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Natural surfaces are mostly anisotropic emitters, contributing to the anisotropic behavior of Land Surface Temperature (LST). This characteristic of thermal infrared emissivity is well known and several studies have tried to simulate this behavior either with physical or empirical models. However, given the high heterogeneity of land surfaces, the translation of the angular dependence of emissivity as provided from measurements or models into satellite pixel scale anisotropy is generally very difficult. Here we propose a reformulation of the Mean Minimum-Maximum Difference (MMD) curve of the Temperature-Emissivity Separation (TES) algorithm to allow a correct adjustment of the TES retrievals by taking into account the emissivity angular distribution. For that purpose, the Multi-Sensor method is used to obtain directional emissivities at different sites in the Saharan and Namib desert. The data is then used to calibrate the view-angle dependence of the new MMD formulation. The TES retrievals obtained with the new formulation show a good agreement with the Multi-Sensor data. Results also suggest that the new coefficients of the MMD can be applied to other sensors with similar spectral channels. The new angle-dependent emissivities may lead to a reduction of LST bias as high as 2 K for view angles above 50o. The proposed formulation is currently only valid over barren surfaces. • We propose updating the TES algorithm to incorporate emissivity directionality. • The multi-sensor method is used to create a calibration dataset. • The MMD equation was revised to include a view angle dependence. • The new formulation can be applied to sensors with similar spectral configurations. [ABSTRACT FROM AUTHOR]

Changes in lake water temperature, observed with the greatest intensity during the last two decades, may significantly affect the functioning of these unique ecosystems. Currently, in situ studies in Poland are conducted only for 38 lakes using the single-point method. The aim of this study was to develop a method for remote sensing monitoring of lake water temperature in a spatio-temporal context based on Landsat 8 imagery. For this purpose, using data obtained for 28 lakes from the period 2013–2020, linear regression (LM) and random forest (RF) models were developed to estimate surface water temperature. In addition, analysis of Landsat Level-2 Surface Temperature Science Product (LST-L2) data provided by United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) was performed. The remaining 10 lakes not previously used in the model development stage were used to validate model performance. The results showed that the most accurate estimation is possible using the RF method for which RMSE = 1.83 °C and R2 = 0.89, while RMSE = 3.68 °C and R2 = 0.8 for the LST-L2 method. We found that LST-L2 contains a systematic error in the coastal zone, which can be corrected and eventually improve the quality of estimation. The satellite-based method makes it possible to determine water temperature for all lakes in Poland at different times and to understand the influence of climatic factors affecting temperature at the regional scale. On the other hand, spatial presentation of thermics within individual lakes enables understanding the influence of local factors and morphometric conditions. [ABSTRACT FROM AUTHOR]

Land surface temperature is linked to a wide range of surface processes. Given the increased development of earth observation systems, a large effort has been put into advancing land surface temperature retrieval algorithms from remote sensors. Due to the very limited number of reliable in situ observations matching the spatial scales of satellite observations, algorithm development relies on synthetic databases, which then constitute a crucial part of algorithm development. Here we provide a database of atmospheric profiles and respective surface conditions that can be used to train and verify algorithms for land surface temperature retrieval, including machine learning techniques. The database was built from ERA5 data resampled through a dissimilarity criterion applied to the temperature and specific humidity profiles. This criterion aims to obtain regular distributions of these variables, ensuring a good representation of all atmospheric conditions. The corresponding vertical profiles of ozone and relevant surface and vertically integrated variables are also included in the dataset. Information on the surface conditions (i.e., temperature and emissivity) was complemented with data from a wide array of satellite products, enabling a more realistic surface representation. The dataset is freely available online at Zenodo. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is a fundamental state variable for land surface processes, long available from satellite observations in the thermal infrared. Here we make an assessment of LST products derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board the geostationary satellite Meteosat Second Generation (MSG), and from the Advanced Very High Resolution Radiometer (AVHRR) on-board Metop polar-orbiting satellites, as provided by the Satellite Application Facility on Land Surface Analysis (LSA-SAF). The validation of LSA-SAF LST products uses ground observations taking into account the representativeness of each station at pixel scale. The in situ - satellite comparisons reveal overall accuracies of 0.13 °C for SEVIRI LST and −0.32 °C for AVHRR-based LST. Better matches are usually found for night-time observations, highlighting the influence of LST spatial and temporal variability as well as of sensor view angle on satellite daytime estimates. The averaged difference between AVHRR and SEVIRI LST products remains mostly within ±1 °C. The impact of different viewing and illumination angles is particularly pronounced for daytime matchups, leading to higher standard deviation of differences than in the case of night-time. Over heterogeneous landscapes, this primarily results from illumination-shadowing effects in daytime observations present in all LST estimates derived from infrared observations. These differences are smaller for night-time observations, or over homogeneous surfaces in general: in these cases, the dependency of LST differences on viewing geometry is smoother and likely explained by the impact of view angle on surface emissivity. Overall the LSA-SAF SEVIRI and AVHRR LST datasets are consistent and meet high accuracy standards. Their production is ensured by the LSA-SAF throughout the life-cycle of the sensors' satellite platforms, making them good candidates for long term applications and studies. [ABSTRACT FROM AUTHOR]

The effects of photovoltaic solar parks on the local environment are a growing topic of interest, as more solar parks are installed worldwide. Most studies focus on large solar parks, as these usually have more availability for both in situ and remote sensing data. Here, remote sensing data of surface temperature and vegetation were used to assess the impact of a relatively small utility-scale solar park (46 MW) in Portugal, in a region with a Mediterranean climate. Results show that the impact translates into a seasonal pattern, with positive surface temperature anomalies during the colder months and negative ones during the summer, which overall reduces the amplitude of the seasonal cycle. Comparing the operational period of the solar park with the one before its installation yields an increase in surface temperature for the control area of 1.74 °C contrasting with a decrease of 0.23 °C in the solar park area, during summertime (JJA). For the winter months (DJF), surface temperature increased for the solar park and control areas, by 0.4 °C and 1.44 °C, respectively. The effect is similar but less pronounced for anomalies in the inferred air temperature. For vegetation, negative anomalies in winter and little to no change during hotter months are observed. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) values retrieved from satellite measurements in the thermal infrared (TIR) may be strongly affected by spatial anisotropy. Different parametric approaches have been proposed to simulate such effects. These are relatively simple models requiring few input data and therefore appropriate to simulate directional effects in satellite LST retrievals over large areas. The purpose of this study is to consistently evaluate the performance of two parametric models (the so-called Kernel and Hotspot models), and to assess their respective potential to correct directional effects on LST for a wide range of surface conditions, in terms of tree coverage, vegetation density, surface emissivity. We also propose an optimization of the correction of directional effects through a synergistic use of both models. The Kernel model allows an effective simulation of LST directionality associated with shadowing effects and emissivity anisotropy, but results show that it significantly underestimates the amplitude of the angular corrections. The Hotspot model performs better in simulating anisotropy related to shadowing effects. However, it is unable to account for emissivity anisotropy, showing lower performance than the Kernel model for nighttime data and for low tree coverage. The combined Kernel-Hotspot model provides corrections on LST directionality with reliable quality, with particularly improved performance during nighttime and for low tree densities. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a markedly directional variable and its remotely sensed measurement may be strongly affected by viewing and illumination geometries. This study proposes the use of LST products collocated in space and time, but obtained with different viewing angles, to calibrate a simple model capable of characterizing the LST angular variability. The exercise is performed using MODIS (Aqua and Terra) and SEVIRI (Meteosat) LST products, for an area covering Mediterranean Europe and Northern Africa and encompassing the full years of 2011, 2012, 2013 and 2014. The approach relies on a kernel model that is composed by an “emissivity kernel” and a “solar kernel”, associated to observation angle anisotropy and to shadowing/sunlit effects on the surface, respectively. The spatial distribution of the kernel coefficients is shown to reflect characteristics of the landscape, both in terms of vegetation cover and topography. Model performance is assessed through several comparison exercises over the 4-year period under analysis. Cross-validation results show that the angular correction by the kernel model leads to a decrease of the root mean square difference between SEVIRI and MODIS daytime (night-time) LST products, from the original uncorrected values of 3.5 K (1.5 K) to 2.3 K (1.3 K). Comparison of both MSG and MODIS LST products against in situ daytime measurements gathered over 2 years at a validation site in Évora (Portugal) reveals that the angular correction leads to a decrease in root mean square error from 4.6 K (2.0 K) to 3.8 K (1.9 K) for MODIS (SEVIRI). The kernel model may be a useful tool to quantify the LST uncertainties associated with viewing and illumination angles. [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]

Satellite retrieved values of Land Surface Temperature (LST) over structured heterogeneous pixels generally depend on viewing and illumination angles as well as on the characteristics of the land cover. Here we present a method to quantify such dependencies on land surface characteristics, sun illumination and satellite position. The method uses a geometric model to describe the surface elements viewed by an air-borne sensor and relies on parallel-ray geometry to calculate the projections of tree canopies and sunlit and shaded ground: these are considered as basic surface elements responsible for most of the spatial variability of LST. For a woodland landscape we demonstrate that modeling the fractions of these basic surface elements within the sensor field-of-view allows us to quantify the directional effects observed on satellite LST with sufficient accuracy. Geometric models are an effective tool to upscale in situ measurements for the validation of LST over discontinuous canopies (e.g. forests). Here we present the application of a model to observations of brightness temperature from the LSA-SAF validation site in Évora (Portugal), an area of oak woodland, over the one-year period from October 2011 to September 2012. The resulting composite temperature is compared against LSA SAF LST products from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat as well as against MYD11A1/MOD11A1 (collection 5) products from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard AQUA and TERRA. Comparisons with modeled ground LST show that SEVIRI LST has a bias of 0.26°C and a RMSE of 1.34°C, whereas MODIS LST (MYD11A1 and MOD11A1, collection 5) has a bias of −1.54°C and a RMSE of 2.37°C. Both MODIS and SEVIRI LST are closer to in situ values obtained with the geometric model than to those obtained when disregarding the effects of viewing and illumination geometry. These results demonstrate the need to consider the directional character of LST products, especially for validation purposes over heterogeneous land covers. For the new MODIS LST product (MOD21), which is based on the Temperature-Emissivity Separation (TES) algorithm, comparisons with in-situ LST show an improved bias of −0.81°C and a RMSE of 1.48°C (daytime values only). The TES based product presents lower emissivity values than those used for retrieving MYD11A1/MOD11A1 LST, which may partially explain the improved match with in-situ LST. Discrepancies between LST retrievals obtained from different sensors, especially those on different orbits can also be partly explained by their viewing/illumination geometries. In this study the geometric model is used to correct LST deviations between simultaneous MODIS and SEVIRI LST estimations related to those effects. When the model is used to correct the variable MODIS viewing geometry there is a reduction in standard deviation of about 0.5°C. [ABSTRACT FROM AUTHOR]

Cities concentrate people, wealth, emissions, and infrastructure, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This urgently demands for accurate urban climate projections to help organizations and individuals to make climate-smart decisions. However, most of the large ensembles of global and regional climate model simulations do not include sophisticated urban parameterizations (e.g., EURO-CORDEX; CMIP5/6). Here, we explore this shortcoming in ERA5 (the latest generation reanalysis from the European Centre for Medium-Range Weather Forecasts) and in a simulation with the SURFEX (Surface Externalisée) land surface model employing the widely used bulk bare rock approach. The city of Paris is considered as a case study. Subsequently, we apply a more complex urban scheme – SURFEX coupled to the Town Energy Balance (TEB) urban canopy model to assess its benefits on characterizing the Paris urban climate. Both simulations and ERA5 were compared to the LSA SAF (Satellite Application Facility on Land Surface Analysis) land surface temperature product to evaluate the simulation of Parisian surface urban heat island (SUHI). Our results show a significant added value of SURFEX-TEB in reproducing the SUHI during the daytime and the UHI during both the daytime and nighttime (with overall reductions in the bias and root mean square error and improvements in the representation of the statistics of the SUHI/UHI displayed by the Perkins skill score or S score). The improvement in the simulated SUHI is lower during the nighttime due to the lack of land–atmosphere feedbacks in the proposed offline framework. Nonetheless, the offline SURFEX-TEB framework applied here clearly demonstrates the added value of using more comprehensive parameterization schemes to simulate the urban climate and, therefore, allowing the improvement of urban climate projections. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key variable in surface-atmosphere energy and water exchanges. The main goals of this study are to (i) evaluate the LST of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and ERA5 reanalyses over Iberian Peninsula using the Satellite Application Facility on Land Surface Analysis (LSA-SAF) product and to (ii) understand the main drivers of the LST errors in the reanalysis. Simulations with the ECMWF land-surface model in offline mode (uncoupled) were carried out over the Iberian Peninsula and compared with the reanalysis data. Several sensitivity simulations were performed in a confined domain centered in Southern Portugal to investigate potential sources of the LST errors. The Copernicus Global Land Service (CGLS) fraction of green vegetation cover (FCover) and the European Space Agency's Climate Change Initiative (ESA-CCI) Land Cover dataset were explored. We found a general underestimation of daytime LST and slightly overestimation at night-time. The results indicate that there is still room for improvement in the simulation of LST in ECMWF products. Still, ERA5 presents an overall higher quality product in relation to ERA-Interim. Our analysis suggested a relation between the large daytime cold bias and vegetation cover differences between (ERA5 and CGLS FCocver) with a correlation of −0.45. The replacement of the low and high vegetation cover by those of ESA-CCI provided an overall reduction of the large Tmax biases during summer. The increased vertical resolution of the soil at the surface, has a positive impact, but much smaller when compared with the vegetation changes. The sensitivity of the vegetation density parameter, that currently depends on the vegetation type, provided further proof for a needed revision of the vegetation in the model, as there is a reasonable correlation between this parameter and the Tmax mean errors when using the ESA-CCI vegetation cover (while the same correlation cannot be reproduced with the original model vegetation). Our results support the hypothesis that vegetation cover is one of the main drivers of the LST summertime cold bias in ERA5 over Iberian Peninsula. [ABSTRACT FROM AUTHOR]

Cities concentrate people, wealth, emissions, and infrastructures, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This places an urgent demand for accurate urban climate projections to help organizations and individuals making climate smart-decisions. However, most of the state-of-the-art global and regional climate models have an oversimplified representation of (or completely neglect) urban climate processes. Here, we use the city of Paris as a case study to show that this is the case for the fifth (and latest) generation reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) and for simulations employing the widely used bulk bare rock approach to urban climate parameterization. Subsequently, we leveraged on the hourly resolution of ERA5 and the Satellite Application Facility Land Surface Analysis (LSA-SAF) land surface temperature product to demonstrate the significant added value of employing the SURFEX land-surface model coupled to Town Energy Balance (TEB) urban canopy model in simulating the Parisian Surface Urban Heat Island (SUHI) during daytime and the urban heat island during both daytime and nighttime. Our results showed the significant added value of SURFEX-TEB in reproducing the observed daytime and nighttime Parisian urban heat island effect. An annual average bias magnitude reduction of 0.5 °C was observed for daytime and around 1.5 °C for nighttime when compared to ERA5 and bare rock approach. Also, SURFEX-TEB revealed an overall better performance in reproducing the observed daytime SUHI, whilst the added value of SURFEX-TEB was lower during nighttime (but still slightly better than ERA5 and the bare rock approach), due to the lack of land-atmosphere feedbacks in the proposed offline framework. Finally, the offline SURFEX-TEB framework applied here demonstrates the ability to simulate the urban climate, which is an asset to build urban climate projections that allow the development of mitigation and adaptation strategies. [ABSTRACT FROM AUTHOR]

The Land surface temperature (LST) is one of the most relevant parameters when addressing the physical processes that take place at the surface of the Earth. Satellite data are particularly appropriate for measuring LST over large areas with high temporal resolution. The estimation of LST from space-borne sensors has been systematically performed at a global scale for over three decades and LST climate data records (CDRs) are available e.g. from the International Satellite Cloud Climatology Project and the Pathfinder Atmospheres-Extended dataset, opening new perspectives for climate monitoring.Most available long-term databases of Land Surface Temperature (LST) derived from space-borne sensors rely on infrared (IR) observations. Although there is a large number of IR sensors on-board geostationary satellites and polar orbiters providing LST retrievals with different temporal and spatial resolutions, the use of IR observations limits LST estimates to clear-sky conditions. The use of IR LST datasets may therefore result in analyses that are "biased" towards clear-sky conditions and not representative of all-weather conditions. Such "clear-sky bias" of IR-based LST (defined here as the difference between average clear-sky and average all-weather LSTs) has never been quantified. Hence, the true impact of relying only on clear-sky data is still uncertain. On the other hand, an "all-weather" global LST database may be obtained from passive microwave (MW) measurements, which are much less affected by clouds. Using the subset of clear-sky MW LST as a surrogate for IR LST, global maps of "LST clear-sky bias" can therefore be generated that will allow making a first assessment of the spatial and temporal characteristics of the deviations.A 3-year record of all-weather MW LST is used here to provide a first quantification of the clear-sky bias of IR LST at the global scale. The LST dataset used for this purpose relies on MW observations provided by the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) onboard NASA's Aqua satellite and is publicly available through the ESA DUE GlobTemperature project. The amplitude of the bias is closely related to the fraction of clear-sky days and, therefore, arid regions are associated to very low values of bias whereas mid-latitudes present the highest values. During daytime, the input of solar radiation for clear-sky situations leads to higher LST values and therefore the bias is generally positive (e.g, 2-8 K over the mid-latitudes) whereas, during nighttime, the bias is generally negative although with lower amplitude (around -2 K), because of the increased radiative cooling for clear-sky situations. The clear, cloudy and all-sky LSTs are also compared with near-surface air temperature. Although LST is generally higher than air temperature, the contrast between the two may be strongly influenced by local weather conditions. Both the clear-sky bias and differences between LST and air temperature are also analyzed at the local scale taking into account the predominant cloud regime. [ABSTRACT FROM AUTHOR]

Remote sensing presents countless advantages in analyzing phenomena at the Earth surface. The increasing availability of remote sensors allows high spatio-temporal resolution and global coverage, impossible to obtain by other means. With the growing length of remote sensing data records, the use of measurements based on space-borne sensors opens new perspectives to the analysis of the impacts of climate change from local to global scales. The Satellite Application Facility (SAF) on Land Surface Analysis (LSA) is part of the SAF Network, from EUMETSAT (European organization for the Exploitation of Meteorological Satellites) distributed Applications Ground Segment. LSA SAF provides products that enable the analysis of land surface variables, including land surface temperature, albedo, evapotranspiration and vegetation state variables (e.g. fraction of vegetation cover, NDVI, leaf area index), amongst others. Land surface variables allow a comprehensive characterization of surface state, which can have strong impact on local climate conditions. Anthropogenic changes to land cover and its effects on local temperature, for instance, are one of the main topics addressed when discussing human impact on the climate.Recently, the LSA-SAF has released a catalogue of Climate Data Records (CDR) for land surface variables, i.e. homogeneous records over a time-period spanning from 2004 to 2015 and covering Europe, Africa and part of South America. This study takes advantage of the CDRs provided by the LSA-SAF, namely land surface temperature and vegetation products, as listed above, with the goal of deriving their annual cycle parameters (ACP) over Europe. These ACP are then used to assess possible mechanisms for temperature change associated to variability in vegetation cover or state, while also providing a first approach to a consistency analysis among LSA SAF products. Research was performed within the framework of the Satellite Application Facility on Land Surface Analysis (LSA SAF) and FCT- project UID/GEO/50019/2019 - Instituto Dom Luiz. [ABSTRACT FROM AUTHOR]

The correction of directional effects on satellite-retrieved land surface temperature (LST) is of high relevance for a proper interpretation of spatial and temporal features contained in LST fields. This study presents a methodology to correct such directional effects in an operational setting. This methodology relies on parametric models, which are computationally efficient and require few input information, making them particularly appropriate for operational use. The models are calibrated with LST data collocated in time and space from MODIS (Aqua and Terra) and SEVIRI (Meteosat), for an area covering the entire SEVIRI disk and encompassing the full year of 2011. Past studies showed that such models are prone to overfitting, especially when there are discrepancies between the LSTs that are not related to the viewing geometry (e.g., emissivity, atmospheric correction). To reduce such effects, pixels with similar characteristics are first grouped by means of a cluster analysis. The models' calibration is then performed on each one of the selected clusters. The derived coefficients reflect the expected impact of vegetation and topography on the anisotropy of LST. Furthermore, when tested with independent data, the calibrated models are shown to maintain the capability of representing the angular dependency of the differences between LST derived from polar-orbiter (MODIS) and geostationary (Meteosat, GOES and Himawari) satellites. The methodology presented here is currently being used to estimate the deviation of LST products with respect to what would be obtained for a reference view angle (e.g., nadir), therefore contributing to the harmonization of LST products. [ABSTRACT FROM AUTHOR]

Surface net radiation (SNR) is a vital input for many land surface and hydrological models. However, most of the current remote sensing datasets of SNR come mostly at coarse resolutions or have large gaps due to cloud cover that hinder their use as input in models. Here, we present a downscaled and continuous daily SNR product across Europe for 2018–2019. Long-wave outgoing radiation is computed from a merged land surface temperature (LST) product in combination with Meteosat Second Generation emissivity data. The merged 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-3A satellite. This approach makes use of the medium spatial (approx. 5–7 km) but high temporal (30 min) resolution, gap-free data from MSG along with the low temporal (2–3 d) 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. Short-wave outgoing radiation is computed from the incoming short-wave radiation from MSG and the downscaled albedo using 1 km PROBA-V data. MSG incoming short-wave and long-wave 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 mean squared error by ca. 7 % with an increase in spatial detail compared to the original MSG product. The resulting pan-European SNR dataset, as well as the merged LST product, 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. The datasets can be downloaded from 10.5281/zenodo.8332222 and 10.5281/zenodo.8332128. [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]

• Surface temperature from Landsat calculated using different emissivity assumptions • Impact of emissivity prescription on global surface urban heat islands quantified • Landsat-derived estimates compared with coarser MODIS-based values • Strong sensitivity of urban heat island intensity to surface emissivity assumption • Prescribed urban emissivity in models lower than from satellite observations The prescription of surface emissivity (ε) strongly controls satellite-derived estimates of land surface temperature (LST). This is particularly important for studying surface urban heat islands (SUHI) since built-up and natural landscapes are known to have distinct ε values. Given the small signal associated with the SUHI compared to LST, accurately prescribing urban and rural ε would improve our satellite-derived SUHI estimates. Here we test the sensitivity of SUHI to the ε assumption made while deriving LST from Landsat measurements for almost 10,000 global urban clusters for summer and winter days. We find that adjusting the ε values from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) dataset based on pixel-level normalized difference vegetation index (NDVI) increases the summer to winter contrast in daytime SUHI, a constrast that has been noted in previous studies. Overall, the difference between the two methods of prescribing ε , one from ASTER and one after NDVI-adjustment, is moderate; around 10% during summer and around 20% during winter, though this difference varies by climate zone, showing higher deviations in polar and temperate climate. We also combine five different methods of prescribing emissivity to provide the first global estimates of SUHI derived from Landsat. The global ensemble mean SUHI varies between 2.42 °C during summer to 0.46 °C in winter. Regardless of the surface emissivity model used, compared to Moderate Resolution Imaging Spectroradiometer (MODIS) Terra observations, Landsat data show higher SUHI daytime intensities during summer (by more than 1.5 °C), partly due to its ability to better resolve urban pixels. We also find that the ε values prescribed for urban land cover in global and regional weather models are lower than the satellite-derived broadband ε values. Computing sensitivities of urban and rural LST to ε , we demonstrate that this would lead to overestimation of SUHI by these models (by around 4 °C for both summer and winter), all else remaining constant. Our analysis provides a global perspective on the importance of better constraining urban ε for comparing satellite-derived and model-simulated SUHI intensities. Since both the structural and geometric heterogeneity of the surface controls the bulk ε , future studies should try to benchmark the suitability of existing LST- ε separation methods over urban areas. [ABSTRACT FROM AUTHOR]

Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modeling platform (SURFEX-ISBA) for the 2004–2015 period. The results showed that the daily maximum LST simulated by CHTESSEL over Iberia was affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10 ∘ C over wide portions of central and southwestern Iberia. This error was shown to be tightly linked to a misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality. The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) leaf area index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also included a clumping approach that introduces seasonality to the vegetation cover. The results showed significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guiding the developments in CHTESSEL highlighting the importance of using different models. This work has important implications: first, it takes advantage of LST, a key variable in surface–atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve the model's representation of land surface processes. Second, CHTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis; hence systematic errors in land surface variables and fluxes are then propagated into those products. Indeed, we showed that the summer daily maximum LST cold bias over Iberia in CHTESSEL is present in the widely used ECMWF fifth-generation reanalysis (ERA5). Finally, our results provided hints about the interaction between vegetation land–atmosphere exchanges, highlighting the relevance of the vegetation cover and respective seasonality in representing land surface temperature in both CHTESSEL and SURFEX. As a whole, this work demonstrated the added value of using multiple earth observation products for constraining and improving weather and climate simulations. [ABSTRACT FROM AUTHOR]

Earth observations were used to evaluate the representation of Land Surface Temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) - the European Centre for Medium Range Weather Forecasting (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modelling platform (SURFEX-ISBA) for the 2004-2015 period. The results show that the daily maximum LST simulated by CHTESSEL over Iberia is affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10 °C over wide portions of central and southwestern Iberia. This error is shown to be tightly linked to a misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality. The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA -CCI) land cover dataset with the Copernicus Global Land Service (CGLS) Leaf Area Index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also includes a clumping approach that introduces seasonality to the vegetation cover. The results show significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guide the developments in CHTESSEL highlighting the importance of using different models. This work has important implications: first, it takes advantage of LST, a key variable in surface-atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve model's representation of land surface processes. Second, CHTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis, hence systematic errors in land surface variables and fluxes are then propagated into those products. Indeed, we show that the summer daily maximum LST cold bias over Iberia in CHTESSEL is present in the widely used ECMWF fifth generation reanalysis (ERA5). Finally, our results provide hints into the interaction between vegetation land-atmosphere exchanges, highlighting the relevance of the vegetation cover and respective seasonality in representing land surface temperature in both CHTESSEL and SURFEX. As a whole, this work demonstrates the added value in using multiple earth observation products for constraining and improving weather and climate simulations. [ABSTRACT FROM AUTHOR]

Thermal radiation directionality (TRD) characterizes the anisotropic signature of most surface targets in the thermal infrared domain. It causes significant uncertainties in estimating surface upward longwave radiation (SULR) from space observations. In this regard, kernel-driven models (KDMs) are suitable to remove TRD effects from remote sensing dataset as they are computationally efficient. However, KDMs requires simultaneous multi-angle observations as inputs to be well calibrated, which yields a difficulty with geostationary satellites as they can only provide a single-angle observation. To overcome this issue, we proposed a six-parameter time-evolving KDM that combines a four-parameter SULR diurnal variation model and a two-parameter TRD amplitude model to correct the TRD effect for single-angle estimated SULR dataset of geostationary satellites. The significant daytime TRD effect when solar zenith angle is within 60° can be effectively eliminated. The modeling accuracy of the time-evolving KDM is evaluated using a simulated SULR dataset generated by the 3D Discrete Anisotropic Radiative Transfer (DART) model; the TRD correction method based on the new time-evolving KDM is validated using a two-year single-angle estimated SULR dataset derived from data of the Advanced Baseline Imager (ABI) onboard Geostationary Operational Environmental Satellite-16 (GOES-16) against in situ measurements at 20 AmeriFlux sites. Results show that the proposed time-evolving KDM has a high accuracy with an R2 > 0.999 and a small RMSE = 1.5 W/m2; the TRD correction method based on the time-evolving KDM can greatly reduce the GOES-16 SULR uncertainty caused by the TRD effect with an RMSE decrease of 4.5 W/m2 (22.1%) and mean bias error decrease of 7.9 W/m2 (62.7%). Hence, the proposed TRD correction method is practically efficient for the operational TRD correction of SULR products generated from the geostationary satellites (e.g., GOES-16, FY-4A, Himawari-8, MSG). • A 6-parameter time-evolving kernel-driven model for SULR was proposed. • The multi-temporal observations of ABI/GOES-16 are utilized to drive new model. • The TRD effect of SULR product of geostationary satellite was corrected for the first time. • The RMSE and MBE decreased by 22.1% and 62.7% for the ABI/GOES-16 SULR. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A new all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. It is the first all-weather LST product based on visible and infrared observations combining clear-sky LST retrieved from the Spinning Enhanced Visible and Infrared Imager on Meteosat Second Generation (MSG/SEVIRI) infrared (IR) measurements with LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a microwave (MW)-based LST product derived from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product. [ABSTRACT FROM AUTHOR]