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Frequent occurring of haze pollution events and high fine particulate matter (PM(2.5)) concentration in China have attracted more and more attention in the world. Satellite remote sensing can be used to characterize the air pollution. However, haze is usually misidentified as fog, thin cloud or bright surface in NASA’s Moderate Resolution Imaging Spectrometer (MODIS) cloud and clear days’ aerosol products, and the retrieval of its optical properties is not included in MODIS cloud detection and dark target algorithm. This approach first studies the spectral characters of cloud, fog, haze, and land cover pixels. Second, following the previous cloud detection and aerosol retrieval literatures, a threshold algorithm is developed to distinguish haze from other pixels based on MODIS multi-band apparent reflectance and brightness temperature. This algorithm is used to detect the haze distribution over North China Plain in 2008 spring and summer. Our result shows a good agreement with the true-color satellite images, which enhances MODIS’s ability to monitor the severe air pollution episodes. In addition, the high AOD data from Beijing and Xiang Aerosol Robotic NETwork (AERONET) sites indicate nearly 80% haze days are detected by our approach. Finally, we analyze the errors and uncertainties in haze detection algorithm, and put forward the potential improvements.

Ozone is a particularly critical trace gas in the Earth's atmosphere, since this molecule plays a key role in the photochemical reactions and climate change. The TIR measurements can capture the variability of ozone and are weakly sensitive to the lowermost tropospheric ozone content but can provide accurate measurements of tropospheric ozone and higher vertical resolution ozone profiles, with the additional advantage that measurements are also possible during the night. Because of the influence of atmospheric temperature, the ozone profile retrieval accuracy is severely limited. This paper analyze and discuss the ozone absorption spectra and weighting function sensitivity of temperature and its influence on ozone profile retrieval in detail. First, we simulate the change of atmospheric transmittance and radiance by importing 1 K temperature uncertainty, using line-by-line radiative transfer mode under 6 different atmosphere modes. The results show that the transmittance change ratio for 1 K temperature variation was consistent with the transmittance change ratio for 5%-6% change of ozone density variation in all layers of the profile. Then, we calculate the change of weighting function by a temperature error of 1 K, using the Community Radiative Transfer Model (CRTM) for the Cross-track Infrared Sounder (CrIS) on the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite and calculate the corresponding change of retrieval result. The results demonstrate that CrIS is sensitive to Ozone in the middle to upper stratosphere, with the peak vertical sensitivity between 10-100 hPa and the change of weighting function for 1 K temperature variation was consistent with 6% change in the ozone profile. Finally, the paper retrieves ozone profiles from the CrIS radiances with a nonlinear Newton iteration method and use the eigenvector regression algorithm to construct the a priori state. In order to resolve the problem of temperature uncertainty and get high accuracy ozone profile, atmospheric temperature profile and ozone profile are simultaneously retrieved. Comparison of the CrIS retrieved ozone profile with high-vertical-resolution ozonesonde profiles provided by the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) and ERA-Interim ozone profiles indicated that the retrieved ozone profiles are in good agreement with the ozonesonde profiles, and a notable improvement in this algorithm than the retrieval without atmospheric temperature profile, are also better than the ECMWF model profiles. The relative differences are less than 20% for the stratosphere and 50% for the lower troposphere.

Nitrous Oxide is a very important greenhouse gases and ozone-depleting substances. Due to the limited observations, there are still many uncertainties to quantitatively describe the role of nitrous oxide played in both cases. We can retrieve the methane and carbon dioxide gas using thermal infrared satellite data AIRS, but it is rarely for the nitrous oxide retrieval. Therefore, this paper retrieves nitrous oxide profiles from the AIRS data with an Optimal Estimate Method for the first time in China. The issue of the a priori and channels election is discussed. Comparison of the retrieved AIRS profiles with HIPPO profiles show the retrieved profiles are in good agreement with the smoothed HIPPO profiles, and a notable improvement in this algorithm than the eigen vector regression algorithm. For pressures between 300 and 900 hPa, we got the most accurate profiles and the relative error is only 0.1%, which is consistent with the jacobian peaks of the selected channels.

Biomass burning makes up an important part of both trace gases and particulate matter emissions, which can efficiently degrade air quality and reduce visibility, destabilize the global climate system at regional to global scales. Burned area is one of the primary parameters necessary to estimate emissions, and considered to be the largest source of error in the emission inventory. Satellite-based fire observations can offer a reliable source of fire occurrence data on regional and global scales, a variety of sensors have been used to detect and map fires in two general approaches: burn scar mapping and active fire detection. However, both of the two approaches have limitations. In this article, we explore the relationship between hotspot data and burned area for the Southeastern United States, where a significant amount of biomass burnings from both prescribed and wild fire took place. MODIS (Moderate resolution imaging spectrometer) data, which has high temporal-resolution, can be used to monitor ground biomass. burning in time and provided hot spot data in this study. However, pixel size of MODIS hot spot can't stand for the real ground burned area. Through analysis of the variation of vegetation band reflectance between pre- and post-burn, we extracted the burned area from Landsat-5 TM (Thematic Mapper) images by using the differential normalized burn ratio (dNBR) which is based on TM band4 (0.84 μm) and TM band 7(2.22 μm) data. We combined MODIS fire hot spot data and Landsat-5 TM burned scars data to build the burned area estimation model, results showed that the linear correlation coefficient is 0.63 and the relationships vary as a function of vegetation cover. Based on the National Land Cover Database (NLCD), we built burned area estimation model over different vegetation cover, and got effective burned area per fire pixel, values for forest, grassland, shrub, cropland and wetland are 0.69, 1.27, 0.86, 0.72 and 0.94 km2 respectively. We validated the burned area estimates by using the ground survey data from National interagency Fire Center (NIFC), our results are more close to the ground survey data than burned area from Global Fire Emissions Database (GFED) and MODIS burned area product (MCD45), which omitted many small prescribed fires. We concluded that our model can provide more accurate burned area parameters for developing fire emission inventory, and be better for estimating emissions from biomass burning.

In order to get higher vertical resolution atmosphere profile information, the present paper retrieves atmospheric temperature and moisture profiles from the Cross-track Infrared Sounder (CrIS) on the newly-launched Suomi National Polar-orbiting Partnership (Suomi NPP) and future Joint Polar Satellite System (JPSS) with a nonlinear Newton iteration method by using the profiles retrieved via statical regression method as the first guess, and the issue of channel selection is discussed. The retrieved profiles are compared with radiosonde observations, and National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analyses show that the physical retrievals of temperature and moisture are in good agreement with the distributions from GDAS analysis fields and radiosonde observations, and have a notable improvements of the atmospheric profile retrieval accuracy as compared with the eigenvector regression algorithm. For pressures between 200 and 700 hPa the accuracy is of the order of 1 K for the temperature profile, and 20% for the relative humidity profile is consistent with the jacobian peaks of the selected channels.

In the present, for the characteristic of HJ-1 CCD camera, after receiving aerosol optical depth (AOD) from deep blue algorithm which was developed by Hsu et al. assisted by MODerate-resolution imaging spectroradiometer (MODIS) surface reflectance database, bidirectional reflectance distribution function (BRDF) correction with Kernel-Driven Model, and the calculation of viewing geometry with auxiliary data, a new atmospheric correction method of HJ-1 CCD was developed which can be used over vegetation, soil and so on. And, when the CCD data is processed to correct atmospheric influence, with look up table (LUT) and bilinear interpolation, atmospheric correction of HJ-1 CCD is completed quickly by grid calculation of atmospheric parameters and matrix operations of interface define language (IDL). The experiment over China North Plain on July 3rd, 2012 shows that by our method, the atmospheric influence was corrected well and quickly (one CCD image of 1 GB can be corrected in eight minutes), and the reflectance after correction over vegetation and soil was close to the spectrum of vegetation and soil. The comparison with MODIS reflectance product shows that for the advantage of high resolution, the corrected reflectance image of HJ-1 is finer than that of MODIS, and the correlation coefficient of the reflectance over typical surface is greater than 0.9. Error analysis shows that the recognition error of aerosol type leads to 0. 05 absolute error of surface reflectance in near infrared band, which is larger than that in visual bands, and the 0. 02 error of reflectance database leads to 0.01 absolute error of surface reflectance of atmospheric correction in green and red bands.

With the urbanizing in China, haze days occur frequently, which have largely impacted air quality. In the present paper, based on haze physical properties, haze optical properties were calculated by Mie theory and apparent reflectance of haze in Environment Satellite 1 (HJ-1) channels was simulated by Radiative Transfer (RT) 3. Simulated results show that it is reasonable to extract the haze from apparent reflectance in the first and second channels. By Deep Blue algorithm, Haze Optical Depth (HOD) was retrieved from HJ-1 data supported by reflectance database from MODIS product. From HJ-1 data in 2009 over Beijing area, the haze days were monitored and validated by AERONET/PHOTONS Beijing site. The validation shows that the correlation coefficient of HOD is greater than 0.9, but HOD from HJ-1 is greater than that from ground-based measurements. The discussions show that the error from reflectance database is less than 0.1 and radiance resolution of HJ-1 needs to advance for haze monitoring.

Generally, aerosols over land are composed of fine and coarse aerosols, which have different optical properties. How to derive the fine mode and coarse mode aerosols from satellite observations is an important issue in the aerosol retrieving. In the present paper, information of aerosol models (including refractive index and size distribution function) was retrieved from the ground-based measurement of the AERONET/PHOTONS site in Beijing. The retrievals indicate that, aerosols over Beijing have a bi-model distribution, and the optical properties of fine and coarse aerosols are distinct. Based on the dark dense vegetation (DDV) method, optical depth of the fine, coarse and total aerosols were derived from MODIS data over Beijing area in 2007. The validation of these satellite retrievals shows that from MODIS data, the optical depth of fine and total aerosols can be retrieved well (with correlation coefficients greater than 0.8), and so can the Angstrom exponent (having a correlation coefficient of 0.517). However, relatively poor results were obtained when retrieving the optical depth of coarse aerosols.

The potentiality of the retrieval of surface reflectance using CCD camera aboard HJ-1A/B satellite was studied. It is very difficult to use dark targets in atmospheric correction due to the lack of near infrared band. The alternative normalized difference vegetation index (NDVI) and the red/blue reflectance ratio are detected from the spectral experiment in Beijing and the Pearl River Delta. Ground-based spectral data including grass, dense vegetation, water body, soil, residential roof and bright building etc. were used to validate the surface reflectance in Beijing, and the relative error in red, blue band is under 38.7% and 37.2% respectively. Uncertainties of the surface reflectance retrievals were analyzed. The comparison of MODIS surface reflectance product showed that there is a good agreement in the dense targets, and the correlation coefficient (R2) in red, blue band is as high as 0.809 4 and 0.723 9 respectively. HJ-1-CCD data can effectively reduce pixel-mixed impact on the cement roof and bright buildings, and the inversion accuracy is higher than MODIS products.

The Ring effect is a significant limitation to the accuracy of the retrieval of trace gas constituents in atmosphere, while using satellite data with differential optical absorption spectroscopy technique. The Ring effect refers to the filling in of Fraunhofer lines, known as solar absorption lines, caused almost entirely by rotational Raman scattering. The inelastic component of the molecular scattering results in a net increase in radiance in the line because more radiation is shifted to the wavelength of an absorption line than shifted from this wavelength to other wavelengths. The rotational Raman scattering by N2 and Oz in the atmosphere is the main factor that leads to Ring effect. Basically, the Ring effect is considered as a pseudo-absorption process in retrieval of trace gas constituents in atmosphere. The solar spectrum measured by OMI/AURA is convolved with rotational Raman cross sections of N2 and O2, divided by the original solar spectrum, with a cubic polynomial subtracted off, to create differential Ring spectrum. This method has been suggested in order to obtain an effective differential Ring cross-section for the DOAS fitting process. The differential Ring spectrum could be used to improve the accuracy of the retrieval of the trace gases concentration. The results in this paper have been in basic agreement with the corresponding results calculated with RTM, and the R2 Statistic is 0. 966 3.