Abstract
In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation.
Highlights
The use of wireless sensor networks (WSNs) is a novel technique for ground data collection that is currently in high demand
The Extended Kriging method proposed in this study introduces the remote sensing image The Extended Kriging method proposed in this study introduces the remote sensing image spectral information into the traditional interpolation method
These spectral variables are treated in the same manner as the spatial variables used in the algorithm
Summary
The use of wireless sensor networks (WSNs) is a novel technique for ground data collection that is currently in high demand. Radar has the capability to provide high spatial resolution, in order of tens of meters, they are more sensitive to surface roughness, topographic features and vegetation, which means that the soil moisture inversion process is very difficult. Ground observation data, such as that from WSN measurement, are always necessary as supplementary data for remote sensing inversion of soil moisture. Remote sensing information can be viewed as supplementary data to aid the interpolation of ground-measured soil moisture. We propose extending the Kriging method by introducing high-resolution remote sensing imagery spectral variables into the interpolation algorithm.
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