Exploratory Analysis of Urban Climate Using a Gap-Filled Landsat 8 Land Surface Temperature Data Set

Area of Science:

• Earth and Environmental Sciences
• Remote Sensing
• Urban Climatology

Background:

• Urban climate studies often face challenges with missing data in satellite-derived land surface temperature (LST) datasets.
• Landsat 8 provides LST data at a 30-m resolution, but data gaps hinder long-term urban analysis.

Purpose of the Study:

• To present a procedure for creating a gap-free, long-term LST dataset for urban areas using high-resolution Landsat 8 imagery.
• To analyze the LST climatology of Bucharest, Romania, using the gap-filled data.

Main Methods:

• Employed the Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm within the sinkr R package to fill missing LST data.
• Applied the method to 94 Landsat 8 images from 2013-2018 over Bucharest, addressing data gaps ranging from 1.1% to 58.4%.

Main Results:

• Generated a high-spatial-resolution, gap-free LST dataset for Bucharest.
• Explored LST variations at monthly, seasonal, and annual scales, revealing urban temperature patterns.
• Validated the DINEOF method's performance through cross-validation, confirming its reliability.

Conclusions:

• The developed procedure effectively creates gap-free LST datasets essential for urban climate studies.
• The gap-filled LST data supports the development of robust LST-based urban climatology.
• This approach enhances the utility of satellite data for understanding urban environmental dynamics.