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. @en

Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%.

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Using eight years (2006-2014) of passive (MODIS/Aqua and OMI/Aura) and active (CALIOP/CALIPSO) satellite measurements of aerosols, we yield a three-dimensional (3D) distribution of the frequency of occurrence (FoO) of aerosols over China. As an indicator of the vertical heterogeneity of aerosol layers detected by CALIOP, two types of Most Probable Height (MPH), including MPH_FoO and MPH_AOD, are deduced. The FoO of "Total Aerosol" reveals significant geographical dependence. Eastern China showed much stronger aerosol FoD than northwestern China. The FoO vertical structures of aerosol layer are strongly dependent on altitudes. Among the eight typical ROIs analyzed, aerosol layers over the Gobi Desert have the largest occurrence probability located at an altitude as high as 2.83 km, as compared to 1.26 km over Beijing-Tianjin-Hebei. The diurnal variation (nighttime-daytime) in MPH_AOD varies from an altitude as low as 0.07 km over the Sichuan basin to 0.27 km over the Gobi Desert, whereas the magnitude of the diurnal variation in terms of MPH_AOD is six times as large as the MPH_FoO, mostly attributable to the day/night lidar SNR difference. Also, the 3D distribution of dust and smoke aerosols was presented. The multi-sensor synergized 3D observations of dust aerosols, frequently observed in the zonal belt of 38°N-45°N, is markedly different from that of smoke aerosols that are predominantly located in the eastern and southern parts. The 3D FoO distribution of dust indicates a west-to-east passageway of dust originating from the westernmost Taklimakan Desert all the way to North China Plain (NCP). The findings from the multi-sensor synergetic observations greatly improved our understanding on the long-range aerosol dispersion, transport and passageway over China.

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Aerosol optical depth (AOD) product retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) measurements has greatly benefited scientific research in climate change and air quality due to its high quality and large coverage over the globe. However, the current product (e.g., Collection 6) over land needs to be further improved. The is because AOD retrieval still suffers large uncertainty from the surface reflectance (e.g., anisotropic reflection) although the impacts of the surface reflectance have been largely reduced using the Dark Target (DT) algorithm. It has been shown that the AOD retrieval over dark surface can be improved by considering surface bidirectional distribution reflectance function (BRDF) effects in previous study. However, the relationship of the surface reflectance between visible and shortwave infrared band that applied in the previous study can lead to an angular dependence of the AOD retrieval. This has at least two reasons. The relationship based on the assumption of isotropic reflection or Lambertian surface is not suitable for the surface bidirectional reflectance factor (BRF). However, although the relationship varies with the surface cover type by considering the vegetation index NDVISWIR, this index itself has a directional effect and affects the estimation of the surface reflection, and it can lead to some errors in the AOD retrieval. To improve this situation, we derived a new relationship for the spectral surface BRF in this study, using 3 years of data from AERONET-based Surface Reflectance Validation Network (ASRVN). To test the performance of the new algorithm, two case studies were used: 2 years of data from North America and 4 months of data from the global land. The results show that the angular effects of the AOD retrieval are largely reduced in most cases, including fewer occurrences of negative retrievals. Particularly, for the global land case, the AOD retrieval was improved by the new algorithm compared to the previous study and MODIS Collection 6 DT algorithm, with the increase of 2.0 and 4.5 % AOD retrievals falling within the expected accuracy envelope ±(0.05 + 15 %), respectively. This implies that the users can get more accurate data without angular bias, i.e., more meaningful AOD data.@en

This study is to evaluate the sensitivity of Aerosol Optical Depth (AOD τ) to aerosol vertical profile and type, using the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 algorithm over land. Four experiments were performed, using different aerosol properties including 3 possible non-dust aerosol models and 14 vertical distributions. The algorithm intrinsic uncertainty was investigated as well as the interplay effect of aerosol vertical profile and type on the retrieval. The results show that the AOD retrieval is highly sensitive to aerosol vertical profile and type. With 4 aerosol vertical distributions, the algorithm with a fixed vertical distribution gives about 5% error in the AOD retrieval with aerosol loading τ≤0.5 . With pure aerosols (smoke and dust), the retrieval of AOD shows errors ranging from 2% to 30% for a series of vertical distributions. Errors in aerosol type assumption in the algorithm can lead to errors of up to 8% in the AOD retrieval. The interplay effect can give the AOD retrieval errors by over 6%. In addition, intrinsic algorithm errors were found, with a value of >3% when τ> 3.0. This is due to the incorrect estimation of the surface reflectance. The results suggest that the MODIS algorithm can be improved by considering a realistic aerosol model and its vertical profile, and even further improved by reducing the algorithm intrinsic errors.@en

Aerosols in the atmosphere play an important role in the climate system and human health. Retrieval from satellite data, Aerosol Optical Depth (AOD), one of most important indices of aerosol optical properties, has been extensively investigated. Benefiting from the high resolution at spatial and temporal and the maturity of the aerosol retrieval algorithm, MOderate Resolution Imaging Spectroradiometer (MODIS) Dark Target AOD product has been extensively applied in other scientific research such as climate change and air pollution. The latest product – MODIS Collection 6 Dark Target AOD (C6_DT) has been released. However, the accuracy of C6_DT AOD (global mean 0.03) over land is still too low for the constraint on radiative forcing in the climate system, where the uncertainty should be reduced to 0.02. The major uncertainty mainly lies on the underestimation/overestimation of the surface contribution to the Top Of Atmosphere (TOA) radiance since a lambertian surface is assumed in the C6_DT land algorithm. In the real world, it requires considering the heterogeneity of the surface reflection in the radiative transfer process. Based on this, we developed a new algorithm to retrieve AOD by considering surface Bidirectional Reflectance Distribution Function (BRDF) effects. The surface BRDF is much more complicated than isotropic reflection, described as 4 elements: directional-directional, directional-hemispherical, hemispherical-directional and hemispherical-hemispherical reflectance, and coupled into radiative transfer equation to generate an accurate top of atmosphere reflectance. The limited MODIS measurements (three channels available) allow us to retrieve only three parameters, which including AOD, the surface directional-directional reflectance and fine aerosol ratio . The other three elements of the surface reflectance are expected to be constrained by ancillary data and assumptions or “a priori” information since there are more unknowns than MODIS measurements in our algorithm. We validated three case studies with AErosol Robotic NETwork (AERONET) AOD, and the results show that the AOD retrieval was improved compared to C6_DT AOD, with the increase of within expected accuracy (0:05 + 15%) by ranging from 2.7% to 7.5% for the best quality only (Quality Assurance =3), and from 5.8% to 9.5% for the marginal and better quality (Quality Assurance 1). @en