The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station (ISS) on June 29, 2018, and currently provides the highest spatial resolution thermal infrared (TIR) data (38 m $\times \,\, 69$ m) available from space. In this study, we validated the ECOSTRESS level-2 Land Surface Temperature (LST) and emissivity product at fourteen global sites to Stage-1 status. Two primary methods are recommended for the validation of LST data: Temperature-based (T-based) and Radiance-based (R-based) methods. The T-based method requires calibrated measurements of the ground leaving radiance concurrent with the satellite overpass. In contrast, the R-based method uses a radiative closure simulation with external atmospheric profiles and an $a$ priori knowledge of surface emissivity. Using these standard methods, we validated 1139 ECOSTRESS clear-sky observations between August 1, 2018, and March 31, 2020. For LST, the results show good agreement with ground-based measurements with an average root mean square error (RMSE) of 1.07 K, mean absolute error (MAE) of 0.40 K, and $r^{2}>0.988$ at all sites. However, a cold bias of ~0.75 K was identified for temperatures below 295 K linked to calibration issues that will be addressed in future reprocessing of the data. Retrieved emissivity comparisons with laboratory spectra had an RMSE of 0.023 (2.3%) for all bands on average. With the decommissioning of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on Terra in 2023, the multispectral and high-spatial-resolution characteristics of ECOSTRESS data serve as a pathfinder to the National Aeronautics and Space Administration’s (NASA) Surface Biology and Geology (SBG) designated observable with an expected launch in 2026.

Since 2005, the Land Surface Analysis Satellite Application Facility (LSA SAF) operationally retrieves Land Surface Temperature (LST) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG). The high temporal resolution of the Meteosat satellites and their long term availability since 1977 make their data highly valuable for climate studies. In order to ensure that the LSA SAF LST product continuously meets its target accuracy of 2 °C, it is validated with in-situ measurements from four dedicated LST validation stations. Three stations are located in highly homogenous areas in Africa (semiarid bush, desert, and Kalahari semi-desert) and typically provide thousands of monthly match-ups with LSA SAF LST, which are used to perform seasonally resolved validations. An uncertainty analysis performed for desert station Gobabeb yielded an estimate of total in-situ LST uncertainty of 0.8 ± 0.12 °C. Ignoring rainy seasons, the results for the period 2009–2014 show that LSA SAF LST consistently meets its target accuracy: the highest mean root-mean-square error (RMSE) for LSA SAF LST over the African stations was 1.6 °C while mean absolute bias was 0.1 °C. Nighttime and daytime biases were up to 0.7 °C but had opposite signs: when evaluated together, these partially compensated each other.

Global land surface temperature (LST) data derived from satellite-based infrared radiance measurements are highly valuable for various applications in climate research. While in situ validation of satellite LST data sets is a challenging task, it is needed to obtain quantitative information on their accuracy. In the standardised approach to multi-sensor validation presented here for the first time, LST data sets obtained with state-of-the-art retrieval algorithms from several sensors (AATSR, GOES, MODIS, and SEVIRI) are matched spatially and temporally with multiple years of in situ data from globally distributed stations representing various land cover types in a consistent manner. Commonality of treatment is essential for the approach: all satellite data sets are projected to the same spatial grid, and transformed into a common harmonized format, thereby allowing comparison with in situ data to be undertaken with the same methodology and data processing. The large data base of standardised satellite LST provided by the European Space Agency’s GlobTemperature project makes previously difficult to perform LST studies and applications more feasible and easier to implement. The satellite data sets are validated over either three or ten years, depending on data availability. Average accuracies over the whole time span are generally within ±2.0 K during night, and within ± 4.0 K during day. Time series analyses over individual stations reveal seasonal cycles. They stem, depending on the station, from surface anisotropy, topography, or heterogeneous land cover. The results demonstrate the maturity of the LST products, but also highlight the need to carefully consider their temporal and spatial properties when using them for scientific purposes.

Surface skin temperature (T_skin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving T_skin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI T_skin product (i.e., T_ANN) is evaluated against T_skin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI T_ANN and the T_skin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust T_skin dataset suitable for T_skin long-term spatio-temporal variability and trend analysis.

Accurately targeting and tracking climate change mitigation efforts requires detailed bottom-up greenhouse gas (GHG) emission inventories verified by independent atmospheric measurements. While policy decisions often rely on annual national or city-scale inventories, higher-resolution data, both spatially (sub-city) and temporally (daily to hourly), though more uncertain, provide key advantages. First, they are input to inverse modelling of emission sources from atmospheric measurements, which offers a semi-independent approach to verify bottom-up estimates. Second, they can enable simulations to evaluate the impact of interventions, such as changes of policies, industrial standards or household behaviour, on emissions and atmospheric concentrations. Third, by providing localized, near real-time emissions data that can enhance the communication and tracking of climate actions, they can motivate both behavioural and policy shifts.This study presents a straightforward method to create high-resolution carbon dioxide emission inventories for road traffic (street level) and heating (building level) using publicly available data such as OpenStreetMap. Scalable across Germany and adaptable to diverse contexts and applications, the method is demonstrated in Mannheim and Heidelberg, part of the Rhine-Neckar Metropolitan Area.For road traffic, emissions are derived by estimating daily traffic volumes using road type, lane count, and population density, combined with speed- and fuel-specific emission factors and national vehicle fleet data. Heating emission estimates combine building data with gridded census data on heating energy sources and building age.We validate our traffic volume against independent traffic count data and compare our emissions with existing inventories. Road traffic emissions in the Rhine-Neckar region exceeded the regional estimates of TNO (Super et al., 2021), a widely used European inventory, by 1.6%. Building heating emissions were 12% and 8% lower than inventory estimates for Mannheim and Heidelberg, respectively, primarily due to differences in emission factor assumptions.

Achieving climate neutrality requires a socio-ecological transformation of the mobility sector. Consequently, urban traffic infrastructure must be redesigned to promote and support active mobility. By analysing multiple criteria extracted from open-access geodata via OSM (OpenStreetMap), we can assess the current state of the urban traffic infrastructure and evaluate the walkability and bikeability of streets and neighborhoods. HeiGIT’s walkability and bikeability indices – HiWalk and HiBike – provide traffic planners, decision-makers, NGOs, and the general public with quantitative insights into how well a city's traffic infrastructure supports active mobility.In recent years, numerous walkability and bikeability indices have emerged, focusing primarily on accessibility metrics. However, these anlyses often assume that all streets are equally suitable for active mobility, overlooking the specific needs of groups such as the elderly, young children, people with disabilities, and risk-averse cyclists.It is therefore essential to address two critical gaps: (a) providing detailed information on the suitability of urban environments for active mobility, and (b) ensuring that traffic infrastructure transformation is inclusive. To meet these needs, we collaborate with practitioners and NGOs to co-create street-level indices of walkability (HiWalk) and bikeability (HiBike). Our goal is to offer practical applications that go beyond general index values for cities or neighborhoods. By assessing bikeability and walkability at the street-level, we can better inform routing engines and support accessibility analyses of inclusive “15-minute cities“.HiWalk and HiBike consist of indicators that assess the user-friendliness, attractiveness, and safety of paths and streets. They evaluate factors such as the presence of sidewalks and cycling lanes, surface smoothness, and surface material types. HiBike also incorporates information on street-side parking from OSM to identify streets where cyclists may be at risk of “dooring”. Both indices are entirely based on open data and can be adapted to various urban settings worldwide.Since HiWalk and HiBike are still under development, our presentation will focus on the main challenges we have encountered, including (1) their application for cities with different cultural, socioeconomic, and environmental contexts, and (2) the variability in the quality and completeness of OSM data. These challenges underscore the benefits of our co-creation approach in enhancing the indices' usability and impact on policy.