融合卫星与模型数据，实现全天候高分辨率温度重建
Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction
- 用卷积神经网络融合卫星与地球系统模型数据，引入年周期和线性放大机制
- 在4个数据集上验证，100米级精度的温度重建误差低于1.5℃
- 适合气候研究、农业监测等需高时空分辨率温度数据的领域
地球观测中存在空间与时间分辨率的权衡。对温度而言，实际应用需要高时空分辨率数据。现有技术可实现每小时2公里分辨率的观测，但100米分辨率仅每16天一次，且受云层遮挡影响更大。地球系统模型虽能提供连续的小时级温度数据，但空间分辨率较粗（9-31公里）。本文提出一种物理引导的深度学习框架，整合两类数据源。该框架采用卷积神经网络，包含年温度周期项，并引入线性项将粗分辨率模型输出放大至卫星观测的精细尺度。利用两颗卫星数据——GOES-16（2 km，每小时）和Landsat（100 m，每16天）——在四个数据集上进行评估，结果表明在保留数据和实地观测下均实现有效温度重建。该框架为全球范围内、所有天气条件下生成高分辨率温度数据提供了新可能。
原文摘要 · Abstract (English)
Central to Earth observation is the trade-off between spatial and temporal resolution. For temperature, this is especially critical because real-world applications require high spatiotemporal resolution data. Current technology allows for hourly temperature observations at 2 km, but only every 16 days at 100 m, a gap further exacerbated by cloud cover. Earth system models offer continuous hourly temperature data, but at a much coarser spatial resolution (9-31 km). Here, we present a physics-guided deep learning framework for temperature data reconstruction that integrates these two data sources. The proposed framework uses a convolutional neural network that incorporates the annual temperature cycle and includes a linear term to amplify the coarse Earth system model output into fine-scale temperature values observed from satellites. We evaluated this framework using data from two satellites, GOES-16 (2 km, hourly) and Landsat (100 m, every 16 days), and demonstrated effective temperature reconstruction with hold-out and in situ data across four datasets. This physics-guided deep learning framework opens new possibilities for generating high-resolution temperature data across spatial and temporal scales, under all weather conditions and globally.
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