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Open AccessArticle

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

by Maolin Liu 1,†, Yinghai Ke 2,*,†, Qi Yin 1,3, Xiuwan Chen 1,3 and Jungho Im 4,5
1
Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China
2
Beijing Laboratory of Water Security, Base of the State Key Laboratory of Urban Environment Process & Digital Modeling, Capital Normal University, Beijing 100089, China
3
Engineering Research Center of Earth Observation and Navigation (CEON), Ministry of Education of the People’s Republic of China, Beijing 100871, China
4
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Eonyang-eup, Ulju-gun Ulsan 44919, Korea
5
Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry, One Forestry Dr. Syracuse, New York, NY 13210, USA
*
Author to whom correspondence should be addressed.
The first two authors contributed equally to this paper.
Remote Sens. 2019, 11(22), 2612; https://doi.org/10.3390/rs11222612
Received: 5 October 2019 / Revised: 4 November 2019 / Accepted: 6 November 2019 / Published: 7 November 2019
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. View Full-Text
Keywords: spatial and temporal satellite image fusion; spatial heterogeneity; temporal variation; STARFM; FSDAF; Fit-FC; One-pair learning; UBDF spatial and temporal satellite image fusion; spatial heterogeneity; temporal variation; STARFM; FSDAF; Fit-FC; One-pair learning; UBDF
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MDPI and ACS Style

Liu, M.; Ke, Y.; Yin, Q.; Chen, X.; Im, J. Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation. Remote Sens. 2019, 11, 2612.

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