Geostatistical Methods for Filling Gaps in Level-3 Monthly-Mean Aerosol Optical Depth Data from Multi-Angle Imaging SpectroRadiometer
Geostatistical Methods for Filling Gaps in Level-3 Monthly-Mean Aerosol Optical Depth Data from Multi-Angle Imaging SpectroRadiometer
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DOI：
10.4209/aaqr.2016.02.0084
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发表时间：
2017
影响因子：
4
通讯作者：
Manoj K. Singh;P. Venkatachalam;R. Gautam
中科院分区：
文献类型：
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作者：
Manoj K. Singh;P. Venkatachalam;R. Gautam
从TERRA平台上的MISR和MODIS卫星遥感测量中提取的气溶胶光学厚度(AOD)被广泛用于研究区域和全球气溶胶负荷模式。这些传感器的气溶胶产品也被用来分析气溶胶与气候变量之间的反馈和关系，包括云、降水和辐射通量。几种导致理解这种关系的统计技术，包括经验正交函数和时间趋势提取方法，需要空间上完整的AOD数据记录。由于气溶胶遥感的固有特性，云层对气溶胶的反演有很大影响，并导致气溶胶遥感产品的数据缺失。本文展示了广泛使用的地质统计学技术，如联合克立格(CK)和回归克立格(RK)，用于空间填充MISR AOD产品2001-2013年期间的缺失数据。这种数据填充算法的一个独特特点是，它利用了从MODIS获得的额外AOD信息。用CK方法预测MISR AOD的全球平均精度估计为0.05。填补空白的MISR AOD数据还与世界各地的131个陆基气溶胶机器人网络(AERONET)站进行了比较。结果表明，填充空位的AOD数据集和原始MISR AOD产品相对于AERONET数据的均方根误差为0.143。填补空白的AOD数据集可用于不希望出现缺失值的应用，例如用于全球/区域气溶胶可变性和趋势分析。
ABSTRACTThe Aerosol Optical Depth (AOD) retrieved from satellite remote sensing measurements such as from MISR and MODIS, both onboard the Terra platform, are widely used for studying regional and global patterns of aerosol loading. Aerosol products from these sensors are also used for analyzing feedbacks and relationship between aerosols and climatic variables including clouds, precipitation, and radiation fluxes. Several statistical techniques leading to the understanding of such relationships, including empirical orthogonal function and temporal trend extraction methods, require spatially complete AOD data records. Inherent to remote sensing of aerosols, cloud cover significantly affects aerosol retrievals and results in missing data across the AOD products. This paper demonstrates widely-used geostatistical techniques, such as Co-Kriging (CK) and Regression Kriging (RK), for spatially-filling missing data in the MISR AOD product for the period 2001–2013. Among the unique characteristics of this data-filling algorithm is that it utilizes additional AOD information obtained from MODIS. The mean accuracy of the predicted MISR AOD using CK method is estimated to be 0.05, globally. The gap-filled MISR AOD data are also compared with 131 ground-based Aerosol Robotic Network (AERONET) stations, located around the world. It is found that Root Mean Squared Error of the gap-filled AOD dataset and the original MISR AOD product with respect to AERONET data are 0.143. The gap-filled AOD dataset can be used in applications where the presence of missing values is undesirable such as for global/regional aerosol variability and trend analysis.