Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-401
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
06 Sep 2016
Review status
This discussion paper has been under review for the journal Hydrology and Earth System Sciences (HESS). The revised manuscript was not accepted.
An Extended Kriging method to interpolate soil moisture data measured by wireless sensor network
Jialin Zhang1,2, Xiuhong Li1,2, Qiang Liu1,2, Long Zhao1,2, and Baocheng Dou1,2 1College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
2State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China
Abstract. In recent years, wireless sensor network (WSN) has emerged as a new technique to collect Earth observation data at a relatively low cost and minimal labor over large areas. However, WSN observations are still point data. To determine the spatial distribution of a land surface parameter, interpolation of these point data is necessary. Some geostatistical interpolation methods, such as the Ordinary Kriging method, Co-Kriging method, and Regression Kriging method, have been used in various fields. However, capturing the spatial distribution pattern of heterogeneous land surface parameters is still difficult. For example, near-surface soil moisture is a critical parameter for agriculture management, and hydrological and ecological research. However, as soil moisture is related to many factors such as topography, soil type, and vegetation, even a WSN observation grid is not sufficiently dense to reflect its spatial distribution pattern. This study developed a method to interpolate WSN-measured soil moisture with the aid of remote sensing images. The underlying idea is extension of the traditional Kriging algorithm by introducing spectral variables, specifically, vegetation index (VI) and albedo, from satellite imagery as supplementary information to aid interpolation. Thus, the new Extended Kriging algorithm operates on spatial and spectral combined space. The algorithm has been applied to WSN-measured data in the HiWATER campaign to generate daily soil moisture maps in the 5 km ×: 5 km oasis area in the middle reaches of the Heihe River, western China, from June 10 to July 15, 2012. Visual inspections indicate that the result from the Extended Kriging algorithm shows more spatial details than that of the traditional Kriging algorithm, and the temporal variation of patch-average soil moisture is, in general, consistent with precipitation/irrigation data. Leave-one-out cross-validation was also adopted to estimate the interpolation accuracy. The Root Mean Square Error (RMSE) of the Extended Kriging method was also found to be smaller than that of the original Ordinary Kriging method. Analysis with minimum variance of error (σk), a self-uncertainty indicator given by the Kriging algorithm, also gave the same conclusion. Further testing also indicated that if high-resolution land surface temperature maps are available, they can be added to the spectral variables and further improve the interpolation accuracy.

Citation: Zhang, J., Li, X., Liu, Q., Zhao, L., and Dou, B.: An Extended Kriging method to interpolate soil moisture data measured by wireless sensor network, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-401, 2016.
Jialin Zhang et al.
Jialin Zhang et al.

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Short summary
This study developed the Extended Kriging algorithm to interpolate ground observed soil moisture data with the aid of remote sensing images. This new algorithm is similar to the Kriging algorithm, but operates on the spatial and spectral combined space. The result from the Extended Kriging algorithm shows more spatial details than that of the traditional Kriging algorithm, and the temporal variation of patch-average soil moisture is consistent with precipitation/irrigation data.
This study developed the Extended Kriging algorithm to interpolate ground observed soil moisture...
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