Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019
Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019
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2003-2019年台湾气溶胶光学深度全覆盖一公里估算及时空趋势
DOI：
10.1016/j.apr.2022.101579
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发表时间：
2022-10
影响因子：
4.5
通讯作者：
Haoran Zhu
中科院分区：
文献类型：
--
作者：
Weihang Wang;Qingqing He;Ming Zhang;Wenting Zhang;Haoran Zhu
卫星衍生的气溶胶光学深度（AOD）提供了一种研究大气气溶胶的全球和区域变化的有效方法，但是，由于云覆盖率和表面反射率，AOD数据集从卫星仪器中得出对AOD数据的不确定性并限制其下游来解决此问题，这项研究使用了基于空间插值的两阶段方法和一个随机的森林模型来填补由大气校正（MAIAC）Aerosol Retrooce产生的数据的空白算法为全球公众提供了最佳的AOD产品。台湾的两阶段模型可与地面AOD测量值相当的准确性（确定系数= 0.52，根平方误差= 0.22），以提高了先前研究的精度。空间分辨率的AOD估计为100％的空间覆盖率。每日1公里AOD数据集的估计，并探索台湾AOD的时空趋势，适用于本研究的实际方法。 。 - 嵌入式森林•以相当的精度，每天的AOD覆盖率提高到100％。
Satellite-derived aerosol optical depth (AOD) provides an effective way to investigate global and regional variations in atmospheric aerosols. However, due to cloud cover and surface reflectance, AOD datasets derived from satellite instruments generally have non-random missing values, which introduces additional uncertainty into AOD data and limits its downstream. To remedy this problem, this study used a two-stage approach based on spatial interpolation and a random forest model to fill the gaps in data generated by the Multiangle Implementation of Atmospheric Correction (MAIAC) aerosol retrieval algorithm, which provides the best-available AOD product to the global public. The relationship between ground-level fine particulate matter concentrations and satellite AOD was considered in the modeling. By gap-filling daily 1-km MAIAC AOD data from 2003 to 2019 over Taiwan, the two-stage model achieved comparable accuracy (coefficient of determination = 0.52, root-mean-square error = 0.22) against ground-level AOD measurements to the accuracy that has been achieved by previous studies. Furthermore, it improved daily high-spatial-resolution AOD estimates to 100% of spatial coverage. Comparisons between the full-coverage estimates and MAIAC retrievals showed that the MAIAC AOD dataset generally underestimated monthly/seasonal/annual mean AOD values in Taiwan. We also used the long-term estimates of the daily 1-km AOD dataset with full coverage to explore the spatiotemporal trends in AOD in Taiwan. The practical approach developed in this study is suitable for application in long- and short-term studies of air pollution and its effects on public health. • Daily high-resolution AOD values were gap-filled in Taiwan, China from 2003 to 2019. • PM 2.5 -AOD relationship was used to produce the intermediate AOD pattern. • Missing MAIAC AOD values were filled using a two-stage model embedded random forest. • Improved daily coverage of AOD estimates to 100% with comparable accuracy. • Satellite AOD tends to underestimate annual and seasonal AOD values in Taiwan.
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影响因子：
11.4
作者：
Kloog I;Nordio F;Coull BA;Schwartz J
通讯作者：
Schwartz J
影响因子：
56.9
作者：
M. Mishchenko;I. Geogdzhayev;W. Rossow;B. Cairns;B. Carlson;A. Lacis;Li Liu;L. Travis
通讯作者：
M. Mishchenko;I. Geogdzhayev;W. Rossow;B. Cairns;B. Carlson;A. Lacis;Li Liu;L. Travis
影响因子：
5
作者：
Gizem Tuna Tuygun;S. Gündoğdu;T. Elbir
通讯作者：
Gizem Tuna Tuygun;S. Gündoğdu;T. Elbir
影响因子：
4
作者：
Manoj K. Singh;P. Venkatachalam;R. Gautam
通讯作者：
Manoj K. Singh;P. Venkatachalam;R. Gautam
影响因子：
5
作者：
Hengchao Xu;J. Guang;Yong Xue;G. Leeuw;Y. Che;Jianping Guo;Xingwei He;T. K. Wang
通讯作者：
Hengchao Xu;J. Guang;Yong Xue;G. Leeuw;Y. Che;Jianping Guo;Xingwei He;T. K. Wang