用张量低秩模型修复卫星大气数据缺失，提升云遮挡下污染热点识别精度。
Do Tensorized Large-Scale Spatiotemporal Dynamic Atmospheric Data Exhibit Low-Rank Properties?
- 将大气数据转为张量，利用CP分解与交替最小二乘法实现低秩逼近。
- 在连续四年美国区域数据上，成功填补云遮挡导致的空缺，重建效果优于传统插值。
- 适用于高时空分辨率环境监测，尤其适合处理大规模卫星遥感数据缺失问题。
本研究首次探究了大规模时空动态大气变量的低秩特性。聚焦于覆盖美国本土（CONUS）四年内哨兵-5号对流层NO2产品（S5P-TN）数据，证实该类动态变量具备低秩近似可行性。采用基于CP分解与交替最小二乘法（ALS）的低秩张量模型（LRTM），对S5P-TN数据中的缺失值进行补全。通过与地统计空间插值方法对比，评估了LRTM在应对长期云遮蔽、异常值预测及热点识别方面的性能。结果表明，当数据在扩展的空间与时间尺度上张量化后，张量补全能有效重构缺失值，显著提升数据完整性与分析可靠性。
原文摘要 · Abstract (English)
In this study, we investigate for the first time the low-rank properties of a tensorized large-scale spatio-temporal dynamic atmospheric variable. We focus on the Sentinel-5P tropospheric NO2 product (S5P-TN) over a four-year period in an area that encompasses the contiguous United States (CONUS). Here, it is demonstrated that a low-rank approximation of such a dynamic variable is feasible. We apply the low-rank properties of the S5P-TN data to inpaint gaps in the Sentinel-5P product by adopting a low-rank tensor model (LRTM) based on the CANDECOMP / PARAFAC (CP) decomposition and alternating least squares (ALS). Furthermore, we evaluate the LRTM's results by comparing them with spatial interpolation using geostatistics, and conduct a comprehensive spatial statistical and temporal analysis of the S5P-TN product. The results of this study demonstrated that the tensor completion successfully reconstructs the missing values in the S5P-TN product, particularly in the presence of extended cloud obscuration, predicting outliers and identifying hotspots, when the data is tensorized over extended spatial and temporal scales.
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