Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation
With rising urbanization and extreme heat events, effective monitoring of urban heat stress is essential. Satellite-derived land surface temperature (LST) is valuable for this task, but current technologies face a trade-off between spatial resolution and revisit frequency, leading to gaps in high re...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Science of Remote Sensing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000409 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849689311164760064 |
|---|---|
| author | Matteo Piccardo Emanuele Massaro Luca Caporaso Alessandro Cescatti Grégory Duveiller |
| author_facet | Matteo Piccardo Emanuele Massaro Luca Caporaso Alessandro Cescatti Grégory Duveiller |
| author_sort | Matteo Piccardo |
| collection | DOAJ |
| description | With rising urbanization and extreme heat events, effective monitoring of urban heat stress is essential. Satellite-derived land surface temperature (LST) is valuable for this task, but current technologies face a trade-off between spatial resolution and revisit frequency, leading to gaps in high resolution thermal data. While numerous methods for downscaling coarse resolution imagery exist, their increasing complexity and variability make it challenging to standardize accuracy and identify key improvement factors. In response, we introduce the Convolutional Linear-Regression Model (CLRM), a minimal complexity approach that focuses on two key assumptions: (i) correlations between LST at different times and spatial resolutions are considered without additional variables, and (ii) these correlations are modelled using linear relationships. Unlike the conventional fusion-based methods, CLRM leverages temporal correlations to perform pixel-wise linear interpolations, thereby avoiding the complexities of pixel unmixing, and eliminating the need for a fine resolution reference image. We use CLRM to generate Landsat 8 imagery at 30 m resolution using data from Moderate-resolution Imaging Spectroradiometer (MODIS) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) over Madrid, Brussels, and Helsinki, representing three different climate regions. CLRM achieves robust accuracy, with a mean absolute error (MAE) of approximately 1.5 °C, a root mean square error (RMSE) typically below 2 °C - aligning with the error ranges reported for LST downscaling methods in the literature. Finally, we apply CLRM to reconstruct the high-resolution daily LST profile over Madrid, illustrating its potential for improved urban heat monitoring and thermal data enhancement. |
| format | Article |
| id | doaj-art-584e2469e48c4a3e9002facdde1b9fcd |
| institution | DOAJ |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-584e2469e48c4a3e9002facdde1b9fcd2025-08-20T03:21:42ZengElsevierScience of Remote Sensing2666-01722025-06-011110023410.1016/j.srs.2025.100234Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimationMatteo Piccardo0Emanuele Massaro1Luca Caporaso2Alessandro Cescatti3Grégory Duveiller4European Commission Joint Research Centre collaborator, Ispra, Italy; Corresponding author.European Commission Joint Research Centre, Ispra, ItalyEuropean Commission Joint Research Centre, Ispra, Italy; National Research Council of Italy, Institute of BioEconomy, Rome, ItalyEuropean Commission Joint Research Centre, Ispra, Italy; Corresponding author.Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, GermanyWith rising urbanization and extreme heat events, effective monitoring of urban heat stress is essential. Satellite-derived land surface temperature (LST) is valuable for this task, but current technologies face a trade-off between spatial resolution and revisit frequency, leading to gaps in high resolution thermal data. While numerous methods for downscaling coarse resolution imagery exist, their increasing complexity and variability make it challenging to standardize accuracy and identify key improvement factors. In response, we introduce the Convolutional Linear-Regression Model (CLRM), a minimal complexity approach that focuses on two key assumptions: (i) correlations between LST at different times and spatial resolutions are considered without additional variables, and (ii) these correlations are modelled using linear relationships. Unlike the conventional fusion-based methods, CLRM leverages temporal correlations to perform pixel-wise linear interpolations, thereby avoiding the complexities of pixel unmixing, and eliminating the need for a fine resolution reference image. We use CLRM to generate Landsat 8 imagery at 30 m resolution using data from Moderate-resolution Imaging Spectroradiometer (MODIS) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) over Madrid, Brussels, and Helsinki, representing three different climate regions. CLRM achieves robust accuracy, with a mean absolute error (MAE) of approximately 1.5 °C, a root mean square error (RMSE) typically below 2 °C - aligning with the error ranges reported for LST downscaling methods in the literature. Finally, we apply CLRM to reconstruct the high-resolution daily LST profile over Madrid, illustrating its potential for improved urban heat monitoring and thermal data enhancement.http://www.sciencedirect.com/science/article/pii/S2666017225000409LandsatMODISSEVIRILST downscalingConvolutional Linear-Regression ModelLST daily profile |
| spellingShingle | Matteo Piccardo Emanuele Massaro Luca Caporaso Alessandro Cescatti Grégory Duveiller Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation Science of Remote Sensing Landsat MODIS SEVIRI LST downscaling Convolutional Linear-Regression Model LST daily profile |
| title | Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation |
| title_full | Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation |
| title_fullStr | Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation |
| title_full_unstemmed | Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation |
| title_short | Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation |
| title_sort | exploring a minimal convolutional linear regression model for urban land surface temperature estimation |
| topic | Landsat MODIS SEVIRI LST downscaling Convolutional Linear-Regression Model LST daily profile |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000409 |
| work_keys_str_mv | AT matteopiccardo exploringaminimalconvolutionallinearregressionmodelforurbanlandsurfacetemperatureestimation AT emanuelemassaro exploringaminimalconvolutionallinearregressionmodelforurbanlandsurfacetemperatureestimation AT lucacaporaso exploringaminimalconvolutionallinearregressionmodelforurbanlandsurfacetemperatureestimation AT alessandrocescatti exploringaminimalconvolutionallinearregressionmodelforurbanlandsurfacetemperatureestimation AT gregoryduveiller exploringaminimalconvolutionallinearregressionmodelforurbanlandsurfacetemperatureestimation |