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Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
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Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

Author(s)
Liu, MaolinKe, YinghaiYin, QiChen, XiuwanIm, Jungho
Issued Date
2019-11
DOI
10.3390/rs11222612
URI
https://scholarworks.unist.ac.kr/handle/201301/30730
Fulltext
https://www.mdpi.com/2072-4292/11/22/2612
Citation
REMOTE SENSING, v.11, no.22, pp.2612
Abstract
In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications.
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
ISSN
2072-4292
Keyword (Author)
spatial and temporal satellite image fusionspatial heterogeneitytemporal variationSTARFMFSDAFFit-FCOne-pair learningUBDF
Keyword
MODIS SURFACE REFLECTANCEBLENDING LANDSATTIME-SERIESRESOLUTIONSUPERRESOLUTIONTEMPERATUREMULTISENSORALGORITHMFRAMEWORK

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