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.

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.

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.

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 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%.

The four-stream discrete ordinates method and the four-stream successive orders of scattering method are compared for the radiative flux calculations in this paper. With the Rayleigh scattering phase function and the Henyey-Greenstein phase function, the effects of the two four-stream method are investigated. The delta-M method is used to deal with the strong forward-peaked scattering and the 3 2-stream DISORT is used as the benchmark for assessments of the relative accuracy of the two methods. With the comparisons for the accuracy of flux, the two four-stream methods are roughly comparable in accuracy and the absorbing media lead to larger errors of flux compared with that of the non-absorbing case.

The two-stream discrete ordinates method and the two-stream successive orders of scattering method are compared, and the key features of two methods are discussed. Based on the convergence characteristics of successive scattering, we use a semi-empirical model to improve the computing efficiency in the SOS method. Using the delta-M method, we investigate the effect of the two two-stream method for non-absorbing and absorbing case respectively. The 32-stream DISORT is used as the benchmark for assessments of the relative accuracy of the two methods investigated. With the comparisons for the accuracy of flux, the results of the two two-stream methods are almost the same in general and the absorbing media lead to larger errors of flux compared with that of the non-absorbing case.

Based on a great deal of spectral data for different kinds of ground objects which were denoised by wavelet transform method, the spectral characteristics and changing rules of water, paddy field, wheat, cole and vegetable greenhouse in Chao Lake basin were analyzed with ASD portable spectrum analyzer by field investigation and plot survey. The spectral data of typical ground objects in Chao Lake basin were also processed by using mathematics technology such as de-noising technology of derivative spectrum technology and normalization processing technology. It was shows that wavelet transform had advantage in de-noising because it could remove the noise from signal as well as preserve the detail information; derivative spectrum and normalization processing technology had better practicability in suppressing background effect and emphasizing the signal of objects.

This paper demonstrates the Ruddick's algorithm to utilize atmospheric correction with the hyper-spectral imagery over Chinese turbid lake water obtained by China first hyperspectral imager (HSI) onboard HJ-1A. The paper studies on the sensor characteristics and analyzes the optical properties of turbid lake water in Taihu Lake. Based on consideration about real circumstances, the paper recalibrates parameterαof value taken as 1.43. Results indicate that the recalibrated parameter could enhance the algorithm performance and improve the accuracy through comparison with the in situ measurements.