Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal Data
Super-Resolution Land Surface Temperature (LST<sub>SR</sub>) maps are essential for urban heat island (UHI) analysis and temperature monitoring. While much of the literature focuses on improving the resolution of low-resolution LST (e.g., MODIS-derived LST) using high-resolution space-bo...
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2025-04-01
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| author | Mahdiyeh Fathi Hossein Arefi Reza Shah-Hosseini Armin Moghimi |
| author_facet | Mahdiyeh Fathi Hossein Arefi Reza Shah-Hosseini Armin Moghimi |
| author_sort | Mahdiyeh Fathi |
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| description | Super-Resolution Land Surface Temperature (LST<sub>SR</sub>) maps are essential for urban heat island (UHI) analysis and temperature monitoring. While much of the literature focuses on improving the resolution of low-resolution LST (e.g., MODIS-derived LST) using high-resolution space-borne data (e.g., Landsat-derived LST), Unmanned Aerial Vehicles (UAVs)/drone thermal imagery are rarely used for this purpose. Additionally, many deep learning (DL)-based super-resolution approaches, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), require significant computational resources. To address these challenges, this study presents a novel approach to generate LST<sub>SR</sub> maps by integrating Low-Resolution Landsat-8 LST (LST<sub>LR</sub>) with High-Resolution PlanetScope images (I<sub>HR</sub>) and UAV-derived thermal imagery (T<sub>HR</sub>) using the Kolmogorov–Arnold Network (KAN) model. The KAN efficiently integrates the strengths of splines and Multi-Layer Perceptrons (MLPs), providing a more effective solution for generating LST<sub>SR</sub>. The multi-step process involves acquiring and co-registering T<sub>HR</sub> via the DJI Mavic 3 thermal (T) drone, I<sub>HR</sub> from Planet (3 m resolution), and LST<sub>LR</sub> from Landsat-8, with T<sub>HR</sub> serving as reference data while I<sub>HR</sub> and LST<sub>LR</sub> are used as input features for the KAN model. The model was trained at two sites in Germany (Oberfischbach and Mittelfischbach) and tested at Königshain, achieving reasonable performance (RMSE: 4.06 °C, MAE: 3.09 °C, SSIM: 0.83, PSNR: 22.22, MAPE: 9.32%), and outperforming LightGBM, XGBoost, ResDensNet, and ResDensNet-Attention. These results demonstrate the KAN’s superior ability to extract fine-scale temperature patterns (e.g., edges and boundaries) from I<sub>HR</sub>, significantly improving LST<sub>LR</sub>. This advancement can enhance UHI analysis, local climate monitoring, and LST modeling, providing a scalable solution for urban heat mitigation and broader environmental applications. To improve scalability and generalizability, KAN models benefit from training on a more diverse set of UAV thermal imagery, covering different seasons, land use types, and regions. Despite this, the proposed approach is effective in areas with limited UAV data availability. |
| format | Article |
| id | doaj-art-14bfbffe71164df1b81d3057a605b89a |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-14bfbffe71164df1b81d3057a605b89a2025-08-20T03:13:32ZengMDPI AGRemote Sensing2072-42922025-04-01178141010.3390/rs17081410Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal DataMahdiyeh Fathi0Hossein Arefi1Reza Shah-Hosseini2Armin Moghimi3i3mainz, Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, 55128 Mainz, Germanyi3mainz, Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, 55128 Mainz, GermanySchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14399-57131, IranDepartment of Photogrammetry and Remote Sensing, Geomatics Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, IranSuper-Resolution Land Surface Temperature (LST<sub>SR</sub>) maps are essential for urban heat island (UHI) analysis and temperature monitoring. While much of the literature focuses on improving the resolution of low-resolution LST (e.g., MODIS-derived LST) using high-resolution space-borne data (e.g., Landsat-derived LST), Unmanned Aerial Vehicles (UAVs)/drone thermal imagery are rarely used for this purpose. Additionally, many deep learning (DL)-based super-resolution approaches, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), require significant computational resources. To address these challenges, this study presents a novel approach to generate LST<sub>SR</sub> maps by integrating Low-Resolution Landsat-8 LST (LST<sub>LR</sub>) with High-Resolution PlanetScope images (I<sub>HR</sub>) and UAV-derived thermal imagery (T<sub>HR</sub>) using the Kolmogorov–Arnold Network (KAN) model. The KAN efficiently integrates the strengths of splines and Multi-Layer Perceptrons (MLPs), providing a more effective solution for generating LST<sub>SR</sub>. The multi-step process involves acquiring and co-registering T<sub>HR</sub> via the DJI Mavic 3 thermal (T) drone, I<sub>HR</sub> from Planet (3 m resolution), and LST<sub>LR</sub> from Landsat-8, with T<sub>HR</sub> serving as reference data while I<sub>HR</sub> and LST<sub>LR</sub> are used as input features for the KAN model. The model was trained at two sites in Germany (Oberfischbach and Mittelfischbach) and tested at Königshain, achieving reasonable performance (RMSE: 4.06 °C, MAE: 3.09 °C, SSIM: 0.83, PSNR: 22.22, MAPE: 9.32%), and outperforming LightGBM, XGBoost, ResDensNet, and ResDensNet-Attention. These results demonstrate the KAN’s superior ability to extract fine-scale temperature patterns (e.g., edges and boundaries) from I<sub>HR</sub>, significantly improving LST<sub>LR</sub>. This advancement can enhance UHI analysis, local climate monitoring, and LST modeling, providing a scalable solution for urban heat mitigation and broader environmental applications. To improve scalability and generalizability, KAN models benefit from training on a more diverse set of UAV thermal imagery, covering different seasons, land use types, and regions. Despite this, the proposed approach is effective in areas with limited UAV data availability.https://www.mdpi.com/2072-4292/17/8/1410super-resolutionLSTplanetUAVsKANs |
| spellingShingle | Mahdiyeh Fathi Hossein Arefi Reza Shah-Hosseini Armin Moghimi Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal Data Remote Sensing super-resolution LST planet UAVs KANs |
| title | Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal Data |
| title_full | Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal Data |
| title_fullStr | Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal Data |
| title_full_unstemmed | Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal Data |
| title_short | Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal Data |
| title_sort | super resolution of landsat 8 land surface temperature using kolmogorov arnold networks with planetscope imagery and uav thermal data |
| topic | super-resolution LST planet UAVs KANs |
| url | https://www.mdpi.com/2072-4292/17/8/1410 |
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