WGAST: Weakly-Supervised Generative Network for Daily 10 m Land Surface Temperature Estimation via Spatio-Temporal Fusion

• URL: http://arxiv.org/abs/2508.06485v1
• Date: Fri, 08 Aug 2025 17:49:46 GMT
• Title: WGAST: Weakly-Supervised Generative Network for Daily 10 m Land Surface Temperature Estimation via Spatio-Temporal Fusion
• Authors: Sofiane Bouaziz, Adel Hafiane, Raphael Canals, Rachid Nedjai,
• Abstract summary: We present WGAST, a Weakly-performing Genedrative Network for Daily 10 m LST resolution via SSupervise-Temporal Fusion of MODIS, Landsat 8, and Sentinel-2.<n>It adopts a conditional generative adversarial architecture with a generator composed of four stages: feature extraction, fusion, LST reconstruction, and noise suppression.<n>Experiments demonstrate that WGAST outperforms existing methods in both quantitative and qualitative evaluations.
• Score: 3.344876133162209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urbanization, climate change, and agricultural stress are increasing the demand for precise and timely environmental monitoring. Land Surface Temperature (LST) is a key variable in this context and is retrieved from remote sensing satellites. However, these systems face a trade-off between spatial and temporal resolution. While spatio-temporal fusion methods offer promising solutions, few have addressed the estimation of daily LST at 10 m resolution. In this study, we present WGAST, a Weakly-Supervised Generative Network for Daily 10 m LST Estimation via Spatio-Temporal Fusion of Terra MODIS, Landsat 8, and Sentinel-2. WGAST is the first end-to-end deep learning framework designed for this task. It adopts a conditional generative adversarial architecture, with a generator composed of four stages: feature extraction, fusion, LST reconstruction, and noise suppression. The first stage employs a set of encoders to extract multi-level latent representations from the inputs, which are then fused in the second stage using cosine similarity, normalization, and temporal attention mechanisms. The third stage decodes the fused features into high-resolution LST, followed by a Gaussian filter to suppress high-frequency noise. Training follows a weakly supervised strategy based on physical averaging principles and reinforced by a PatchGAN discriminator. Experiments demonstrate that WGAST outperforms existing methods in both quantitative and qualitative evaluations. Compared to the best-performing baseline, on average, WGAST reduces RMSE by 17.18% and improves SSIM by 11.00%. Furthermore, WGAST is robust to cloud-induced LST and effectively captures fine-scale thermal patterns, as validated against 33 ground-based sensors. The code is available at https://github.com/Sofianebouaziz1/WGAST.git.

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