Synchronization monitoring atmospheric corrector (SMAC) sensor is equipped with polarization and intensity data, providing the possibility of retrieving more accurate aerosol parameters for main sensor’s atmospheric correction. In this study, a novel aerosol retrieval algorithm based on intensity and polarization data was proposed. First, we analyze and construct the ratio relationship of different intensity and polarization channels of SMAC on different surface types and analyze the correlation between these ratios and normalized difference vegetation index (NDVI) and scattering angle (SCA). Then, for the first time, high-precision surface prior knowledge for SMAC aerosol retrieval is constructed, and then, intensity and polarization data were used to retrieve high-precision aerosol products simultaneously. These results are compared and validated with Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based observations aerosol products, and aerosol optical depth (AOD) product comparison between the SMAC and MODIS showed that they had similar spatial distribution, scattered dots of them with a Pearson correlation coefficient (R) of 0.84 and a root-mean-square error (RMSE) of 0.11. Meanwhile, their differences were also analyzed. The validation between the ground-based sites and the SAMC retrieval results also showed a good performance with R of 0.88 and RMSE of 0.10. These results revealed that the new algorithm is an effective method for retrieving reliable AOD products for the main sensor’s atmosphere correction.

Monitoring aerosols over the oceans is critical for understanding Earth’s climate and air quality. Although polarization can substantially reduce uncertainty in aerosol retrievals, current algorithms rely mainly on multi-view polarimeters, and no dedicated algorithm is available for single-view polarimeters over the ocean. Here, we present the first ocean algorithm for a spaceborne single-view polarimeter, demonstrated with the Particulate Observing Scanning Polarimeter (POSP) onboard the GF-5(02) satellite. Our algorithm combines multi-spectral polarization with machine-learning-accelerated radiative transfer calculation and seasonally clustered global aerosol models. Validation with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data demonstrates high accuracy, with RMSEs of 0.061, 0.479, and 0.037 for AOD550, AE670-870, and SSA550using AERONET, and 0.030 and 0.259 for AOD550and AE670-870using MAN, respectively. Comparison with retrievals from the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm confirms that our algorithm performs comparably to GRASP products. These results underscore the necessity and feasibility of developing specialized aerosol retrieval algorithms for single-view polarimeters, and pave the way for global aerosol over the ocean monitoring.

Single-angle sensors typically use radiative transfer simulations based on Lambertian surface, even though the surface reflectance obtained exhibits directional characteristics. Fully accounting for the contribution of surface directional characteristics to the top of atmosphere (TOA) reflectances can further improve the accuracy of aerosol retrievals. In this study, we propose an enhanced aerosol retrieval algorithm for the Particulate Observing Scanning Polarimeter (POSP), by further considering the surface directional characteristics. Combined with an updated aerosol model, this approach achieves high-accuracy retrievals. We used historical bidirectional reflectance distribution function (BRDF) products to construct stable surface constraints. By exploring the strong empirical statistical relationships between adjacent blue bands, we have realized the joint inversion of multiple blue bands. In addition, we used an optimization algorithm that incorporates boundary constraints, simultaneously accounting for errors in the surface constraint model, and satellite observation errors. The global aerosol optical thickness (AOD) at 550 nm over land was retrieved from November 2021 to April 2022. Validation of POSP AOD versus AErosol RObotic NETwork (AERONET) data shows a high consistency, with correlation coefficient (R) of 0.93, root mean square error (RMSE) of 0.086, bias of 0.004, fraction within expected error (EE) of 80.8%, and fraction within Global Climate Observing System (GCOS) of 52.1%. Comparison with MODIS aerosol products shows that the accuracy of POSP AOD is better than that of MODIS AOD. According to the matching results, for DB, R of 0.936/0.907 and fraction within EE of 82.2%/74.9% (POSP/MODIS DB); for DT, R of 0.937/0.915 and fraction within EE of 83.5%/ 72.0% (POSP/MODIS DT). The spatial distribution difference between POSP AOD and DB AOD is small, indicating good consistency, and POSP AOD captured the intensity of aerosol pollution well. In summary, the enhanced aerosol algorithm achieves reliable high-precision AOD retrieval and because of its generality could also be applied to other sensors.

China’s new generation of polar-orbiting meteorological satellite, FengYun-3D (FY-3D), is equipped with the Medium Resolution Spectral Imager II (MERSI-II) sensor, which greatly enhances Chinese capacity for comprehensive space environment detection and meteorological remote sensing. MERSI-II has two mid-infrared (MIR) channels with the potential to apply split-window (SW) algorithms. We have developed an improved nonlinear SW (NSW) algorithm to retrieve the ground brightness temperature (GBT)$T_{\text {gb}}^{0}$without the contribution of the solar direct beam from MERSI-II MIR data, enabling the accurate retrieval of surface bidirectional reflectivity$\rho _{b}$based on the radiative transfer equation (RTE). Compared with the traditional estimation method, this article assumed a linear relationship between$\rho _{b}$of MIR channels 20 and 21, and believed that under dry-cool atmospheric conditions,$T_{\text {gb}}^{0}$is not equal. Considering different solar zenith angles (SZAs), by iterating the combination of atmospheric and surface parameters under reasonable variations, the MODerate spectral resolution atmospheric TRANsmittance (MODTRAN) model version 5.2 was used to obtain the simulation datasets for determining the NSW coefficients. In the practical retrieval, the precise algorithm coefficients are only relevant to SZA. The$R^{2}$fit by the NSW algorithm is 0.99, and the root-mean-square error (RMSE) is less than 0.58 K for different SZAs. Under different SZAs, comparing the actual value of$\rho _{b}$with the proposed method, the bias is less than$1.57\times 10^{-3}$, and the RMSE is less than$2.21\times 10^{-3}$. A detailed sensitivity analysis found that the errors in atmospheric water vapor content (WVC), CO2 concentrations, O3 concentrations, horizontal visibilities (VISs), and instrumental noise were below the order of$10^{-4}$, and their effects on the retrieval of$\rho _{b}$were negligible. This study employed two methods to validate$T_{\text {gb}}^{0}$, thereby indirectly validating$\rho _{b}$. First, the moderate resolution imaging spectrometer (MODIS) land surface emissivity (LSE) product MYD11C1 and the ERA5-Land land surface temperature (LST) product were used to validate$T_{\text {gb}}^{0}$, yielding a bias of 0.9 K and an RMSE of 1.13 K. Second, in situ measurements from the surface radiation budget network (SURFRAD) sites were used to validate$T_{\text {gb}}^{0}$, resulting in a bias of 0.74 K and an RMSE of 1.83 K. Overall, results showed that the proposed algorithm has good accuracy and can improve its applications in relevant fields.

This study presents the first year-long global aerosol retrieval results over land derived from the Chinese Multi-Angle Polarimeter (MAP), DPC-2/GF-5(02). Aerosol products, including Aerosol Optical Depth at 550 nm (AOD550), Ångström Exponent between 440 nm and 670 nm (AE440-670), and Single Scattering Albedo at 440 nm (SSA440), have been produced using the Remote-sensing of Trace-gas and Aerosol Products (RemoTAP) algorithm. Validations against ground-based observations from AErosol RObotic NETwork (AERONET) and Sun-sky radiometer Observation NETwork (SONET) show good agreement. For AERONET validation the Root Mean Square Error (RMSE) and bias are 0.109 and -0.006 (AOD550), 0.488 and -0.151 (AE440-670), and 0.044 and 0.003 (SSA440). For SONET validation the RMSE and bias are 0.059 and -0.017 (AOD550), 0.261 and -0.002 (AE440-670), and 0.041 and -0.002 (SSA440). The effectiveness of vicarious calibration is also evaluated, revealing substantial improvements in agreement between forward model results and satellite measurements. Comparisons with MODIS Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol products across four seasons further confirm the consistency of our retrievals. The global distributions of annual mean aerosol properties align well with expected spatial patterns, highlighting the potential of DPC-2/GF-5(02) for robust global aerosol monitoring. Future work will focus on improving retrievals over bright surfaces and in high-altitude regions.

Aerosol properties, including aerosol optical depth (AOD), aerosol absorption optical depth (AAOD), single scattering albedo (SSA), and fine mode fraction (FMF), are essential in studying aerosol climate effects. Spectral multiangle polarimetry (MAP) has been recognized as a promising technique for comprehensive retrievals of global aerosol optical properties from space. As one of the very few MAP sensors in space, the Directional Polarimetric Camera (DPC) onboard the GaoFen (GF)-5 satellite has great potential to provide these critical aerosol parameters. However, retrievals of aerosol parameters from DPC, especially SSA and AAOD, still remain limited. This study introduces a machine-learning algorithm using the eXtreme gradient boosting (XGBoost) model to retrieve AOD, AAOD, SSA, FMF, as well as surface albedo (expressed as the directional hemispherical reflectance, DHR) over land from DPC multiangle reflectances and degree of linear polarization (DOLP), using AERONET aerosol measurements and Moderate Resolution Imaging Spectroradiometer (MODIS) DHR data as the training target. Cross-validation indicates high retrieval accuracy, with correlations exceeding 0.75 for all parameters under sufficient aerosol loading. Notably, the accuracy of SSA retrieval is comparable to that of the Polarization and Directionality of the Earth’s Reflectance (POLDER) products, with 73% of the independently retrieved 670-nm SSA falling within the ±0.03 error envelope (EE) when 670-nm AOD is above 0.30. Gridded products also effectively capture the spatial and seasonal variability of aerosol properties worldwide, such as in regions dominated by biomass burning and dust. This study confirms the capability of DPC for aerosol property retrievals, which could serve as an important technique and data source for global aerosol and climate monitoring.

The short-wave infrared Polarization and Multi-Angle Imager (PMAI) onboard Fengyun-3 precipitation satellite is a new spaceborne imaging polarimeter for clouds and aerosols, with polarization channels of 1030, 1370, and 1640 nm. This study presents a detailed description and assessment of the calibration model of PMAI. For radiometric intensity calibration, multiple parameters in the radiometric model are fitted into a single coefficient to simplify calibration. Results show that the radiometric calibration uncertainty of the full image plane is better than 0.02, and the calibration coefficient increases as field of view increases. The maximal unsaturated incident radiance of all channels is equivalent to 100% albedo, and signal-to-noise ratio at the referenced radiance is greater than 115 and 182 for the polarized and unpolarized channels, respectively. The response of all channels shows high linearity and good uniformity of the full image plane. Based on results of intensity calibration, a polarization calibration model using a fully linear polarized light source is introduced with a polarization measurement matrix established by a simplified method and a calculation method. Assessment of polarization measurement indicates that the uncertainties of the obtained degree of linear polarization (DoLP) and angle of linear polarization (AoLP) based on the two methods are highly consistent. When fully linearly polarized light is incident, the measurement error of DoLP using the simplified polarization measurement matrix is within 0.02 and that of AoLP is less than 1°. Therefore, the simplified radiometric intensity and polarization calibration model meets the measurement accuracy requirements and improves the calibration efficiency.

Large-scale and high spatiotemporal resolution sea surface temperature (SST) products can provide important support for monitoring dynamic changes in the marine environment, energy development, and assimilation models. We develop an algorithm to obtain the high spatiotemporal resolution SST product by fusion of the high spatial resolution SST product from the medium resolution spectral imager (MERSI) on the Fengyun-3E (FY-3E) satellite and the high temporal SST product from the advanced geosynchronous radiation imager (AGRI) on the Fengyun-4E (FY-4E) satellite. During data preprocessing, MERSI products have large data volumes stored in chunks, and we use the multicore computer for batch re-projection to generate the matrix of temperature values and satellite observation times. The matrix has high spatial coverage and removes the anomalous data, increasing the stability of the algorithm. AGRI data have gaps due to cloud cover, and we build a pixel-by-pixel linear fitting model to extract more valid change information from multihours data, which also improve the coverage of fusion results. During the fusion process, we calculate the spectral and temporal weighting factors in real time, divide data into regular blocks for multicores fusion to get 1 km/1 h SST fusion product covering most areas of the Eastern Hemisphere. Using Argo buoy data to verify the accuracy of the results for 15 days, the root mean square error (RMSE) is$1.089~^{\circ }$C and the average deviation is$0.867~^{\circ }$C. Multicore parallel computing makes algorithm fast and efficient. The spatiotemporal resolution of results is improved significantly and the effect of missing original data of the results is reduced.

Polarized Scanning Atmospheric Corrector (PSAC) is an optical sensor onboard HuanjingJianzai-2 (HJ-2) A/B satellites. One of its missions is to monitor precipitable water vapor (PWV) by using its near-infrared (NIR) channels. Since the accuracy of the commonly used NIR PWV retrieval algorithm developed based on radiative transfer model (RTM) would be significantly affected by radiometric decay of sensors, and the recalibration of decayed sensors is a complex process, it is interesting and necessary to find a robust PWV retrieval algorithm that is not affected by sensor decay. At present, a semi-empirical algorithm constructed based on the matching results between ground-based PWV data and the actual PSAC observations has been used for the PWV retrieval of PSAC. Since the systematic calibration error of PSAC is considered in constructing the algorithm, it should be able to remove the negative effects of sensor decay on PWV retrieval results. Because the above inference has not been confirmed quantitatively, it is necessary to evaluate the accuracy of the algorithm in the presence of sensor decay. The evaluation results based on simulated data show that the accuracy of the semi-empirical algorithm does not change regardless of the presence or absence of radiometric decay in PSAC. Moreover, the algorithm is used for PWV retrieval of MODIS to test its effectiveness. Compared with the official PWV data developed based on RTM, the MODIS PWV data developed by using the semi-empirical algorithm are reduced by more than 50% in both absolute and relative errors.

The Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun-4A (FY4A) satellite has good cloud observation ability, but it still absents all-weather and high-precision official cloud products. This study develops a deep-learning ResUnet model for all-weather retrieval of cloud phase (CLP) and cloud properties using the brightness temperature from water vapor and longwave infrared channels of AGRI. The ResUnet model is trained with the Himawari-8 satellite Level-2 (H8-L2) cloud products as true targets, and adopts image-by-image way to learn the spatial structure information of clouds, which compensates for the difficulty of retrieving thick clouds by thermal infrared radiation at night to some extent. On an independent testing dataset, the model has an overall accuracy of 90.64% for CLP identification and performs well at retrieving cloud top height (CTH). Even without using visible and near-infrared radiation, the root mean square error of cloud effective radius (CER) and cloud optical thickness (COT) estimations still reaches 7.14 μm and 9.01 in the range of 0–60. To further illustrate the reliability and applicability, CLP and cloud properties provided by the CALIPSO and MODIS are used as benchmarks to assess the quality of cloud products from FY4A satellite Level-2 (FY4A-L2), H8-L2 and ResUnet model retrieval. The ResUnet model provides a significant improvement over FY4A-L2 for the accuracy of cloud identification and in the quality of CTH products. In the range of 0–40 μm (0–60), the CER (COT) product of ResUnet model retrieval has a reliable and higher precision that is comparable with H8-L2.

Polarized Scanning Atmospheric Corrector (PSAC) onboard the Huanjing Jianzai (HJ)-2 A/B satellites is a cross-track scanning polarimetric remote sensor that measures the intensity and direction of light reflected by the Earth and its atmosphere by 9 full polarized spectral bands from near-ultraviolet (near-UV) to shortwave infrared (SWIR). In particular, geolocation accuracy is an important factor for polarization observations. An automatic coastline inflection method (CIM) is implemented for PSAC geolocation error estimation. Over five months of globally middle or low latitude coastline area measurements are used to obtain statistical result. The results of the comparison with the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) show PSAC geolocation error is smaller than 0.38 ground sample distance (GSD) or 3.25 km in 95% confidence level. In cross-track direction, the geolocation error estimation is affected by the instrument sampling characteristics like spatial response function (SRF). Thus, the correction method is proposed by establishing relationship between measurement radiance in CIM and offset proportion of PSAC GSD. The biases are obviously reduced after correction.

Obtaining accurate atmospheric parameters, e.g., aerosol optical depth (AOD) and column water vapor (CWV), is important for the quantitative atmospheric correction (AC) of the high-spatial resolution remote sensing images. However, due to the strong temporal and spatial changes of the atmospheric parameters, it will be a challenge to ensure spatiotemporal registration of the satellite images given the AC parameters obtained separately from ground-based or other satellite products, which affects significantly the accuracy of the AC. The China National Space Administration launched a high resolution and multimode imaging satellite [Gao Fen Duo Mo (GFDM)] in July 2020, which has multifunctional observation modes and flexible mobility, with a high-spatial resolution imaging sensor (0.42 m in panchromatic and 1.6 m in multispectrum) and equipped the synchronization monitoring atmospheric corrector (SMAC) sensor. As the first atmospheric corrector with polarization detection capability on-board high-spatial resolution satellite, SMAC is designed to obtain multispectral intensity and polarized data and to retrieve synchronously AC parameters in the same field of view with main sensor. Based on the SMAC in-orbit test data, a lookup table method using the optimized inversion framework and a dual-channel ratio retrieval method are developed to derive AOD and CWV, respectively, in this article. The AOD and CWV results are validated against the AERosol RObotic NETwork (AERONET). The preliminary test of AC performance on the multispectral images of GFDM satellite indicates that SMAC is of great potential to improve the quality of the main sensor’s image.

As the successors of the overdue HuanjingJianzai-1 (HJ-1) satellites and new members in Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HuanjingJianZai-2 series satellites (HJ-2 A/B) have been launched on September 27, 2020. Each satellite carries four sensors, including the Polarized Scanning Atmospheric Corrector (PSAC), the charge-coupled device (CCD) camera, the hyperspectral imager (HSI) and the infrared spectroradiometer (IRS). Among them, PSAC is mainly used for the monitoring of atmospheric parameters to provide data support for atmospheric environmental monitoring and atmospheric correction of data from other sensors. To test the in-orbit performance of PSAC, we develop the “day-1” aerosol and water vapor retrieval algorithms. The preliminary validation results based on ground-based observations show that the aerosol optical depth (AOD) and columnar water vapor (CWV) datasets developed based on PSAC data have high accuracy and can effectively characterize the temporal trends of AOD and CWV. The accuracy of PSAC AOD dataset is better than the expected error ±(0.05 + 0.2 * AODAERONET), and the accuracy of PSAC CWV dataset is better than the expected error ±(0.5 + 0.15 * CWVAERONET). To eliminate the negative impact of the atmosphere on CCD data and expand its application range, aerosol and water vapor data developed based on PSAC are used for atmospheric correction of CCD data. Compared with L1 CCD data, the texture details and clarity of CCD data after atmospheric correction have been significantly improved.

As the successors of the HuanjingJianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HJ-2 A/B satellites have been successfully launched on the September 27 of 2020. The Polarized Scanning Atmospheric Corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring high speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network based AOD retrieval model (named the AODNet), which takes full advantage of the multispectral measurements of PSAC for AOD retrieval with a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either of Sun-sky radiometer Observation NETwork (SONET) AOD or AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85 and more than 60% of the AODNet AOD can fall into the expected error envelope of ±(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS Dark Target (DT) and Deep Blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data.

The Advanced Geosynchronous Radiation Imager (AGRI) is one of the main imaging sensors onboard the Fengyun-4A (FY-4A) satellite. Because of its high observation frequency, AGRI is suitable for continuous monitoring of atmospheric aerosols. In this study, we propose an aerosol optical depth (AOD) retrieval algorithm called the multichannel (MC) algorithm, which uses four channels (0.65, 0.83, 1.61, and$2.25~\mu \text{m}$) of AGRI. The algorithm assumes that the ratios between surface reflectance of different channels remain unchanged within two weeks, and the ratios are calculated by using Moderate-Resolution Imaging Spectroradiometer (MODIS)-combined AOD data to perform atmospheric correction on AGRI data under low pollution conditions (AOD at 550 nm less than 0.5). Since this algorithm is not developed for specific surface types, AOD retrieval can be achieved over both dark targets and bright surfaces. This algorithm has been applied to aerosol retrieval in South Asia. The accuracy assessment of the AGRI AOD dataset in 2019 and 2020 using the ground-based data from 11 aerosol robotic network (AERONET) sites shows that the AGRI AOD dataset has a high accuracy, and the statistical parameters of AGRI AOD dataset are slightly better than those of MODIS-combined AOD dataset. The root-mean-square error (RMSE), mean absolute error (MAE), relative mean bias (RMB), and percentage of data with errors within the expected error$\pm (0.05+0.15 \times {{\text {AOD}}}_{{\text {AERONET}}})$(EE15) of AGRI AOD dataset are 0.16, 0.12, 0.23, and 63.71%, respectively. The RMSE, MAE, RMB, and EE15 of MODIS-combined AOD dataset are 0.18, 0.13, 0.24, and 61.06%, respectively.

Himawari-8 (H8), as a geostationary satellite, observing the full disk image of the earth per 10 min, is a very powerful tool to investigate atmospheric aerosol temporal variations. The advanced himawari imager (AHI) onboard H8 has several spectral bands similar with those of Moderate Resolution Imaging Spectroradiometer (MODIS). Aerosol optical depth (AOD) is thus possible to be retrieved from AHI data using the dark target (DT) method of MODIS. Based on a statistics study of infrared bands of these two satellite instruments, we found that the difference of shortwave infrared bands between AHI (2.3 μm) and MODIS (2.12 μm) in DT area can be ignored for our purpose. Meanwhile, for AHI sensor, the normalized difference vegetation index (NDVI) calculated from 0.86 μm (instead of traditional 1.24 μm) and 2.3 μm can also be a good indicator of surface spectral reflectance. Therefore, first, a new NDVI of AHI sensor is proposed in this paper to improve the estimation of surface reflectance based on DT retrieval method. Then, AOD over DT area was retrieved using a lookup table strategy and tested based on AHI observation over China region from August 2015 to July 2016. All retrieved results are validated against groundbased measurements of the aerosol robotic network and the sun- sky radiometer observation network. The coincident AOD results between AHI retrieval- and ground-based network measurements show good agreements with R2of 0.81. The intercomparison between AHI and MODIS DT AOD products also shows a good agreement with R2of 0.92.

The composition and mixing states of atmospheric aerosols are the essential properties as they decide aerosol optical and physical properties. This study uses microphysical parameters obtained by sun-sky radiometer measurements to estimate main tropospheric aerosol components, including black carbon, brown carbon, mineral dust, particulate organic matter, ammonia sulfate-like, sea salt and water uptake. Different internal mixing models are compared, and Maxwell-Garnett effective medium approximation is selected for forward modeling of refractive index. The composition-to-microphysical parameters look-up table is established and then an optimization procedure is employed to find the best fit between models and observations. Aerosol components under clean conditions during a campaign held in Beijing are quantitatively estimated, and aerosol optical, microphysical and compositional properties are comprehensively analysed.

Atmospheric aerosol has important influences on the global climate either directly by scattering and absorption of the solar radiation or indirectly by affecting cloud droplet concentration or cloud radiative properties[1-2]. A high proportion of aerosol in the Earth's atmosphere consists of non-spherical mineral dust particles[3]. Light scattering by non-spherical particle such as mineral dust is commonly known as a major difficulty in aerosol characterization[1,4]. Compared with the total radiance, polarization is more sensitive to aerosol particle shape. It has a distinct advantage in study non-spherical aerosol particles. In the sky under some atmospheric conditions (e.g., clear sky, cloudy sky, hazy sky), it usually exists a characteristic polarization pattern, which is related to the position of the sun, the distribution of various atmospheric constituents, and the properties of the underlying surface[5]. The polarization pattern can be applied not only in navigation, but also in studying of atmospheric aerosol properties.

The impact of environmental control measures during 26th World University Summer Games on aerosol loadings is analyzed in this work. The aerosol optical depth (AOD), size distributions as well as angström index during and after the games are compared between two measurement stations. The results indicate that the AODs during the games are significate lower than afterwards at two stations. And the comparison of size distribution shows that fine mode aerosols which mainly produced by human activities are obviously declined during the games at both stations.

While the successive order of scattering (SOS) method is used to solve the radiative transfer equation, however, for the large optical depth with a high single scattering albeldo, the slow convergence will spend a lot of computing time. To speed up the convergence of SOS method, an improved semi-analytic model is developed and the two notes fitting method is used to get the ratio of two successive scattering radiances after the thirteenth scattering. The new semi-analytic model is accurate and efficient and makes the SOS method applicable for optically thick scattering media. With the improved semi-analytic model, the efficiency of the SOS method can be greatly improved.

In this paper, we compared the aerosol optical properties (single scattering albedo(SSA), real part part of refractive index(mr)) at 0.44 µm retrieved from two different (principal plane(PP) and almucantar(ALM)) measurement of sky radiance, which observed from ground-based sun-sky radiometer CE318. We found 75% cases of SSA and mr retrieval are higher retrieved from ALM compared with PP, and SSA has a high correlation (R2=0.848) between these two measurement, while mr has a low(R2=0.019).

An improved scheme has been developed to retrieve volume fraction of aerosol chemical composition like black carbon (BC), mineral dust (DU), ammonium sulfate-like (AS) and water content from aerosol complex refractive indices and size distribution remotely obtained from ground-based sun-sky radiometer measurements. Typical observations in Beijing during 2011 were selected to investigate chemical composition retrieval results at clear, haze and dusty days respectively. The retrieved BC mass concentration was compared with coincident aethalometer.

Black carbon, organic matter, and mineral dust are the main absorption components in atmospheric aerosols and their fractions determine the wavelength dependence of absorption. We analyze the spectral measurements of imaginary refractive indices (k) from 440 to 1020 nm obtained from ground-based AErosol RObotic NETwork sun-sky radiometer located in Beijing. The results show that the k spectra in Beijing have a wavelength dependent characteristic, with k at 440 nm significantly higher than that at the other three bands. Hence, the ratio of k(440 nm) to k(670 nm) is established to express the k spectral behavior. A significant seasonal variations of k(440 nm)/k(670 nm) is found in Beijing, with maximum value of about 3.5 appeared in March, and minimum value of about 0.9 in August. This is consistent with the variation trends of absorption angstrom exponent, indicating that k(440 nm)/k(670 nm) is also a useful tool in identifying aerosol composition.

In this paper the authors employ accurate vector radiative transfer calculations using two vector radiative transfer models VLIDORT and SOS to perform a systematic study of the errors for the homogeneous, plane-parallel Rayleigh and Mie scattering atmosphere above a Lambertian surface. We calculate percent errors in upwards radiance for various directions of light incidence and reflection. The errors increase rapidly when the directions of incidence and reflection is closed to 10 and 80 degree. And the relative errors are lager in Mie scattering atmosphere than that in Rayleigh scattering atmosphere. The maximum errors of Stokes parameters I and Q in molecular scattering case are less than 15% and -0.0011, respectively. In the case involving the impacts of aerosols, the maximum relative error in I is 20.3%.

This paper researches on the method how to retrieve the water-leaving reflectance from Chinese HJ1 CCD imagery. The instrument characteristics are first analyzed and then the monochromic atmosphere correction equation is described. Using the radiative transfer model, the correction parameters are worked out by varying different conditions such as sun-observation geometry, aerosol models and so on. The results are stored in look-up table (LUT). Moreover, the MODIS data are used to make certain aerosol load and water vapor volume, which are the input parameters of the pixel-by-pixel procedure. The retrievals of water-leaving reflectance are finally compared to the in Situ measurement and MODIS results. It is found that the correction accuracy is good especially in the blue and green bands. This paper demonstrates that this method is a simple and efficient approach and well worth applying to future conventional production.