Design of an Optimal Soil Moisture Monitoring Network Using SMOS Retrieved Soil Moisture


Several methods are proposed to pick sites for grid-scale soil moisture monitoring networks; but, calibration/validation activities also need data regarding where to position grid representative monitoring sites. In order to style a soil moisture network for this task in the Great Lakes Basin (52two 00zero km2), the dual-entropy multiobjective optimization algorithm was used to maximise the data content and minimize the redundancy of knowledge in a potential soil moisture monitoring network. Soil moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) mission during the frost-free periods of 2010–2013 were filtered for knowledge quality and then utilized in a multiobjective search to seek out Pareto optimum network styles based on the joint entropy and total correlation measures of knowledge content and data redundancy, respectively. Differences in the data content of SMOS ascending and descending overpasses resulted in distinctly different network styles. Entropy from the SMOS ascending overpass was found to be spatially consistent, whereas descending overpass entropy had many peaks that coincided with areas of high subgrid heterogeneity. A mix of each ascending and descending overpasses made network designs that incorporated aspects of information from each overpass. Initial networks were designed to incorporate fifteen monitoring sites, but the addition of network price as an objective demonstrated that a network with similar information content may be achieved with fewer monitoring stations.

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