4.7 Article

A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data

期刊

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 59, 期 12, 页码 10738-10752

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3053284

关键词

Land surface temperature; Spatiotemporal phenomena; Clouds; Land surface; Temperature sensors; MODIS; Remote sensing; China; gapfilling; land surface temperature (LST); remote sensing; interpolation

资金

  1. National Natural Science Foundation of China [41771360, 41975044, 41801021, 41905032, 42001314, 42001016]
  2. Fundamental Research Funds for National Universities, China University of Geosciences (Wuhan)

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The study introduced an enhanced hybrid (EH) method for gapfilling, utilizing information from similar LST products to improve accuracy. Results showed that the EH method had higher accuracy compared to other methods, demonstrating the effectiveness of using information from other similar products.
Satellite-derived land surface temperatures (LSTs) are a critical parameter in various fields. Unfortunately, there are numerous gaps in LST products due to cloud contamination and orbital gaps. In previous studies, various gapfilling methods have been developed. However, most of those methods use only spatiotemporal information to fill gaps. In this study, a gapfilling method called the enhanced hybrid (EH) method that integrates spatiotemporal information and information from other similar LST products was proposed. The accuracy of the EH method was compared with the accuracies of three other gapfilling methods that only use spatiotemporal information: Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST), interpolation of the mean anomalies (IMAs), and Gapfill. It was found that the correlations between the four LST products were strong, indicating that using information from other products may improve the accuracy of gapfilling. On average, the mean absolute errors (MAEs) of the data filled using the EH method were 23.7%-52.7% lower than those of RSDAST, 35.4%-38.7% lower than those of IMA, and 38.5%-46.9% lower than those of the Gapfill method. The usage of information from other similar LST products was the main reason for the high accuracy observed for the EH method. In addition, the LST images filled using the RSDAST and IMA methods had some outliers, while there were fewer obvious outliers in the LST images filled with the EH method. It was concluded that the EH method is a robust gapfilling method with a high accuracy.

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