A series of studies of hyperspectral remote sensing had been carried out to develop a hyperspectral remote sensing technique for aerosol retrieval in the previous works, including the theoretical framework, information content analysis and application to the real data, in which a hyperspectral inversion algorithm was developed to simultaneously retrieved the aerosol and surface properties, and the surface reflectance spectra were decomposed into different principal components, thus only several weighting coefficients of principal components (PCs) were needed to be retrieved. In this study, based on the optimal estimation (OE) framework, we extend the OE-based hyperspectral inversion algorithm to multispectral remote sensing, and the synthetic multispectral intensities of Polarized Scanning Atmospheric Corrector (PSAC) centered in 410, 443, 555, 670, 865, 1610 and 2250 nm are used to test the inversion framework. Principal component analysis (PCA) has been conducted for the spectral dataset of 4 typical surface types with 7 channels of PSAC, in which the PC’s contribution and spectra, the spectral reconstruction results and constraints of PC’s weighting coeffects are discussed. Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) is used as the forward model, and 1% Gaussian distribution errors has been added to the simulated radiance at the top of the atmosphere for multispectral inversion test. The iterative process of multispectral normalized intensities and the reconstructed surface reflectance during the OE iteration are investigated, and the normalized cost function values are well convergent. This study can provide key support to the development of OE-based inversion algorithms for multispectral remote sensing
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.
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.
For the along-track scanning mode, the same place along the ground track could be detected by the Advanced Multi-angular Polarized Radiometer (AMPR) with several different scanning angles from -55 to 55 degree, which provides a possible means to get the multi-angular detection for some nearby pixels. However, due to the ground sample spacing and spatial footprint of the detection, the different sizes of footprints cannot guarantee the spatial matching of some partly overlap pixels, which turn into a bottleneck for the effective use of the multi-angular detected information of AMPR to study the aerosol and surface polarized properties. Based on our definition and calculation of t he pixel coincidence rate for the multi-angular detection, an effective multi-angle observation’s pixel matching method is presented to solve the spatial matching problem for airborne AMPR. Assuming the shape of AMPR’s each pixel is an ellipse, and the major axis and minor axis depends on the flying attitude and each scanning angle. By the definition of coordinate system and origin of coordinate, the latitude and longitude could be transformed into the Euclidian distance, and the pixel coincidence rate of two nearby ellipses could be calculated. Via the traversal of each ground pixel, those pixels with high coincidence rate could be selected and merged, and with the further quality control of observation data, thus the ground pixels dataset with multi-angular detection could be obtained and analyzed, providing the support for the multi-angular and polarized retrieval algorithm research in t he next study.
The Geostationary Ocean Color Imager (GOCI) provides multispectral imagery of the East Asia region hourly from 9:00 to 16:00 local time (GMT+9) and collects multispectral imagery at eight spectral channels (412, 443, 490, 555, 660, 680, 745, and 865 nm) with a spatial resolution of 500 m. Thus, this technology brings significant advantages to high temporal resolution environmental monitoring. We present the retrieval of aerosol optical depth (AOD) in northern China based on GOCI data. Cross-calibration was performed against Moderate Resolution Imaging Spectrometer (MODIS) data in order to correct the land calibration bias of the GOCI sensor. AOD retrievals were then accomplished using a look-up table (LUT) strategy with assumptions of a quickly varying aerosol and a slowly varying surface with time. The AOD retrieval algorithm calculates AOD by minimizing the surface reflectance variations of a series of observations in a short period of time, such as several days. The monitoring of hourly AOD variations was implemented, and the retrieved AOD agreed well with AErosol RObotic NETwork (AERONET) ground-based measurements with a good R2 of approximately 0.74 at validation sites at the cities of Beijing and Xianghe, although intercept bias may be high in specific cases. The comparisons with MODIS products also show a good agreement in AOD spatial distribution. This work suggests that GOCI imagery can provide high temporal resolution monitoring of atmospheric aerosols over land, which is of great interest in climate change studies and environmental monitoring.
This paper aims at bridging the gap between the academic research and practical application in water environment
monitoring by remote sensing. It mainly focuses on how to rapidly construct the Inland and coastal Water Environment
Remote Sensing Monitoring System (IWERSMS) in a software perspective. In this paper, the remote sensed data
processing framework, dataflow and product levels are designed based on the retrieval algorithms of water quality
parameters. The prototype is four-tier architecture and modules are designed elaborately. The paper subsequently
analyzes the strategy and key technology of conglutinating hybrid components, adopting semantic metafiles and tiling
image during rapid construction of prototype. Finally, the paper introduces the successful application to 2008 Qingdao
enteromorpha prolifra disaster emergency monitoring in Olympics Sailing Match fields. The solution can also fit other
domains in remote sensing and especially it provides a clue for researchers who are in an attempt to establish a prototype
to apply research fruits to practical applications.
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