Normalized Differential Vegetation Index (NDVI), usually calculated by surface reflectance at red and near-infrared bands (NDVI_Surf), which is an essential index in remote sensing. NDVI_Surf is generally used to discriminate different surface cover types and adopted in many surface models as a vital adjustable parameter to estimate the surface reflectance in remote sensing aerosol retrieval. However, NDVI_Surf is challenging to obtained directly and usually calculated by the red and near-infrared reflectance at the top of atmosphere (NDVI_TOA). NDVI_TOA is very susceptible to the atmosphere with different angles. If NDVI_Surf is replaced by the NDVI_TOA, it will cause an error of surface reflectance estimation and then make the wrong aerosol retrieval. In this study, Second Simulation of a Satellite Signal in the Solar Spectrum, Vector version (6SV) radiative transfer code was used to analyze the effects of NDVI_TOA on a surface Bidirectional Polarization Distribution Function (BPDF) model under different atmosphere and multi-angles conditions. The results display that the NDVI_TOA decreases with the rise of AOD. Within scattering angle (SA) of 60° to 115°, the influences of NDVI_TOA on BPDF are great and increases with the AOD reduces. Within the SA between 115° to 180°, the effects of NDVI_TOA on BPDF are small and remain unchanged with the AOD decreases. The simulation and analysis results have a great significance for polarized aerosol retrieval.
The Atmosphere-surface Radiation Automatic Instrument (ASRAI) is a newly developed hyper-spectral apparatus by Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (AIOFM, CAS), measuring total spectral irradiance, diffuse spectral irradiance of atmosphere and reflected radiance of the land surface for the purpose of in-situ calibration. The instrument applies VIS-SWIR spectrum (0.4~1.0 μm) with an averaged spectral resolution of 0.004 μm. The goal of this paper is to describe a method of deriving both aerosol optical depth (AOD) and aerosol modes from irradiance measurements under free cloudy conditions. The total columnar amounts of water vapor and oxygen are first inferred from solar transmitted irradiance at strong absorption wavelength. The AOD together with total columnar amounts of ozone and nitrogen dioxide are determined by a nonlinear least distance fitting method. Moreover, it is able to infer aerosol modes from the spectral dependency of AOD because different aerosol modes have their inherent spectral extinction characteristics. With assumption that the real aerosol is an idea of “external mixing” of four basic components, dust-like, water-soluble, oceanic and soot, the percentage of volume concentration of each component can be retrieved. A spectrum matching technology based on Euclidean-distance method is adopted to find the most approximate combination of components. The volume concentration ratios of four basic components are in accordance with our prior knowledge of regional aerosol climatology. Another advantage is that the retrievals would facilitate the TOA simulation when applying 6S model for satellite calibration.
Because of the special geographical location and meteorology conditions, Beijing is a dust-prone city for a long history especially in the spring season. But these years, the most common air pollution in Beijing is haze which is mainly composed of fine particles. The dust is transported from north (Inner Mongolia province and Mongolia country), and the haze is transported from south (Hebei, Shandong and other provinces). Generally, the severities of dust and haze are opposite for the different weather causes. On March 28, 2015, the spring coming earlier for the relatively high temperature, a severe dust weather process happened suddenly in the long-term hazy days. In this dust process, the PM10 concentration was more than 1000μg/m3; the visibility was no more than 3km; and the aerosol optical depth was more than 2, which reached a severe pollution level. We used ground-based remote sensing instruments to observing the heavy dust episode. The data of two conditions were analyzed optical and microphysical parameters contrastively including the Aerosol Optical Depth, Single Scattering Albedo, Size distribution, Complex refractive index, Fine-mode Fraction. The vertical distribution characteristics were also analyzed by the lidar measurements. The results show that big differences between the dust and haze aerosol properties. But we found that fine mode particle pollution was assignable in the dust pollution weather in 2015 spring in Beijing. Our preliminary inference is that this dust episode was not only caused by transportation, but also contributed by the local raise dust.
A hyper spectral ground-based instrument named Atmosphere-Surface Radiation Automatic Instrument (ASRAI) has been developed for the purpose of in-situ calibration of satellites. The apparatus has both upward and downward looking views, and thus can observe both the atmosphere and land surface. The solar transmitted irradiance can be derived from the measured full spectral irradiance and diffused spectral irradiance of atmosphere within visible spectrum (0.4-1.0μm). A method similar to that of King et al. which originally intended to apply to multi-wavelength measurements, is adopted to determine absorptive gaseous columnar amount from hyper spectrum. The solar irradiance at top of atmosphere and absorption coefficients of water vapor (H2O), ozone (O3), oxygen (O2) and nitrogen dioxide (NO2) are recalculated at an instrumental spectral resolution by convolution method. Based on the gaseous characteristics of absorption, the total columnar amounts of water vapor and oxygen are first inferred from solar transmitted irradiance at strong absorption wavelength of 0.934μm and 0.763μm respectively. The total columnar amounts of ozone and nitrogen dioxide, together with aerosol optical depth, are determined by a nonlinear least distance fitting method which minimizes a χ2 statistic to obtain optimal solutions. ASRAI was deployed for observation in Dunhuang site in China in August of 2014. Our results demonstrate that the algorithm is reasonable. Although the validation is preliminary, the hyper spectrum measured by ASRAI exhibits good ability to retrieve the abundance of absorptive gases and aerosols.
Carbon dioxide is commonly considered as the most important greenhouse gas. Ground-based remote sensing technology of acquiring CO2 columnar concentration is needed to provide validation for spaceborne CO2 products. A new groundbased sunphotometer prototype for remotely measuring atmospheric CO2 is introduced in this paper, which is designed to be robust, portable, automatic and suitable for field observation. A simple quantity, Differential Absorption Index (DAI) related to CO2 optical depth, is proposed to derive the columnar CO2 information based on the differential absorption principle around 1.57 micron. Another sun/sky radiometer CE318, is used to provide correction parameters of aerosol extinction and water vapor absorption. A cloud screening method based on the measurement stability is developed. A systematic error assessment of the prototype and DAI is also performed. We collect two-year DAI observation from 2010 to 2012 in Beijing, analyze the DAI seasonal variation and find that the daily average DAI decreases in growing season and reaches to a minimum on August, while increases after that until January of the next year, when DAI reaches its highest peak, showing generally the seasonal cycle of CO2. We also investigate the seasonal differences of DAI variation and attribute the tendencies of high in the morning and evening while low in the noon to photosynthesis efficiency variation of vegetation and anthropogenic emissions. Preliminary comparison between DAI and model simulated XCO2 (Carbon Tracker 2011) is conducted, showing that DAI roughly reveals some temporal characteristics of CO2 when using the average of multiple measurements.
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