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In this paper, we assessed and compared land surface temperature (LST) in urban centers using data from Landsat, MODIS, and the Simple Biosphere model (SiB2). We also evaluated the sensitivity of the model's LST to different land cover types, fractions (percentages), and emissivities compared to reference points derived from Landsat thermal data. This was demonstrated in three climatologically- and morphologically-different cities of Atlanta, GA, New York, NY, andWashington, DC. Our results showed that in these cities SiB2 was sensitive to both the emissivity and the land cover type and fraction, but much more sensitive to the latter. The practical implications of these results are rather significant since they imply that the SiB2 model can be used to run different scenarios for evaluating urban heat island (UHI) mitigation strategies. This study also showed that using detailed emissivities per land cover type and fractions from Landsat-derived data caused a convergence of the model results towards the Landsat-derived LST for most of the studied cases. This study also showed that SiB2 LSTs are closer in magnitude to Landsat-derived LSTs than MODIS-derived LSTs. It is important, however, to emphasize that both Landsat and MODIS LSTs are not direct observations and, as such, do not represent a ground truth. More studies will be needed to compare these results to in situ LST data and provide further validation. [ABSTRACT FROM AUTHOR]

The seasonal and spatial variability of surface heat fluxes is crucial to the understanding of urban heat island phenomenon and dynamics. To estimate energy fluxes, remote sensing derived biophysical variables need to be integrated with surface atmospheric parameters measured in meteorological stations or in situ field measurements. In this study, based on the two-source energy balance algorithm, we applied a method to estimate surface energy fluxes by combined use of multispectral ASTER images and routine meteorological data, and applied it to the City of Indianapolis, United States, aiming at in-depth understanding of the spatial patterns of energy fluxes. By computing the fluxes by land use and land cover (LULC) type, we further investigated the spatial variability of heat fluxes. Results show that the energy fluxes possessed a strong seasonality, with the highest net radiation in summer, followed by spring, fall and winter. Sensible heat flux tended to change largely with surface temperature, while latent heat was largely modulated by the change in vegetation abundance and vigor and the accompanying moisture condition. The fluctuation in all heat fluxes tended to be high in the summer months and low in the winter months. Sensible and latent heat fluxes showed a stronger spatial variability than net radiation and ground heat flux. The variations of net radiation among the land use/cover types were mainly attributable to surface albedo and temperature, while the within-class variability in the turbulent heat fluxes was more associated with the changes in vegetation, water bodies, and other surface factors. [ABSTRACT FROM PUBLISHER]

Using a geographic transect in Central Mexico, with an elevation/climate gradient, but uniformity in socio-economic conditions among study sites, this study evaluates the applicability of three widely-used remote sensing (RS) products to link weather conditions with the local abundance of the dengue virus mosquito vector, Aedes aegypti (Ae. aegypti). Field-derived entomological measures included estimates for the percentage of premises with the presence of Ae. aegypti pupae and the abundance of Ae. aegypti pupae per premises. Data on mosquito abundance from field surveys were matched with RS data and analyzed for correlation. Daily daytime and nighttime land surface temperature (LST) values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua cloud-free images within the four weeks preceding the field survey. Tropical Rainfall Measuring Mission (TRMM)-estimated rainfall accumulation was calculated for the four weeks preceding the field survey. Elevation was estimated through a digital elevation model (DEM). Strong correlations were found between mosquito abundance and RS-derived night LST, elevation and rainfall along the elevation/climate gradient. These findings show that RS data can be used to predict Ae. aegypti abundance, but further studies are needed to define the climatic and socio-economic conditions under which the correlations observed herein can be assumed to apply. [ABSTRACT FROM AUTHOR]

Abstract: This study provides an introduction to the HyspIRI mission a National Research Council “Decadal Survey” mission that combines a 213 channel visible, near−infrared and shortwave infrared (VSWIR) imaging spectrometer with an 8 channel multispectral thermal infrared (TIR) instrument and evaluates some of its potential in urban science. Potential synergies between VSWIR and TIR data are explored using analogous airborne data acquired over the Santa Barbara metropolitan region in June, 2008. These data were analyzed at both their native spatial resolutions (7.5m VSWIR and 15m TIR), and aggregated 60m spatial resolution similar to HyspIRI. A spectral library of dominant urban materials (e.g., grass, trees, soil, roof types, roads) was developed from field and airborne-measured spectra using Multiple-Endmember Spectral Mixture Analysis (MESMA) and used to map fractions of impervious, soil, green vegetation (GV, e.g., trees, lawn) and non-photosynthetic vegetation (NPV). Land Surface Temperature (LST) and emissivity were also retrieved from the airborne data. Co-located pixels from the VSWIR and TIR airborne data were used to generate reflectance/emissivity spectra for a subset of urban materials. MESMA was used to map GV, NPV, soil and impervious fractions at the different spatial resolutions and compare the fractional estimates across spatial scales. Important surface energy parameters, including albedo, vegetation cover fraction, broadband emissivity and surface temperature were also determined for and evaluated for 14 urban and natural land-cover classes in the region. Fractions were validated using 1m digital photography. Fractions for GV and NPV were highly correlated with validation fractions at all spatial scales, producing a near 1:1 relationship but with a <10% overestimate of GV from MESMA. Similar, high correlations were observed for impervious surfaces, although impervious was significantly underestimated in most urban areas and soil overestimated. Comparison of fractions across scales showed high correlation between GV and NPV at 7.5 and 60m resolution, suggesting that HyspIRI will provide accurate measures of these two measures in urban areas. An inverse relationship between vegetation cover and LST was observed. Albedo proved to be highly variable and poorly correlated with LST. Broadband emissivity was far less variable with high emissivity surfaces (~0.95) including vegetation, water and asphalt, and low emissivity surfaces (<0.95) including selected roof types, beach sands and senesced grasslands. Residential and commercial areas showed a general pattern of increasing LST with increasing impervious fraction with the highest impervious fractions mapped in commercial areas, roads and roofs. Fine scale spatial structure in cover fractions and LST demonstrated important departures from a simple inverse relationship between GV and LST, even at 60m. The results demonstrate the utility of HyspIRI data for urban studies and provide an insight of what will be possible on a global scale when HyspIRI data become available. [Copyright &y& Elsevier]