Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data

Qunming Wang, Yihang Zhang, Alex O. Onojeghuo, Xiaolin Zhu, Peter M. Atkinson

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)

Abstract

Spatio-temporal fusion of MODIS and Landsat data aims to produce new data that have simultaneously the Landsat spatial resolution and MODIS temporal resolution. It is an ill-posed problem involving large uncertainty, especially for reproduction of abrupt changes and heterogeneous landscapes. In this paper, we proposed to incorporate the freely available 250 m MODIS images into spatio-temporal fusion to increase prediction accuracy. The 250 m MODIS bands 1 and 2 are fused with 500 m MODIS bands 3-7 using the advanced area-to-point regression kriging approach. Based on a standard spatio-temporal fusion approach, the interim 250 m fused MODIS data are then downscaled to 30 m with the aid of the available 30 m Landsat data on temporally close days. The 250 m data can provide more information for the abrupt changes and heterogeneous landscapes than the original 500 m MODIS data, thus increasing the accuracy of spatio-temporal fusion predictions. The effectiveness of the proposed scheme was demonstrated using two datasets.
Original languageEnglish
Article number7934036
Pages (from-to)4116-4123
Number of pages8
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume10
Issue number9
DOIs
Publication statusPublished - 1 Sep 2017

Keywords

  • Downscaling
  • geostatistics
  • image fusion
  • Landsat
  • MODIS
  • spatio-temporal fusion

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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