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- W4319458388 abstract "First posted February 8, 2023 For additional information, contact: Director, Earth Resources Observation and Science CenterU.S. Geological Survey47914 252nd StreetSioux Falls, SD 57198Contact Pubs Warehouse 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." @default.
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- W4319458388 date "2023-01-01" @default.
- W4319458388 modified "2023-09-27" @default.
- W4319458388 title "Improving temporal frequency of Landsat surface temperature products using the gap-filling algorithm" @default.
- W4319458388 doi "https://doi.org/10.3133/ofr20231006" @default.
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