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Comparison of Spatiotemporal Fusion Models: A Review

by 1, 2 and 1,3,*
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
Department of Geography and Resource Management and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Ministry of Education Key Laboratory for Earth System Modelling, Center of Earth System Science, Tsinghua University, Beijing 100084, China
Author to whom correspondence should be addressed.
Academic Editors: Raul Zurita-Milla and Prasad S. Thenkabail
Remote Sens. 2015, 7(2), 1798-1835;
Received: 8 October 2014 / Accepted: 29 January 2015 / Published: 5 February 2015
Simultaneously capturing spatial and temporal dynamics is always a challenge for the remote sensing community. Spatiotemporal fusion has gained wide interest in various applications for its superiority in integrating both fine spatial resolution and frequent temporal coverage. Though many advances have been made in spatiotemporal fusion model development and applications in the past decade, a unified comparison among existing fusion models is still limited. In this research, we classify the models into three categories: transformation-based, reconstruction-based, and learning-based models. The objective of this study is to (i) compare four fusion models (STARFM, ESTARFM, ISTAFM, and SPSTFM) under a one Landsat-MODIS (L-M) pair prediction mode and two L-M pair prediction mode using time-series datasets from the Coleambally irrigation area and Poyang Lake wetland; (ii) quantitatively assess prediction accuracy considering spatiotemporal comparability, landscape heterogeneity, and model parameter selection; and (iii) discuss the advantages and disadvantages of the three categories of spatiotemporal fusion models. View Full-Text
Keywords: spatiotemporal fusion; comparison; prediction modes; assessment spatiotemporal fusion; comparison; prediction modes; assessment
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Chen, B.; Huang, B.; Xu, B. Comparison of Spatiotemporal Fusion Models: A Review. Remote Sens. 2015, 7, 1798-1835.

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