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A major challenge for the urban community is the exploitation of Earth Observation intelligence in the multidimensional nature of urban sustainability, towards enhancing urban resilience, particularly in relation to the challenges of climate change. Here, we present the ways in which the Horizon2020 funded project CURE synergistically exploited Copernicus Core Services (CCS) to support urban resilience. CURE provides spatially disaggregated environmental intelligence at a local scale, as well as a proof-of-concept that urban planning and management strategies, enhancing the resilience of cities, can be supported by CCS. CURE has developed eleven cross-cutting applications between CCS, reflecting the main urban sustainability dimensions and are relevant to user needs, which were identified based on a strong stakeholders’ engagement. CURE is built on Data and Information Access Services, as a system integrating these cross-cutting applications, capable of supporting downstream services across Europe, enabling its incorporation into operational Copernicus products portfolio in the future.

The accurate land cover mapping of the Earth’s surface using Earth observation data is one of the most studied, but yet the most challenging tasks of remote sensing field, particularly when it comes to urban areas. The large spectral variability of man-made structures, as well as the mixed pixel phenomenon, imposes the use of computational demanding techniques, which are not always effective for real case applications. Support vector machines (SVMs) are supervised learning models with associated learning algorithms, which are mainly used for classification and regression analysis. Specifically, a support vector classifier (SVC) constructs a hyperplane or a set of hyperplanes in a high-dimensional space, which separates the training data into different classes. These are then used to classify a whole image, or series of images. The current standard SVM algorithm for classification used by the most popular mapping software (e.g., ENVI, EnMAP) is the C-SVC. The parameterization of a C-SVC strongly affects the final classification result. Yet, there is no rule of thumb to choose the optimal parameters when classifying satellite imagery. Optimal parameterization totally depends on the training data, and to determine it for a specific case, a time-consuming trial-and-error process is inevitable. In this work, advancements for the C-SVC algorithm are proposed to enhance its performance when used to classify remote sensing data, eliminating the need for a part of manual parametrization, while ensuring increasing its performance.

Air temperatures in urban environments are usually obtained from sparse weather stations that provide limited information with regard to spatial patterns. Effective methods that predict air temperatures (Tair) in urban areas are based on statistical models which utilize remotely sensed and geographic data. This work aims to compute Tair predictions for diurnal and nocturnal time intervals using predictive models that do not exploit information on Land Surface Temperatures. The models are developed based on explanatory variables that describe the urban morphology, land cover and terrain, aggregated at 100 m × 100 m resolution, combined with in situ Tair measurements from urban meteorological stations. The case study is the urban and per-urban area of Heraklion, Greece, where a dense meteorological station network is available since 2016. Moran’s eigenvector filtering and an autoregressive moving average residual specification are implemented to account for spatial and temporal correlations. The statistical models display satisfactory predictive performance, with mean annual Mean Absolute Error (MAE) equal to 0.36 °C, 0.34 °C, 0.42 °C and 0.54 °C, for 11:00–12:00, 14:00–15:00, 22:00–23:00 and 02:00–03:00 (UTC + 2), respectively. The minimum (maximum) MAE for the estimated datasets is 0.22 °C (0.81 °C). The mean annual MAE for all Tair interpolations is 0.42 °C, the mean annual Root Mean Square Error (RMSE) is 0.49 °C and the mean annual bias

The rate at which global climate change is happening is arguably the most pressing environmental challenge of the century and it affects our cities. Temperature is one of the most important parameters in climate monitoring and Earth Observation (EO) systems and the advances in remote sensing science increase the opportunities for monitoring the surface temperature from space. The EO4UTEMP project examines the exploitation of EO data for monitoring the urban surface temperature (UST). Large variations in surface temperatures can be observed within a couple of hours, particularly when referring to urban surfaces. The geometric, radiative, thermal, and aerodynamic properties of the urban surface are unique and exert particularly strong control on the surface temperature. EO satellites provide excellent means for mapping the land surface temperature, but the particular properties of the urban surface and the unique urban geometry in combination with the trade-off between temporal and spatial resolution of the current satellite missions impose the development of new sophisticated surface temperature retrieval methods particularly designed for urban areas. EO4TEMP develops a novel UST algorithm exploiting multi-temporal, multi-sensor, multi-resolution EO data, to be validated with in-situ measurements in urban sites and to be applied to Sentinel-3 and Sentinel-2 data. Therefore, EO4UTEMP will provide an advanced methodology for deriving frequent UST estimations at local scale (100 m), capable of resolving the diurnal variation of UST and contribute to the study of the urban energy balance.

Physical models simulating the radiative budget (RB) and remote sensing (RS) observation of three-dimensional (3D) landscapes are critical to better understand human and natural components of the Earth system and further develop RS technology. DART is one of the most comprehensive 3D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (https://dart.omp.eu). Here, five recent advances are presented. 1) Atmosphere RT. 2) RT in non repetitive topography. 3) Monte Carlo modelling for fast RS image simulation of large landscapes. 4) SIF modelling for vegetation simulated as facets and turbid cells. 5) RS image inversion for mapping the optical properties of urban material and the urban radiative budget.

Resilience has become an important necessity for cities, particularly in the face of climate change. Mitigation and adaptation actions that enhance the resilience of cities need to be based on a sound understanding and quantification of the drivers of urban transformation and settlement structures, human and urban vulnerability, and of local and global climate change. Copernicus, as the means for the establishment of a European capacity for Earth Observation (EO), is based on continuously evolving Core Services. A major challenge for the EO community is the innovative exploitation of the Copernicus products in dealing with urban sustainability towards increasing urban resilience. Due to the multidimensional nature of urban resilience, to meet this challenge, information from more than one Copernicus Core Services, namely the Land Monitoring Service (CLMS), the Atmosphere Monitoring Service (CAMS), the Climate Change Service (C3S) and the Emergency Management Service (EMS), is needed. Furthermore, to address urban resilience, the urban planning community needs spatially disaggregated environmental information at local (neighbourhood) scale. Such information, for all parameters needed, is not yet directly available from the Copernicus Core Services mentioned above, while several elements - data and products - from contemporary satellite missions consist valuable tools for retrieving urban environmental parameters at local scale. The H2020-Space project CURE (Copernicus for Urban Resilience in Europe) is a joint effort of 10 partners from 9 countries that synergistically exploits the above Copernicus Core Services to develop an umbrella cross-cutting application for urban resilience, consisting of individual cross-cutting applications for climate change adaptation/mitigation, energy and economy, as well as healthy cities and social environments, at several European cities. These cross-cutting applications cope with the required scale and granularity by also integrating or exploiting third-party data, in-situ observations and modelling. CURE uses DIAS (Data and Information Access Services) to develop a system capable of supporting operational applications and downstream services across Europe. The CURE system hosts the developed cross-cutting applications, enabling its incorporation into operational services in the future. CURE is expected to increase the value of Copernicus Core Services for future emerging applications in the domain of urban resilience, exploiting also the improved data quality, coverage and revisit times of the future satellite missions. Thus, CURE will lead to more efficient routine urban planning activities with obvious socioeconomic impact, as well as to more efficient resilience planning activities related to climate change mitigation and adaptation, resulting in improved thermal comfort and air quality, as well as in enhanced energy efficiency. Specific CURE outcomes could be integrated into the operational Copernicus service portfolio. The added value and benefit expected to emerge from CURE is related to transformed urban governance and quality of life, because it is expected to provide improved and integrated information to city administrators, hence effectively supporting strategies for resilience planning at local and city scales, towards the implementation of the Sustainable Development Goals and the New Urban Agenda for Europe.

Surface albedo is one of the essential climate variables as it influences the radiation budget and the energy balance. Because it is used in a variety of scientific fields, from local to global scale, spatially and temporally disaggregated albedo data are required, which can be derived from satellites. Satellite observations have led to directional-hemispherical (black-sky) and bi-hemispherical (white-sky) albedo products, but time series of high spatial resolution true (blue-sky) albedo estimations at global level are not available. Here, we exploit the capabilities of Google Earth Engine (GEE) for big data analysis to derive global snow-free land surface albedo estimations and trends at a 500-m scale, using satellite observations from 2000 to 2015. Our study reveals negative albedo trends mainly in Mediterranean, India, south-western Africa and Eastern Australia, whereas positive trends mainly in Ukraine, South Russia and Eastern Kazakhstan, Eastern Asia, Brazil, Central and Eastern Africa and Central Australia. The bulk of these trends can be attributed to rainfall, changes in agricultural practices and snow cover duration. Our study also confirms that at local scale, albedo changes are consistent with land cover/use changes that are driven by anthropogenic activities.

The study of urban climate and the estimation of urban energy fluxes requires frequent and accurate monitoring of land surface temperature (LST), at the local scale. Current and forthcoming space-borne sensors though, do not provide frequent thermal infrared imagery at high spatial and temporal resolution. In this study, a downscaling technique is applied for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. LST maps of 100 m spatial resolution multiple times per day were derived for three cities, London, United Kingdom, Basel, Switzerland and Heraklion, Greece. The maps were validated using radiation measurements from flux towers. The accuracy of the derived maps was also assessed using LST data from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer).

Climate change influences the vulnerability of urban populations worldwide. To improve their adaptive capacity, the implementation of nature-based solutions (NBS) in urban areas has been identified as an appropriate action, giving urban planning and development an important role towards climate change adaptation/mitigation and risk management and resilience. However, the importance of extensively applying NBS is still underestimated, especially regarding its potential to induce significantly positive environmental and socioeconomic impacts across cities. Concerning environmental impacts, monitoring and evaluation is an important step of NBS management, where earth observation (EO) can contribute. EO is known for providing valuable disaggregated data to assess the modifications caused by NBS implementation in terms of land cover, whereas the potential of EO to uncover the role of NBS in urban metabolism modifications (e.g., energy, water, and carbon fluxes and balances) still remains underexplored. This study reviews the EO potential in the monitoring and evaluation of NBS implementation in cities, indicating that satellite observations combined with data from complementary sources may provide an evidence-based approach in terms of NBS adaptive management. EO-based tools can be applied to assess NBS’ impacts on urban energy, water, and carbon balances, further improving our understanding of urban systems dynamics and supporting sustainable urbanization.

Detailed, frequent, and accurate land surface temperature (LST) estimates from satellites may support various applications related to the urban climate. When satellite-retrieved LST is used in modeling, the level of uncertainty is important to account for. In this letter, an uncertainty estimation scheme based on Monte Carlo simulations is proposed for local-scale LST products derived from image fusion. The downscaling algorithm combines frequent low-resolution thermal measurements with surface cover information from high spatial resolution imagery. The uncertainty is estimated for all the intermediate products, allowing the analysis of individual uncertainties and their contribution to the final LST product. Uncertainties of less than 2 K was found for most part of the test area. The uncertainty estimation method, although demanding in terms of computations, can be useful for the uncertainty analysis of other satellite products.

The high spatial diversity of man-made structures, the spectral variability of urban materials, and the three-dimensional structure of the cities make the mapping of urban surfaces using Earth Observation data, one of the most challenging tasks in remote sensing field. Spectral unmixing techniques can be proven useful with medium spectral resolution data to assess urban surface cover information on a subpixel level. Due to the large spectral variability of urban materials and the multiple scattering of light between surfaces in urban areas, multiple endmembers should be used, and the nonlinearity of spectral mixture should be accounted for. In this study, these issues are addressed using an artificial neural network trained with endmember and nonlinearly mixed synthetic spectra to inverse the pixel spectral mixture in Landsat imagery. A spectral library is built, consisting of endmember spectra collected from the image and synthetic spectra, produced using a nonlinear model specifically developed for urban areas. The method was tested over a case study, and the validation against higher resolution products revealed an accuracy of around 90% for all abundance maps. The comparison performed between the linear and nonlinear implementation of the method proved the need for including the nonlinear term, especially for improving the built-up abundance map. The proposed method is easily transferable to any city and fast in terms of computations, which makes it ideal for the implementation of operational urban services.

The successful implementation of the European Commission&rsquo, s Common Agricultural Policy (CAP) and the insurance coverage in case of a natural disaster requires precise and regular mapping of crop types and detailed delineation of the disasters&rsquo, effects by frequent and accurate controls. Free and open access policy to Copernicus Sentinel data offers a big volume of data to the users on a consistent and complete basis. Today, the Sentinels are involved in an increasing number of agriculture applications, but their effective exploitation is still being investigated and the development of efficient tools, aligned to the user&rsquo, s needs, is yet to be realised. To this end, the DiAS (Disaster and Agriculture Sentinel Applications) project proposes methods for decision support in agriculture using Sentinel data for crop type mapping, as well as mapping of the extend of fire and flood effects in agricultural areas. The DiAS Decision Support System (DSS) is designed in consultation with potential users in participatory approach and aims to provide a prototype tool, which provides assistance to the responsible paying agencies and insurance organizations to make decisions on farmers&rsquo, subsidies and compensations. The DiAS DSS prototype and its functionalities are presented in this paper and its use is demonstrated through example applications for two test sites in Greece. The DiAS DSS demonstrates the necessity for the development of similar tools, as this emerges from the user&rsquo, s requirements, and wishes to stimulate and inspire further research and development.

Besides new economical, managerial and social challenges associated with growing cities, the modifications caused in the energy budget of the urban surface intensifies the existing urban heat island (UHI). UHI can vary temporally and spatially according to meteorological conditions, landscape and urban typologies. Urban cover and form, as well as anthropogenic activities, pose an important effect on the city’s thermal behaviour that influence UHI and therefore the quality of life of the citizens. In this study, we focus on quantifying the air temperature spatiotemporal patterns across the urban and peri-urban area of Heraklion, Greece at a grid of 100 m x 100 m cells. We use point air temperature observations from the Wireless Sensors Network of Heraklion and interpolate spatially by means of sophisticated geostatistical modelling parameterized with satellite derived predictors. Regression kriging interpolation technique is implemented over the study area, using different predictors to minimize the uncertainty in air temperature estimation. We deal for multicollinearity between predictors and spatio-temporal correlations between measurements. A maximum magnitude of UHI ~ 4 oC has been observed between 04:00-05:00 (UTC+3). Cross-validations indicate a mean MAE ~0.86 oC in the estimated air temperature maps.

An image dataset from the Landsat OLI spaceborne sensor is compared with the Landsat TM in order to evaluate the excellence of the new imagery in urban landcover classification. Widely known pixel-based and object-based image analysis methods have been implemented in this work like Maximum Likelihood, Support Vector Machine, k-Nearest Neighbor, Feature Analyst and Sub-pixel. Classification results from Landsat OLI provide more accurate results comparing to the Landsat TM. Object-based classifications produced a more uniform result, but suffer from the absorption of small rare classes into large homogenous areas, as a consequence of the segmentation, merging and the spatial parameters in the spatial resolution (30 m) of Landsat images. Based exclusively on the overall accuracy reports, the SVM pixel-based classification from Landsat 8 proved to be the most accurate for the purpose of mapping urban land cover, using medium spatial resolution imagery.

The advances in satellite sensor technology, the launch of new satellites and the continuous development of remote sensing technology constantly increase the opportunities for monitoring the thermal behaviour of cities from space. Not only the surface temperature, but several other parameters related to the urban climate can be quantified from Earth Observation (EO) data. In this study, remote sensing techniques are applied to derive EO data products and a methodology is proposed that combines this information to discriminate between zones with different thermal behaviour in cities. Information on the building, pervious and paved surface cover, the surface albedo, the mean building/tree height and the sky view factor is quantified in local scale and then used to identify possible zones with homogeneous thermal characteristics.

Detailed mapping of urban surfaces is one of the most challenging tasks in remote sensing due to the three-dimensional structure of cities, spatial diversity, and material spectral variability. Satellite urban applications demand better spatial, spectral, and temporal resolution, although there are strict technical constraints among them. Therefore, the development of sophisticated methods that exploit both high spectral and spatial data sources becomes necessary. A hierarchical multiple endmember spectral mixture analysis (MESMA) approach is developed and applied on Sentinel-2 imagery for the detailed quantification of the urban land cover, taking advantage of Worldview-2 high spatial resolution. The case study is the urban and peri-urban area of Heraklion, Greece. The area to point regression kriging (ATPRK) method is applied to downscale Sentinel-2 bands from 10 and 20 m to 2 m (WorldView-2 spatial resolution) and create a spectral library (SL) of urban materials, which contain 180 separate spectra. The urban SL is then used in the developed hierarchical MESMA approach to estimate the abundances of 11 urban land cover classes based on the original Sentinel-2 image. The estimated land cover fractions validate against a very high-resolution (1 m) land cover map of the area. It is proved that the complexity of the urban land cover can be efficiently investigated by the proposed methodology. Error analysis shows good accuracy of the results in all estimated class fractions. Moreover, the good validation results lead to the conclusion that ATPRK fusion algorithm between Sentinel-2 and WorldView-2 bands produced reliable urban material spectra, capable for advanced spectral analysis on Sentinel-2 imagery. The developed methodology is easily transferable to other cities since it is based exclusively on earth observation data and is suitable for multiple urban applications related to urban climate, urban sprawl, and urban regeneration.

The objective of atmospheric correction is to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects. The process determines the optical characteristics of the atmosphere and then applies it in order to correct the atmospheric effects on satellite imagery. Two main categories of atmospheric correction methods can be identified, the ones that rely on radiative transfer modeling and the image-based ones. In this study, three methods are compared, two physical-based (6S, FLAASH) and one image-based (DOS) for their effectiveness on atmospheric correction of Sentinel-2 high resolution optical imagery. A Sentinel-2 image, acquired on a clear day over Heraklion, Greece was used. Ancillary information on the aerosol optical thickness from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used for the physically based methods. In line with similar studies using Landsat imagery, the physically based methods perform better than the image-based ones also for the Sentinel-2 imagery. Nevertheless, their high computational demand and the need for ancillary atmospheric information makes them difficult to apply. Different atmospheric correction methods showed different results for specific land cover types, suggesting that the selection of the suitable method is also application dependent.

Spectral unmixing provides information on a sub-pixel level, which is extremely useful for studying the urban areas. Nevertheless, the high spatial diversity of man-made structures, the spectral variability of urban materials and the three-dimensional structure of the cities makes the sub-pixel mapping of urban surfaces one of the most challenging tasks of remote sensing science. In this study, these issues are addressed using an artificial neural network trained with endmember and non-linearly mixed synthetic spectra to inverse the pixel spectral mixture in high resolution multispectral imagery. A spectral library is built, consisting of endmember spectra collected from the images and synthetic spectra, produced using a non-linear model specifically developed for urban scenes. The proposed method is easily transferable to any city and fast in terms of computations, which makes it ideal for implementation with operational services for cities.

The recently launched H2020 project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites) investigates the potential of EO to retrieve anthropogenic heat flux, as a key component in the Urban Energy Budget (UEB). URBANFLUXES advances existing Earth Observation (EO) based methods for estimating spatial patterns of turbulent sensible and latent heat fluxes, as well as urban heat storage flux at city scale and local scale. Independent methods and models are engaged to evaluate the derived products and statistical analyses provide uncertainty measures. Optical, thermal and SAR data are exploited to improve the accuracy of the UEB components spatial distribution calculation. Synergistic use of different types and of various resolution EO data allows estimates in local and city scale. Ultimate goal of the URBANFLUXES is to develop a highly automated method for estimating UEB components to use with Copernicus Sentinel data, enabling its integration into applications and operational services.

Earth Observation (EO) systems and the advances in remote sensing technology increase the opportunities for monitoring the thermal behaviour of cities. Several parameters related to the urban climate can be quantified from EO data products, providing valuable support for advanced urban studies and climate modelling. In this study, remote sensing techniques are applied to derive quantitative information necessary to identify the Local Climate Zones (LCZ). Parameters like the pervious and impervious surface fraction, the surface albedo, the building density, the mean building/tree height and the sky view factor are quantified and used to map possible zones with homogeneous thermal characteristics, considered as LCZ.

This study explores the estimation of land surface temperature (LST) for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm’s implementation to any area of interest. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a) LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), as higher-level products based on the temperature-emissivity separation approach; (b) Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error) of 1.52 °C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated “on-the-fly” and are available on-line via a web application.

Information about the spatial distribution of urban surface emissivity is essential for surface temperature estimation which is an important component of urban microclimate and it is critical in many applications, like turbulent sensible and latent heat fluxes estimation, energy budget, urban canopy modeling, bio-climatic studies and urban planning. The proposed method presents an improvement in emissivity estimation as compared with existing methods, such as the look-up table approach, wherein emissivity and other biophysical parameters are assigned to grid cells based on land cover types. The basic premise of this method is a sub-pixel classification of urban surface into vegetation, impervious and soil, based on spectral mixture analysis. The proposed approach was applied to Landsat-7 ETM + observations over the area of Athens, Greece. Spatial distributions of surface emissivity, as well as land surface temperature in the spectral region of 10.4–12.5 μm were derived. ASTER (Advanced Spectral Reflection and Emission Radiometer) emissivity and surface temperature products were used for evaluation.

Following a plethora of validations and demonstrations SAR Interferometry (InSAR) has been established as a mature space geodetic technique for providing valuable insights for various phenomena related to geohazards. One of the main advantages of space borne SAR systems with respect to GNSS is the continuous spatial coverage. However, the impact of temporal decorrelation especially in repeat-pass interferometry has been observed during the historical development of InSAR applications. Interferometric coherence can be considered as expression of temporal decorrelation. It is understood that interferometric coherence decreases with time between SAR acquisitions because of changes in surface reflectivity, reducing the quality of SAR phase measurements. This is an intrinsic characteristic of the design of SAR systems that has a significant contribution at longer time scales. Since the majority of geohazards rely on long term observation scenarios, the effect of temporal decorrelation is evident as coherence becomes dominated by temporal changes. Although in the past there was not sufficient amount of SAR data to extract robust statistical metrics, in the present study it is demonstrated that tailored analysis of interferometric coherence by exploiting the large SAR archive as well as data from operational missions such as the Sentinels, available by the European Space Agency (ESA), enables the accurate quantification of temporal decorrelation. A methodology to translate the observed rate of coherence loss into decorrelation times over two volcanic landscape, namely the Santorini volcanic complex and Mt. Etna is the subject treated in this study. Specifically, a sensitivity analysis was performed on a large data stack of interferometric pairs to quantify at a pixel level the time beyond which the interferometric phase becomes practically unusable due to decorrelation. The estimation and mapping within a geostatistical context of the spatial distribution of the temporal decorrelation times in an area without a necessary a priori knowledge of its surface characteristics is a fundamental parameter for the design and establishment of local GNSS networks as well as the definition of optimal monitoring strategy for various geohazards. The dependence of decorrelation on various land cover/use types is also analysed by postprocessing in a GIS environment. Though the dependence of decorrelation on various land cover/use types is already documented the provision of additional information regarding the expected time of decorrelation is of practical use especially when EO data are utilized in operational activities. The performed analysis is viewed within the improved capacity of current and future SAR systems, while underlining the necessity for exploitation of archive data.