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田信鹏1, 高志强1, 刘强2, 王跃启1
1.中国科学院 烟台海岸带研究所 海岸带环境过程与生态修复重点实验室;2.北京师范大学

地气分离是气溶胶光学厚度(Aerosol Optical Depth,AOD)卫星遥感反演中的难点之一,当前大多反演算法一般将地表假定为朗伯体,这使得反演结果在城市等高异质地区具有较大的不确定性。本文利用长时间序列MODIS BRDF/Abledo产品,通过离散余弦变换的惩罚最小二乘估计时空滤波算法构建了地表BRDF形状因子先验知识数据集。通过耦合考虑地表各向异性反射特性的辐射传输前向模型及半经验核驱动模型,以先验知识数据集为驱动进行地表参数估算,实现考虑地表BRDF效应的气溶胶遥感反演。基于该算法,以北京为研究区开展了Landsat-8 OLI传感器AOD反演实验,并使用AERONET地基观测数据及朗伯假设反演和地表反射率数据库支持反演进行交叉对比,结果表明,新算法在城市/植被地区的反演点对在期望误差线内比重为84.6%/86.0%,表明可以有效地改善朗伯假设对AOD的高估。此外,通过与MODIS气溶胶产品(MOD04及MCD19A2)对比验证,新算法获取的AOD与AERONET观测值具有更高的一致性(R > 0.987,RMSE < 0.117),城市站点反演结果在误差线内的占比较MODIS DT、DB、DTDB及MAIAC产品分别提高了46.8%、13.9%、14.7%及4.4%,且有效对比点数高于MODIS产品。新算法可以获取500 m空间分辨AOD,能提供空间更为连续的分布信息,显示了在支持区域污染精细管控、重点城市污染传输通道监测及污染物溯源等领域的应用潜力。

Retrieval of aerosol optical depth over urban area by coupling the characteristics of surface directional reflection

The impacts of atmospheric aerosols on radiance balance of the earth is an important factor affecting global climate change. Aerosol optical depth (AOD) is a crucial fundamental parameter for meteorological observation and a basic optical property of aerosol derived from satellites. Over land, the aerosol contribution in satellite signals is small compared with surface, which makes it difficult to separate the aerosols path radiance from satellite measurements, particularly over bright urban surface. Over the past several decades, numerous different AOD retrieval algorithms have been proposed by using different satellite sensors, but most of them does not consider the surface anisotropy. The main purpose of this paper is to improve the accuracy of aerosol retrievals and reduce uncertainty of the operational MODIS AOD products over mixed surfaces. For this, a new generic high-performance aerosol retrieval algorithm is presented and explained. The new method is developed by coupling non-Lambertian atmospheric radiative transfer model and semiempirical linear kernel-driven BRDF model. First, a priori surface BRDF shape parameters database is constructed using the daily MODIS BRDF/Albedo product by penalized least square regression based on three-dimensional discrete cosine transform (DCT-PLS) method. Then, the estimation of surface reflectances, including bidirectional reflectance, directional to hemispheric reflectance, hemispheric to directional reflectance and bi-hemispheric reflectance (also called white-sky albedo, WSA), is based on this database and kernel-driven BRDF model. The presented method is tested on the Landsat-8 OLI images around Beijing area, which features highly heterogeneous surfaces and severe air pollution problems. AOD retrievals with 500 m resolution can be successfully obtained over both dark and bright surfaces. An accuracy assessment of new algorithm, WSA-derived and HARLS AOD retrievals against AERONET AOD from four selected stations indicated the superiority of new algorithm, which is reflected in the high PWE and low RMSE. The comparison results show that the new algorithm is in good agreement with ground-based AOD (R=0.911) as compared to the WSA-derived and HARLS AOD retrievals. At the same time, the new algorithm and MODIS aerosol algorithms have similar spatial pattern of AOD, and new algorithm significantly improves the accuracy of aerosol retrievals, which is verified by AERONET AOD data, especially over brighter surfaces, due to the surface anisotropy is considered in this algorithm. The new algorithm can provide a detailed AOD spatial distribution over mixed surfaces and shows a high ability to capture fine-scale features. Both new algorithm and MAIAC AOD retrievals have a very similar spread of uncertainty envelopes. However, the new algorithm AOD retrievals have a higher correlation and smaller RMSE than the MAIAC retrievals, and the number of collections with AERONET for new algorithm is almost 1.5 times those for MAIAC. We hope that this improvement will provide a possibility for high-precision urban aerosol remote sensing monitoring, and then solve other pressing issues such as long-term trend analysis of urban aerosols and air quality conditions, especially in heavily polluted areas. For collocated observations, new algorithm achieved satisfactory retrieval accuracy, however, there are still several issues to be solved in the future works. First, retrieval errors of the MODIS BRDF kernels parameters are also a major source of uncertainty. Second, more analyses of aerosol models and model selection are required. Third, to evaluate the applicability of new algorithm, the application in other regions and sensors is required in further work.



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