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.

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.

Due to the limitations in the number of satellites and the swath width of satellites (determined by the field of view and height of satellites), it is impossible to monitor global aerosol distribution using polar orbiting satellites at a high frequency. This limits the applicability of aerosol optical depth (AOD) data sets in many fields, such as atmospheric pollutant monitoring and climate change research, where a high-temporal data resolution may be required. Although geostationary satellites have a high–temporal resolution and an extensive observation range, three or more satellites are required to achieve global monitoring of aerosols. In this article, we obtain an hourly and global AOD data set by integrating AOD data sets from four geostationary weather satellites [Geostationary Operational Environmental Satellite (GOES-16), Meteosat Second Generation (MSG-1), MSG-4, and Himawari-8]. The integrated data set will expand the application range beyond the four individual AOD data sets. The integrated geostationary satellite AOD data sets from April to August 2018 were validated using Aerosol Robotic Network (AERONET) data. The data set results were validated against: the mean absolute error, mean bias error, relative mean bias, and root-mean-square error, and values obtained were 0.07, 0.01, 1.08, and 0.11, respectively. The ratio of the error of satellite retrieval within ±($0.05+ 0.2\times $AODAERONET) is 0.69. The spatial coverage and accuracy of the MODIS/C61/AOD product released by NASA were also analyzed as a representative of polar orbit satellites. The analysis results show that the integrated AOD data set has similar accuracy to that of the MODIS/AOD data set and has higher temporal resolution and spatial coverage than the MODIS/AOD data set.

The Arctic has a unique geographical environment. In recent years, the Arctic has undergone major changes, including an increase in temperature, a decrease in the extent and thickness of sea ice, and the reasons for these changes have yet to be further studied. In the Arctic, aerosol is an important factor that causes the temperature and environmental change. The lack of ground-based observation data in the Arctic makes satellite remote sensing an effective means for aerosol monitoring.We first selected the C61 version of the MODIS Level2 10 KM aerosol product from 2000 to 2018 as the Arctic's aerosol monitoring data. Then the products Aerosol Optical Depth (AOD) was evaluated by 20 ground-based AERONET (AErosol RObotic NETwork) sites in the Arctic. Based on the data with high quality control, the monthly averaged AOD in the Arctic were analyzed. Results show that: 1. MODIS AOD is overestimated in the Arctic and requires quality control to obtain more reliable results; 2. The monthly averaged AOD in the Arctic region does not exceed 0.3 with maximum 0.25 and minimum 0.027, and the average monthly AOD value of 18 years is 0.111. Usually, AOD peaks in summer and has a valley in autumn and winter, but it rises in spring with the Arctic haze events.

The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: R = 0.88, root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of ±0.2·AODAERONET±0.05. A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land.

Within the European Space Agency Climate Change Initiative (CCI) project Aerosol_cci, there are three aerosol optical depth (AOD) data sets of Advanced Along-Track Scanning Radiometer (AATSR) data. These are obtained using the ATSR-2/ATSR dual-view aerosol retrieval algorithm (ADV) by the Finnish Meteorological Institute, the Oxford-Rutherford Appleton Laboratory (RAL) Retrieval of Aerosol and Cloud (ORAC) algorithm by the University of Oxford/RAL, and the Swansea algorithm (SU) by the University of Swansea. The three AOD data sets vary widely. Each has unique characteristics: the spatial coverage of ORAC is greater, but the accuracy of ADV and SU is higher, so none is significantly better than the others, and each has shortcomings that limit the scope of its application. To address this, we propose a method for converging these three products to create a single data set with higher spatial coverage and better accuracy. The fusion algorithm consists of three parts: the first part is to remove the systematic errors; the second part is to calculate the uncertainty and fusion of data sets using the maximum likelihood estimate method; and the third part is to mask outliers with a threshold of 0.12. The ensemble AOD results show that the spatial coverage of fused data set after mask is 148%, 13%, and 181% higher than those of ADV, ORAC, and SU, respectively, and the root-mean-square error, mean absolute error, mean bias error, and relative mean bias are superior to those of the three original data sets. Thus, the accuracy and spatial coverage of the fused AOD data set masked with a threshold of 0.12 are improved compared to the original data set. Finally, we discuss the selection of mask thresholds.

In order to improve the accuracy and spatial coverage of AOD datasets, we proposed a method to obtain a consistent dataset with higher spatial coverage and better accuracy from Deep Blue (DB) AOD and Dark Target (DT) AOD products. The fusion algorithm consists of three parts: the first part is to remove the system errors, the second part is to calculate the uncertainty and fusion of datasets using the maximum likelihood estimate method, and the third part is to mask outliers. The MBE, MAE, RMB and RMSE of DB AOD in 2015 are 0.04, 0.13, 1.10 and 0.20 respectively, the MBE, MAE, RMB and RMSE of DT AOD in 2015 are 0.07, 0.12, 1.18 and 0.17 respectively, the MBE, MAE, RMB and RMSE of combined AOD provided by MODIS in 2015 are 0.05, 0.11, 1.12 and 0.16 respectively, and the MBE, MAE, RMB and RMSE of fusion data after mask with a threshold of 0.20 in 2015 are 0.03, 0.10, 1.08 and 0.15 respectively. The accuracy of fusion data after mask is obviously superior to the original data and the combined data provided by MODIS. In addition, the spatial coverage of the data has also been significantly improved.