用AI重建加拿大养禽区大气二氧化碳分布，精度高且带不确定性分析。
AI-Driven Carbon Monitoring: Transformer-Based Reconstruction of Atmospheric CO2 in Canadian Poultry Regions
- 结合小波分析与Transformer，融合气象、植被等多源数据重建CO2场。
- 在OCO-2数据上达R2=0.984，92.3%预测误差小于±1 ppm。
- 适合碳核算、政策评估及养殖场排放监测，可支撑精准畜牧管理。
精确绘制农业景观上的柱平均二氧化碳（XCO2）分布对指导减排策略至关重要。本文提出一种时空视觉小波变换器（ST-ViWT）框架，基于OCO-2卫星数据重建加拿大南部养禽密集区连续的、带不确定性的XCO2场。模型融合小波时频表示与变压器注意力机制，输入包括气象、植被指数、地形和土地利用数据。在2024年OCO-2数据上，模型达到R²=0.984，均方根误差（RMSE）为0.468 ppm；92.3%的插补预测值位于±1 ppm以内。独立验证显示与TCCON数据一致（偏差=-0.14 ppm；相关系数r=0.928），并准确复现了夏末碳汇下降特征。对14个养禽区的空间分析表明，设施密度与XCO2呈中度正相关（r=0.43），高密度区季节振幅达9.57 ppm，夏季变率更高。相比传统插值与标准机器学习基线，该方法生成无缝0.25度分辨率的CO2表面，并明确标注不确定性，实现稀疏观测下的全年覆盖。该方法支持将卫星约束融入国家清单与智慧养殖平台，用于排放基准校准、区域因子优化及干预措施验证。更重要的是，基于变压器的地球观测实现了可扩展、透明、空间显式的碳核算、热点识别与政策相关减量评估。
原文摘要 · Abstract (English)
Accurate mapping of column-averaged CO2 (XCO2) over agricultural landscapes is essential for guiding emission mitigation strategies. We present a Spatiotemporal Vision Transformer with Wavelets (ST-ViWT) framework that reconstructs continuous, uncertainty-quantified XCO2 fields from OCO-2 across southern Canada, emphasizing poultry-intensive regions. The model fuses wavelet time-frequency representations with transformer attention over meteorology, vegetation indices, topography, and land cover. On 2024 OCO-2 data, ST-ViWT attains R2 = 0.984 and RMSE = 0.468 ppm; 92.3 percent of gap-filled predictions lie within +/-1 ppm. Independent validation with TCCON shows robust generalization (bias = -0.14 ppm; r = 0.928), including faithful reproduction of the late-summer drawdown. Spatial analysis across 14 poultry regions reveals a moderate positive association between facility density and XCO2 (r = 0.43); high-density areas exhibit larger seasonal amplitudes (9.57 ppm) and enhanced summer variability. Compared with conventional interpolation and standard machine-learning baselines, ST-ViWT yields seamless 0.25 degree CO2 surfaces with explicit uncertainties, enabling year-round coverage despite sparse observations. The approach supports integration of satellite constraints with national inventories and precision livestock platforms to benchmark emissions, refine region-specific factors, and verify interventions. Importantly, transformer-based Earth observation enables scalable, transparent, spatially explicit carbon accounting, hotspot prioritization, and policy-relevant mitigation assessment.
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