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.

Land surface temperature (LST) and emissivity for large areas can only be derived from surface-leaving radiation measured by satellite sensors. These measurements represent the integrated effect of the surface and are, thus, for many applications, superior to point measurements on the ground, e.g. in Earth's radiation budget and climate change detection. Over the years, a substantial amount of research was dedicated to the estimation of LST and emissivity from passive sensor data. This article provides the theoretical basis and gives an overview of the current status of this research. Sensors operating in the visible, infrared and microwave range onboard various meteorological satellites are considered, e.g. Meteosat-MVIRI, NOAA-AVHRR, ERS-ATSR, Terra-MODIS, Terra-ASTER and DMSP-SSM/I. Atmospheric effects on measured brightness temperatures are described and atmospheric corrections using radiative transfer models (RTM) are explained. The substitution of RTM with neural networks (NN) for faster forward calculations is also discussed. The methods reviewed for LST estimation are the single-channel method, the split-window techniques (SWT), and the multi-angle method, and, for emissivity estimation, the normalized emissivity method (NEM), the thermal infrared spectral indices (TISI) method, the spectral ratio method, alpha residuals, normalized difference vegetation index (NDVI )-based methods, classification-based emissivity and the temperature emissivity separation (TES) algorithm.

Land surface temperature (LST) is estimated from thermal infrared data provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), using a generalized split-window (GSW) algorithm. The uncertainty of the LST retrievals is highly dependent on the input accuracy and retrieval conditions, particularly the sensor view angle and the atmospheric water vapor content. This paper presents a quantification of the uncertainty of LST estimations, taking into account error statistics of the GSW under a globally representative collection of atmospheric profiles, and a careful characterization of the uncertainty of input data, particularly the surface emissivity and forecasts of the total water vapor content. Such analysis is the basis for LST uncertainty estimation, also distributed to users, in the form of error bars, along with the LST retrievals. Moreover, the spatial coverage of SEVIRI LST is essentially determined by the LST expected uncertainty, instead of being restricted to view zenith angles below a given threshold (e.g., 60°). Within the MSG disk, the atmosphere is often dry for clear-sky conditions where angles are large (e.g., Northern and Eastern Europe and Saudi Arabia). By considering several factors that contribute to LST inaccuracies, it is possible to increase the spatial coverage to regions such as those mentioned earlier. Retrieved values are also compared with in situ observations collected in Namibia, covering a seasonal cycle. The two data sets are in good agreement with root-mean-square differences ranging between 1°C and 2°C, which is well below the average error estimated for the satellite retrievals.