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 ground-based 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 R² of 0.81. The intercomparison between AHI and MODIS DT AOD products also shows a good agreement with R² of 0.92.

The DPC is a multiangle sensor that detects atmospheric parameters. However, the retrieval of high-precision and high-spatial-resolution aerosol products from the DPC remains a great challenge due to the ill-posed nature of the problem. Thus, a novel aerosol optical depth (AOD) retrieval algorithm was proposed using visible surface reflectance relationships (VISRRs). The VISRR algorithm accounts for the surface anisotropy and needs neither a shortwave infrared band nor a surface reflectance database that can retrieve AOD over dark and bright land cover. Firstly, moderate-resolution imaging spectroradiometer (MODIS) surface reflectance (MYD09) products were used to derive the preceding surface reflectance relationships (SRRs), which are related to surface types, scattering angle, and normalized difference vegetation index (NDVI). Furthermore, to solve the problem of the NDVI being susceptible to the atmosphere, an innovative method based on an iterative atmospheric correction was proposed to provide a realistic NDVI. The VISRR algorithm was then applied to the thirteen months of DPC multiangle data over the China region. AOD product comparison between the DPC and MODIS showed that they had similar spatial distribution, but the DPC had both high spatial resolution and coverage. The validation between the ground-based sites and the retrieval results showed that the DPC AOD performed best, with a Pearson correlation coefficient (R) of 0.88, a root mean square error (RMSE) of 0.17, and a good fraction (Gfrac) of 62.7%. Then, the uncertainties regarding the AOD products were discussed for future improvements. Our results revealed that the VISRR algorithm is an effective method for retrieving reliable, simultaneously high-spatial-resolution and full-surface-coverage AOD data with good accuracy.

The directional polarimetric camera (DPC) is a Chinese satellite sensor with a large field of view (FOV) (±50° both along-track and cross-track) and a high spatial resolution (about 3.3 km at nadir) that operates in a sun-synchronous orbit. It is a difficult task to calibrate the in-flight relative radiometric variation of the sensors with such a wide FOV. In this study, a new method based on Rayleigh scattering over the ocean is developed to estimate the radiometric sensitivity variation over the whole FOV of DPC. Firstly, the theoretical uncertainty of the method is analyzed to calibrate the relative radiometric response. The calibration uncertainties are about 2–6.9% (depending on the wavelength) when the view zenith angle (VZA) is 0° and decrease to about 1–3.8% when VZA increases to 70°. Then, the method is applied to evaluate the long-term radiometric drift of the DPC. It is found that the radiometric response of DPC/GaoFen-5 over the whole FOV is progressively drifting over time. The sensitivity at shorter bands decreases more strongly than longer bands, and at the central part of the optics decreases more strongly than the marginal part. During the 14 months (from March 2019 to April 2020) of operational running in-orbit, the DPC radiometric responses of 443 nm, 490 nm, 565 nm, and 670 nm bands drifted by about 4.44–23.08%, 4.75–16.22%, 3.86–9.81%, and 4.7–16.86%, respectively, from the marginal to the central part of the FOV. The radiometric sensitivity has become more stable since January 2020. The monthly radiometric drift is separated into the relative radiometric part and the absolute radiometric part. The relative radiometric drift of DPC is found to be smoothly varying with VZA, which can be parameterized as a polynomial function via VZA. At last, the temporal radiometric drift of DPC/GaoFen-5 is corrected by combining the relative and absolute radiometric coefficients. The correction is convincing by cross calibration with MODIS/Aqua observation over the desert sites and improving the aerosol retrievals. The Rayleigh method in this study is efficient for the radiometric sensitivity calibration of wide FOV satellite sensors.

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.

China is planning to launch more and more optical remote-sensing satellites with high spatial resolution and multistep gains. Field calibration, the current operational method of satellite in-flight radiometric calibration, still does not have enough capacity to meet these demands. Gaofen-1 (GF-1), as the first satellite of the Chinese High-resolution Earth Observation System, has been specially arranged to obtain 22 images over clean ocean areas using the Wide Field Viewing camera. Following this, Rayleigh scattering calibration was carried out for the visible channels with these images after the appropriate data processing steps. To guarantee a high calibration precision, uncertainty was analyzed in advance taking into account ozone, aerosol optical depth (AOD), seawater salinity, chlorophyll concentration, wind speed and solar zenith angle. AOD and wind speed were found to be the biggest error sources, which were also closely coupled to the solar zenith angle. Therefore, the best sample data for Rayleigh scattering calibration were selected at the following solar zenith angle of 19–22° and wind speed of 5–13 m/s to reduce the reflection contributed by the water surface. The total Rayleigh scattering calibration uncertainties of visible bands are 2.44% (blue), 3.86% (green), and 4.63% (red) respectively. Compared with the recent field calibration results, the errors are −1.69% (blue), 1.83% (green), and −0.79% (red). Therefore, the Rayleigh scattering calibration can become an operational in-flight calibration method for the high spatial resolution satellites.