用多源卫星数据生成每日10米地表温度图，提升精度与细节。
FuseTen: A Generative Model for Daily 10 m Land Surface Temperature Estimation from Spatio-Temporal Satellite Observations
- 融合多源卫星数据，用生成模型重建高分辨率地表温度。
- 相比线性方法，定量指标提升32.06%，视觉质量提高31.42%。
- 首次实现每日10米级地表温度生成，适合城市热环境研究。
城市热浪、干旱和土地退化是气候变化下的紧迫挑战，需精确的时空地表信息支持研究。地表温度（LST）是评估这些现象的关键变量，通常由卫星获取，但存在空间与时间分辨率间的权衡。为此，本文提出FuseTen，一种新型生成框架，通过融合Sentinel-2、Landsat 8和Terra MODIS的时空观测数据，生成每日10米分辨率的地表温度产品。该模型采用基于物理原理的平均监督策略，集成注意力与归一化模块，并使用PatchGAN判别器增强结果真实性。实验表明，跨多个日期，其在定量指标上平均优于线性基线32.06%，视觉保真度提升31.42%。据我们所知，这是首个实现每日10米级地表温度生成的非线性方法。
原文摘要 · Abstract (English)
Urban heatwaves, droughts, and land degradation are pressing and growing challenges in the context of climate change. A valuable approach to studying them requires accurate spatio-temporal information on land surface conditions. One of the most important variables for assessing and understanding these phenomena is Land Surface Temperature (LST), which is derived from satellites and provides essential information about the thermal state of the Earth's surface. However, satellite platforms inherently face a trade-off between spatial and temporal resolutions. To bridge this gap, we propose FuseTen, a novel generative framework that produces daily LST observations at a fine 10 m spatial resolution by fusing spatio-temporal observations derived from Sentinel-2, Landsat 8, and Terra MODIS. FuseTen employs a generative architecture trained using an averaging-based supervision strategy grounded in physical principles. It incorporates attention and normalization modules within the fusion process and uses a PatchGAN discriminator to enforce realism. Experiments across multiple dates show that FuseTen outperforms linear baselines, with an average 32.06% improvement in quantitative metrics and 31.42% in visual fidelity. To the best of our knowledge, this is the first non-linear method to generate daily LST estimates at such fine spatial resolution.
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