To meet the demanding of spectral reconstruction in the visible and near-infrared wavelength, the spectral reconstruction method for typical surface types is discussed based on the USGS/ASTER spectral library and principal component analysis (PCA). A new spectral reconstructed model is proposed by the information of several typical bands instead of all of the wavelength bands, and a linear combination spectral reconstruction model is also discussed. By selecting 4 typical spectral datasets including green vegetation, bare soil, rangeland and concrete in the spectral range of 400−900 nm, the PCA results show that 6 principal components could characterized the spectral dataset, and the relative reconstructed errors are smaller than 2%. If only 6−7 selected typical bands are employed to spectral reconstruction for all the surface reflectance in 400−900 nm, except that the reconstructed error of green vegetation is about 3.3%, the relative errors of other 3 datasets are all smaller than 1.6%. The correlation coefficients of those 4 datasets are all larger than 0.99, which can effectively satisfy the needs of spectral reconstruction. In addition, based on the spectral library and the linear combination model of 4 common used bands of satellite remote sensing such as 490, 555, 670 and 865 nm, the reconstructed errors are smaller than 8.5% in high reflectance region and smaller than 1.5% in low reflectance region respectively, which basically meet the needs of spectral reconstruction. This study can provide a reference value for the surface reflectance processing and spectral reconstruction in satellite remote sensing research.
GaoFen-4 (GF-4) is China’s first optical remote sensing geostationary satellite, which observes the Earth’s surface every 20 s. GF-4 has a gaze camera with a ground resolution of 50 m in the visible spectral bands that are sensitive to aerosol optical depth (AOD). AOD retrieval was completed using initial prior surface reflectance in ground-atmosphere decoupling. Surface reflectance was then updated using satellite measurements. The algorithm used in this study was based on two time-adjacent images of GF-4 (which have a constant viewing angle in the same area) and on the following two assumptions: (1) AOD varies quickly with time, but slowly with location and (2) surface reflectance varies quickly with location, but slowly with time. AOD retrieval was then accomplished using a lookup table strategy. The data from June 2016, in the North China Plain were selected for the AOD retrieval test. GF-4-derived AOD was validated using ground measurements from the aerosol robotic network and Sun–Sky radiometer network with a correlation coefficient of R = 0.794. The derived results agreed reasonably well with the moderate resolution imaging spectroradiometer collection 6.0 aerosol product, with a correlation coefficient of R = 0.893. The atmospheric distribution and changes in some parts of the North China Plain were analyzed using the aerosol products of GF-4. The results showed that it is feasible to use the time-series imaging method to conduct high spatiotemporal resolution aerosol inversion using GF-4’s data, which can be used for detecting changes in air pollution.
Aerosol plays a key role in the assessment of global climate change and environmental health, while observation is one of important way to deepen the understanding of aerosol properties. In this study, the newly instrument – lunar photometer is used to measure moonlight and nocturnal column aerosol optical depth (AOD, τ) is retrieved. The AOD algorithm is test and verified with sun photometer both in high and low aerosol loading. Ångström exponent (α) and fine/coarse mode AOD (τf, τc) 1 is derived from spectral AOD. The column aerosol properties (τ, α, τf, τc) inferred from the lunar photometer is analyzed based on two month measurement in Beijing. Micro-pulse lidar has advantages in retrieval of aerosol vertical distribution, especially in night. However, the typical solution of lidar equation needs lidar ratio(ratio of aerosol backscatter and extinction coefficient) assumed in advance(Fernald method), or constrained by AOD2. Yet lidar ratio is varied with aerosol type and not easy to fixed, and AOD is used of daylight measurement, which is not authentic when aerosol loading is different from day and night. In this paper, the nocturnal AOD measurement from lunar photometer combined with mie scattering lidar observations to inverse aerosol extinction coefficient(σ) profile in Beijing is discussed.
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.
Information on the vertical distribution of aerosol is important for understanding its transport characteristics as well as aerosol retrieval uncertainty. In this paper, the believable lidar ratio under clear sky condition during December 2014 is determined from ground-based lidar and sun-photometer site in Beijing. Then two methods are adopted to derive typical aerosol extinction profiles by averaging attenuated backscatter and retrieved extinction profiles respectively. The results indicate that the former vertical gradient of dispersion (standard deviation) is smaller than the latter. Moreover, the comparison of the aerosol extinction coefficient profiles shows a good consistency above 2km but significant difference below that altitude.
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.
The reflected Solar radiance at top of atmosphere (TOA) are, to some degree, sensitive to the vertical distribution of absorbing aerosols, especially at short wavelengths (i.e. blue and UV bands). If properly exploited, it may enable the extraction of basic information on aerosol vertical distribution. In recent years, rapid development of the advanced spectral multi-angle polarimetric satellite observation technology and aerosol inversion algorithm makes the extraction of more aerosol information possible. In this study, we perform a sensitivity analysis of the reflection function at TOA to the aerosol layer height, to explore the potential for aerosol height retrievals by using multi-angle total and polarized reflectance passive observations at short wavelength. Employing a vector doubling-adding method radiative transfer code RT3, a series of numerical experiments were conducted considering different aerosol model, optical depth (AOD), single-scattering albedo (SSA), and scale height (H), also the wavelength, solar-viewing geometry, etc. The sensitivity of both intensity and polarization signals to the aerosol layer height as well as the interacted impactions with SSA and AOD are analyzed. It’s found that the sensitivity of the atmospheric reflection function to aerosol scale height increase with aerosol loading (i.e. AOD) and aerosol absorption (i.e. SSA), and decrease with wavelength. The scalar reflectance is sensitive to aerosol absorption while the polarized reflectance is more influenced by the altitude. Then the aerosol H and SSA may be derived simultaneously assuming that the total and polarized radiances in UV bands deconvolve the relative influences of height and absorption. Aerosol layer height, Atmospheric reflection function, Sensitivity, Ultraviolet (UV) band.
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