Urban surface temperature time series estimation at the local scale by spatial-spectral unmixing of satellite observations

The study of urban climate requires frequent and accurate monitoring of land surface temperature (LST), at the local scale. Since currently, no space-borne sensor provides frequent thermal infrared imagery at high spatial resolution, the scientific community has focused on synergistic methods for retrieving LST that can be suitable for urban studies. Synergistic methods that combine the spatial structure of visible and near-infrared observations with the more frequent, but low-resolution surface temperature patterns derived by thermal infrared imagery provide excellent means for obtaining frequent LST estimates at the local scale in cities. In this study, a new approach based on spatial-spectral unmixing techniques was developed for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. The method was applied to an urban area in Crete, Greece, for the time period of one year. The results were evaluated against independent high-resolution LST datasets and found to be very promising, with RMSE less than 2 K in all cases. The developed approach has therefore a high potential to be operationally used in the near future, exploiting the Copernicus Sentinel (2 and 3) observations, to provide high spatio-temporal resolution LST estimates in cities

Urban energy exchanges monitoring from space

One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. However, satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (-64.1, +69.3 W m-2 for ±2 K perturbation); and also underestimate anthropogenic heat flux. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling

Incorporating bio-physical sciences into a decision support tool for sustainable urban planning

Deciding upon optimum planning actions in terms of sustainable urban planning involves the consideration of multiple environmental and socio-economic criteria. The transformation of natural landscapes to urban areas affects energy and material fluxes. An important aspect of the urban environment is the urban metabolism, and changes in such metabolism need to be considered for sustainable planning decisions. A spatial Decision Support System (DSS) prototyped within the European FP7-funded project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism), enables accounting for the urban metabolism of planning actions, by exploiting the current knowledge and technology of biophysical sciences. The main aim of the BRIDGE project was to bridge the knowledge and communication gap between urban planners and environmental scientists and to illustrate the advantages of considering detailed environmental information in urban planning processes. The developed DSS prototype integrates biophysical observations and simulation techniques with socio-economic aspects in fiveEuropean cities, selected as case studies for the pilot application of the tool. This paper describes the design and implementation of the BRIDGE DSS prototype, illustrates some examples of use, and highlights the need for further research and development in the field

Remote Sensing Studies of Urban Canopies: 3D Radiative Transfer Modeling

Need for better understanding and more accurate estimation of radiative fluxes in urban environments, specifically urban surface albedo and exitance, motivates development of new remote sensing and three‐dimensional (3D) radiative transfer (RT) modeling methods. The discrete anisotropic radiative transfer (DART) model, one of the most comprehensive physically based 3D models simulating Earth/atmosphere radiation interactions, was used in combination with satellite data (e.g., Landsat‐8 observations) to better parameterize the radiative budget components of cities, such as Basel in Switzerland. After presenting DART and its recent RT modeling functions, we present a methodological concept for estimating urban fluxes using any satellite image data

Non-linear spectral mixture analysis of Landsat imagery by means of neural networks

Urban surfaces are highly inhomogeneous because of the high spatial and spectral diversity of man-made structures. Spectral unmixing techniques although developed to be used with hyperspectral data, are useful for assessing sub-pixel information on multispectral data as well. The large spectral variability imposes the use of multiple endmember spectral mixture analysis techniques, in which many possible mixture models are considered to produce the best fit. The use of many endmembers and mixture models result in prohibitive computational time. In this study, an artificial neural network is used to inverse the pixel spectral mixture in Landsat imagery. Endmember spectra, collected from the image were used to train the network and capture the spectral variability of man-made structures

Spectral unmixing of urban Landsat imagery by means of neural networks

Mapping urban surfaces using Earth Observation data is one the most challenging tasks of remote sensing field, because of the high spatial and spectral diversity of man-made structures. Spectral unmixing techniques, although designed and mainly used with hyperspectral data, can be proven useful for use with spectral data as well to assess sub-pixel information. For urban areas, the large spectral variability imposes the use of multiple endmember spectral mixture analysis techniques, which are very demanding in terms of computation time. In this study, an artificial neural network is used to inverse the pixel spectral mixture in Landsat imagery. To train the network, a spectal library was created, consisting of pure endmember spectra collected from the image and synthetic mixed spectra produced from combinations of the pure ones. Among the advantages of using a neural network is its low computational demand and its ability to capture non-linearities in the spectral mixture

Mapping the urban surface in a sub-pixel level with multispectral high resolution satellite imagery

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

Nonlinear Spectral Unmixing of Landsat Imagery for Urban Surface Cover Mapping

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

Uncertainty Estimation of Local-Scale Land Surface Temperature Products over Urban Areas Using Monte Carlo Simulations

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