发表机构
Politecnico di Torino; Fondazione LINKS; Institute of Integrated Methodologies for Earth Observation, National Research Council (IMIOT/CNR); University of Calabria（都灵理工大学; LINKS基金会; 地球观测综合方法研究所，国家研究委员会（IMIOT/CNR）; 卡拉布里亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种双编码器Transformer，用于从卫星辐射率估算行星边界层高度，在保留的全球测试集上达到155.8米MAE，优于所有基线，并验证了其在分布外数据上的泛化能力。
AI 中文摘要
从卫星观测估算行星边界层高度（PBLH）是一个具有挑战性的回归问题，因为大气顶部辐射率与近地表大气结构之间存在间接关系。进展一直受到两方面限制：缺乏能够处理卫星过境多模态、空间不完整特性的架构，以及缺乏合适的数据集。在本文中，我们基于先前工作中引入的大规模数据集（将MetOp辐射率与ERA5 PBLH标签配对），做出三项贡献。首先，我们建立了涵盖八种方法的基准，包括像素级回归、条带级序列模型，以及在全轨道通道上运行的卷积和Transformer模型。其次，我们通过输入块上的分组Shapley分解，量化了所得模型实际依赖的内容。第三，我们展示了性能最佳的架构：一种双编码器Transformer，其掩码输入处理使其能够在所有天气条件下运行。所提出的模型在保留的全球测试集上达到MAE = 155.8米，在所有评估子集上均优于所有基线。在TEAMx观测活动重叠的两天获取的30个分布外条带上，它达到MAE = 165.3米，优于在同一数据上训练的像素级基线（MAE = 197米）。
英文摘要
Estimating the Planetary Boundary Layer Height (PBLH) from satellite observations is a challenging regression problem due to the indirect relationship between top-of-atmosphere radiances and near-surface atmospheric structure. Progress has been limited both by the lack of architectures capable of handling the multimodal, spatially incomplete nature of satellite overpasses, and by the scarcity of suitable datasets. In this paper, we build upon the large-scale dataset pairing MetOp radiances with ERA5 PBLH labels that we introduced in our previous work, making three contributions. First, we establish a benchmark across eight approaches spanning pixel-wise regression, swath-wise sequence models, and convolutional and Transformer models operating on the full orbital passage. Second, we quantify what the resulting model actually relies on, using grouped Shapley decomposition over the input blocks. Third, we present the best-performing architecture found: a dual-encoder Transformer whose masked-input handling lets it operate in all weather conditions. The proposed model achieves MAE = 155.8 m on the held-out global test set, outperforming all baselines on every evaluation subset. On 30 out-of-distribution granules acquired on two days overlapping the TEAMx observational campaign, it achieves MAE = 165.3 m, outperforming a pixel-wise baseline trained on the same data (MAE = 197 m).
Comments13 pages, 3 figures, 2 tables. Extended version of the paper accepted at the MACLEAN workshop, ECML PKDD 2026. Code: https://github.com/links-ads/pblh-transformer
