The accuracy of land surface temperature (LST) observations is critical to many applications. Any observation of LST is subject to incomplete knowledge, so an accurate assessment of the uncertainty budget is critical. We present a comprehensive and consistent approach to determining an uncertainty budget for LST products. We apply this approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Aqua satellite. In order to generate the uncertainty model, a new implementation of the generalised split-window algorithm is applied, in which retrieval coefficients are categorised by viewing angle and water vapour. Validation of the LST against in situ data shows a mean absolute bias of 0.37 K for daytime and 0.73 K for nighttime. The average standard deviation per site is 1.53 K for daytime and 1.21 K for nighttime. Uncertainties from the implemented model are estimates in their own right and are also validated. We do this by comparing the standard deviation of the differences between the satellite and in situ LSTs, and the total uncertainties of the validation matchups. We show that the uncertainty model provides a good fit. Our approach offers a framework for quantifying uncertainties for LST that is equally applicable across different sensors and different retrieval approaches.

To ensure optimal and consistent algorithm usage within climate studies utilizing satellite-derived Land Surface Temperature (LST) datasets, an algorithm intercomparison exercise was undertaken to assess the various operational and scientific LST retrieval algorithms in use. This study was focused on several LST products including single-sensor products for AATSR, Terra-MODIS, SEVIRI, SSM/I and SSMIS; a Climate Date Record (CDR), which is a combined dataset drawing from AATSR, SLSTR and MODIS; and finally a merged low Earth orbit/geostationary product using data from AATSR, MODIS and SEVIRI. Therefore, the analysis included 14 algorithms: seven thermal infrared algorithms and seven microwave algorithms. The thermal infrared algorithms include five split-window coefficient-based algorithms, one optimal estimation algorithm and one single-channel inversion algorithm, with the microwave focusing on linear regression and neural network methods. The algorithm intercomparison assessed the performance of the retrieval algorithms for all sensors using a benchmark database. This approach was chosen due to the lack of sufficient in situ validation sites globally and the bias this limited set engendered on the training of particular algorithms. A simulated approach has the ability to test all parameters in a consistent, fair manner at a global scale. The benchmark database was constructed from European Centre for Medium-Range Weather Forecasts Re-analysis 5 (ERA5) atmospheric data, Combined ASTER and MODIS Emissivity for Land (CAMEL) infrared emissivity data, and Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM) emissivity data for the period of 2013–2015. The best-performing algorithms had biases of under 0.2 K and standard deviations of approximately 0.7 K. These results were consistent across multiple sensors. Areas of improvement, such as coefficient banding, were found for all algorithms as well as lines for further inquiry that could improve the global and regional performance.

Land Surface Temperature (LST) is integral to our understanding of the radiative energy budget of the Earth’s surface since it provides the best approximation to the thermodynamic temperature that drives the outgoing longwave flux from surface to atmosphere. Since 5 July 2017, an operational LST product has been available from the Sentinel-3A mission, with the corresponding product being available from Sentinel-3B since 17 November 2018. Here, we present the first paper describing formal products, including algorithms, for the Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard Sentinel-3A and 3B (SLSTR-A and SLSTR-B, respectively). We evaluate the quality of both the Land Surface Temperature Climate Change Initiative (LST_cci) product and the Copernicus operational LST product (SL_2_LST) for the years 2018 to 2021. The evaluation takes the form of a validation against ground-based observations of LST across eleven well-established in situ stations. For the validation, the mean absolute daytime and night-time difference against the in situ measurements for the LST_cci product is 0.77 K and 0.50 K, respectively, for SLSTR-A, and 0.91 K and 0.54 K, respectively, for SLSTR-B. These are an improvement on the corresponding statistics for the SL_2_LST product, which are 1.45 K (daytime) and 0.76 (night-time) for SLSTR-A, and 1.29 K (daytime) and 0.77 (night-time) for SLSTR-B. The key influencing factors in this improvement include an upgraded database of reference states for the generation of retrieval coefficients, higher stratification of the auxiliary data for the biome and fractional vegetation, and enhanced cloud masking.