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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change

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Spatial Downscaling of MODIS Land Surface Temperature: Recent Research Trends, Challenges, and Future Directions

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Title
Spatial Downscaling of MODIS Land Surface Temperature: Recent Research Trends, Challenges, and Future Directions
Author
Yoo, CheolheeIm, JunghoPark, SuminCho, Dongjin
Issue Date
2020-08
Publisher
대한원격탐사학회
Citation
대한원격탐사학회지, v.36, no.4, pp.609 - 626
Abstract
Satellite-based land surface temperature (LST) has been used as one of the major parameters in various climate and environmental models. Especially, Moderate Resolution Imaging Spectroradiometer (MODIS) LST is the most widely used satellite-based LST product due to its spatiotemporal coverage (1 km spatial and sub-daily temporal resolutions) and longevity (> 20 years). However, there is an increasing demand for LST products with finer spatial resolution (e.g., 10-250 m) over regions such as urban areas. Therefore, various methods have been proposed to produce high-resolution MODIS-like LST less than 250 m (e.g., 100 m). The purpose of this review is to provide a comprehensive overview of recent research trends and challenges for the downscaling of MODIS LST. Based on the recent literature survey for the past decade, the downscaling techniques classified into three groups—kernel-driven, fusion-based, and the combination of kernel-driven and fusion-based methods—were reviewed with their pros and cons. Then, five open issues and challenges were discussed: uncertainty in LST retrievals, low thermal contrast, the nonlinearity of LST temporal change, cloud contamination, and model generalization. Future research directions of LST downscaling were finally provided.
URI
https://scholarworks.unist.ac.kr/handle/201301/49516
DOI
10.7780/kjrs.2020.36.4.9
ISSN
1225-6161
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