The Fengyun-4B satellite is the first operational satellite of China’s new-generation geostationary meteorological satellite series. It is equipped with an advanced geostationary radiation imager (AGRI) that can detect aerosols. This method includes two aspects: 1) we constructed an hourly surface reflectance (SR) database and selected appropriate aerosol models based on seasonal variations. This approach enables the quantitative retrieval of aerosol properties over a variety of land surfaces, including dark pixels, urban areas, and bright desert surfaces, and 2) based on the spatiotemporal variability characteristics of aerosols in the past hour and within a 12-km radius, a quality control scheme was designed. This scheme was used to produce two datasets: the quality-controlled AODpure dataset and the original uncontrolled AODorig dataset. The aerosol products in 2023 were evaluated using Aerosol Robotic Network (AERONET) data. The results showed that the performance of AODpure was optimal during the summer and fall seasons, with the root-mean-square error (RMSE) less than 0.1, and more than 75% of the samples falling within the expected error (EE). However, due to the overestimation of low values in winter and underestimation of high values in spring, only 64.06% of the samples fell within the EE throughout the year. For the EE criterion, the number of samples of AODorig against AERONET was 10% lower than that of AODpure against AERONET. The diurnal variations of AODpure show more consistency with AERONET, proving the necessity of hourly SR. Both aerosol optical depth (AOD) datasets are capable of capturing dust events in spring and haze events in winter. Considering the cross-comparison, the quantitative ability of FY-4B/AGRI enhanced by this work is currently superior to that of Himawari-9/AHI.

The measured CO2mixing ratio is significantly influenced by atmospheric water vapor due to its rapid spatial-temporal variability. In greenhouse gas remote sensing, the dry-air mixing ratio is commonly used to represent CO2concentration, which reduces errors arising from water vapor fluctuations. Early satellite spectrometers were often equipped with the O2-A band to retrieve atmospheric O2concentration. Leveraging the stable ratio between O2and dry air, CO2can be normalized to derive its dry-air mixing ratio. This approach not only reduces systematic errors but also plays a crucial role in minimizing instrumental biases, thereby enhancing measurement accuracy. However, advancements in satellite instrument calibration have diminished the necessity for O2-A band normalization. Therefore, this study proposes an alternative normalization method based on meteorological data to alleviate hardware requirements and costs. CO2retrieval results from O2-A band normalization and meteorological data normalization were compared using observations from the Orbiting Carbon Observatory-2 (OCO-2) satellite, and these results were validated against data from the Total Carbon Column Observing Network (TCCON).The findings indicate that, in comparison to TCCON, the mean biases of the O2-A band scheme are 0.17 ppm, 0.25 ppm, and - 0.005 ppm in glint, nadir, and target modes, whereas the biases for the meteorological normalization method are 0.35 ppm, 0.45 ppm, and 0.19 ppm, respectively. Overall, the systematic bias of the meteorological normalization method is approximately 0.2 ppm greater than that of the O2-A band scheme, potentially attributable to the spatial-temporal resolution limitations of the Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2) dataset. Nonetheless, this method remains a viable alternative to O2-A band normalization for CO2retrieval.

Atmospheric carbon dioxide (CO2) is the primary anthropogenic driver of climate change, accounting for more than half of the total effective radiative forcing (ERF). Active remote-sensing technique using differential absorption Lidar (DIAL) is recognized as the most promising remote sensing means for atmospheric carbon dioxide measurements. The Aerosol and Carbon Detection Lidar (ACDL) instrument onboard the DQ-1 is dedicated to quantifying the global spatial distribution of atmospheric CO2. To meet the requirement of accuracy and precision, a reasonable averaging scheme for ACDL measurements is needed to minimize the effect of random noise of observations on CO2retrievals. In this study, three averaging schemes were conducted in the retrieval process: averaging of CO2columns (AVX), averaging of differential absorption optical depth (AVD), and averaging of signals (AVS). The performances were compared at three representative sites. The experiments were first carried out on simulations. The results show that the optimal size of the averaging window is 50 km, corresponding to an averaging of measurements over 150 pulse pairs. In addition, the AVX and AVD schemes are less affected by altitude variations and can be applied to surfaces with moderate and severe topographic variation, such as hills and mountains. Whereas the AVS method is more suitable for surfaces with slight topographic variation, such as oceans, plains, and terraces. Furthermore, the ACDL observations were also retrieved by applying three averaging schemes and validated against ground-based TCCON measurements at Xianghe station. The AVS scheme exhibits better performance than the AVX and AVD methods with the lowest biases of less than 0.5 ppm, which is consistent with the simulation results.

This study explores a new scheme to retrieve the global aerosol optical depth (AOD) over land for the advanced Medium Resolution Spectral Imager (MERSI-II) aboard the Fengyun-3D (FY-3D) satellite based on the dark target (DT) algorithm. The main improvement is that the global surface reflectance (SR) model nonlinearly varies with the solar zenith angle and normalized difference vegetation index (NDVIswir) is made, which is more complex relative to that of Moderate-Resolution Imaging Spectro-Radiometer (MODIS) operational algorithm. Our AOD retrievals are compared with an aerosol robotic network (AERONET) AOD and cross evaluated with Aqua/MODIS, respectively. Overall, the MERSI-II retrieved results over the global scale have good consistency with the AERONET observations; on the same condition, the percentage of matchups within the expected error (EE: ±0.05 ± 0.15AOD) is 67.06%, which is slightly lower than the percentage of MODIS (79.84%). On a spatial scale, the coverage of MERSI-II retrievals at one granule is significantly higher than that of MODIS, which is related to successful inversion of haze pixels and has retrieval ability in urban, grassland, and other surface types. The monthly mean AOD values retrieved by MERSI-II are close to those of MODIS, indicating that MERS-II has similar quantitative capability and application potential as its international counterparts.

The payload MEdium Resolution Spectral Imager - low light (MERSI-LL) of Fengyun-3E (FY-3E) equipped with a low light band (LLB) first enabled the FY series satellite to detect low lights at night. Due to the early morning orbit of FY-3E, MERSI-LL/LLB only obtains nighttime observations in one hemisphere during the winter half-year, and nighttime observations are only found at high latitudes for the ascending orbit (at dusk) and globally covered for the descending orbit (at dawn). Using MERSI-LL/LLB data, we developed nighttime lights (FY-NTL) product for socioeconomic use. Publicly released FY-NTL data include monthly and annual products for both ascending and descending orbits, which are composited from multitemporal MERSI-LL/LLB data after quality control. At present, the quality control procedure consists of moonlit data identification, stray light removal, cloud screening, and natural illumination exclusion. The absolute radiometric accuracy of FY-NTL degraded in the stray light removal process, and the comparison results with VIIRS NTL suggested that FY-NTL may bear a negative bias. Nonetheless, FY-NTL first images global nighttime lights at dawn and provides us with an opportunity to study city lights in different periods of the night.

A cross-sensor calibration technique is developed and applied to improve upon the prelaunch radiance calibration and characterization for the Total Ozone Unit (TOU) onboard the FengYun-3/A satellite. The Level 3 products from the National Aeronautics and Space Administration Ozone Monitoring Instrument (OMI) onboard the Earth Observing System Aura are used as input to a radiative transfer model to predict the TOU radiances and characterize the biases for the measurements over the Pacific Ocean in low- and midlatitudes. The coefficients are derived from a regression algorithm to adjust the TOU radiances. It is shown that, after the measurement bias correction, the biases between the retrieved total column ozone products from the TOU with those from the OMI Total Ozone Mapping Spectrometer (TOMS)-Version 8 products and those from a set of ground-based station measurements are 3 % and 5% , respectively. The variations in the estimated total ozone amounts from the TOU are consistent with those derived from Solar Backscatter Ultraviolet Radiometer instruments and OMI for a period from January 2010 to February 2011.

In this paper, we describe the methodology and field experimentations of measuring the Gobi surface emissivity using CE312 and infragold board at Dunhuang radiometric calibration site of China. The radiance of the land surface and infragold board were measured by CE312, and calculated the land surface emissivity. Then we performed atmospheric correction including gas absorption and path radiance which calculated with atmospheric radiative transfer code MODTRAN 4.0; predict the radiance at the satellite sensor entrance pupil. Inversion the TOA brightness temperature measured by the satellites and compared with the land surface brightness temperature, which obtained from the land surface physical temperature divided by the calculated emissivity. The results showed that the methodology described in this paper for measuring the Gobi surface emissivity is feasible and with a tolerable accuracy.