An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions

Xiaolin Zhu, Jin Chen, Feng Gao, Xuehong Chen, Jeffrey G. Masek

Research output: Journal article publicationJournal articleAcademic researchpeer-review

805 Citations (Scopus)


Due to technical and budget limitations, remote sensing instruments trade spatial resolution and swath width. As a result not one sensor provides both high spatial resolution and high temporal resolution. However, the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change science. One approach is to "blend" the radiometry from daily, global data (e.g. MODIS, MERIS, SPOT-Vegetation) with data from high-resolution sensors with less frequent coverage (e.g. Landsat, CBERS, ResourceSat). Unfortunately, existing algorithms for blending multi-source data have some shortcomings, particularly in accurately predicting the surface reflectance of heterogeneous landscapes. This study has developed an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm, and has tested it with both simulated and actual satellite data. Results show that ESTARFM improves the accuracy of predicted fine-resolution reflectance, especially for heterogeneous landscapes, and preserves spatial details. Taking the NIR band as an example, for homogeneous regions the prediction of the ESTARFM is slightly better than the STARFM (average absolute difference [AAD] 0.0106 vs. 0.0129 reflectance units). But for a complex, heterogeneous landscape, the prediction accuracy of ESTARFM is improved even more compared with STARFM (AAD 0.0135 vs. 0.0194). This improved fusion algorithm will support new investigations into how global landscapes are changing across both seasonal and interannual timescales.
Original languageEnglish
Pages (from-to)2610-2623
Number of pages14
JournalRemote Sensing of Environment
Issue number11
Publication statusPublished - 1 Nov 2010
Externally publishedYes


  • Data fusion
  • Landsat
  • Multi-source satellite data
  • Reflectance
  • Time-series

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Soil Science
  • Geology


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