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Improving temporal frequency of Landsat surface temperature products using the gap-filling algorithm

February 8, 2023

Remotely sensed surface temperature (ST) has been widely used to monitor and assess landscape thermal conditions, hydrologic modeling, and surface energy balance. Landsat thermal sensors have continuously measured the Earth surface thermal radiance since August 1982. The thermal radiance measurements are atmospherically compensated and converted to Landsat STs and delivered as part of the U.S. Geological Survey Landsat Collection 1 U.S. Analysis Ready Data; however, the low satellite revisit cycles combined with the presence of clouds and cloud shadows reduce the number of valid retrievals. This reduction can limit the ability to monitor annual or seasonal variations in the surface thermal budget. These factors reduce the ability to use the temperature data to fit time series for historical trend analysis to match background climate variations. In this study, we implemented an approach that uses linear harmonic least absolute shrinkage and selection operator regression models to fill gaps because of clouds, shadows, and coarse temporal resolution. The gap-filled data provide increased temporal density of Landsat ST records. The gap-filled Landsat ST, therefore, can allow for an improved monitoring of annual, seasonal, or even monthly landscape thermal conditions.

Publication Year 2023
Title Improving temporal frequency of Landsat surface temperature products using the gap-filling algorithm
DOI 10.3133/ofr20231006
Authors George Z. Xian, Hua Shi, Saeed Arab, Chase Mueller, Reza Hussain, Kristi Sayler, Danny Howard
Publication Type Report
Publication Subtype USGS Numbered Series
Series Title Open-File Report
Series Number 2023-1006
Index ID ofr20231006
Record Source USGS Publications Warehouse
USGS Organization Earth Resources Observation and Science (EROS) Center
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