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Sensors 2016, 16(8), 1245; doi:10.3390/s16081245

Spatiotemporal Interpolation for Environmental Modelling

Data61, CSIRO, College Road, Sandy Bay TAS 7005, Australia
College of Engineering and Science, Victoria University, Footscray VIC 3011, Australia
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 22 June 2016 / Revised: 28 July 2016 / Accepted: 2 August 2016 / Published: 6 August 2016
(This article belongs to the Section Sensor Networks)
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A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method. In this study, we utilised one year of Tasmania’s South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW) method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications. View Full-Text
Keywords: spatiotemporal interpolation; ordinary kriging; inverse distance weighting; triangular irregular network; distribution-based distance weighting spatiotemporal interpolation; ordinary kriging; inverse distance weighting; triangular irregular network; distribution-based distance weighting

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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MDPI and ACS Style

Susanto, F.; de Souza, P., Jr.; He, J. Spatiotemporal Interpolation for Environmental Modelling. Sensors 2016, 16, 1245.

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