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Remote Sens. 2015, 7(4), 4112-4138; doi:10.3390/rs70404112

Toward the Estimation of Surface Soil Moisture Content Using Geostationary Satellite Data over Sparsely Vegetated Area

1
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
2
ICube, UdS, CNRS, Boulevard Sebastien Brant, CS10413, Illkirch 67412, France
3
Key Laboratory of Agri-Informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 10 November 2014 / Revised: 4 February 2015 / Accepted: 24 March 2015 / Published: 7 April 2015
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Abstract

Based on a novel bare surface soil moisture (SSM) retrieval model developed from the synergistic use of the diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR) (Leng et al. 2014. “Bare Surface Soil Moisture Retrieval from the Synergistic Use of Optical and Thermal Infrared Data”. International Journal of Remote Sensing 35: 988–1003.), this paper mainly investigated the model’s capability to estimate SSM using geostationary satellite observations over vegetated area. Results from the simulated data primarily indicated that the previous bare SSM retrieval model is capable of estimating SSM in the low vegetation cover condition with fractional vegetation cover (FVC) ranging from 0 to 0.3. In total, the simulated data from the Common Land Model (CoLM) on 151 cloud-free days at three FLUXNET sites that with different climate patterns were used to describe SSM estimates with different underlying surfaces. The results showed a strong correlation between the estimated SSM and the simulated values, with a mean Root Mean Square Error (RMSE) of 0.028 m3·m−3 and a coefficient of determination (R2) of 0.869. Moreover, diurnal cycles of LST and NSSR derived from the Meteosat Second Generation (MSG) satellite data on 59 cloud-free days were utilized to estimate SSM in the REMEDHUS soil moisture network (Spain). In particular, determination of the model coefficients synchronously using satellite observations and SSM measurements was explored in detail in the cases where meteorological data were not available. A preliminary validation was implemented to verify the MSG pixel average SSM in the REMEDHUS area with the average SSM calculated from the site measurements. The results revealed a significant R2 of 0.595 and an RMSE of 0.021 m3·m−3. View Full-Text
Keywords: surface soil moisture; geostationary satellite; sparsely vegetated area surface soil moisture; geostationary satellite; sparsely vegetated area
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Leng, P.; Song, X.; Li, Z.-L.; Wang, Y.; Wang, R. Toward the Estimation of Surface Soil Moisture Content Using Geostationary Satellite Data over Sparsely Vegetated Area. Remote Sens. 2015, 7, 4112-4138.

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