Machine Learning and Bias Correction of MODIS Aerosol Optical Depth

Lary D. J.; Remer L. A.; MacNeill D.; Roscoe B.; Paradise S.

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Machine Learning and Bias Correction of MODIS Aerosol Optical Depth

机译：MODIS气溶胶光学深度的机器学习和偏差校正

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Machine-learning approaches (neural networks and support vector machines) are used to explore the reasons for a persistent bias between aerosol optical depth (AOD) retrieved from the MODerate resolution Imaging Spectroradiometer (MODIS) and the accurate ground-based Aerosol Robotic Network. While this bias falls within the expected uncertainty of the MODIS algorithms, there is room for algorithm improvement. The results of the machine-learning approaches suggest a link between the MODIS AOD biases and surface type. MODIS-derived AOD may be showing dependence on the surface type either because of the link between surface type and surface reflectance or because of the covariance between aerosol properties and surface type.

机译：机器学习方法（神经网络和支持向量机）用于探讨从MODerate分辨率成像光谱仪（MODIS）检索到的气溶胶光学深度（AOD）与精确的地面气溶胶机器人网络之间存在持续偏差的原因。尽管此偏差落在MODIS算法的预期不确定性之内，但仍有改进算法的空间。机器学习方法的结果表明，MODIS AOD偏差与表面类型之间存在联系。源自MODIS的AOD可能显示出对表面类型的依赖性，这可能是由于表面类型与表面反射率之间的联系，或者是由于气溶胶特性与表面类型之间存在协方差。

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《Geoscience and Remote Sensing Letters, IEEE》 |2009年第4期|p.694-698|共5页
作者
Lary D. J.; Remer L. A.; MacNeill D.; Roscoe B.; Paradise S.;
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正文语种 eng
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Aerosol optical depth (AOD); machine learning; neural networks; support vector machines (SVMs);

机译：气溶胶光学深度（AOD）;机器学习;神经网络;支持向量机（SVM）;

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1. An Improved Aerosol Optical Depth Map Based on Machine-Learning and MODIS Data: Development and Application in South America [J] . Bethania L. Lanzaco, Luis E. Olcese, Gustavo G. Palancar, Aerosol and Air Quality Research . 2016,第6期

机译：基于机器学习和MODIS数据的改进的气溶胶光学深度图：在南美的开发与应用
2. An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals [J] . E. J. Hyer, J. S. Reid, J. Zhang Atmospheric Measurement Techniques . 2011,第3期

机译：用于过滤，校正和聚合MODIS Collection 5光学深度检索数据的陆上气溶胶光学深度数据集
3. An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals [J] . E. J.Hyer, J. S.Reid, J.Zhang Atmospheric Measurement Techniques . 2011,第3期

机译：用于过滤，校正和聚合MODIS Collection 5光学深度检索数据的陆上气溶胶光学深度数据集
4. Towards Identification of Relevant Variables in the observed Aerosol Optical Depth Bias between MODIS and AERONET observations [C] . N. K. Malakar, D. J. Lary, D. Gencaga, International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering . 2013

机译：探讨了Modis和AeroNet观测之间观察到的气溶胶光学深度偏差中的相关变量
5. Investigations on the Impacts of Ship Traffic in the Bering Sea on Aerosol Optical Depth Using Automatic Identification Systems Data, and Modis Collection 6.1 Data [D] . Summers, Tyler. 2021

机译：使用自动识别系统数据和MODIS集合6.1数据调查船舶交通对膨胀海洋气孔光学深度的影响。
6. Spatial analysis of MODIS aerosol optical depth PM2.5 and chronic coronary heart disease [O] . Zhiyong Hu 2009

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7. An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals [O] . E. J. Hyer, J. S. Reid, J. Zhang 2011

机译：用于过滤，校正和聚合MODIS Collection 5光学深度检索数据的陆上气溶胶光学深度数据集

Machine Learning and Bias Correction of MODIS Aerosol Optical Depth

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