WGAST：基于弱监督生成网络的10米分辨率日地表温度时空融合估算

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

August 8, 2025

作者: Sofiane Bouaziz, Adel Hafiane, Raphael Canals, Rachid Nedjai

cs.AI

摘要

城市化、气候变化和农业压力正日益提升对精准及时环境监测的需求。地表温度（LST）作为这一背景下的关键变量，通常通过遥感卫星获取。然而，这些系统在空间与时间分辨率之间面临权衡。尽管时空融合方法提供了有前景的解决方案，但鲜有研究致力于实现每日10米分辨率的LST估算。本研究提出了WGAST，一种基于Terra MODIS、Landsat 8和Sentinel-2数据时空融合的弱监督生成网络，用于每日10米LST估算。WGAST是首个专为此任务设计的端到端深度学习框架，采用条件生成对抗网络架构，其生成器包含四个阶段：特征提取、融合、LST重建及噪声抑制。第一阶段通过一组编码器从输入中提取多层次潜在表示，随后在第二阶段利用余弦相似度、归一化及时间注意力机制进行融合。第三阶段将融合特征解码为高分辨率LST，并通过高斯滤波器抑制高频噪声。训练采用基于物理平均原理的弱监督策略，并辅以PatchGAN判别器强化。实验表明，WGAST在定量与定性评估上均优于现有方法。相较于表现最佳的基线模型，WGAST平均将RMSE降低了17.18%，SSIM提升了11.00%。此外，WGAST对云层导致的LST变化具有鲁棒性，并能有效捕捉细微热力模式，这一能力已通过33个地面传感器验证。代码公开于https://github.com/Sofianebouaziz1/WGAST.git。

English

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.