Generating Comparable and Fine-Scale Time Series of Summer Land Surface Temperature for Thermal Environment Monitoring

Satellite images have been widely used for urban heat island (UHI) monitoring in recent studies, among which the summer UHI has attracted more attention. However, the studies based on high spatial resolution images have to use single-day land surface temperature (LST) to analyze the summer UHI, due to the low temporal resolution, which is not representative of the summer and leads to incomparability in the time series. The studies based on low spatial resolution images can generate a time series of representative LSTs for summer (e.g., summer mean LSTs), due to the high temporal resolution, but these LSTs lack sufficient spatial details for a refined analysis. To fill these gaps, we propose to integrate the respective advantages of the above approaches to generate a comparable and fine-scale LST time series with a high spatiotemporal resolution. By normalizing the LSTs between the different satellite images via robust fitting with Huber's M-estimator and moment matching, the comparability is ensured. Furthermore, the high-spatial resolution and high-temporal resolution are combined via the spatiotemporal fusion. Overall, we propose a procedure to produce a comparable time series of annual and fine-scale summer mean LSTs, which can serve as a basis for elaborate analysis of the thermal environment.

Automated summary

Satellite images are commonly used to monitor urban heat islands, with a focus on summer heat islands. Current approaches based on high and low spatial resolution images each have their limitations. A proposed solution integrates the advantages of both approaches to generate a comparable and detailed LST time series for analysis of the thermal environment.