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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.

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.