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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. [ABSTRACT FROM AUTHOR]

The impact of ecosystem conservation and restoration activities are rarely monitored from a global, multidimensional and multivariable perspective. Here we present an approach to quantify the environmental impact of landscape restoration using long-term and high-resolution satellite observations. For two restoration areas in Tanzania, we can likely attribute an increase in the amount of water retained by the soil (∼0.01 m³ m⁻³, ∼13% average increase), a soil temperature drop (∼-0.5°C) and an increase in surface greenness (∼50% average increase) in 3.5 years. These datasets illuminate the impact of restoration initiatives on the landscape and support the reporting of comprehensive metrics to donors and partners. Satellite observations from commercial providers and space agencies are now achieving the frequency, resolution, and accuracy that can allow for the effective evaluation of restoration activities. [ABSTRACT FROM AUTHOR]

The assessment of satellite-derived land surface temperature (LST) data is essential to ensure their high quality for climate applications and research. This study intercompared seven LST products (i.e., ATSR_3, MODISA, MODIST, SLSTRA, SLSTRB, SEVIR2 and SEVIR4) of the European Space Agency's (ESA) LST Climate Change Initiative (LST_cci) project, which are retrieved for polar and geostationary orbit satellites, and three operational LST products: NASA's MODIS MOD11/MYD11 LST and ESA's AATSR LST. All data were re-gridded on to a common spatial grid of 0.05° and matched for concurrent overpasses within 5 min. The matched data were analysed over Europe and Africa for monthly and seasonally aggregated median differences and studied for their dependence on land cover class and satellite viewing geometry. For most of the data sets, the results showed an overall agreement within ±2 K for median differences and robust standard deviation (RSD). A seasonal variation of median differences between polar and geostationary orbit sensor data was observed over Europe, which showed higher differences in summer and lower in winter. Over all land cover classes, NASA's operational MODIS LST products were about 2 K colder than the LST_cci data sets. No seasonal differences were observed for the different land covers, but larger median differences between data sets were seen over bare soil land cover classes. Regarding the viewing geometry, an asymmetric increase of differences with respect to nadir view was observed for day-time data, which is mainly caused by shadow effects. For night-time data, these differences were symmetric and considerably smaller. Overall, despite the differences in the LST retrieval algorithms of the intercompared data sets, a good consistency between the LST_cci data sets was determined. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) and Surface Net Radiation (SNR) are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions. Although high-resolution LST and SNR retrievals are available, they have large gaps due to cloud-cover that hinder their use as input in models. Here, we present a downscaled and continuous daily LST and SNR product across Europe for 2018-2019. The LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite, and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel 3 satellites. The product combines the medium spatial (approx. 5-7 km) but high temporal (30 minute) resolution, gap-free data from MSG, with the low temporal (2-3 days) but high spatial (1 km) resolution of the Sentinel 3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias-correction and Kalman Filter assimilation. Longwave outgoing radiation is computed from the merged LST product in combination with MSG-based emissivity data. Shortwave outgoing radiation is computed from the incoming shortwave radiation from MSG and downscaled albedos using 1 km PROBA-V data. MSG incoming shortwave and longwave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the root mean squared error by ca. 8% with a substantial increase in spatial detail compared to the original MSG product. The resulting pan-European LST and SNR dataset can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future. [ABSTRACT FROM AUTHOR]

Previous studies have indicated that summer-time aerosol optical depths (AOD) over the southeastern US are dependent on temperature but the reason for this dependence and its radiative effects have so far been unclear. To quantify these effects we utilized AOD and land surface temperature (LST) products from the Advanced Along-Track Scanning Radiometer (AATSR) with observations of tropospheric nitrogen dioxide (NO2) column densities from the Ozone Monitoring Instrument (OMI). Furthermore, simulations of the global aerosol-climate model ECHAM-HAMMOZ have been used to identify the possible processes affecting aerosol loads and their dependence on temperature over the studied region. Our results showed that the level of AOD in the southeastern US is mainly governed by anthropogenic emissions but the observed temperature dependent behaviour is most likely originating from non-anthropogenic emissions. Model simulations indicated that biogenic emissions of volatile organic compounds (BVOC) can explain the observed temperature dependence of AOD. According to the remote sensing data sets, the non-anthropogenic contribution increases AOD by approximately 0.009 ± 0.018 K−1 while the modelled BVOC emissions increase AOD by 0.022 ± 0.002 K−1. Consequently, the regional direct radiative effect (DRE) of the non-anthropogenic AOD is −0.43 ± 0.88 W/m²/K and −0.17 ± 0.35 W/m²/K for clear-and all-sky conditions, respectively. The model estimate of the regional clear-sky DRE for biogenic aerosols is also in the same range: −0.86 ± 0.06 W/m²/K. These DRE values indicate significantly larger cooling than the values reported for other forested regions. Furthermore, the model simulations showed that biogenic emissions increased the number of biogenic aerosols with radius larger than 100 nm (N100, proxy for cloud condensation nuclei) by 28 % per one degree temperature increase. For the total N100, the corresponding increase was 4 % which implies that biogenic emissions could also have a small effect on indirect radiative forcing in this region. [ABSTRACT FROM AUTHOR]

The Copernicus Global Land Service provides a wide range of satellite and earth observation derived products focused on monitoring of the terrestrial environment. For these products to be of use for environmental decision-making they must be validated using independent data sources to ensure they are of high quality and consistency.Validation of satellite-derived products has traditionally been through intensive field campaigns, which enable the spatial variability of the variable of interest to be well-characterised over a particular site but are limited in time and space. In recent decades, numerous ground-based monitoring sites have been established as part of several wider ground-based observation networks. The spatial variability is not as well-characterised for these sites, but they provide a better sampling of data and provide useful information in terms of temporal consistency of products.The Ground-Based Observations for validation of Copernicus Global Land Products (GBOV) project aims to develop and distribute robust in-situ datasets from these ground-based monitoring sites for a systematic and quantitative validation of all seven Copernicus Global Land Products (CGLPs). In GBOV, observations from identified monitoring sites are processed into various point-scale reference measurements and then upscaled in order to be more representative of the equivalent CGLP pixel, which will cover a much wider spatial area.Here we present the methods used to upscale in situ Land Surface Temperature (LST) data for validation of the Copernicus Global Land Service LST product. We describe the derivation of in situ LST data from radiometric observations at several ground-based monitoring sites. We then outline the upscaling method used to estimate LSTs which are equivalent to a CGLP pixel from the derived point-scale in situ LSTs. We also present results comparing the validation of the Copernicus Global Land Service LST product with upscaled LST data instead of point in situ LST data. [ABSTRACT FROM AUTHOR]

This contribution presents the results of a systematic validation of several state-of-the-art satellite data sets of land surface temperature (LST) against in situ data. LST can be obtained with satellite remote sensing and is a useful quantity for various applications within climate research. Information on the quality of satellite derived LST data is usually gained by validating it against in situ data, which is generally based on investigating the absolute differences between the two data sets. In the work presented here, LST data sets from several polar-orbiting and geostationary satellites, namely AATSR, GOES, MODIS and SEVIRI, are compared over two sets of in situ stations, operated by Karlsruhe Institute of Technology (KIT) and by the National Oceanic and Atmospheric Administration (NOAA) (Surface Radiation Budget Network - SURFRAD stations). The stations are located throughout the world and cover different land surface types, climates, biomes and topographies. Three years of satellite LST data are validated over all stations and, thanks to longer data availability, ten years over the SURFRAD sites. All satellite, in situ and matched data sets were produced in a harmonized format developed in the framework of ESA's GlobTemperature (GT) project. The obtained results are directly comparable to each other, as all validations follow a standardized approach. The results of the validations show that the differences between satellite and in situ LST are strongly influenced by spatial homogeneity, land cover class and orography. For the years 2010 – 12, overall differences between satellite and in situ LST are often within 2 K, with generally better results during night. During day, when shadows are present and the influence of heterogeneous land covers is higher, the observed differences increase. AATSR and MODIS LST tend to exceed the corresponding in situ LST values, whereas SEVIRI and GOES LST tend to be lower than the in situ LST. Time series of LST differences were also investigated at each station separately. A seasonal cycle was observed for most data sets, but its magnitude varied significantly with station and satellite data set. It was strongest for a station located in a heterogeneous landscape and for a station exposed to rainy seasons. At another station located in a valley, a considerable influence from topography was found. At two stations, directional effects from bushes or trees were clearly visible in the analyses. The results show that it is important to investigate the differences between the area-integrated satellite measurements and the in situ point measurement for each site separately in order to be able to interpret the many factors influencing the results at each station. In situ validation of LST is a well-established and reliable method to gain information on the quality of satellite LST data sets and the large GT data base makes it feasible to perform standardised validations and inter-comparisons. The results presented here provide a baseline for further validation work and for constructing a larger and more comprehensive data base within ESA's Climate Change Initiative (CCI) LST project. [ABSTRACT FROM AUTHOR]