Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature

• Published in: IEEE transactions on geoscience and remote sensing Vol. 55; no. 1; pp. 477 - 490
• Main Authors: Gao, Lun, Zhan, Wenfeng, Huang, Fan, Quan, Jinling, Lu, Xiaoman, Wang, Fei, Ju, Weimin, Zhou, Ji
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brightness temperature; Climate; Computer applications; Disaggregation; Geography; global regression strategy (GWS); Globalization; High resolution; Land cover; Land surface; Land surface temperature; land surface temperature (LST); Land use; local regression strategy (LWS); Localization; Methods; moving-window size (MWS); Optimization; Predictions; Regression; Remote sensing; Resolution; Scaling factors; Spatial discrimination; Spatial resolution; Statistical analysis; Temperature; Temperature sensors; Thermal conductivity; thermal remote sensing
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2016.2608987

The past decade has witnessed the disaggregation of remotely sensed land surface temperature (DLST), which aims for the generation of high temporal and spatial resolution land surface temperature (LST) and which has steadily evolved into a relatively independent subfield of thermal remote sensing. Limited by Tobler's first law of geography, DLST methods require a regression between LSTs and scaling factors using image pixels within a globalized or a localized regression window. Recommendations regarding the selection of the regression window have been provided, but they are mainly subjective and based on highly specific examples. In this context, 100 DLST samples with diversified land cover types and climates were employed to assess the global window strategy (GWS) and the local window strategy (LWS). To optimize disaggregation accuracy and computational complexity, the assessments show that the optimal moving-window size (MWS) for the LWS can be estimated by the resolution ratio between pre- and postdisaggregated LSTs. To identify the better strategy between the GWS and the LWS, an indirect criterion based on aggregation-disaggregation (ICAD) was formulated, which determines the better strategy from medium to high resolution according to the associated performances from low to medium resolution. Validations demonstrate that the accuracy predicted by the ICAD achieves 72%, and in cases in which predictions are incorrect, the performances of the GWS and the LWS are similar. Further evidences indicate that the use of historical high-resolution LSTs improves the LWS by using a locally varying MWS. These findings are able to guide researchers in choosing the most suitable regression window for any particular DLST.