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

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.

[1]  N. Cressie The origins of kriging , 1990 .

[2]  Gong Peng Wireless Sensor Network as a New Ground Remote Sensing Technology for Environmental Monitoring , 2007 .

[3]  Bo Gao,et al.  Derivation of Land Surface Albedo at High Resolution by Combining HJ-1A/B Reflectance Observations with MODIS BRDF Products , 2014, Remote. Sens..

[4]  Xiuyu Liang,et al.  Co-Kriging Estimation of Nitrate-Nitrogen Loads in an Agricultural River , 2016, Water Resources Management.

[5]  Hua Li,et al.  Wireless Sensor Network of Typical Land Surface Parameters and Its Preliminary Applications for Coarse-Resolution Remote Sensing Pixel , 2016, Int. J. Distributed Sens. Networks.

[6]  F. S. Nakayama,et al.  The Dependence of Bare Soil Albedo on Soil Water Content. , 1975 .

[7]  S. A. Barber,et al.  Development and Distribution of the Corn Root System Under Field Conditions1 , 1974 .

[8]  Roberto Benedetti,et al.  On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna , 1993 .

[9]  Yong Tang,et al.  Evaluation of the Airborne CASI/TASI Ts-VI Space Method for Estimating Near-Surface Soil Moisture , 2015, Remote. Sens..

[10]  B. Minasny,et al.  The Matérn function as a general model for soil variograms , 2005 .

[11]  Jiemin Wang,et al.  Intercomparison of surface energy flux measurement systems used during the HiWATER‐MUSOEXE , 2013 .

[12]  Xiaoming Lai,et al.  Uncertainty analysis in near-surface soil moisture estimation on two typical land-use hillslopes , 2016, Journal of Soils and Sediments.

[13]  Philippe Cantet,et al.  Mapping the mean monthly precipitation of a small island using kriging with external drifts , 2015, Theoretical and Applied Climatology.

[14]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .

[15]  Marko Wagner,et al.  Geostatistics For Environmental Scientists , 2016 .

[16]  Li Jianzhong and Gao Hong,et al.  Survey on Sensor Network Research , 2008 .

[17]  Qiang Liu,et al.  Coarse scale in situ albedo observations over heterogeneous snow-free land surfaces and validation strategy: A case of MODIS albedo products preliminary validation over northern China , 2016 .

[18]  H. Wackernagle,et al.  Multivariate geostatistics: an introduction with applications , 1998 .

[19]  A. Warrick,et al.  Estimating Soil Water Content Using Cokriging1 , 1987 .

[20]  Jianguang Wen,et al.  The Combination of Ground-Sensing Network and Satellite Remote Sensing in Huailai County , 2016, IEEE Sensors Journal.

[21]  A. Castrignanò,et al.  ESTIMATING SOIL WATER CONTENT USING COKRIGING , 1990 .

[22]  Fumio Yamazaki,et al.  Fragility curves for expressway embankments based on damage datasets after recent earthquakes in Japan , 2010 .

[23]  Mohd Fauzi Othman,et al.  Wireless Sensor Network Applications: A Study in Environment Monitoring System , 2012 .

[24]  Irena Hajnsek,et al.  Soil Moisture Estimation Using Hybrid Polarimetric SAR Data of RISAT-1 , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Fei Lin,et al.  Effects of Soil Water and Nitrogen on Growth and Photosynthetic Response of Manchurian Ash (Fraxinus mandshurica) Seedlings in Northeastern China , 2012, PloS one.

[26]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[27]  Guodong Jia,et al.  Response of Soil Respiration to Soil Temperature and Moisture in a 50-Year-Old Oriental Arborvitae Plantation in China , 2011, PloS one.

[28]  Tingting Wu,et al.  Spatial interpolation of temperature in the United States using residual kriging , 2013 .

[29]  Qing Xiao,et al.  Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design , 2013 .

[30]  Jin Li,et al.  A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors , 2011, Ecol. Informatics.

[31]  Y. Ge,et al.  Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces , 2016 .

[32]  Erwin Zehe,et al.  Analyzing spatial data: An assessment of assumptions, new methods, and uncertainty using soil hydraulic data , 2008 .

[33]  N. Baghdadi,et al.  Soil moisture retrieval over irrigated grassland using X-band SAR data , 2016 .

[34]  David W. S. Wong,et al.  An adaptive inverse-distance weighting spatial interpolation technique , 2008, Comput. Geosci..

[35]  K. Bradbury,et al.  A simple daily soil–water balance model for estimating the spatial and temporal distribution of groundwater recharge in temperate humid areas , 2007 .

[36]  G. Matheron Principles of geostatistics , 1963 .

[37]  V. Castillo,et al.  Spatial patterns and temporal stability of soil moisture across a range of scales in a semi‐arid environment , 2000 .

[38]  D G Krige,et al.  A statistical approach to some mine valuation and allied problems on the Witwatersrand , 2015 .

[39]  J. Yamamoto An Alternative Measure of the Reliability of Ordinary Kriging Estimates , 2000 .

[40]  Baoping Yan,et al.  A Nested Ecohydrological Wireless Sensor Network for Capturing the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin, China , 2014, IEEE Geoscience and Remote Sensing Letters.

[41]  Qiang Liu,et al.  The Design and Implementation of the Leaf Area Index Sensor , 2015, Sensors.

[42]  Xin Li,et al.  Regression Kriging-Based Upscaling of Soil Moisture Measurements From a Wireless Sensor Network and Multiresource Remote Sensing Information Over Heterogeneous Cropland , 2015, IEEE Geoscience and Remote Sensing Letters.

[43]  A. McBratney,et al.  Optimal interpolation and isarithmic mapping of soil properties: V. Co-regionalization and multiple sampling strategy , 1983 .

[44]  Zhongli Zhu,et al.  Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[45]  A. Viña,et al.  Drought Monitoring with NDVI-Based Standardized Vegetation Index , 2002 .

[46]  H. Wackernagel,et al.  Mapping temperature using kriging with external drift: Theory and an example from scotland , 1994 .

[47]  R. Webster,et al.  Kriging: a method of interpolation for geographical information systems , 1990, Int. J. Geogr. Inf. Sci..

[48]  Qiang Liu,et al.  Calibration and data validation of wireless sensor network , 2015, Intelligent Earth Observing Systems.