Dense Satellite Image Time Series Analysis: Opportunities, Challenges, and Future Directions

Desheng Liu, Xiaolin Zhu

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

3 Citations (Scopus)

Abstract

Earth observation satellites provide important data for monitoring land surface dynamics. In recent years, with the development of new satellite constellations, supercomputing, artificial intelligence, and cloud computing, remote sensing studies of land surface changes have been gradually shifted from sparse time series analysis to dense time series anslysis. Dense satellite image time series dramatically improve our capability for capturing frequent changes in the land surface. It has changed the research questions, data processing techniques, and applications compared with the traditional sparse time series analysis. This chapter discussed the opportunities, challenges, and future directions of dense satellite time series data analysis. It can help researchers from the remote sensing community or other disciplines apply dense satellite time series analysis to solve real-world problems.

Original languageEnglish
Title of host publicationNew Thinking in GIScience
PublisherSpringer Nature
Pages233-242
Number of pages10
ISBN (Electronic)9789811938160
ISBN (Print)9789811938153
DOIs
Publication statusPublished - 1 Jan 2022

Keywords

  • Dense satellite image time series
  • Remote sensing
  • Time series analysis

ASJC Scopus subject areas

  • General Computer Science
  • General Earth and Planetary Sciences

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