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 this https URL.
Abstract (translated)
城市化、气候变化和农业压力正在增加对精确且及时的环境监测需求。地表温度（Land Surface Temperature，LST）是这一背景下的关键变量，并通过遥感卫星获取数据。然而，这些系统在空间分辨率与时间分辨率之间存在权衡。尽管时空融合方法提供了有前景的解决方案，但鲜少有关于如何估算每日10米分辨率LST的研究。在这项研究中，我们提出了WGAST——一种基于Terra MODIS、Landsat 8和Sentinel-2卫星数据时空融合的弱监督生成网络，用于估计每日10米分辨率的地表温度。WGAST是第一个为这一任务设计的端到端深度学习框架。它采用了一种条件生成对抗架构，其中生成器包含四个阶段：特征提取、融合、LST重建和噪声抑制。第一阶段利用一组编码器从输入中提取多层潜在表示，然后在第二阶段使用余弦相似度、归一化以及时间注意力机制进行融合。第三阶段将融合的特征解码为高分辨率LST，并通过高斯滤波来消除高频噪声。训练采用基于物理平均原理的弱监督策略，并由PatchGAN鉴别器加强。实验表明，WGAST在定量和定性评估中都优于现有方法。相比最佳基准线，在平均情况下，WGAST使RMSE降低了17.18%，SSIM提升了11%。此外，WGAST对云引起的LST具有鲁棒性，并有效捕捉到了细尺度的热模式，通过33个地面传感器进行了验证。代码可在以下网址获取：[提供链接]。
URL
https://arxiv.org/abs/2508.06485
