Qin, Jun)3; Jin, R
刊名	REMOTE SENSING OF ENVIRONMENT
	2017
卷号	191 期号:0 页码:232-245
关键词	GEOSTATISTICAL ANALYSIS SPATIAL-DISTRIBUTION RETRIEVAL REANALYSIS CATCHMENT OBSERVATORIES DESIGN PART BAND
DOI	10.1016/j.rse.2017.01.027
文献子类	Article
英文摘要	Soil moisture distributions with high spatio-temporal resolution are scarce but beneficial for understanding ecohydrological processes and closing the water cycle at the basin scale. Sensor networks are innovative in their ability to capture the spatio-temporal heterogeneity and dynamics of soil moisture; however, they cannot be used to directly derive spatially continuous soil moisture distributions. A Bayesian-based upscaling algorithm that utilizes MODIS-derived apparent thermal inertia is used to map daily soil moisture spatial patterns with a resolution of 1 km in the Babao River Basin, China. The 2-4 cm soil moisture observations from seven automatic meteorological stations located in different elevation zones from 3000 m to 3500 m are employed to validate the mapping algorithm. The correlation coefficient and unbiased root-mean-square error (RMSE) averaged 0.880 and 0.031 cm(3)/cm(3), respectively, which indicate satisfactory estimation accuracy. The 1 km resolution soil moisture products are re-sampled to a resolution of 25 km and then compared to the level 3 Soil Moisture and Ocean Salinity Mission (SMOS) soil moisture product. The results show that both products exhibit strong temporal consistency; however, due to complex topography, the SMOS soil moisture is generally lower than that of the upscaling results. Semivariograms and an empirical orthogonal function (EOF) analysis are used to analyze the space-time heterogeneities of soil moisture at the 1 km scale. In the summer, rainfall results in soil moisture with low spatial variability and a complex spatial structure. After the rainy season, the spatial heterogeneity of soil moisture is affected by other factors, such as soil texture, evapotranspiration and topography. From the perspective of temporal variation, the upscaled soil moisture shows a well-defined seasonal cycle, which represents the effects of decreased rainfall from August to October. Because more rain falls in the summer due to the mountain microclimate, the oscillation in soil moisture is more pronounced over 20% of the area compared to that in other regions. Based on a validation analysis of the mapping results, the upscaling method is proven feasible, and the upscaled soil moisture can be used to analyze eco-hydrological processes and validate remote sensing products. (C) 2017 Elsevier Inc. All rights reserved.
学科主题	自然地理学
WOS研究方向	Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology
语种	英语
WOS记录号	WOS:000397360500018
内容类型	期刊论文
源URL	[http://ir.itpcas.ac.cn/handle/131C11/8314]
专题	青藏高原研究所_图书馆
通讯作者	Jin, R
作者单位	1.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China. 2.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China. 3.Chinese Acad Sci, Inst Tibetan Plateau Res, Lab Tibetan Environm Changes & Land Surface Proc, POB 2871, Beijing 100101, Peoples R China.
推荐引用方式 GB/T 7714	Zhang, Y ,Kang, J ,Jin, R ,et al. High spatio-temporal resolution mapping of soil moisture by integrating wireless sensor network observations and MODIS apparent thermal inertia in the Babao River Basin, China[J]. REMOTE SENSING OF ENVIRONMENT,2017,191(0):232-245.
APA	Zhang, Y .,Kang, J .,Jin, R .,Li, X .,Ma, CF .,...&Jin, R.(2017).High spatio-temporal resolution mapping of soil moisture by integrating wireless sensor network observations and MODIS apparent thermal inertia in the Babao River Basin, China.REMOTE SENSING OF ENVIRONMENT,191(0),232-245.
MLA	Zhang, Y ,et al."High spatio-temporal resolution mapping of soil moisture by integrating wireless sensor network observations and MODIS apparent thermal inertia in the Babao River Basin, China".REMOTE SENSING OF ENVIRONMENT 191.0(2017):232-245.