Retrieval of aerosol optical depth over urban area by coupling the characteristics of surface directional reflection
- Vol. 26, Issue 11, Pages: 2219-2233(2022)
Published： 07 November 2022
DOI： 10.11834/jrs.20210217
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摘要
地气分离是气溶胶光学厚度AOD（Aerosol Optical Depth）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传感器反演实验，并使用AERONET地基观测数据及与朗伯假设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%，可以有效改善朗伯假设对AOD的高估。通过与MODIS气溶胶产品（MOD04/MCD19A2）对比，新算法获取的AOD与AERONET观测值具有更高的一致性，城市站点反演结果在误差线内占比较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气溶胶细节信息，显示了在支持区域污染精细管控及污染物溯源等领域的应用潜力。
Abstract
Aerosols play an important role in determining the Earth's radiation budget and its impact on 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 the surface
making it difficult to separate the aerosol path radiance from satellite measurements
particularly over the urban area. In the past several decades
numerous different AOD retrieval algorithms have been proposed by using different satellite sensors
but most of them do not consider surface anisotropy.
The main purpose of this work is to improve the accuracy of aerosol retrievals and reduce the uncertainty of the operational MODIS AOD products over mixed surfaces. On this basis
a new generic high-performance aerosol retrieval algorithm is presented and explained. The new method is developed by coupling the non-Lambertian atmospheric radiative transfer model and semiempirical linear kernel-driven BRDF model. First
an
a priori
surface BRDF shape parameter database is constructed using the daily MODIS BRDF/Albedo product by using penalized least square regression based on a 3D discrete cosine transform (DCT-PLS) method. Then
the estimation of surface reflectance
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 the Beijing area
which features highly heterogeneous surfaces and severe air pollution problems. AOD retrievals with 500 m resolution can be successfully obtained over dark and bright surfaces.
An accuracy assessment of the new algorithm
WSA-derived and HARLS AOD retrievals against AERONET AOD
from the 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) compared with the WSA-derived and HARLS AOD retrievals. Furthermore
the new algorithm and MODIS aerosol algorithms have similar spatial patterns of AOD. The new algorithm significantly improves the accuracy of aerosol retrievals
which is verified by AERONET AOD data
especially over brighter surfaces
because surface anisotropy is considered in this algorithm. The new algorithm can provide a detailed AOD spatial distribution over mixed surfaces and shows high ability in capturing fine-scale features. The new algorithm and MAIAC AOD retrievals have a 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 the new algorithm is almost 1.5 times those for MAIAC.
This new AOD retrieval algorithm can provide a possibility for high-precision urban aerosol remote sensing monitoring and solve other pressing issues
such as long-term trend analysis of urban aerosols and air quality conditions
especially in heavily polluted areas. Based on the collocated observations
the new algorithm achieved satisfactory retrieval accuracy. However
several issues remain to be solved in the future. First
the retrieval errors of the MODIS BRDF kernel parameters are also a major source of uncertainty. Second
more analyses of the aerosol models and model selection are required. Third
the application in other regions and sensors is required in further work to evaluate the applicability of new algorithm.
关键词
气溶胶光学厚度地表方向反射核驱动模型MODISLandsat 8 OLI
Keywords
aerosol optical depthsurface anisotropykernel-driven BRDF modelMODISLandsat 8 OLI
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Geomatics College, Shandong University of Science and Technology
School of Geomatics & Planning, Jiangsu Normal University
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College of Remote Sensing and Engineering, Faculty of Geographical Science, Beijing Normal University
State Key Laboratory of Remote Sensing Science, Beijing Normal University
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