Abstract
The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.
Highlights
Reliable estimates of land surface temperature (LST) are crucial for understanding the land–atmosphere energy budget, hydrological and biogeochemical cycles, climate change, as well as for land surface model data assimilation [1,2,3,4]
An example of one of the most widely used Thermal infrared (TIR)–based LST products is obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) derived LST estimates, and it has been successfully used for numerous applications worldwide [3,4,8,9,10,11]
The root–mean–square error (RMSE) in the Spatial Downscaling Method (SDM) LST is less than 1.5 K in both cases. These error metrics again show that SDM has the potential to downscale Geostationary Operational Environmental Satellites—R (GOES–R) LST over the urban region with reasonable accuracy
Summary
Reliable estimates of land surface temperature (LST) are crucial for understanding the land–atmosphere energy budget, hydrological and biogeochemical cycles, climate change, as well as for land surface model data assimilation [1,2,3,4]. Weng et al [23] proposed a spatiotemporal adaptive data fusion algorithm for temperature mapping to obtain finer resolution LST by blending daily MODIS and periodic Landsat datasets over Los Angeles County, California, USA, and an accuracy of 1.3–2 K was reported. This model was unable to predict changes in LST due to limitations in model assumptions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
