Abstract
The Landsat 8 satellites have retrieved land surface temperature (LST) resampled at a 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the city of interest. This paper endorses a procedure for building a long-term gap-free LST data set in an urban area using the high-resolution Landsat 8 imagery. The study is applied on 94 images available through 2013–2018 over Bucharest (Romania). The raw images containing between 1.1% and 58.4% missing LST data were filled in using the Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm implemented in the sinkr R packages. The resulting high-spatial-resolution gap-filled land surface temperature data set was used to explore the LST climatology over Bucharest (Romania) an urban area, at a monthly, seasonal, and annual scale. The performance of the gap-filling method was checked using a cross-validation procedure, and the results pledge for the development of an LST-based urban climatology.
Highlights
The increasing population and the permanent quest for comfort and safe shelter have triggered intense urbanization processes taking place all over the world, mainly in the 20th and 21st centuries.The built-up areas substantially modify the environment, and the features of the local atmospheric envelope are changed to the point that a new type of climate is formed
This study addresses the need for temporal and spatial continuity of remote sensing data, and it explores the climatology of the land surface temperature (LST) in a large urban area (Bucharest, Romania) based on 94 high-spatial-resolution images retrieved from Landsat 8
The results of this study demonstrated that 30-m-spatial-resolution Landsat 8 imagery can be
Summary
The built-up areas substantially modify the environment, and the features of the local atmospheric envelope are changed to the point that a new type of climate is formed. The urban climate is the resultant of a different composition of the radiation budget, higher temperature, and lower humidity values than the surrounding rural areas. Green areas are cooler and more humid than impervious patches, urban canyons and squares disturb the wind flow, and building heights and density are so influential for the urban climate that they form the base of the definition of local climate zones (LCZs) [1,2,3]. Urban climate modelling and weather forecasting require adequate data relevant for different urban microclimates
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