Downscaling landsat land surface temperature over the urban area of Florence

A new downscaling algorithm for land surface temperature (LST) images retrieved from Landsat Thematic Mapper (TM) was developed over the city of Florence and the results assessed against a high-resolution aerial image. The Landsat TM thermal band has a spatial resolution of 120 m, resampled at 30 m by the US Geological Survey (USGS) agency, whilst the airborne ground spatial resolution was 1 m. Substantial differences between Landsat USGS and airborne thermal data were observed on a 30 m grid: therefore a new statistical downscaling method at 30 m was developed. The overall root mean square error with respect to aircraft data improved from 3.3 °C (USGS) to 3.0 °C with the new method, that also showed better results with respect to other regressive downscaling techniques frequently used in literature. Such improvements can be ascribed to the selection of independent variables capable of representing the heterogeneous urban landscape

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

Scaling Effect of Fused ASTER-MODIS Land Surface Temperature in an Urban Environment

There is limited research in land surface temperatures (LST) simulation using image fusion techniques, especially studies addressing the downscaling effect of LST image fusion. LST simulation and associated downscaling effect can potentially benefit the thermal studies requiring both high spatial and temporal resolutions. This study simulated LSTs based on observed Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST imagery with Spatial and Temporal Adaptive Reflectance Fusion Model, and investigated the downscaling effect of LST image fusion at 15, 30, 60, 90, 120, 250, 500, and 1000 m spatial resolutions. The study area partially covered the City of Los Angeles, California, USA, and surrounding areas. The reference images (observed ASTER and MODIS LST imagery) were acquired on 04/03/2007 and 07/01/2007, with simulated LSTs produced for 4/28/2007. Three image resampling methods (Cubic Convolution, Bilinear Interpolation, and Nearest Neighbor) were used during the downscaling and upscaling processes, and the resulting LST simulations were compared. Results indicated that the observed ASTER LST and simulated ASTER LST images (date 04/28/2007, spatial resolution 90 m) had high agreement in terms of spatial variations and basic statistics based on a comparison between the observed and simulated ASTER LST maps. Urban developed lands possessed higher LSTs with lighter tones and mountainous areas showed dark tones with lower LSTs. The Cubic Convolution and Bilinear Interpolation resampling methods yielded better results over Nearest Neighbor resampling method across the scales from 15 to 1000 m. The simulated LSTs with image fusion can be used as valuable inputs in heat related studies that require frequent LST measurements with fine spatial resolutions, e.g., seasonal movements of urban heat islands, monthly energy budget assessment, and temperature-driven epidemiology. The observation of scale-independency of the proposed image fusion method can facilitate with image selections of LST studies at various locations

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Using SMOS and Sentinel 3 satellite data to obtain high resolution soil moisture maps

Surface soil moisture is a critical climate variable and strongly influences water and energy cycles at the surface-atmosphere interface. It is widely used to improve numerical climate and weather models, rainfall and drough estimation, vegetation monitoring, among others. Traditionally, there were two main ways to retrieve soil moisture data. On one hand, soil moisture sensors networks placed and maintained in situ to obtain long term distributed measurements, which is expensive and time-consuming. On the other hand, soil moisture data could be obtained by using numerical model products combined with ground observations. But, in both cases, the data resolution provided was not enough to characterize soil moisture at large scale. Nowadays, microwave remote sensing allows the global monitoring of soil moisture. SMOS (Soil Moisture and Ocean Salinity) mission, launched in 2009, was the first mission with this objective and providing an acceptable spatial resolution. It aims to monitor soil moisture over land surfaces, surface salinity over the oceans, and surfaces covered by snow and ice, by performing microwave radiometric measurements at L-band, characterized by being unaffected by cloud cover and variable surface solar illumination. The SMOS soil moisture data has a spatial resolution of 35-50 km, which is enough for global applications. But, local applications such as hydrological, fire prevention, agricultural and water management, require higher soil moisture resolution. In order to cover this necessity, several downscaling methodologies have been developed to improve the spatial resolution. The Department of Signal Theory in the UPC developed a downscaling algorithm based on the synergistic usage of low resolution soil moisture map and data provided by other satellites, that computed soil moisture maps at 1 km resolution (Maria Piles, 2010 [32]). This algorithm combines the SMOS soil moisture with NDVI and LST measurements from Aqua and Terra missions obtained by MODIS instrument. Later, this algorithm was improved by using an adaptive sliding window, which is the version we will use in this project (Gerard Portal, 2017 [24]). The aim of this project is to substitute the NDVI and LST measurements from MODIS used as ancillary data in the downscaling algorithm by the ones provided by Sentinel 3, comparing its differences and the variation of the high resolution soil moisture maps (SM HR maps) obtained. Also, it will include the evaluation of the data download and preparation process workflow

Chapter Earth Observation for Urban Climate Monitoring: Surface Cover and Land Surface Temperature

The rate at which global climate change is happening is arguably the most pressing environmental challenge of the century, and it affects our cities. Climate change exerts added stress on urban areas through increased numbers of heat waves threatening people’s well-being and, in many cases, human lives. Earth observation (EO) systems and the advances in remote sensing technology increase the opportunities for monitoring the thermal behavior of cities. The Sentinels constitute the first series of operational satellites for Copernicus, a program launched to provide data, information, services, and knowledge in support of Europe’s goals regarding sustainable development and global governance of the environment. This chapter examines the exploitation of EO data for monitoring the urban climate, with particular focus on the urban surface cover and temperature. Two example applications are analyzed: the mapping of the urban surface and its characteristics, using EO data and the estimation of urban temperatures. Approaches, like the ones described in this chapter, can become operational once adapted to Sentinels, since their long-term operation plan guarantees the future supply of satellite observations. Thus, the described methods may support planning activities related to climate change mitigation and adaptation in cities, as well as routine urban planning activities

Future permafrost conditions along environmental gradients in Zackenberg, Greenland

The future development of ground temperatures in permafrost areas is determined by a number of factors varying on different spatial and temporal scales. For sound projections of impacts of permafrost thaw, scaling procedures are of paramount importance. We present numerical simulations of present and future ground temperatures at 10 m resolution for a 4 km long transect across the lower Zackenberg valley in northeast Greenland. The results are based on stepwise downscaling of future projections derived from general circulation model using observational data, snow redistribution modeling, remote sensing data and a ground thermal model. A comparison to in situ measurements of thaw depths at two CALM sites and near-surface ground temperatures at 17 sites suggests agreement within 0.10 m for the maximum thaw depth and 1 °C for annual average ground temperature. Until 2100, modeled ground temperatures at 10 m depth warm by about 5 °C and the active layer thickness increases by about 30%, in conjunction with a warming of average near-surface summer soil temperatures by 2 °C. While ground temperatures at 10 m depth remain below 0 °C until 2100 in all model grid cells, positive annual average temperatures are modeled at 1 m depth for a few years and grid cells at the end of this century. The ensemble of all 10 m model grid cells highlights the significant spatial variability of the ground thermal regime which is not accessible in traditional coarse-scale modeling approaches