Abstract
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, and Washington, 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.
Highlights
Land surface temperature (LST) is of fundamental importance in many land surface physical processes
We evaluate the sensitivity of SiB2 land surface temperature (LST) to different land cover (LC) types, fractions, and emissivities compared to same baseline/reference points derived from Landsat thermal data
(LST) in in urban urban centers, centers, The aim of and for for that weaim used two different approaches: one by using purely remote sensing, sensing, and the other other by Thethat main oftwo thisdifferent study was to evaluateone theby land surface temperature (LST) in urban centers, and we used approaches: using purely remote and the by using physical modeling assimilated with remotely-sensed data
Summary
Land surface temperature (LST) is of fundamental importance in many land surface physical processes. It is a key indicator of climate change, vegetation monitoring, and urban climate [1,2,3]. Satellite thermal infrared sensors provide global coverage of different spatial resolution data that can be used to estimate land surface temperatures. Model-simulated land surface temperatures can offer homogeneous spatial coverage and desired temporal resolution of the diurnal cycle. The main aim of this study is to evaluate the LST in urban centers, and for that we used two different approaches: one, by using purely remote sensing, and the other by using physical modeling assimilated with remotely-sensed data
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