Data gaps exist in the measured spectral reflectance and atmospheric data from the Radiometric Calibration Network (RadCalNet) due to instrument malfunctions or weather-related interferences, which severely impedes the application of the data. Therefore, developing a method to fill these missing RadCalNet data is a pressing issue. This study focuses on four RadCalNet sites with distinct surface types and proposes a high-precision Bottom-of-Atmosphere (BOA) spectral reflectance model. With on-site atmospheric data from RadCalNet, the predicted results achieve a Root Mean Square Error (RMSE) of no more than 1.26%. In scenarios where in-situ atmospheric conditions are completely missing, the ERA5 dataset is used as a substitute and validated with Landsat 8 surface reflectance products; the absolute errors for all sites did not exceed 4.58%, validating the proposed method’s effectiveness. Additionally, the importance of input parameters and the impact of their uncertainties on prediction accuracy are discussed.

Near real-time precipitation estimation from geostationary satellites plays an important role in flood forecasting, water resource management, and disaster prevention and reduction. Currently, near real-time precipitation products based on geostationary satellites still face great challenges in accurately detecting precipitation and monitoring small-scale precipitation. In this study, a novel Geostationary Satellite Precipitation Estimation (GSPE) algorithm for near real-time condition was developed to retrieve precipitation at a spatial resolution of 0.05°×0.05° every 10 minutes both day and night. The major highlight of the GSPE is that a new precipitation detection scheme was created by introducing a newly proposed precipitation detection index (PDI) and 24-hour continuous cloud microphysical parameters for the first time. Another highlight is that a dynamic updating scheme was proposed in building conversion models between brightness temperature of geostationary satellite and precipitation to keep the accuracy and stability of the estimated precipitation. Furthermore, the 10-minute temporal resolution of the estimated precipitation could accurately capture the evolution of a short precipitation process and improve the calculation of total precipitation amount. According to the validation using rain gauges observations from Chinese mainland, the Heidke Skill Score of the GSPE in hourly scale could reach up to 0.39 which was improved by 11.43% compared to the GSMaP_NOW. The root mean square error of precipitation from the GSPE in hourly, daily, and monthly scale are 1.66mm, 13.65mm, and 97.76mm respectively, and were improved by 15.74%, 13.17%, and 21.01% respectively compared to that of the GSMaP_NOW.

The Medium Resolution Spectral Imager-III (MERSI-III), the third generation of the MERSI series, began observations from the Feng Yun-3F (FY-3F) in a sun-synchronous morning orbit on August 3, 2023. MERSI-III collects data from 25 spectral bands, including 19 reflective solar bands (RSBs) and 6 thermal emissive bands with wavelengths ranging from 0.4 to 2.15 μm and 3.7 to 12.5 μm, respectively. Compared with its predecessors, MERSI-III features an enhanced on-orbit calibration capability for RSBs thanks to the incorporation of a newly designed visible on-board calibrator (VOC). This VOC is a solar diffuser (SD)-based on-board calibration system consisting of an SD panel, a solar attenuation plate positioned in front of the SD panel, and an on-board SD degradation monitor. We provide an overview of the MERSI-III instrument, its VOC functions, and the corresponding on-orbit calibration methodologies. The performance of the on-orbit calibration is also assessed using a wide dynamic-integrated vicarious calibration (VC) technique, which combines multiple VC methods, including inter-comparison using invariant pixels, absolute calibration with pseudo-invariant calibration sites, and Rayleigh scattering calibration. Based on three months of validation results, the on-orbit calibration demonstrates strong performance in both absolute accuracy and radiometric stability. Among the 14 validated RSBs, 13 bands meet the basic calibration accuracy requirement of a calibration bias of less than 5%, while 7 bands meet the desired requirement of a calibration bias of less than 3%.

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.

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.

The geostationary interferometric infrared sounder (GIIRS) onboard the Fengyun-4B (FY-4B) is the first operational geostationary hyperspectral infrared (IR) sounder. This study analyzes the first-year FY-4B/GIIRS on-orbit calibration performance by comparing it to the collocated IR atmospheric sounder interferometer (IASI) observations and radiative transfer (RT) simulations. The results reveal that the mid-wave IR (MWIR) channels had a slightly larger calibration bias compared to the long-wave IR (LWIR) channels. However, the operational FY-4B/GIIRS showed improved performance compared to the experimental FY-4A/GIIRS. Furthermore, this study also found that most channels exhibited negligible annual and weak diurnal variations in calibration bias. However, there was a significant degradation in the LWIR channels (<850 cm1) and the weak diurnal variation in the MWIR channels. Finally, the calibration performance of FY-4B/GIIRS demonstrates a reduced dependence on brightness temperature (BT), except for the channel at wavenumber 703.125 cm1. Overall, it concluded that FY-4B/GIIRS demonstrated consistent calibration stability and high accuracy, highlighting its capability for precise quantitative applications.

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.

The Chinese early-morning-orbiting Fengyun-3E (FY-3E) satellite fills the 6-h initial observation window for data assimilation in numerical weather prediction (NWP). The low-light band (LLB) on the medium-resolution spectral imager low light (MERSI-LL) of FY-3E can detect extremely low radiances at night, significantly enhancing nighttime observation capabilities as well as elevating data assimilation quality by improving the nighttime cloud mask algorithm. However, severe and nonlinear stray light contamination affects most nocturnal FY-3E/MERSI-LL LLB images, particularly those from the Southern Hemisphere, hindering further visualization applications. The analysis concluded that the stray light is closely associated with the refraction and reflection of sunlight entering the MERSI-LL, solar zenith angle (SZA), and detector number. To obtain clear and enhanced images, this study designed a fully automated and adaptive stray light correction and enhancement algorithm for the nocturnal low-light images of FY-3E/MERSI-LL. Three typical stray-light-contaminated scenarios were categorized for all nighttime images. The restored results showed that after processing, the “fog” stray light and stripes were essentially removed, and the details became richer and more prominent, significantly improving the visual effect and usability of the images. This algorithm is simple, efficient, and highly applicable, and will be integrated into the processing system of the FY-3E satellite to support near real-time applications of LLB images. However, some strong or unusual stray light still affects the local continuity of the images. Future low-light imagers of FY-3 satellites will feature more sophisticated instruments to reduce incident stray light in their optical system.

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.

This study presents a novel approach to the radiometric inter-calibration between two sensors onboard the same satellite based on pseudo-invariant pixels (PIPs) using iteratively re-weighted multivariate alteration detection (IR-MAD) method. The IR-MAD algorithm can statistically select pseudo-invariant pixels from the multispectral image pair to assess the radiometric differences between them. Analysis of multiple image pairs from different acquisition times can provide long-term inter-calibration results of the two sensors. The procedure is applied to Fengyun(FY)-3A&3B Visible Infrared Radiometer (VIRR), with the Medium Resolution Spectral Imager (MERSI) onboard the same platform as the reference. Consistency of the spatial distribution of the PIPs selected by IR-MAD with pseudo-invariant calibration sites (PICS) given by other scientists demonstrates the effectiveness of our method. The long-term time series trending of top-of-atmosphere VIRR reflectance over LIBYA1 and LIBYA4 after inter-calibration correction shows that the inter-calibrated VIRR has good agreement with MERSI, with a mean bias of less than 1% and an uncertainty of less than 2% for most channels. The approach requires no prior knowledge of the inter-calibration targets and extends PICS to the pixel-level targets, which results in more diverse samples, broader dynamic ranges and lower uncertainty, yielding consistent and reliable long-term inter-calibration results.

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 short-wave infrared Polarization and Multi-Angle Imager (PMAI) onboard Fengyun-3 precipitation satellite is a new spaceborne imaging polarimeter for clouds and aerosols, with polarization channels of 1030, 1370, and 1640 nm. This study presents a detailed description and assessment of the calibration model of PMAI. For radiometric intensity calibration, multiple parameters in the radiometric model are fitted into a single coefficient to simplify calibration. Results show that the radiometric calibration uncertainty of the full image plane is better than 0.02, and the calibration coefficient increases as field of view increases. The maximal unsaturated incident radiance of all channels is equivalent to 100% albedo, and signal-to-noise ratio at the referenced radiance is greater than 115 and 182 for the polarized and unpolarized channels, respectively. The response of all channels shows high linearity and good uniformity of the full image plane. Based on results of intensity calibration, a polarization calibration model using a fully linear polarized light source is introduced with a polarization measurement matrix established by a simplified method and a calculation method. Assessment of polarization measurement indicates that the uncertainties of the obtained degree of linear polarization (DoLP) and angle of linear polarization (AoLP) based on the two methods are highly consistent. When fully linearly polarized light is incident, the measurement error of DoLP using the simplified polarization measurement matrix is within 0.02 and that of AoLP is less than 1°. Therefore, the simplified radiometric intensity and polarization calibration model meets the measurement accuracy requirements and improves the calibration efficiency.

Atmospheric motion vectors (AMVs) from satellite measurements serve as critical indicators of atmospheric dynamics, playing an essential role in enhancing the prediction precision of numerical weather prediction (NWP) models through data assimilation (DA). The implementation of finer satellite-derived vector products has the potential to significantly augment the accuracy of atmospheric flow field data in high-resolution regional NWP model simulations, thereby fulfilling the burgeoning requirements of operational weather nowcasting and forecasting. This study is focused on the development of mesoscale AMV (MAMV) products, which are distinguished by their exceptional quality and spatiotemporal resolution, leveraging data from the geostationary high-speed imager aboard the Fengyun-4B geostationary meteorological satellite (FY-4B/GHI). MAMVs of FY-4B/GHI feature an enhanced horizontal resolution of 3 km, enabling more accurate identification and monitoring of nongeostrophic flow patterns of mesoscale weather systems, as well as their fast-evolving dynamical structures and characteristics. Furthermore, a comparative analysis with radiosonde measurements highlights the precision of MAMV products, as evidenced by a speed bias (SB) of 0.37 m/s, a speed root mean square error (sRMSE) of 4.68 m/s, and a direction root mean square error (dRMSE) of 26.35°. The prospects of high-resolution satellite wind field data hold great potential for propelling scientific advancement and enriching our comprehension of atmospheric dynamics. This is particularly valuable in the context of typhoon monitoring and forecasting, where such data can lead to significant improvements in predictive capabilities.

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.

Surface albedo plays a significant role in Earth’s energy budget and global climate change. The spaceborne remote sensing technique is efficient for deriving and monitoring long-term surface albedo over large regions. Numerous satellite surface albedo products have been established and used for climate change research. However, the surface daily mean albedo is not regularly produced from satellite observations, even though it is more critical than instantaneous albedo for calculating daily shortwave radiation budget. Compared to polar-orbiting satellites, the geostationary satellites offer greater potential for mapping daily albedo with more diurnal observations. This study proposes an innovative multisensor combined direct estimation algorithm, which takes advantage of multiple clear-sky observations from new-generation geostationary satellite sensors FengYun-4 Advanced Geostationary Radiation Imager (AGRI) and Himawari-8 Advanced Himawari Imager (AHI) taken during the same day to improve the surface albedo estimation. Compared to albedo estimates derived from a single sensor, the root mean squared error (RMSE) decreases from 0.030 to 0.022 at OzFlux sites and from 0.040 to 0.031 at Heihe sites. Moreover, an information index of top-of-atmosphere (II_TOA) reflectance is proposed to quantify the amount of information that multiangular TOA observations carry in the surface albedo estimation. As a result, combining observations from two sensors increases such information by 20%, as compared to observations from a single sensor. This study demonstrates the combined multisensor direct estimation method has significant potential for improving surface albedo estimation.

Reflected global navigation satellite system (GNSS) signals from Earth surface can be received by receivers at low Earth orbit for the remote sensing of geophysical parameters. While the technique has been studied for around 30 years, most early spaceborne GNSS reflectometry missions only adapted to receive GPS signals and the studies of reflected Galileo (GAL) signals in space are limited. The Navigation Satellite System Occultation Sounder II (GNOS-II) payload onboard the FY-3E satellite is the first mission that can operationally receive reflected GPS, BeiDou (BDS), and GAL signals at the same time. This letter presents the GAL reflectometry measurements from GNOS-II together with their calibration and wind speed (WS) retrieval methods. Results show that while GAL has a different signal modulation, the observables can be used to retrieve WSs using the same geophysical model functions (GMFs) of GPS after a dedicated calibration. The retrieved WSs from GAL also have a comparable accuracy as those from GPS and BDS.

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.

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.

Knowing how much measurement noise is in a signal is critical for evaluating the overall performance of a satellite observation. We developed a triple collocation observation (TCO) algorithm for estimating measurement noise by collocation comparing the local deviations of three satellite data sets. When we evaluated our algorithm with a synthetic data set, the results showed that the algorithm effectively derived measurement noise from satellite signals despite the many intermission signal differences among the satellites. The TCO algorithm produced <6.66% uncertainty in the measurement noise estimates that we derived from the synthetic data set. In addition, to maximally isolate measurement noise from ocean color images, we developed a set of data quality control criteria to apply when identifying synchronous pixel pairs. Using images from the Medium Resolution Spectral Imager II (MERSI II), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, we applied our data quality control criteria and found that the TCO algorithm produced measurement noise consistent with the measured prelaunch or specifications for VIIRS and MERSI II instrument noise. However, the TCO measurement noise was significantly lower than the spaced MODIS noise because MODIS’s extended service time likely produced instrument degradation. Overall, MODIS performed better than MERSI II but worse than VIIRS. Furthermore, we found that the residual error in remote sensing reflectance exponentially decreased as the measurement signal-to-noise ratio (MSNR) increased. Because of this exponential relationship, the MSNR should not be lower than 181 to achieve the <5% uncertainty goal of remote sensing reflectance at 443 nm that NASA proposed. Our results suggest that the TCO algorithm is an effective approach for comprehensively estimating and comparing instrument performance.

The quality of thermal infrared (TIR) radiances from the FY-4A advanced geostationary radiation imager (AGRI) and their derived products is very important for weather predictions. The demand for more accurate weather forecasting models is increasing, so it is necessary to evaluate and improve the calibration accuracy of AGRI. This study aims to evaluate the AGRI TIR radiometric calibration accuracy by comparing two well-calibrated hyperspectral radiometers, the cross-track infrared sounder (CrIS), and the infrared atmospheric sounding interferometer (IASI). Most of the AGRI TIR channels are calibrated with an absolute brightness temperature (Tb) bias less than 0.5 K currently. The Tb bias of AGRI and CrIS\IASI depends on the target temperature, and it is smaller and more stable for higher Tb and increases for lower Tb. CH_14 shows the largest Tb bias (−0.53 K) and standard deviation (0.64 K), CH_10 and CH_11 record slightly positive Tb biases, and the other four channels exhibit negative Tb biases. The diurnal mean Tb bias is very consistent except for CH_11, which varies greatly among different time bins. The Tb bias of CH_09 is very consistent after the calibration update in February 2018. CH_10 shows a gradual bias decrease with bias from 1.0 to 0.6 K. CH_11 shows a gradual bias decrease with bias from 0.4 to 0.1 K with large fluctuations. CH_14 shows a gradual bias decrease with large fluctuation and like CH_11. CH_12 and CH_13 exhibit seasonal variations where the Tb bias reaches its maximum around June and minimum around February. In general, the Tb bias shows different spatial patterns in different channels and CH_11 shows obvious spatial distribution characteristics that is monthly dependent.

This study mainly focuses on the diurnal and long-term variation characteristics of the calibration performance of the geostationary (GEO) meteorological satellite Fengyun-4A/ Advanced Geostationary Radiation Imager (FY-4A/AGRI) infrared (IR) channels from June 1, 2017 to December 31, 2020. An improved algorithm is developed in this research based on thein situobservations from ocean drifters, which can capture the diurnal variations of the calibration performance of FY-4A/AGRI IR channels. The results suggest that there are significant and nonnegligible diurnal variations of the uncertainties and biases for the brightness temperature (TB) observed by the FY-4A/AGRI, especially the IR channels at 3.71, 8.61, 10.83, and$12.07 \mu \text{m}$. Among them, the calibration performance from 16:00 to 18:00 UTC (around the time of local midnight at the subsatellite point of FY-4A) is worse at$3.71 \mu \text{m}$, while TB biases from 04:00 to 16:00 UTC are relatively large at$12.07 \mu \text{m}$. Moreover, after the operational calibration and update in June 2020, the long-term TB biases at channel 08 ($3.71 \mu \text{m}$) obviously decrease from about 5–2.5 K, and the TB biases at channel 14 ($13.54 \mu \text{m}$) increase from about 0.5–3 K, implying a possible positive or negative impact of the calibration update on the calibration performance of IR channels. Overall, this method based on thein situdrifter data can well monitor the on-orbit calibration performance of GEO satellite imaging sensor’s IR channels such as the FY-4A/AGRI, allowing an objective assessment on the effect of calibration update events.

Under the assumption that clouds are homogeneous and single-layered (SL), most current operational cloud top height (CTH) products derived from passive radiometers may largely underestimate the CTH of overlapping clouds. This article proposes a statistics-based extrapolation algorithm for retrieving the CTHs of overlapping clouds using only existing cloud property products available for most operational radiometers, and the method is successfully employed for the advanced himawari imager (AHI) observations. Because regional clouds within the same “system” have relatively continuous geometric properties, especially CTH, due to similar atmospheric conditions, upper-layer ice cloud CTHs (ITHs) and lower-layer water cloud CTHs (WTHs) are inferred using the CTH retrievals of well-chosen neighboring SL ice and water clouds, respectively. The proposed algorithm uses the latest machine-learning-based model to reasonably distinguish overlapping clouds from SL clouds, and optimizes the extrapolation by considering three physical constraints on neighboring, cloud phase, and cloud optical thickness (COT). Validated using active observations from CloudSat and cloud-aerosol Lidar and infrared pathfinder satellite observation (CALIPSO), our algorithm improves the AHI CTH mean bias for overlapping clouds from −5.1 to −2.6 km. More importantly, the algorithm provides CTH information of underlying water clouds that are unavailable from existing radiometer-based products. With the simultaneous retrieval of ITH and WTH, this algorithm increases our capability to detect the vertical structures of overlapping clouds and better evaluate the cloud radiative effects (CREs).

Cloud top properties (CTPs) are important satellite products. However, the failures of CTPs derived from the Advanced Geostationary Radiation Imager of FengYun-4A (FY-4A/AGRI) have been occasionally reported by users. To this end, the feasibility of the operational CTP algorithm has been reviewed. First, this study reveals the slight differences in brightness temperature (BT) maps of thermal infrared (TIR) bands between two different imagers. Further analyses found that the nonretrieved pixels of CTP products from FY-4A/AGRI usually have a negative value down to −13 K in 10.8–13.5-$\mu \text{m}$BT difference (BTD$_{10.8-13.5\,\mu \text {m}}$), and the joint distribution related to BTD$_{10.8-13.5\,\mu \text {m}}$and BTD$_{10.8-12\,\mu \text {m}}$is well separated from those successfully retrieved ones. These findings are confirmed by the simulations of the radiative transfer forward model (approaching −12 K or lower) and the cross-validation between the products from AGRI/FY-4A and the Infrared Atmospheric Sounding Interferometer of Meteorological Operational Satellite Program-Satellite B. In essence, the bias at the 13.5-$\mu \text{m}$band is mainly affected by the relatively low accuracy and stability at the low-temperature end. Based on these findings, we have proposed a novel method to estimate calibration-related measurement biases of TIR bands and track their in-orbit stability. The statistical study based on the FY-4A/AGRI observations reveals significant daily and diurnal variations in performance and provides an insight into its stability at the TIR band ($13.5~\mu \text{m}$).

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.

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.

The Medium Resolution Spectral Imager (MERSI) was installed as a key payload on the FengYun-3C (FY-3C) polar-orbit meteorological satellite, which was successfully launched on September 23, 2013. We used 14 months of continuous FY-3C/MERSI Level-1B data (from November 2013 to December 2014) to evaluate the on-orbit image spatial quality. Based on a polar ice block image, a sharp target modulation transfer function (MTF) estimation method is used to quantitatively estimate the MTF value at the Nyquist frequency, which is an index for the spatial quality. The results show very good stability in the first year and a relatively lower spatial quality (MTF approximately 0.15) along the FY-3C/MERSI scan direction. This lower spatial quality is primarily attributed to the known and fixed 27% overlapped scan mode of MERSI, which can significantly reduce the image contrast. By using this fixed overlapped proportion (27%), we develop a fast and robust image restoration algorithm based on the Gaussian elimination (GE) method with lower and upper triangular matrix decomposition (LU). The speed-up ratio of this GE with LU decomposition method can attain a value of 626.30 compared with the traditional GE method when it solves linear equations with 2048 MERSI scan pixels. After the image restoration process, significant enhancement in the image spatial quality along the scan direction for every band of FY-3C/MERSI can be found with an increased MTF value of approximately 0.30. However, we evaluate the possible effect of this restoration algorithm on the original digital number (DN) and reflectance values. We find a slight decrease in the total averaged DN (0.5) and reflectance (<; 0.5%, relative bias) values. The variation in DN or reflectance after the image restoration process exhibits a positive correlation with homogeneity of the original target. Moreover, a sensitivity study on the reflectance reveals that it has a more significant impact on the inhomogeneous pixel with a low DN value.

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.

The MEdium Resolution Spectral Imager (MERSI) is the keystone instrument onboard FengYun-3 (FY-3). After FY-3A, FY-3B MERSI is the second launched in November 2010. Nineteen of the 20 MERSI spectral bands are the reflective solar bands, which cannot be absolutely calibrated onboard. The annual vicarious calibration (VC) based on synchronous in situ measurements at the Dunhuang site is the baseline calibration method for MERSI. To assure frequent and stable calibration updates, a multisite calibration tracking method is developed. This paper presents the FY-3B MERSI postlaunch daily calibration updating method based on multisite calibration tracking with the Dunhuang VC correction, the long-term sensor response on-orbit change, and the calibration performance evaluation. A reflectance-based method is used for the Dunhuang VC, and the reflectance calibration uncertainties are within 3% for most MERSI bands. The multisite calibration tracking method relies on simulated radiation over several stable sites without synchronous in situ measurements. A postlaunch daily calibration updating model is established using a linear function of days since launch to describe the long-term trend. The calibration updating model is validated by the Dunhuang VC, showing the relative bias within 3.5% for most bands. It is found that the shortwave channels of MERSI experience large degradation, particularly the 412-nm band with an annual degradation rate of approximately 18%, whereas most red and near-infrared bands are relatively stable. Using the calibration updating model with the Dunhuang VC correction, the recalibrated MERSI data are validated against Moderate Resolution Imaging Spectroradiometer by near synchronous-nadir-observation analysis, and good agreement is achieved.

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.

The MEdium-Resolution Spectral Imager (MERSI), onboard the second-generation Chinese polar-orbit meteorological satellite FY-3A, is a MODIS-like sensor with 19 solar bands and one thermal infrared band. Although there is a visible onboard calibration device, it can only be used for tracking temporal instrument degradation. The vicarious calibration (VC) campaign at the Dunhuang site, conducted once a year, has been the main postlaunch absolute radiometric calibration method for MERSI in the solar bands. To increase the in-flight calibration frequency, a multisite radiometric calibration tracking method is presented. This method relies on simulated radiation over several stable sites, and a daily calibration updating model is built from long-term trending of calibration coefficient series. The MERSI calibration reference is evaluated against the observations of Aqua MODIS, showing mean relative biases within 5% from 0.4 to 2.1 μm . The short-wave channels of MERSI are found to experience large degradation, particularly the 412-nm band with an annual degradation rate of 9.7%, whereas the red and near-infrared bands are relatively stable with annual degradation rates within ±1%. Several approaches have been used to analyze the reliability of MERSI calibration results. A comparison of the calibration slopes shows that the relative biases between the multisite method and the annual Dunhuang VC campaign are below 3.8%. Aqua MODIS is used as a reference to monitor the data quality of the recalibrated MERSI. A double-difference analysis shows that the mean relative biases are almost within 5% over stable deserts, and the synchronous nadir observation analysis also reveals good agreement.