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纸质出版日期： 2022-05-07 ，
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周佩，汪洋，徐玲琳，程志强，盖宸德，庄留文.2022.基于AERONET数据的气溶胶光学特性分析.遥感学报，26（5）： 953-970
Zhou P，Wang Y，Xu L L，Cheng Z Q，Ge C D and Zhuang L W. 2022. Study of aerosol optical properties based on AERONET data. National Remote Sensing Bulletin， 26（5）：953-970
大气气溶胶的监测对全球气候变化、区域空气质量和公共健康等研究具有重要的意义，而中国台湾岛四面环海，地理位置特殊，若忽略其大气环流和局地排放源造成的气溶胶特征时空异质性将会导致气溶胶参数反演误差。因此本研究使用中国台湾岛多个具有代表性的AERONET（AErosol RObotic NETwork）观测站历史数据和MODIS气溶胶光学厚度AOD（Aerosol Optical Depth）反演产品，分析5个典型站点气溶胶参数及其类型的时空变化特征及差异，分析结果表明：（1）各站点AOD年平均值逐年下降，呈现春季最高（0.5257）的季节变化特征和双峰结构的日变化规律，主导气溶胶类型为城市工业型（仅鹿林站点为海洋型）。（2）中国台湾地区风向多为东北风，风速越大，AOD值越低，海洋型气溶胶占比越高；反之则以城市工业型气溶胶为主。（3）Ångström波长指数（AE）、单次散射比（SSA）、复折射指数虚部、不对称因子平均值分别为1.3283、0.9564、0.0054、0.7292；相比于北京（39.9768°N，116.3813°E）站，台湾“中央大学”AOD年平均值、季节变化、主导气溶胶类型均存在较大的差异。（4）MODIS AOD分站点验证精度较高，而在高山鹿林站的验证精度稍低（
R
2
=0.5925）；而利用不同气溶胶类型的分类验证结果显示，城市工业（
R
2
=0.7238）、生物质燃烧（
R
2
=0.6161）和次大陆型（
R
2
=0.5116）精度较高，但海洋型（
R
2
=0.1585）、大陆型（
R
2
=0.1111）AOD验证精度显著降低。本研究表明，中国台湾岛气溶胶类型呈现西南沿岸站点秋冬季次大陆型占比上升，西北沿岸大陆型上升的时空特征差异，细化气溶胶参数的时间差异和时间动态变化信息将对气溶胶卫星反演算法在环流特征明显的近海区域有着重要指导作用。
The study of atmospheric aerosols lays the foundation of global climate change
air quality
and public health research. Different from those in the Chinese mainland
the special atmospheric circulation and local emission sources in Taiwan
china from its seagirt terrain have led to the difference in aerosols’ characteristics. Lack of information on aerosol temporal and spatial characteristics might cause errors in the retrieval of satellite aerosol parameters.
On the basis of the historical data of several representative Aerosol Robotic NETwork (AERONET) sites in Taiwan
this study explored the temporal and spatial variation characteristics and differences of aerosol parameters and types of typical sites in Taiwan
China. First
the change trend of aerosol optical parameters was analyzed. The observation sample points of AERONET were divided into six categories
namely
maritime
continental
desert dust
sub-continental
urban industry
and biomass burning aerosols
using the graphical classification method. The differences in aerosol types at different sites and the effects of wind direction and speed on Aerosol Optical Depth (AOD) and aerosol types were explored
and they were comparing with the aerosol optical parameters of Beijing. Second
the MODIS data were validated against the AERONET data.
The annual average of AOD at each station is decreasing annually
which suggests the highest seasonal variation in spring (0.5257) and the diurnal variation of bimodal structure. The dominant aerosol type is urban industrial
and only Lulin station is maritime type. The northeast wind prevails in Taiwan
China
and the AOD is lower and the maritime aerosol type occupies larger proportion when the wind speed is higher. Conversely
urban industrial aerosols dominate. The average values of Ångström Exponent
Single Scattering Albedo
Refractive Index-Imaginary Part
and Asymmetry Factor are 1.3283
0.9564
0.0054
and 0.7292
respectively. Compared with those in the Beijing site (39.9768°N
116.3813°E)
the annual average of AOD
seasonal variation
and dominant aerosol types in the “Central University” shows a dramatic difference. For the remote retrieval products
MODIS AOD has higher verification accuracy at different sites
and only the Lulin site is slightly lower (
R
2
=0.5925). As for the verification results of different aerosol types
urban industry (
R
2
=0.7238)
biomass burning (
R
2
=0.6161)
and sub-continental (
R
2
=0.5116) have higher accuracy
while maritime (
R
2
=0.1585) and continental (
R
2
=0.1111) have significantly lower accuracy.
The types of aerosols in Taiwan
China show differences in temporal and spatial characteristics. The proportion of sub-continental types in the southwest coastal sites increases in autumn and winter
while the continental types in the northwest coast increase. Refining the characteristic changes of aerosol parameters plays an important role in guiding the aerosol satellite retrieval algorithm for island regions with distinct features of circulation.
大气遥感气溶胶AERONET气溶胶光学特性时空分布
atmospheric remote sensingaerosolAERONETaerosol optical propertiestemporal and spatial analysis
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