Publication Type
Journal Article
Version
acceptedVersion
Publication Date
4-2020
Abstract
Land surface temperatures (LST) in urban landscapes are typically more heterogeneous than can be monitored by the spatial resolution of satellite-based thermal infrared sensors. Thermal sharpening (TS) methods permit the disaggregation of LST based on finer-grained multispectral information, but there is continued debate over which spectral indices are most appropriate for urban TS, and how they should be configured in a predictive regression framework. In this study, we evaluate the stability of various TS kernels with respect to LST at different spatial (Landsat 8) and diurnal (MODIS) scales, and present a new TS method, global regression for urban thermal sharpening (SGRUTS), based on these findings. Of the spectral indices examined, the normalized difference built-up index (NDBI) and the normalized multi-band drought index (NMDI) were the most spatially stable for Landsat 8 and MODIS overall. Kernel performance varied diurnally, with the index-based impervious surface index (IBI) and broadband α selected for 1030 h, NDBI and NMDI selected for 1330 h, and IBI and NMDI selected for 2230 h and 130 h, respectively. Over a range of field-validated metrics, the SGRUTS scheme comprising a two-factor interaction between NDBI and NMDI was competitive with the best alternative TS models compared. This SGRUTS model is essentially a refinement of the Enhanced Physical Method for urban applications in terms of kernel selection and configuration, and has interpretative advantages over more complex statistical schemes.
Keywords
Impervious surface, Normalized differences, Regression method, Spatial resolution, Statistical scheme, Thermal infrared sensors, Urban applications, Urban land surface temperature
Discipline
Environmental Sciences | Political Science | Urban Studies and Planning
Research Areas
Political Science
Publication
International Journal of Remote Sensing
Volume
41
Issue
8
First Page
2986
Last Page
3009
ISSN
0143-1161
Identifier
10.1080/01431161.2019.1697009
Publisher
Taylor & Francis
Citation
WANG, James. W., CHOW, Winston T. L., & WANG, Yi-Chen.(2020). A global regression method for thermal sharpening of urban land surface temperatures from MODIS and Landsat. International Journal of Remote Sensing, 41(8), 2986-3009.
Available at: https://ink.library.smu.edu.sg/soss_research/3048
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/01431161.2019.1697009
Included in
Environmental Sciences Commons, Political Science Commons, Urban Studies and Planning Commons