A review of reconstructing remotely sensed land surface temperature under cloudy conditions

Yaping Mo, Yongming Xu*, Huijuan Chen, Shanyou Zhu

*Corresponding author for this work

Research output: Contribution to journalLiterature reviewpeer-review

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Abstract

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.
Original languageEnglish
Article number2838
Number of pages21
JournalRemote Sensing
Volume13
Issue number14
DOIs
Publication statusPublished - 19 Jul 2021

Keywords

  • land surface temperature
  • reconstruction
  • validation
  • cloud cover
  • gap-filling

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