Abstract
Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE.
Highlights
Land Surface Temperature (LST) is an important component of the Earth’s energy budget, closely linked to the partitioning between sensible and latent heat fluxes
TOA brightness temperatures for the Landsat’s thermal infrared (TIR) channels are provided by the United States Geological Survey (USGS) and are fully available and ready to use in Google Earth Engine (GEE) for Landsats 4–8, collection 1
Landsat 5 shows the largest errors, followed by Landsat 7, 4, and 8—these discrepancies are related to the different spectral characteristics of the sensors
Summary
Land Surface Temperature (LST) is an important component of the Earth’s energy budget, closely linked to the partitioning between sensible and latent heat fluxes. High-resolution satellite-derived LST is increasingly used in various applications related to the assessment of land surface conditions, including mapping the urban extent and the intensity of urban micro-climates, estimating high-resolution evapotranspiration for the management of water resources and assessing vegetation stress [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution that are appropriate for local and small-scale studies. While most algorithms are simple to implement, they require users to provide the necessary input data and calibration coefficients, which are generally not readily available. Some datasets are available online (e.g., [25,26]); they generally require users to be able to handle large volumes of data. Until now high-resolution LST datasets from Landsat have been unavailable in GEE
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