The atmospheric products of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm include column water vapor (CWV) at a 1 km resolution, derived from daily overpasses of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Aqua and Terra satellites. We have recently shown that machine learning using extreme gradient boosting (XGBoost) can improve the estimation of MAIAC aerosol optical depth (AOD). Although MAIAC CWV is generally well validated (Pearson's R > 0.97 versus CWV from AERONET sun photometers), it has not yet been assessed whether machine-learning approaches can further improve CWV. Using a novel spatiotemporal cross-validation approach to avoid overfitting, our XGBoost model, with nine features derived from land use terms, date, and ancillary variables from the MAIAC retrieval, quantifies and can correct a substantial portion of measurement error relative to collocated measurements at AERONET sites (26.9 % and 16.5 % decrease in root mean square error (RMSE) for Terra and Aqua datasets, respectively) in the Northeastern USA, 2000–2015. We use machine-learning interpretation tools to illustrate complex patterns of measurement error and describe a positive bias in MAIAC Terra CWV worsening in recent summertime conditions. We validate our predictive model on MAIAC CWV estimates at independent stations from the SuomiNet GPS network where our corrections decrease the RMSE by 19.7 % and 9.5 % for Terra and Aqua MAIAC CWV. Empirically correcting for measurement error with machine-learning algorithms is a postprocessing opportunity to improve satellite-derived CWV data for Earth science and remote sensing applications.

中文翻译：

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梯度增强机器学习可改善卫星衍生的柱状水蒸气测量误差

大气校正多角度实施 (MAIAC) 算法的大气产品包括分辨率为 1 公里的柱状水蒸气 (CWV)，源自 Aqua 和 Terra 卫星上 NASA 中分辨率成像光谱辐射计 (MODIS) 仪器的日常立交桥。我们最近表明，使用极限梯度增强 (XGBoost) 的机器学习可以改进 MAIAC 气溶胶光学深度 (AOD) 的估计。尽管 MAIAC CWV 总体上得到了很好的验证（与 AERONET 太阳光度计的 CWV 相比，Pearson 的R  > 0.97），但尚未评估机器学习方法是否可以进一步改进 CWV。我们的 XGBoost 模型采用新颖的时空交叉验证方法来避免过度拟合，该模型具有从土地利用术语、日期和 MAIAC 检索的辅助变量导出的九个特征，可以量化并纠正相对于并置测量的大部分测量误差。 2000-2015 年美国东北部的 AERONET 站点（Terra 和 Aqua 数据集的均方根误差 (RMSE) 分别下降 26.9% 和 16.5%）。我们使用机器学习解释工具来说明测量误差的复杂模式，并描述最近夏季条件下 MAIAC Terra CWV 恶化的正偏差。我们在 SuomiNet GPS 网络的独立站点上验证了我们对 MAIAC CWV 估计的预测模型，其中我们的修正将 Terra 和 Aqua MAIAC CWV 的 RMSE 降低了 19.7% 和 9.5%。使用机器学习算法根据经验纠正测量误差是一个后处理机会，可以改善地球科学和遥感应用中卫星衍生的 CWV 数据。