Paper Info
Title
Mapping High-Resolution Soil Moisture over Heterogeneous Cropland Using Multi-Resource Remote Sensing and Ground Observations
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 (similar to 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 m(3).m(-3) to 0.033 m(3).m(-3). The coefficient of determination (R-2) 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 m(3.)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 m(3).m(-3), R-2 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.
Year
DOI
Venue
2015
10.3390/rs71013273
REMOTE SENSING
Keywords
Field
DocType
soil moisture,Bayesian Maximum Entropy,soil evaporative efficiency,irrigation,PLMR,ASTER,wireless sensor network,heterogeneous cropland
Aster (genus),Soil science,Irrigation statistics,Remote sensing,Water content,Coefficient of determination,Advanced Spaceborne Thermal Emission and Reflection Radiometer,Geology,Image resolution,Radiometer,Spatial distribution
Journal
Volume
Issue
ISSN
7
10
2072-4292
Citations 
PageRank 
References 
1
0.36
17
Authors
7
Name
Order
Citations
PageRank
lei fan12515.44
qing xiao280.94
Jianguang Wen36721.30
qiang liu4141.78
Rui Jin59016.41
Dongqin You63810.25
Xiaowen Li711929.28