Carbonaceous aerosols remote sensing from geostationary satellite observation, Part I: Algorithm development using critical reflectance

Abstract

Current aerosol remote sensing without using multi-view satellite sensors still fail to retrieve its components directly, such as black carbon (BC) and organic carbon (OC) simultaneously due to the limitations on available observations. In this study, A new general carbonaceous aerosol retrieval strategy for geostationary single-view satellite observations is implemented based on a method of critical reflectance, to retrieve the parameters of BC and extra OC concentration from measured radiance without prior quantification of Aerosol Optical Thickness (AOT). The method is based on measuring the change in upward reflectance between clear and hazy days over a varying surface reflectance, with the aim of finding a balance between brightening due to scattering and darkening due to absorption. Since aerosols in the proposed algorithm are considered as a mixture of BC, OC and clustered shell aerosols, this balance can be used to retrieve concentration parameters of these components via radiative transfer models. Sensitivity tests indicate that different scenarios of BC and OC volume fractions can introduce certain sensitivity pattern on critical reflectance. The retrieval is achieved by minimizing the differences between measured critical reflectance and a set of look-up tables (LUTs). Random sampling consensus (RANSAC) is also used in the strategy to reduce the influence of clouds and anomalous pixels on the retrievals. Since critical reflectance should be calculated by comparing geometrically consistent clear and hazy images, the algorithm is more suitable for geostationary satellites with constant viewing geometries. An initial validation and application applied to Himawari images over the North China Plain (NCP) shows that the carbonaceous aerosol retrievals are highly consistent with the expectations in terms of spatial and temporal patterns. The retrievals of BC and OC concentration follow the daily fluctuations of the Aethalometers observations and feature small differences in mean values throughout the month. Additionally, the AOTs at 0.55 μm can be also satisfactorily reproduced based on the carbonaceous aerosol retrievals, resulting in a mean absolute error of 0.089 and a correlation coefficient of 0.870. The main errors in the method arise from shell model assumptions, RANSAC fitting bias, inconsistent clear reference AOT in LUT, and geometric inconsistency between the clear reference and hazy images. This work indicates that the BC and OC concentration in high resolution can be acquired through the geostationary single-view remote sensing for the further air quality and climate studies.