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Spatio-temporal interpolation of soil moisture, temperature and salinity (in 2D+T and 3D+T) coming from automated sensor networks

TitleSpatio-temporal interpolation of soil moisture, temperature and salinity (in 2D+T and 3D+T) coming from automated sensor networks
Publication TypeConference Paper
Year of Publication2014
AuthorsGasch CK, Hengl T, Brown DJoseph, Graeler B
Conference NameDailyMeteo.org/2014
Date Published06/2014
PublisherFaculty of Civil Engineering, University of Belgrade
Conference LocationBelgrade
Abstract

Comprehension of dynamic soil properties at the field scale requires measurements with high spatial and temporal resolution. Sensor networks provide frequent in situ measurements of soil properties at fixed locations, providing data in 2- or 3-dimensions and through time (3D+T). Spatio-temporal interpolation of 3D+T point data produces continuous estimates (maps) that can then be used for prediction at unsampled locations, as input for process models, and can simply aid in visualization of properties through space and time. Regression-kriging with 2D+T data has successfully been implemented for a daily air temperature dataset using terrain and temporal imagery as covariates. In this paper, we extend that approach to develop models for mapping soil moisture, temperature, and salinity using regression-kriging on 3D+T data. Currently, the field of geostatistics lacks an analytical framework for modeling 3D+T data, so our long term objective is to develop robust 3D+T models for mapping soil data that has been collected with high spatial and temporal resolution.

For this analysis, we use the Cook Farm dataset, which includes hourly measurements of soil volumetric water content, temperature, and electrical conductivity at 42 points and five depths, collected over five years. Cook Farm is a 37 hectare experimental farm in the northwest of the United States; it is host to variable soils, cropping systems, microclimates, and landscape positions. The dataset also includes a digital elevation model, topographic wetness index, soil unit description map, daily meteorological data, and annual satellite imagery (for 2011-2013). The sensor data, combined with the spatial and temporal covariates, provide an ideal dataset for developing 3D+T models. The presentation will include preliminary results and address main implementation strategies.

Refereed DesignationNon-Refereed

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Hengl_DailyMeteo_Cookfarm.pdf1.67 MB

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