Retrieval of Aerosol Optical Depth Over Land by Inverse Modeling of Multi-Source Satellite Data

Abstract

The Aerosol Optical Depth (AOD), a measure of the scattering and absorption of light by aerosols, has been extensively used for scientific research such as monitoring air quality near the surface due to fine particles aggregated, aerosol radiative forcing (cooling effect against the warming effect by carbon dioxide CO2 ), aerosol long-term trend analysis and the climate change on regional and global scale.

Aerosols vary greatly over time and space. This is because of the short lifetime of aerosols (a few hours to a week), and also because of the heterogeneous distribution of sources and the variable effectiveness of atmospheric mixing though turbulence. To monitor aerosols, observations by space-borne instruments have a huge advantage (nearly global coverage daily) over ground-based measurements (point observation). Global quantitative aerosol information has been derived from satellite measurements for decades. The MODerate resolution Imaging Sepctroradiometer (MODIS) AOD product is proven to be mature and is extensively applied in different scientific fields. The current AOD product generated with the collection 6 (C6) Dark Target (C6_DT) algorithm over land is still suffering from errors or biases due to parameterization, assumptions, modeling, and retrieval techniques as well as ill-posed problems, presenting large uncertainties, including regional bias, angular effects and a large number of unphysical negative values. Chapter 1 discusses the challenges and limitations in the current satellite aerosol retrieval algorithm.

Owing to the use of static aerosol properties (predefined aerosol models and fixed vertical profile over the globe), the MODIS algorithm may give serious errors since aerosols can change over time and are distributed very diversely at different altitude levels. To quantify these errors, in Chapter 3 the sensitivity of AOD retrieval to the variation of aerosol vertical profiles and types with the MODIS algorithm is evaluated by a set of experiments. It was found that the AOD retrieval shows a high sensitivity to different vertical profiles and types.

As suggested by the sensitivity study, it is necessary to investigate the impact of dynamical aerosol properties in a real case. To do this, an adaptive development of the MODIS C6_DT algorithm was implemented to consider realistic aerosol vertical profile in the retrieval (Chapter 4). MODIS and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements were used. Inferred from CALIPSO data, the vertical profile was applied into the new algorithm to generate an accurate Top Of the Atmosphere (TOA) reflectance for the retrieval. The AOD retrieval was compared between C6_DT and the new algorithm with cases of heavy smoke and dust. The difference in the retrieval was significant between C6_DT and the new algorithm, which demonstrated that C6_DT would give large errors in the retrieval for these cases.

In the MODIS algorithm, the assumption of the surface with isotropic reflection (Lambertian) is inconsistent with the well-known fact that the surface has a strong anisotropic reflection (non-Lambertian), and could lead to large uncertainties in estimating the surface contribution to satellite measurements, with resulting errors in the AOD retrieval. Chapter 5 describes a newly developed algorithm (BRF_DT) by considering non-Lambertian surface reflectance characterized by Bidirectional Distribution Reflectance Function (BRDF), where the surface reflection is described by four reflectance properties — bidirectional, directional-hemispherical, hemispherical-directional, and bihemispherical reflectance and coupled into the radiative transfer process to generate an accurate TOA reflectance. In addition, a parameterization of spectral relationship inherited from C6_DT was applied to constrain the surface BRF. The remaining three components are determined by MODIS BRDF/albedo product. As shown by sample plots and histograms as well as analysis and comparison against AERONET measurements, the AOD retrievals were significantly improved by BRF_DT especially for areas with heavy aerosol loading.

For the case of areas with light aerosol loading, the parameterization of spectral surface BRF should be further refined to yield a better retrieval. Chapter 6 shows that a new parameterization was derived for the BRF_DT algorithm (called BRF_DT2) by using 3 years of BRF data from AERONET-based Surface Reflectance Validation Network (AS-RVN). The contribution to the TOA reflectance dominated by the surface BRF was well estimated. As a result, negative retrievals and angular biases were significantly reduced in BRF_DT2. A summary of the current and future research of satellite aerosol retrieval is introduced in Chapter 7.