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 (EE) ±(0.05 + 0.2 * AOD _AERONET ), and the accuracy of PSAC CWV dataset is better than the EE ±(0.05 + 0.15 * CWV _AERONET ). 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 Polarization CrossFire (PCF) suite onboard the Chinese GaoFen-5(02) satellite has been sophisticatedly composed by the Particulate Observing Scanning Polarimeter (POSP) and the Directional Polarimetric Camera (DPC). Among them, DPC is a multi-angle sequential measurement polarization imager, while POSP is a cross-track scanning simultaneous polarimeter with corresponding radiometric and polarimetric calibrators, which can theoretically be used for cross comparison and calibration with DPC. After the data preprocessing of these two sensors, we first select local homogeneous cluster scenes by calculating the local variance-to-mean ratio in DPC’s Level 1 product projection grids to reduce the influence of scale differences and geometry misalignment between DPC and POSP. Then, taking the observation results after POSP data quality assurance as the abscissa and taking the DPC observation results under the same wavelength band and geometric conditions as the same ordinate, a two-dimensional radiation/polarization feature space is established. Results show that the normalized top of the atmosphere (TOA) radiances of DPC and POSP processed data at the nadir are linearly correlated. The normalized TOA radiance root mean square errors (RMSEs) look reasonable in all common bands. The DPC and POSP normalized radiance ratios in different viewing zenith angle ranges at different times reveal the temporal drift of the DPC relative radiation response. The RMSEs, mean absolute errors (MAEs), relative errors (REs), and scatter percentage of DPC degree of linear polarization (DoLP) falling within the expected error (EE = ±0.02) of POSP measured DoLP are better than 0.012, 0.009, 0.066, and 91%, respectively.

As the latest members of Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HuanjingJianzai-2 (HJ-2) series satellites were launched on 27 September 2020 by China and are usually abbreviated as HJ-2 A/B satellites. The polarized scanning atmospheric corrector (PSAC) is one of main sensors onboard HJ-2 A/B satellites, which is mainly used to monitor atmospheric components such as water vapor and aerosols. In this study, a columnar water vapor (CWV) retrieval algorithm using two bands (865 and 910 nm) is developed for PSAC. The validation results of PSAC CWV data based on ground-based CWV data derived from Aerosol Robotic Network (AERONET) show that PSAC CWV data has a high accuracy, and all statistical parameters of PSAC CWV data are better than those of Moderate-resolution Imaging Spectroradiometer (MODIS) CWV data released by NASA. Overall, there is no obvious overestimation or underestimation in PSAC CWV data. The root mean square error (RMSE), mean absolute error (MAE), relative error (RE), and percentage of CWV data with error within $\pm \left(0.05+0.10\ast CW{V}_{AERONET}\right)$ (PER10) of PSAC CWV data are 0.17 cm, 0.13 cm, 0.08, and 78.19%, respectively. The RMSE, MAE, RE, and PER10 of MODIS CWV data are 0.59 cm, 0.48 cm, 0.28, and 16.55%, respectively. Compared with MODIS CWV data, PSAC CWV data shows a 71% decrease in RMSE, a 73% decrease in MAE, a 71% decrease in RE, and a 372% increase in PER10. In addition, the results of day-to-day comparisons between PSAC CWV data and AERONET data show that PSAC CWV data can effectively characterize the change trend of CWV.

The development of long-term, continuous, consistent, and high-quality satellite remote sensing aerosol and surface products is crucial to constrain climate models and improve our understanding of climate change. Particulate Observing Scanning Polarization (POSP) is the first space-borne multi-spectral (UV–VIS–NIR–SWIR) cross-track scanning polarimeter dedicated to complementing the Directional Polarimetric Camera (DPC) multi-angle polarimetric measurements and performing synergistic observations, namely the polarization crossfire (PCF) suite on board the Chinese GF-5(02) (Gaofen-5 series) satellite. The POSP's unique single-view spectral (UV–VIS–NIR–SWIR) high-precision polarimetric measurements provide rich information for atmospheric aerosol and surface characterization. Here, we developed aerosol and surface products from POSP/GF-5(02) based on the Generalized Retrieval of Atmosphere and Surface Properties (GRASP)/Models approach. The detailed retrieval approach and processing scheme are provided. The baseline Level 2 product was generated for the first 18 months of POSP measurements from December 2021 to May 2023 and is publicly available and registered at https://doi.org/10.57760/sciencedb.14748 (Chen et al., 2024c). The obtained POSP/GF-5(02) aerosol and surface products are validated and intercompared with the ground-based AERONET reference aerosol dataset and other independent satellite products, such as the NOAA-20 VIIRS/DB aerosol product and the MODIS MCD43 surface product. The results show generally good consistency of POSP products with AERONET, the VIIRS/NOAA-20 aerosol dataset, and the MODIS surface product. Moreover, the developed POSP product includes not only total aerosol optical depth (AOD) but also detailed properties of aerosols (aerosol size, absorption, layer height, type, etc.), along with full-surface bidirectional reflectance distribution function (BRDF), bidirectional polarization distribution function (BPDF), black-sky albedo, white-sky albedo, and normalized difference vegetation index (NDVI). These parameters are of high importance to constrain the Earth–atmosphere radiation budget. The retrieval of these properties seems to be possible due to the polarimetric capabilities and wide UV–VIS–NIR–SWIR spectral range of POSP observations and advances of the used GRASP/Models retrieval approach. Finally, some potential improvements for the POSP Level 1–Level 2 processing chain are identified, and the limitations and lessons learned are discussed.

Polarized scanning atmospheric corrector (PSAC) onboard the HuanjingJianzai-2 (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 nine 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 (GSHHG) Database show that the 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, such as 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.

The Directional Polarimetric Camera (DPC) carried by the Chinese GaoFen-5-02 (GF-5-02) satellite has the ability for multiangle, multispectral, and polarization detection and will play an important role in the inversion of atmospheric aerosol and cloud characteristics. To ensure the validity of the DPC on-orbit multiangle and multispectral polarization data, high-precision image registration and geolocation are vital. High-precision geometric model parameters are a prerequisite for on-orbit image registration and geolocation. Therefore, on the basis of the multiangle imaging characteristics of DPC, an on-orbit autonomous geometric calibration method without ground reference data is proposed. The method includes three steps: (1) preprocessing the original image of the DPC and the satellite attitude and orbit parameters; (2) scale-invariant feature transform (SIFT) algorithm to match homologous points between multiangle images; (3) optimization of geometric model parameters on-orbit using least square theory. To verify the effectiveness of the on-orbit autonomous geometric calibration method, the image registration performance and relative geolocation accuracy before and after DPC on-orbit geometric calibration were evaluated and analyzed using the SIFT algorithm and the coastline crossing method (CCM). The results show that the on-orbit autonomous geometric calibration effectively improves the DPC image registration and relative geolocation accuracy. After on-orbit calibration, the multiangle image registration accuracy is better than 1.530 km, the multispectral image registration accuracy is better than 0.650 km, and the relative geolocation accuracy is better than 1.275 km, all reaching the subpixel level (<1.7 km).