Limited by the on-orbital calibration capability, scaling the measured radiance in accuracy and stability is challenging for the Earth observation satellites in the reflective solar bands (RSBs). Although the lunar calibration is a well-developed technique in the RSBs, limited work has been done for Chinese Earth observation satellites. To improve the on-orbital calibration performance, the advanced MEdium Resolution Spectral Imager (MERSI II), which is the primary payload of the fourth satellite of the Fengyun 3 Series (FY-3D), expands the space view angle of the imager in order to capture better lunar images. In this study, we propose an absolute radiometric calibration method based on the FY-3D/MERSI lunar observation data. A lunar irradiance model named ROLO/GIRO has been used together with the necessary data procedures, including dark current count estimation, single pixel irradiance calculation, and full disk lunar irradiance calculation. The calibration coefficients obtained by the lunar calibration are compared with the pre-launch laboratory calibration. The results show that the deviations between the two calibration procedures are in a reasonable range in general. However, a relatively high non-linear response was found in the low energy incidence for some detectors, which leads to the large deviation in the corresponding bands. This study explored an ideal and independent method to validate MERSI on-orbit radiometric performance. The lunar calibration practiced for MERSI also gave a valuable example that can be recommended to the other Chinese Earth observation satellites.

Ground-level ozone (O₃), renowned for its adverse impacts on human health and crop production, has garnered significant attention from governmental and public sectors. To address the limitations posed by sparse and uneven ground-level O₃ observations, this study proposes an innovative method for hourly full-coverage ground-level O₃ estimation using machine learning. Meteorological data from National Centers for Environmental Prediction global forecasting system, satellite data from Fengyun-4 A(FY-4 A) and Ozone Monitoring Instrument, emission inventory from Multi-resolution Emission Inventory for China, and other auxiliary data are utilized as input variables, while ground-based O₃ observations serve as the response variable. The method is applied on a monthly basis across China for the year 2022, resulting in the generation of an hourly full-coverage high-resolution (4 km) ground-level O₃ estimation, termed ML-derived-O₃. Cross-validation results demonstrate the robustness of ML-derived-O₃ yielding a coefficient of determination (R ²) of 0.96 (0.91) for sample-based (site-based) evaluations and a root-mean-square error (RMSE) of 9.22 (13.65) µg m⁻³. However, the date-based evaluation is less satisfactory due to the imbalanced training data, resulting from the pronounced daily variations in ground-level O₃ concentrations. Nevertheless, the seasonal and hourly ML-derived-O₃ exhibits high prediction accuracy, with R ² values surpassing 0.95 and RMSE remaining below 7.5 µg m⁻³. This study marks a significant milestone as the first successful attempt to obtain hourly full-coverage ground-level O₃ data across China. The diurnal variation of ML-derived-O₃ demonstrates high consistency with ground-based observations, irrespective of clear or cloudy days, effectively capturing ground-level O₃ pollution exposure events. This novel estimation method will be employed to establish a long-term high spatial-temporal resolution ground-level O₃ dataset, which holds valuable applications for air pollution monitoring and environmental health research in future endeavors.

A lunar observation campaign was conducted using a hyper-spectral imaging spectrometer in Lijiang, China from December 2015 to February 2016. The lunar hyper-spectral images in the visible to near-infrared region (VNIR) have been obtained in different lunar phases with absolute scale established by the National Institute of Metrology (NIM), China using the lamp–plate calibration system. At the same time, the aerosol optical depth (AOD) is measured regularly by a lidar and a lunar CE318U for atmospheric characterization to provide nightly atmosphere extinction correction of lunar observations. This paper addressed the complicated data processing procedure in detail from raw images of the spectrometer into the spectral lunar irradiance in different lunar phases. The result of measurement shows that the imaging spectrometer can provide lunar irradiance with uncertainties less than 3.30% except for absorption bands. Except for strong atmosphere absorption region, the mean spectral irradiance difference between the measured irradiance and the ROLO (Robotic Lunar Observatory) model is 8.6 ± 2% over the course of the lunar observation mission. The ROLO model performs more reliable to clarify absolute and relative accuracy of lunar irradiance than that of the MT2009 model in different Sun–Moon–Earth geometry. The spectral ratio analysis of lunar irradiance shows that band-to-band variability in the ROLO model is consistent within 2%, and the consistency of the models in the lunar phase and spectrum is well analyzed and evaluated from phase dependence and phase reddening analysis respectively.

In this study, we compare the data of the advanced geostationary radiation imager (AGRI) on board the FY-4B and the advanced meteorological imager (AMI) on board the GK-2A, in terms of overall data, different reflectivity/brightness temperature intervals, different regions, and different underlying surfaces. The results show that the AGRI and AMI data are generally consistent; the mean biases for reflectivity channels show a range of 0.50% to 1.69%, with channel VIR004 being exceptionally good, while brightness temperature (TB) differences in the IR channels ranging from 0.11 to 0.57 K, with channel IR120 being the most accurate. The reflectivity of the AGRI is higher than that of the AMI in terms of mean bias. The dispersion of the reflectivity difference between the AGRI and AMI is smaller at the short-wavelength channels than that at the longer-wavelength channels. The TB data observed by the AGRI are higher than those of AMI at conditions above 310 K. In the case of observing the same target, the difference in infrared brightness temperature due to the random noise signal is small. The differences between the two sensors can be considerably reduced by revising mean biases. In the following studies of quantitative product algorithms, the characteristics of sensor data need to be further analyzed in detail.

The visible infrared radiometer (VIRR) is the first instrument with longest measurements equipped on the Fengyun (FY) polar-orbiting satellites. Through re-processing of the historic VIRR measurements, long-term (over 20 yr) global data can be integrated from multiple participating VIRRs on a consistent radiometric scale, which are valuable to climate and climate change studies. Due to lack of an onboard calibration system for VIRR, the reflective solar bands must be vicariously calibrated. This study applied the multi-site vicarious approach for consistent calibration of the VIRR visible (VIS) and near-infrared (NIR) data, and produced calibration coefficients for five VIRRs on FY-1C/D and FY-3A/B/C. The data quality was then evaluated with observations. The reflectance predicted by using the radiative transfer model over multiple invariant desert and ocean targets was used to derive the calibration slope via a weighted fitting scheme, in which the weights are the inverse of the variance from a radiative transfer simulation evaluated with reference to Aqua moderate resolution imaging spectroradiometer (MODIS). The sensor-specific calibration coefficients were derived on a daily basis by using piecewise polynomials. The calibration reference of the VIRR solar band record was further traced to the Aqua MODIS Collection 6.1 reference calibration with a systematic correction derived from the Libya4 desert. The VIRR record was compared with the Aqua MODIS C6.1 calibration over the polar region based on simultaneous nadir overpass observations. The lifetime relative difference for each sensor are within 3.3% and 4.5% for channels 1 and 2. Invariant deserts were also employed to evaluate the stability and consistency of the VIRR record. In general, the means of the directional and spectral corrected reflectance for each sensor are within 1% of the 20-yr average, implying that the VIRR reflectance of the invariant targets is consistent to within 1% among the sensors for channels 1 and 2. The VIRR data thus derived are reliable.

Inter-calibration is a general method to harmonize the in-orbit spectral radiance of the target instrument with the reference instrument in the visible and infrared spectra. This study proposes and applies an inter-calibration method to the space-based Ultraviolet and visible spectra (UVIS) grating radiometers. Based on pixel pairs derived from simultaneous nadir overpasses (SNO), the in-orbit spectral radiances from the environmental trace gas monitoring instrument onboard the Chinese high-resolution remote sensing satellite GaoFen-5 (EMI/GF-5) and the TROPOspheric Monitoring Instrument onboard the Sentinel-5 Precursor satellite (TROPOMI/S5p) are compared through the double difference (DD) method, where the individual window channel method is also adopted for comparison. Uncertainties from SNO thresholds, instrument spectral specification, and radiative transfer simulation are estimated. The SNO thresholds, including time, distance, viewing angle, and scene uniformity of 300 s, 3 km, 0.01, and 0.01, are determined to get collocations for UVIS instruments. Several factors, including instrument line shape (ILS), instrument spectral accuracy, reference solar spectrum, surface albedo, and atmospheric polarization, can influence the radiometric difference obtained by the DD method. Accurate SAO2010 solar spectrum and wavelength accuracy can substantially reduce the dependence of radiometric difference on wavelength. Errors introduced by scalar approximation and parameterized ILS can be neglected. The surface albedo greatly affects the simulated radiance at wavelengths longward of 330 nm. The results show that radiometric differences obtained by using the DD method are consistent with those derived from the individual window channel method, with a discrepancy of 0.4%. An ocean–land calibration difference of 1.5% is found between EMI and TROPOMI at the UVIS band. Inter-calibration using the DD method can minimize the spectral variations in full spectral radiance inter-comparison for grating hyperspectral instruments, and the radiometric difference can be estimated comprehensively. The wavelength dependence can also be easily derived from the results of the DD method. The DD method is recommended for radiometric comparison for UVIS hyperspectral spectrometers.