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An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model

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Abstract
  • High spatiotemporal resolution satellite imagery is useful for natural resource management and monitoring for land-use and land-cover change and ecosystem dynamics. However, acquisitions from a single satellite can be limited, due to trade-offs in either spatial or temporal resolution. The spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM (ESTARFM) were developed to produce new images with high spatial and high temporal resolution using images from multiple sources. Nonetheless, there were some shortcomings in these models, especially for the procedure of searching spectrally similar neighbor pixels in the models. In order to improve these models’ capacity and accuracy, we developed a modified version of ESTARFM (mESTARFM) and tested the performance of two approaches (ESTARFM and mESTARFM) in three study areas located in Canada and China at different time intervals. The results show that mESTARFM improved the accuracy of the simulated reflectance at fine resolution to some extent.
  • This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by MDPI. The published article can be found at: http://www.mdpi.com/journal/remotesensing.
  • Keywords: Landsat, Image fusion, MODIS, Reflectance
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  • Fu, D., Chen, B., Wang, J., Zhu, X., Hilker, T. (2013). An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model. Remote Sensing, 5(12), 6346-6360. doi:10.3390/rs5126346
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  • 5
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  • 12
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  • This research is supported by the Research Plan of State Key Laboratory of Resources and Environmental Information System (LREIS), Chinese Academy of Sciences (CAS) (grant no. O88RA900PA), the research grant of the Key Project for the Strategic Science Plan in Institute of Geographic Sciences and Natural Resources Research (IGSNRR), CAS (grant no. 2012ZD010), the research grants (41071059 and 41271116) funded by the National Science Foundation of China, the Strategic Priority Research Program “Climate Change: Carbon Budget and Related Issuesˮ of the Chinese Academy of Sciences (Grant no. XDA05040403), the research grants (2010CB950704, 2010CB950902 and 2010CB950904) under the Global Change Program of the Chinese Ministry of Science and Technology and the “One Hundred Talents” program funded by the Chinese Academy of Sciences.
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