An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model

Dongjie Fu, Baozhang Chen, Juan Wang, Xiaolin Zhu, Thomas Hilker

Research output: Journal article publicationJournal articleAcademic researchpeer-review

55 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)6346-6360
Number of pages15
JournalRemote Sensing
Issue number12
Publication statusPublished - 1 Dec 2013
Externally publishedYes


  • Image fusion
  • Landsat
  • Modis
  • Reflectance

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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