Spatiotemporal fusion of multisource remote sensing data: Literature survey, taxonomy, principles, applications, and future directions

Xiaolin Zhu, Fangyi Cai, Jiaqi Tian, Trecia Kay Ann Williams

Research output: Journal article publicationReview articleAcademic researchpeer-review

129 Citations (Scopus)


Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.
Original languageEnglish
Article number527
JournalRemote Sensing
Issue number4
Publication statusPublished - 1 Apr 2018


  • Data blending
  • Satellite images
  • Spatial resolution
  • Spatiotemporal data fusion
  • Temporal resolution

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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