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Article

A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions

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Department of Geoinformatic Engineering, Inha University, Incheon 22212, Korea
2
Department of Civil Engineering and Geomatics, Cyprus University of Technology, 3036 Limassol, Cyprus
3
Eratosthenes Centre of Excellence, 3036 Limassol, Cyprus
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1553; https://doi.org/10.3390/rs12101553
Received: 1 April 2020 / Revised: 8 May 2020 / Accepted: 11 May 2020 / Published: 13 May 2020
Dense time-series with coarse spatial resolution (DTCS) and sparse time-series with fine spatial resolution (STFS) data often provide complementary information. To make full use of this complementarity, this paper presents a novel spatiotemporal fusion model, the spatial time-series geostatistical deconvolution/fusion model (STGDFM), to generate synthesized dense time-series with fine spatial resolution (DTFS) data. Attributes from the DTCS and STFS data are decomposed into trend and residual components, and the spatiotemporal distributions of these components are predicted through novel schemes. The novelty of STGDFM lies in its ability to (1) consider temporal trend information using land-cover-specific temporal profiles from an entire DTCS dataset, (2) reflect local details of the STFS data using resolution matrix representation, and (3) use residual correction to account for temporary variations or abrupt changes that cannot be modeled from the trend components. The potential of STGDFM is evaluated by conducting extensive experiments that focus on different environments; spatially degraded datasets and real Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images are employed. The prediction performance of STGDFM is compared with those of a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM). Experimental results indicate that STGDFM delivers the best prediction performance with respect to prediction errors and preservation of spatial structures as it captures temporal change information on the prediction date. The superiority of STGDFM is significant when the difference between pair dates and prediction dates increases. These results indicate that STGDFM can be effectively applied to predict DTFS data that are essential for various environmental monitoring tasks. View Full-Text
Keywords: spatiotemporal data fusion; resolution; temporal information; deconvolution spatiotemporal data fusion; resolution; temporal information; deconvolution
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MDPI and ACS Style

Kim, Y.; Kyriakidis, P.C.; Park, N.-W. A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions. Remote Sens. 2020, 12, 1553. https://doi.org/10.3390/rs12101553

AMA Style

Kim Y, Kyriakidis PC, Park N-W. A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions. Remote Sensing. 2020; 12(10):1553. https://doi.org/10.3390/rs12101553

Chicago/Turabian Style

Kim, Yeseul, Phaedon C. Kyriakidis, and No-Wook Park. 2020. "A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions" Remote Sensing 12, no. 10: 1553. https://doi.org/10.3390/rs12101553

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