Search

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval. [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]

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 eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at several hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, and Terrestrial Ecosystem Research Network (TERN) / OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the training and validation of ecosystem models. However, insufficient quality, frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions drawn from them. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40_ off-nadir. We offer to the community pre-processed Earth observation data in a radius of 2 km around 338 flux sites based on the MCD43A4/A2, MxD11A1 MODIS products and Landsat collection 1 Tier1 and Tier2 products. The data sets we provide can widely facilitate the integration of activities in the fields of eddy-covariance, remote sensing and modelling. [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]

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]