Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/19216
Title: A cross-resolution, spatiotemporal geostatistical fusion model for combining satellite image time-series of different spatial and temporal resolutions
Authors: Kim, Yeseul 
Kyriakidis, Phaedon 
Park, No Wook 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Deconvolution;Resolution;Spatiotemporal data fusion;Temporal information
Issue Date: 1-May-2020
Source: Remote Sensing, 2020, vol. 12, iss. 10, article no. 1553
Volume: 12
Issue: 10
Project: ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment 
Journal: Remote Sensing 
Abstract: 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.
URI: https://ktisis.cut.ac.cy/handle/10488/19216
ISSN: 2072-4292
DOI: 10.3390/rs12101553
Rights: © by the authors
Type: Article
Affiliation : Inha University 
Cyprus University of Technology 
ERATOSTHENES Centre of Excellence 
Appears in Collections:Publications under the auspices of the EXCELSIOR H2020 Teaming Project/ERATOSTHENES Centre of Excellence

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