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Remote Sens. 2015, 7(10), 13273-13297; doi:10.3390/rs71013273

Mapping High-Resolution Soil Moisture over Heterogeneous Cropland Using Multi-Resource Remote Sensing and Ground Observations

1,2,3
,
1,* , 1,3,* , 4
,
5,6
,
1,3
and
7,†
1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Joint Center for Global Change Studies (JCGCS), Beijing 100875, China
4
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
5
Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
6
Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China
7
School of Geography, Beijing Normal University, Beijing 100875, China
Deceased.
*
Authors to whom correspondence should be addressed.
Academic Editors: Xin Li, Yuei-An Liou, Qinhuo Liu, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 24 July 2015 / Revised: 27 September 2015 / Accepted: 29 September 2015 / Published: 9 October 2015
View Full-Text   |   Download PDF [1523 KB, uploaded 9 October 2015]   |  

Abstract

High spatial resolution soil moisture (SM) data are crucial in agricultural applications, river-basin management, and understanding hydrological processes. Merging multi-resource observations is one of the ways to improve the accuracy of high spatial resolution SM data in the heterogeneous cropland. In this paper, the Bayesian Maximum Entropy (BME) methodology is implemented to merge the following four types of observed data to obtain the spatial distribution of SM at 100 m scale: soil moisture observed by wireless sensor network (WSN), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived soil evaporative efficiency (SEE), irrigation statistics, and Polarimetric L-band Multi-beam Radiometer (PLMR)-derived SM products (~700 m). From the poor BME predictions obtained by merging only WSN and SEE data, we observed that the SM heterogeneity caused by irrigation and the attenuating sensitivity of the SEE data to SM caused by the canopies result in BME prediction errors. By adding irrigation statistics to the merged datasets, the overall RMSD of the BME predictions during the low-vegetated periods can be successively reduced from 0.052 m3·m−3 to 0.033 m3·m−3. The coefficient of determination (R2) and slope between the predicted and in situ measured SM data increased from 0.32 to 0.64 and from 0.38 to 0.82, respectively, but large estimation errors occurred during the moderately vegetated periods (RMSD = 0.041 m3·m−3, R = 0.43 and the slope = 0.41). Further adding the downscaled SM information from PLMR SM products to the merged datasets, the predictions were satisfactorily accurate with an RMSD of 0.034 m3·m−3, R2 of 0.4 and a slope of 0.69 during moderately vegetated periods. Overall, the results demonstrated that merging multi-resource observations into SM estimations can yield improved accuracy in heterogeneous cropland. View Full-Text
Keywords: soil moisture; Bayesian Maximum Entropy; soil evaporative efficiency; irrigation; PLMR; ASTER; wireless sensor network; heterogeneous cropland soil moisture; Bayesian Maximum Entropy; soil evaporative efficiency; irrigation; PLMR; ASTER; wireless sensor network; heterogeneous cropland
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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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Fan, L.; Xiao, Q.; Wen, J.; Liu, Q.; Jin, R.; You, D.; Li, X. Mapping High-Resolution Soil Moisture over Heterogeneous Cropland Using Multi-Resource Remote Sensing and Ground Observations. Remote Sens. 2015, 7, 13273-13297.

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