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Sensors 2017, 17(6), 1390; doi:10.3390/s17061390

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

1
Center for Global Change Studies, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
2
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences Beijing Normal University, Beijing 100101, China
3
Chinese Research Academy of Environmental Sciences, Beijing 100012, China
*
Author to whom correspondence should be addressed.
Academic Editors: Huajun Tang, Wenbin Wu and Yun Shi
Received: 28 February 2017 / Revised: 9 June 2017 / Accepted: 10 June 2017 / Published: 15 June 2017
(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)
View Full-Text   |   Download PDF [4320 KB, uploaded 15 June 2017]   |  

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. View Full-Text
Keywords: wireless sensor network; Kriging interpolation; soil moisture; spectral variables wireless sensor network; Kriging interpolation; soil moisture; spectral variables
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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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Zhang, J.; Li, X.; Yang, R.; Liu, Q.; Zhao, L.; Dou, B. An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks. Sensors 2017, 17, 1390.

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