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.

This study exploits the unique capabilities of the FY-3E WindRAD scatterometer, the first spaceborne dual-frequency (Ku- and C-band) and dual-polarization (hhandvv) rotating fan-beam scanning measurements, to investigate the backscatter characteristics of open water (OW), first-year ice (FYI), and multi-year ice (MYI) under different seasonal, wavelength, and polarization conditions throughout 2022 in the Arctic. Four types of feature parameters were defined for systematic analysis based on WindRAD swath data. It is concluded that the mean backscatter coefficient σp,λand the wavelength gradient ratioGRpare key indicators for distinguishing between FYI and MYI, with the Ku-band exhibiting superior performance outside the melt season due to enhanced volume scattering from desalinated ice and bubble structures. During melting, however, both ice types become indistinguishable as meltwater increases dielectric loss and reduces penetration depth. Furthermore, the standard deviation of the backscatter coefficient Δσp,λand the polarization ratio γλprove highly effective in separating sea ice from OW with the C-band showing particular advantage owing to a wider incidence angle range and stronger angular sensitivity of Bragg scattering over water. The γλapproaches 1 for both FYI and MYI due to depolarizing rough surfaces, whereas OW exhibits lower values dominated by Bragg scattering. This study provides a systematic observational basis for exploring the benefits of dual-frequency joint detection in enhancing sea ice monitoring capabilities, providing vital support for the development and refinement of algorithms for FY-3E WindRAD operational sea ice products.

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.

Sea surface wind speed (SSWS) is an important oceanic dynamical parameter, extensively utilized in numerical simulations of climate change and weather forecasts, as well as in storm intensity assessment. Microwave radiometers onboard sun-synchronous satellites can provide a large amount of SSWS data globally. Atmospheric attenuation caused by large raindrops and rainwater contamination can both lead to estimation errors, especially for the sensors without L-band and C-band channels such as the Microwave Radiation Imager (MWRI) sensor onboard the Fengyun-3D (FY3D) satellite. To investigate the potential of MWRI in SSWS detection under bad weather conditions, a lightweight deep neural network (LWDNN) is used based on the Global Change Observation Mission First-Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2) all-weather SSWS product in 2021. The SSWS product from AMSR2, soil moisture active passive (SMAP), buoys, and the ERA5 reanalysis data have been utilized to validate the LWDNN under all weather conditions. The overall root mean square error (RMSE) of MWRI SSWS is less than 2.0 m/s under all weather conditions and less than 1.5 m/s in the clear-sky region. In order to test the retrieval effectiveness of LWDNN in cyclone regions, the scenes of cyclones are collected. Results show that the RMSEs of the MWRI maximum wind speed (VMAX) product relative to the AMSR2 and SMAP data are 6.31 and 6.66 m/s, respectively, in the wind speed range of 20–70 m/s, and there are no systematic biases. The RMSEs of the MWRI SSWS relative to the Stepped-Frequency Microwave Radiometer (SFMR) is 5.21 m/s in the wind speed range of 6–56 m/s, and the SSWS of MWRI and SFMR is generally consistent.

Fengyun-3 (FY-3) series are operational satellite missions that can provide global GNSS-R observations using multiple GNSS systems. This abstract highlights the advancements in GNSS-R product development from FY-3E, FY-3F, and FY-3G. Currently available to the public are operational Level 1 and Level 2 wind products with a 25-km resolution. Additionally, a raw intermediate frequency product is accessible for scientific research. Upcoming product developments include the release of Level 2 12.5-km wind, Level 2 land soil moisture, Level 2 sea ice thickness, and Level 3 wind. Their algorithms and scientific impact will be discussed.

Antarctic sea ice has experienced unique and complex changes in the past decades, the sea ice extent of which reaches the lowest record in February 2023. There are few studies on Antarctic sea ice classification since it is more difficult to be identified due to its characteristics of being younger and more dynamic compared to Arctic sea ice. This letter presents a classification algorithm for Antarctic sea ice based on the first-ever spaceborne dual-frequency scatterometer called WindRAD on board Fengyun-3E (FY-3E). The feature parameters are first extracted based on WindRAD orbital data. Then the$k$-means method with an optimized feature vector is used for sea ice classification retrieval. Finally, suspicious multiyear ice (MYI) is corrected based on an image dilation algorithm. The intercomparison of WindRAD Antarctic sea ice classification results with other sea ice type products shows quite good consistency not only in the spatial distribution characteristics, but also in the time series of MYI extent, verifying the capability of FY-3E WindRAD in monitoring Antarctic sea ice type.

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.

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard China’s Fengyun-4A (FY-4A) geostationary (GEO) meteorological satellite provides high-spectral-resolution infrared (IR) observations for targeted observing areas with high temporal resolution. Due to the high uncertainties in radiative transfer modeling of cloudy radiances, it is challenging to take full advantage of the thermodynamic information from GIIRS in all-sky conditions. The Advanced Geostationary Radiation Imager (AGRI) onboard the same platform provides a variety of cloud products with high spatial resolution. A bias-corrected optimal cloud-clearing (BCOCC) approach is introduced to generate GIIRS cloud-cleared radiances (CCRs) with the help of AGRI-collocated clear radiances (CLRs). The bias correction (BC) scheme is based on the inter-comparisons between GIIRS and AGRI for each field-of-view (FOV) and different scene temperatures. The BC method ensures the radiometric consistency between GIIRS and AGRI. Evaluations of GIIRS CCRs show that the mean biases are 0.09, −0.06, and 0.06 K when compared with the three AGRI IR bands, B12, B13, and B14. In addition, BCOCC significantly increases the data yields of successful CCRs by three times that of the optimal cloud-clearing (OCC) approach without BC. For 15 days from September 16–30, 2021, around 37% more GIIRS partially cloudy footprints than clear sky are cloud cleared successfully. The CCRs can be assimilated as CLRs in numerical weather prediction (NWP) models without worrying about the cloud impact. This study provides evidence of the importance of placing an advanced hyperspectral IR sounder and imager onboard the same GEO platform for better quantitative applications.

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.

Remote sensing of cloud properties based on multispectral or hyperspectral observations from satellites is important for earth radiation budget and climate change studies. Currently, most retrieval algorithms for the hyperspectral measurements are developed based on the O2-A band to derive cloud optical thickness (COT) and cloud top height (CTH) via the optimal estimation theory. Nevertheless, there are few studies on the retrieval of COT and CTH using the O4band, where the direct computation of slant column density and spectral information in the blue band provide a faster yet flexible inversion strategy. In this study, we develop a novel cloud retrieval algorithm based on neural networks using the O4band (CRANN-O4) for the simultaneous derivation of COT and CTH. CRANN-O4 employs a transfer learning strategy that combines the radiative transfer model (RTM) and multisource satellite data, for which the deep neural network module is pretrained based on the simulation data from RTM to enhance its adaptability and interpretability, following a fine-tuning scheme using multisource satellite data. To evaluate the CRANN-O4 performance, we apply CRANN-O4 to TROPOMI and make an intercomparison with its official products, which is generated based on the O2-A band. The results indicate that the CRANN-O4-derived spatial distributions of COT and CTH are generally similar to the official TROPOMI cloud product but are more consistent with the SNPP-VIIRS cloud product. The RMSEs of COT and CTH derived by CRANN-O4 are approximately 15.88 and 2.33 km, respectively, while those of the TROPOMI cloud product are 20.85 and 3.00 km, respectively. In addition, the validation of CRANN-O4-derived CTH using CALIOP measurements demonstrates better agreement than that of the TROPOMI official cloud product, with RMSE decreasing from 2.7 km to 2.2 km. The methodology presented in this study provides innovative insight into cloud parameter retrieval for hyperspectral instruments with O4channels, such as FY-3F/OMS.

The MEdium Resolution Spectral Imager (MERSI) onboard the Fengyun-3 (FY-3) series satellites can provide the long-term series data with favorable spectral and spatial resolution on the global scale since 2008. Such datasets are valuable for the studies of climate change. However, due to the lack of stable and reliable onboard calibration equipment and inconsistent in-orbit calibration methods, the MRESI historical data have poor long-term stability and unreliable accuracy, which affects the quantitative application of the data. This study reveals the overall status of the FY-3A/B/C MERSI-I historical data and proposes the recalibration methods for the reflective solar bands (RSBs) and thermal emission bands (TEBs). For the RSBs, by using FY-3A as the radiative transfer reference, an integrated transfer calibration method is developed for the calibration of FY-3B, which is then used to recalibrate FY-3C. The degradation tracking model of FY-3 MERSI-I is established first in the recalibration process by integrating multiple calibration methods. Then, based on the overlapping observations over the Libyan Desert, the linear consistency transfer model of the reference and target satellites is established, and the consistent correction coefficient between them is obtained. For the TEBs, a retrospective transfer recalibration scheme is proposed to achieve the reevaluation of the in-orbit radiometric calibration parameters based on intercalibration and to conduct the recalibration of historical data without permanent dependence on reference instruments. All the historical data of FY-A/B/C MERSI-I (from February 2008 to March 2017) are reprocessed with the same calibration method. The reprocessed datasets show remarkable improvements in calibration accuracy and stability compared with the operational datasets. The overall radiometric biases are found to be small and highly stable during the entire mission cycle of the instrument. The calibration biases of reprocessed data are less than 3% and 0.5 K for the RSBs and TEBs, respectively, much better than those of the operational datasets. There are also substantial improvements in the seasonal fluctuations and deviation discontinuities. This reprocessed long-term MERSI data with high intersensor consistency can provide valuable insights into global climate monitoring and model assessment.

Precise spatial distribution of latent heat released during precipitation formation is crucial to enhance weather forecasting and climate prediction accuracy. This study introduces an innovative convolutional neural network heating (CNNH) algorithm. The algorithm incorporates the vertical gradient of precipitation rate and air temperature at different altitudes as key inputs. It combines additional information from adjacent vertical layers and neighboring horizontal areas. To mitigate possible misjudgments of negative heating, a punishing mechanism was introduced with a latent heating (LH)-structure loss function within the optimization framework. By employing the adaptive differential evolution (ADE) algorithm, the optimal configuration of the network structure optimizes accurate LH retrieval. To evaluate the CNNH algorithm’s efficacy, a self-consistency check using weather research and forecasting (WRF) model simulation data was conducted. Furthermore, an inter-comparison study with four other algorithms using real satellite observations was undertaken. Evaluations found that the CNNH algorithm could precisely retrieve the primary characteristics of the horizontal and vertical structure, along with the temporal evolution process and statistical information of WRF simulated LH, in eastern China in August 2017. It effectively addressed the issues of overestimation of near-surface cooling and mixing layer heating, thereby outperforming selected artificial intelligence (AI) and physics-based LH algorithms. The intercomparison study between LHCNNH with four other published LH products based on the same GPM observations reveals that CNNH got comparable performance among them. However, specific differences among these algorithms highlight considerable uncertainties in multiple LH satellite remote sensing products and underscore the necessity for further improvements in satellite LH algorithms.

Accurate cloud detection via satellites is important for cloud radiative forcing estimation and disaster weather monitoring. Current polar-orbiting satellite cloud observation are limited during early morning orbit and contain notable uncertainty due to dimness measurements in visible bands. FY-3E\MERSI-LL is the first early morning orbit satellite worldwide and can realize global cloud observation under early morning scenarios. In this study, a dynamic threshold cloud detection algorithm is proposed based on the FY-3E\MERSI-LL infrared channel, combined with auxiliary data such as sea surface temperature, land surface temperature, snow cover mask and terrain elevation. The algorithm can detect clouds against complex land surface background, but faces classification difficulties over some plateau, high-latitude and snow surface regions, especially during early morning observation periods. Compared to coincident Himawari-8 and GOES-16 cloud measurements in the Eastern and Western Hemispheres, respectively, our algorithm recognizes reasonable cloud distributions. Furthermore, Himawari-8 and GOES-16 cloud products are used for quantitative cloud algorithm evaluation. The results show that at low-middle latitudes (60°N-60°S), the average cloud and clear hit rates during the various seasons are 73.24% and 76.46%, respectively, the cloud leakage and false alarm rates are 14.46% and 8.15%, respectively, and the total accuracy (cloud and clear) is 77.33%. The algorithm performance is better over the ocean than over land. Ground site MPLCMASK products are also used to verify the FY-3E cloud results in middle- and high-latitude areas. This algorithm provides a cloud detection reference during early morning orbit based on infrared channels.

A hybrid algorithm based on radiative transfer simulations and machine learning for dust aerosol detection, is developed for the Advanced Himawari Imager (AHI) carried by the geostationary satellite Himawari-8. The sensitivities of the AHI thermal infrared (TIR) channels for dust aerosols are analyzed through radiative transfer simulations. The sensitivity study demonstrates that the simulated clear-sky brightness temperatures (BTs) show an obvious improvement in identifying dust aerosols compared to brightness temperature difference techniques, especially optically thin dust. Therefore, the simulated clear-sky BTs and AHI TIR observed BTs, in addition to ground information, are used as inputs to add physical knowledge in the machine learning model. The performance of an artificial neural network constructed for dust aerosol detection is evaluated by comparing its results with those of active Cloud-Aerosol Lidar with Orthogonal Polarization measurements. The proposed algorithm effectively achieves dust aerosol detection during both daytime and nighttime, with a precision of over 86% and a recall of over 85% on an independent testing dataset. The proposed algorithm is applied to three typical dust events to further illustrate its applicability. Although some thin dust aerosols near the ground are misclassified due to weak signals, most dust aerosols are successfully detected, and the identification is generally not affected by other types of aerosols. The results of the regional classification demonstrate that our algorithm is superior in detecting tenuous dust aerosols compared to Dust RGB images using the AHI TIR channels and physical-based algorithm.

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.

Aerosols play an important role in global climate change, which requires long-term data records. Advanced very high-resolution radiometer (AVHRR) provides continuous observations for up to 40 years since 1979, which makes it worthwhile to retrieve aerosol optical depth (AOD) from AVHRR over land. A novel algorithm for retrieving AOD from AVHRR is developed based on the machine learning (ML) method. The AVHRR observations from pathfinder atmospheres–extended (PATMOS-x) Level-2 dataset and corresponding AOD products ($0.55~\mu \mathrm {m}$) from moderate resolution imaging spectroradiometer (MODIS) in 2014 are used as training data. And AOD products in three years (2015, 2006, and 1998) named AVHRR XGB-AOD were generated for evaluation. Comparisons show that the AVHRR XGB-AOD is consistent with the MODIS AOD with correlation coefficients greater than 0.80 and RMSE less than 0.18 for most months in 2015 and 2006. The temporal and spatial characteristics from AVHRR XGB-AOD are similar to those from the MODIS AOD, but those from the previous AVHRR AOD with deep blue (DB) algorithm are significantly different. Validation with AERONET indicates that more than 68% of the matchups fall within expected error [EE, ±($0.05\,\,\pm \,\,0.25\times {\mathrm {AOD}}_{\mathrm {AERONET}}\mathrm {)] }$in 2015 and 2006, while the fraction is 66% in 1998. Compared to the DB algorithm, the ML-based algorithm performs better in high-AOD conditions over vegetated regions, such as in Southeast Asia, where the DB algorithm significantly underestimates. In low-AOD conditions, the ML-based algorithm performs better over western North America and Australia, where the aerosol composition varies greatly.

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 (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 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 double difference 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 double difference method are consistent with those derived from individual window channel method, with the discrepancy within 0.4%. An ocean-land calibration difference of 1.5% is found between EMI and TROPOMI at the UVIS band. Inter-calibration using the double difference 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 double difference method. The double difference method is recommended for radiometric comparison for UVIS hyperspectral spectrometers.

The spectral response function (SRF) is a crucial parameter in multispectral radiometers, and it influences the radiometric calibration accuracy and quantitative application capabilities. The in-flight SRF often has errors due to prelaunch contamination or postlaunch degradation. This study proposes an innovative new method to retrieve SRFs of multispectral radiometers based on intercomparisons with hyperspectral sounders via the functional data analysis (FDA) technique. Under the FDA framework, all variables, including the hyperspectral radiance and SRF, are regarded as functions rather than discrete data by expanding in the Fourier functional basis. The forward convolution equation is processed directly into a functional integration model rather than a normally pointwise summation; this ensures that the unknown quantities are transformed from numerous SRF samples to several function parameters, thus avoiding the ill-posed problem. The proposed algorithm is verified with both simulated and real data from multiple thermal infrared bands of the FY-3 IRAS and FY-4 AGRI using collocations with METOP-B IASI. All these results demonstrate our algorithm’s qualitative and quantitative effectiveness for infrared SRF retrieval. Although the demonstrations are particularly relevant to infrared spectra, the algorithm is universal and also applicable to other spectral bands.

A long-term historical aerosol optical depth (AOD) data set from 1982 to 2011 over China (15-45° N; 75-135° E) with 0.1 spatial resolution has been produced from Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres-Extended level-2B data. The spatial distribution pattern shows that high AOD values are found in central and eastern China over the entire period with AODs larger in summer and spring than in autumn and winter. As the high-quality products from AERONET were absent for this period over mainland China, AOD data obtained using the broadband extinction method from solar radiation stations have been used to verify the quality of the AVHRR AOD data set over China. The intercomparison results show that the interannual variation of AOD has been well captured in the variation curve of the AOD monthly mean and the variation trend is also consistent over the whole period. The correlation coefficient of the monthly mean is mostly larger than 0.55, the agreement index is larger than 0.57, and the relative error is less than 21%. Both AVHRR and visibility data sets show high values in regions with rapid economic development. Using Moderate Resolution Imaging Spectroradiometer AOD data as references, it is found that AVHRR AOD from this paper has better accuracy in general than that from Deep Blue (DB) algorithm over China, especially over eastern and southern China, while DB provides more coverage especially over bright surface such as northwest China. This long-term historic AOD data set can be used together with other AOD data sets to study the climate and environmental changes, especially in the 1980s and 1990s.

The advanced MEdium Resolution Spectral Imager (MERSI II) is a major instrument onboard the Chinese FengYun 3D satellite, which was launched in November 2017. Extensive measurements were performed during MERSI II prelaunch testing to ensure effective characterization for on-orbit calibration. This paper gives a brief overview of the prelaunch performance testing conducted for MERSI II, as well as its improvements in terms of instrument design compared to MERSI I. The prelaunch calibration methodology and radiometric performance are detailed, including dynamic range, signal-to-noise ratio, noise equivalent differential temperature, linearity, and response uniformity. The assessment results indicate that most bands perform effectively with mirror specification noncompliances in a few reflective solar bands (RSBs). In addition, investigation of the stability and uniformity of the spherical integrating source indicates that they have a critical impact on the performance assessment and prelaunch calibration of RSBs. The temperature-dependence features of thermal emissive bands' performances are also discussed in terms of their sensitivity to the operating temperature of the focal plane assembly and instrument circumstance. This paper also shows that the self-stability of the calibration source and the representation of the assessment methods are important as they affect the results of instrument performance evaluation.

So far, the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) are the only two missions designed to measure the column-averaged CO2dry air mole fraction (XCO2). To improve our understanding of global carbon source and sink, these two XCO2products are compared in this study. The result reveals that the OCO-2 XCO2product show the wider spatial coverage than those of GOSAT from 30°S ∼ 90°N latitude. At the same time, GOSAT and OCO-2 XCO2products shows a good agreement with correlation coefficient (R2) of 0.69 and bias of −1.0 ppm. However, the discrepancy is still existed in some region. The discrepancy between these two products implies that it is necessary to make them complement each other to better improve our knowledge of global carbon cycle or even climate change.

Infrared Difference Dust Index (IDDI) is often used as a satellite dust product to detect the change of mineral dust aerosols in the atmosphere. And aerosol optical depth (AOD) is also a main measurement for mineral dust aerosol. To qualify dust loading on the regional or global scale, it is very necessary to understand the relation between IDDI and AOD. Therefore, this study investigates the impact of sensitivity factors including surface temperature and surface type to the IDDI by using MODTRAN (Moderate Resolution Transmittance code) model to better evaluate the relation between IDDI and AOD. The result shows that the simulated IDDI from MODTRAN are extremely sensitive to the surface temperature and surface type. The IDDI is growing with the increased surface temperature. And the sensitivity of farm and forest type to IDDI is similar and their difference is very small. But the sensitivity of desert type and ocean to IDDI is obviously different from other two types and the surface type is also a key parameter to IDDI. The result also implies that there is an exponential relationship between IDDI and AOD. These results will be very helpful to further establish the relation between IDDI and AOD.

As Advanced Very High Resolution Radiometer (AVHRR) lacks a 2.1-μm band, the widely used “dark target” algorithm cannot be used to retrieve aerosol optical depth (AOD) from AVHRR over land. Instead, a multiple regression algorithm has been developed to process a time series of AVHRR Level_1b measurements over China (15°-45° N, 75°-135° E) for AOD retrieval. As the apparent reflectance of AVHRR is closely related to AOD, which can be provided by Moderate Resolution Imaging Spectroradiometer (MODIS), spatially and temporally collocated Aqua/MODIS AOD and AVHRR Level_1b measurements from four years (January 2008-December 2011) were used to generate the regression coefficients. Angle information, normalized difference vegetation index, water vapor, and surface elevation are chosen in addition to the apparent reflectance as predictors for different surface types. By applying the regression coefficients to AVHRR, the AOD product from independent AVHRR Level_1b measurements (May 2003-December 2007) was generated. Validation with AErosol RObotic NETwork (AERONET) AOD and comparison with MODIS AOD products have been conducted to evaluate the uncertainty of the AVHRR AOD from May 2003 to December 2007. The distribution pattern of the seasonal mean AOD from AVHRR is consistent with that of the MODIS AOD. Taking regions with rapid economic development, for example, the regional monthly mean AOD for these two data sets agrees well, with a consistent tendency and high correlation coefficients. When compared with AERONET from four sites in China, the results are also encouraging. Results show that the multiple regression method offers the potential to generate an AOD climatology data record from a long-term AVHRR Level_1b data set over land.

Hyperspectral radiances from the Infrared Atmospheric Sounding Interferometer (IASI) and Atmospheric Infrared Sounder (AIRS) are used as a reference to improve the calibration accuracy for FengYun-2 (FY-2) infrared (IR) channel radiances. It is shown that the previous FY-2 operational calibration for IR bands produces significant bias in brightness temperatures that can exceed 1.1 K. In particular, the FY-2 IR3 band (6.7 μm) has the largest bias of 2.0 K. The daytime double-difference temperature (DDT) between AIRS and IASI using FY-2 imagers as a transfer medium showed an excellent consistency, is within 0.2 K at 290 K, and is stable over time for FY-2C/2D/2E. This only indicates the robust calibrations applied for both the AIRS and IASI measurements. During the nighttime of the Earth observation, stray light in space affects the long-term stability of the FY-2 DDT, particularly for the Earth scene at 220 K. FY-2E satellite which was launched in 2009 has an instrument design improvement. Intercalibrating FY-2 four times using AIRS and IASI data can reveal the diurnal features of the FY-2 instrument calibration. The temporal DDT appears very large during the spring and autumn eclipse times. Not only can the global-space-based-intercalibration-system intercalibration method provide an excellent operational calibration for the FY-2 imager, but it can also help improve the design of future instruments and onboard blackbody calibration.

Focusing on Asian dust aerosols, the Community Radiative Transfer Model (CRTM) developed at JCSDA under NOAA/NESDIS is used to simulate the effects of dust on the observations from 10-12μm split-window channels of the Visible and InfraRed Radiometor(VIRR) on Chinese FengYun-3A (FY-3A) satellite. Based on the simulation, an infrared dust retrieving algorithm is developed with VIRR data, in which the effective radius of Asian dust is defined with the ground measurements from SKYNET. The optical thickness and height of the dust layer are retrieved simultaneously with this algorithm. The results show the optical thickness of dust layer is reliable comparing with the height. The errors in the calibration of sensors and surface temperature may have large effect on the retrieving.

The Medium Resolution Spectral Imager (MERSI) is a key instrument onboard Fengyun-3 (FY-3), the second generation of polar-orbiting meteorological satellites in China. This paper summarizes the knowledge of MERSI instrument in terms of sensor design, calibration algorithm, prelaunch and on-orbit characterization, and performance verification. The calibration monitoring of its reflective solar bands (RSBs) is primarily conducted using a visible onboard calibrator and found that it has a significant degradation on the order of 10% in its shorter RSB bands (<; 500 nm), with the largest in band 8 of about 20% during the past two years. However, the performance at longer wavelength bands is relatively stable with a change of less than 5%. It is shown that the postlaunch calibration of the two short-wavelength infrared bands has frequent fluctuations because of random jumps in their electronic gains. These results are consistently verified by two kinds of vicarious calibration (VC) methods: China Radiometric Calibration Sites VC and intercalibration using Terra/Moderate Resolution Imaging Spectroradiometer over Dunhuang desert. The overall uncertainty in the MERSI top-of-atmosphere radiance or reflectance is less than 5%. These results provide the important reference and evaluation for the update of the FY-3A/MERSI calibration coefficients.