The Moon is an attractive target for radiometric calibration. The integrated signal from the lunar disk has been used to derive several empirical irradiance models, which are particularly useful for tracking instrument responsivity over time. Historically, numerous lunar exploration missions have performed photometric corrections on lunar surface observations, enabling comparison across diverse datasets. In this context, very little work has been done to utilize the photometric knowledge of specific lunar surface regions as a reference for the optical instruments of Earth-observing satellites. In this letter, we introduce a method for instrument calibration using the radiance of a defined surface portion through a photometric model developed based on spectral profiler (SP) data on board the SELENE Lunar Orbiter and conduct calibration experiments using lunar views captured by the panchromatic and multispectral sensor 2 (PMS2) of the JiLin-1 GuangPu02 (GP02) satellite operating in low-Earth orbit. Finally, the applicability and reliability of the calibration method are verified by comparison of disk reflectance and radiance. The method can be extended to other similar sensors and is beneficial for the development and application of lunar radiance models.

This paper introduces the Chinese Tianmu-1 GNSS-R constellation of 22 small satellites launched in 2023–2024 and comprehensively evaluates the latest version of the in-orbit data. First, the mission design and instrument technology are described, which largely builds on the Fengyun-3/GNOS-II missions. Notable innovations include full-GNSS compatibility and dual-polarization antenna. Then, the spatiotemporal characteristics of the constellation are analyzed—specifically, coverage percentage and mean revisit time at different latitudes. Next, the accuracy of its science products including ocean surface winds and land soil moisture has been assessed, with two application cases demonstrating the mission’s utility for monitoring tropical cyclones and flooding. Finally, this paper for the first time evaluates Tianmu’s polarized observations including Horizontal (H), Vertical (V), Left-Hand Circularly-Polarized (LHCP) and Right-Hand Circularly-Polarized (RHCP). Analysis of the signal-to-noise ratio and reflectivity shows that the dual-polarimetric observations follow the trend of theoretical models and hold promise for advancing land remote sensing.

Spaceborne scatterometers are active non-imaging radar systems, become one of the most effective sensors in the field of quantitative remote sensing for global observation. However, their nominal resolution of 25 to 50 km limits their applicability in scenarios requiring higher resolution. In this paper, an improved adaptive bilateral-total-variation regularization reconstruction algorithm with Lorentzian norm (LABTV+ RR algorithm) is specifically designed for the world’s first dual-frequency scatterometer Fengyun-3E Wind Radar (FY-3E WindRAD) data. LABTV+ RR algorithm introduces dynamically adaptive regularization parameters based on our previously proposed LABTV RR algorithm, which effectively suppresses the noise while maintaining the image texture details, enabling more flexible adaptation to different image contents and noise levels. In addition, the performance of the LABTV+ RR algorithm is further optimized by employing Barzilai-Borwein (BB) stepsize, which dynamically adjusts the stepsize and accelerates the convergence speed of the solution. In this study, the effectiveness of the LABTV+ RR algorithm is validated by comparing actual data and simulated images. The spatial response function (SRF) derived from the actual antenna patterns is used to validate the algorithm’s performance on FY-3E WindRAD Level 1B (L1B) data. Specifically, the algorithm is tested on C-band with a spatial resolution pixel size of 25 km×0.25 km and Ku-band data with a spatial resolution pixel size of 10 km×0.25 km. The validation includes both horizontally polarized (HH-pol) and vertically polarized (VV-pol) transmitted and received signals. Additionally, two representative regions in China are chosen as the study regions to demonstrate the algorithm’s capability to enhance resolution to 3.125 km in both C-band and Ku-band. Furthermore, a multi-pass reconstruction strategy is proposed to achieve an even higher resolution pixel size of 1.5625 km for FY-3E WindRAD C-band and Ku-band data. The study has demonstrated the effectiveness of the proposed LABTV+ RR algorithm in enhancing the resolution of FY-3E WindRAD data, as evidenced by both qualitative visual assessments and quantitative evaluation metrics.

The Aerosol and Carbon Detection Lidar (ACDL) on board the Atmospheric Environment Monitoring Satellite (DQ-1) has been successfully operationalized, and it possesses the capability of detecting carbon dioxide, aerosols, and clouds around the world. However, in contrast to other spaceborne lidars currently/previously in operation (e.g., the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation-CALIPSO, the Ice, Cloud, and land Elevation Satellite-2 mission-ICESat-2 and so on), the ACDL does not incorporate a background signal monitor. Consequently, it is crucial to develop background signal acquisition algorithm for ACDL and to analyze the background characterization. In this paper, a three-segmented background signal acquisition algorithm is designed for ACDL to ensure that the acquired background signals match the instrument characteristics. Additionally, the multi-channel background signals measured by ACDL during daytime and nighttime are characterized in detail, including the features in single profile, along the orbit, on a monthly, semi-annually basis, and particular regions. The quantitative analysis of the data reveals a decrease in background signal intensity along latitudinal lines during nocturnal periods (<0.0005 V). Concurrently, elevated signal values are observed within specific regions of South Atlantic Anomaly (SAA). Additionally, the daytime background signal exhibits a high degree of sensitivity to the characteristics of the feature, and it is influenced by the relative positions of the Sun and the Earth. These findings corroborate the stability of the background signal and its consistency with the behavior of the background signal collected by CALIPSO. And the background removed signals collected by ACDL showed good agreement (difference less than 0.026 V) in the region of consistent diurnal aerosol loads (18-22 km) under clear-air conditions. The background acquisition algorithm and characterization analysis proposed in this paper provide accurate data and theoretical support for subsequent calibration and product inversion. Additionally, it offers a solution for background signal extraction in the case of spaceborne lidars that are not equipped with a background signal monitor.

The first scatterometer onboard Chinese meteorological satellites is a dual-frequency and dual-polarization scatterometer named Wind Radar (WindRAD). It uses an advanced fan-beam conical scanning mechanism to acquire wind vector observation of global ocean surfaces and other geophysical parameters. Since the launch in 2021, external calibration using ground-based active radar calibrator (ARC) has been carried out to evaluated WindRAD in-orbit situation and observation accuracy. Satellite-ground integrated external calibration process is proposed, and optimal satellite-ground observation mode is specially designed for the WindRAD, greatly reducing the complexity of satellite-ground interaction and improving the efficiency of external calibration observation. This article proposes the algorithm of WindRAD external calibration, with comprehensive consideration of scientific nature and engineering realizability. WindRAD in-orbit observation data as well as ARC data were used to calculate the real antenna patterns and absolute calibration coefficients of each polarization for both C- and Ku-bands. The evaluation results revealed that the in-orbit antenna pattern hadn’t changed much compared with the prelaunch test result, which for the first time confirmed the correctness of the key parameters used in WindRAD calibration. Moreover, the calibration accuracy is better than 1 dB. For the first time, the stability of WindRAD in orbit is confirmed using external active reference target.

Global navigation satellite system-reflectometry (GNSS-R) is capable of typhoon wind speed (WS) detection, benefiting from its unique feature of L-band forward-scattered signal with frequent temporal revisits. However, the fluctuation and deviation in observations limit the performance. Conversely, the C-band scatterometer can provide stable observations with a larger width, while the backscattered signal tends to be saturated when WSs exceed 25 m/s, accompanying rain attenuation. The complementary advantages of GNSS-R and scatterometer offer a novel strategy for typhoon maximum sustained wind (MSW) estimation. This study first explores the potential of typhoon MSW estimation using data provided by the GNSS occultation sounder-reflectometry (GNOS-R) and Wind Radar (WindRAD) codeployed on China’s polar-orbiting meteorological satellite FengYun-3E (FY-3E). A typhoon MSW estimation method using combined GNSS-reflectometry and scatterometer (CoGREAT) was proposed, considering the distance from the typhoon center, the WS, and the rain attenuation. The experimental results of five typhoon cases in the Western Pacific show that CoGREAT outperforms GNOS-R-only and WindRAD-only methods for various grades of typhoons, achieving an overall absolute bias of 2.77 m/s taking the typhoon best-track (BST) dataset as the reference. Removing GNOS-R or WindRAD observations in CoGREAT reduces accuracy by 0.86 or 3.39 m/s, respectively, but still outperforms single-source methods without weight factors. In addition, using the typhoon center information provided by FengYun-4B (FY-4B) further proves that the method can be extended to near-real-time MSW estimation. This study provides a valuable reference for similar observation modes.

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%.

In order to ensure the accuracy and reliability of the observational data from the remote sensor during its on-orbit operation, a sampling correction approach with the interpolation sliding window (ISWSCA) is proposed on the basis of the quadratic fitting and interpolation correction. The ISWSCA mitigates the effects of the inhomogeneous distribution of the reflective properties on the lunar surface, significantly enhancing the sampling correction accuracy of the lunar observation data by incorporating a subpixel correction term. Based on the lunar observation data from the Fengyun-3D (FY-3D)/Medium Resolution Spectral Imager (MERSI)-II collected between 2018 and 2023, the feasibility of the ISWSCA is verified, and the ISWSCA improves the sampling correction accuracy by 2.13% and 5.25% during low and high lunar phases in comparison to the scaling method of the lunar full-disk irradiance (LFDISM), together with the correction accuracy improved by 1.37% and 6% in low and high spatial resolutions. The ISWSCA gives the long time series of the normalized calibration coefficient, together with the calibration uncertainty of the effective data being analyzed, and the results show that the on-orbit stability of the FY-3D satellite is excellent in the visible (VIS) and near-infrared (NIF) bands, with the attenuation rate below 1.29% and the calibration uncertainty within 2.11%. This study has an important significance for the on-orbit radiation calibration of the spatial remote sensor.

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.

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.

The traditional vicarious calibration methods for long-term radiometric trending of sensors are primarily based on invariant or pseudo-invariant sites on Earth, which typically rely on large spatially homogeneous areas and require delicate selection of samples. This study employs big data analysis of global pseudo-invariant pixels (PIPs) to monitor the long-term degradation of the Medium Resolution Spectral Imager (MERSI) onboard the FengYun(FY)-3 series satellites. Specifically, the PIPs are determined from image pairs acquired at different times by the iteratively re-weighted multivariate alteration detection (IR-MAD) technique. The global PIPs from several regions located in the middle to low latitude lands are obtained through the big data analysis of long-term time series data. This method requires no prior knowledge of the targets and extend the calibration sites to the pixel-level targets. Results of instrument degradation trending derived from the big data analysis of PIPs from different regions are consistent, which also align well with those obtained through traditional vicarious calibration methods. This method is not sensor-specific and can significantly enhance the frequency of vicarious calibration.

The Hyperspectral Infrared Atmospheric Sounder (HIRAS) carried on the FengYun(FY)-3D satellite is a Fourier transform infrared spectrometer, which can improve the vertical detection capability of the atmosphere. The radiometric calibration accuracy of HIRAS depends on accurate spectral calibration. During the operation of the satellite, the spectral calibration accuracy of HIRAS will change continuously due to the mirror misalignment and the volatile silica gel gas appearing in the path of the interferometer optical system. Here the long-term absolute accuracy of the HIRAS spectral calibration is assessed by a forward radiative transfer model through a cross-correlation method. Subsequently, A two-step method is developed to determine the final configurations of the four detectors on each of the three HIRAS focal planes. The off-axis apodization correction is then performed using the dynamically updated off-axis parameters for all the detectors, achieving a spectral calibration accuracy superior to 2 ppm.

The third generation Microwave Temperature Sounder (MWTS-III) onboard Fengyun-3E (FY-3E) can obtain the atmospheric temperature profile in the early morning time, which is valuable for numerical weather prediction (NWP), climate analysis and other environment studies. However, calibrated brightness temperature (BT) of the surface-insensitive sounding channels (i.e. channels 7-17) have a sea-land contrast bias. It is 0.2 K-0.3 K for channels 9-17 and -1.33 K and -2.86 K for channels 7 and 8, respectively. The bias characteristic of channels 9-17 is similar to that of the corresponding channels of FY-3C, which is caused by the interference from a window channel and can be corrected by empirical method. Moreover, the spectral response functions of channels 7 and 8 were examined over a wider frequency range by measuring the instrument of similar design and construction to the on-orbit MWTS-III. The measured data shows that channels 7 and 8 have out-of-band response located between 50 GHz to 52 GHz, which close to the response range of channel 4. A linear function developed in this letter is effective for the BT bias correction. The sea-land contrast bias of these two channels has been decreased to -0.35K and -0.16K. And the daily mean bias of channels 7 and 8 are improved from -1.8K and -2.3K to -0.5K and -0.4K, respectively. This correction function has been integrated into the FY-3E MWTS-III operational calibration program in December 7, 2022. Meanwhile, the subsequent MWTS-III has been further improved to avoid the sea-land contrast bias.

The optical components of space-borne sensors are subject to environmental degradation, resulting in changing response characteristics, especially at different wavelengths. Consequently, the implementation of radiometric calibration is imperative to ensure the accuracy and reliability of the sensors. Calibration requires the capability to provide known excitations across the sensor’s spectral range, which is difficult to achieve with the existing means. Thus, this letter proposes a ground-based multiwavelengths active light source for nighttime absolute radiometric calibration (ANARC), capable of generating light with a uniform intensity distribution, three monochromatic wavelengths, and variable intensity within the satellite detection field of view. The absolute intensity of ANARC is precalibrated by an unmanned aerial vehicle (UAV) equipped with an optical power meter. The atmospheric transmittance during satellite overpasses is measured by co-located lidar systems. We conducted two test experiments targeting the NOAA-20 satellite. The deviations of the top-of-atmosphere (TOA) ANARC emissivity from the measured emissivity from the Visible/Infrared Imaging Radiometer Suite Day Night Band were −8.06% and 1.02%, respectively. At the same time, ANARC can cover the dynamic range of the high gain stage in the low light band (LLB) of the moderate resolution spectral imager-low light (MERSI-LL) in FengYun-3 E. This preliminary verification suggests the feasibility of using ANARC for nighttime sensor calibration, which provides a new opportunity to improve radiometric calibration accuracy. It could also facilitate hyperspectral sensor calibration due to three monochromatic wavelengths of ANARC.

FengYun-3G satellite was launched in April 2023, carrying onboard a precipitation measurement radar (PMR). In this study, FY-3G PMR data at Ku and Ka bands are assessed for retrieving cloud hydrometeor contents. A novel algorithm is developed to simultaneously retrieve the liquid and ice water contents (IWCs) and solid particle shapes at varying heights. For Typhoon Khanun, the hydrometeor content retrieved using this algorithm is close to the ERA5 rainwater and snow water at its developing and mature stages. The results show that the PMR minimum detectable reflectivity factor at Ku and Ka bands exceeds the design specifications, with values of 10 and 6 dBz, respectively. Furthermore, PMR signals primarily originate from the backscatter of larger particles, including rain, hail, and graupel. Using a prior relationship between the reflectivity and hydrometeor content at different temperatures, IWC and liquid water content (LWC) are retrieved and validated. It is found that Typhoon Khanun during the mature stage, compared to the development stage, IWC decreases while LWC increases. Moreover, based on Ku and Ka retrieval result, the most frequent shapes of typhoon solid particles are large column aggregate, Evans snow aggregate, gem graupel, and six-bullet rosette. Graupel particles are mainly concentrated in areas with high values of hydrometeor content. In some areas with high values of hydrometeors, large column aggregate is more prevalent, while Evans snow aggregate is mainly distributed in the areas with low values of hydrometeors.

Cloud liquid water path (LWP) quantifies liquid water amount within the atmosphere and is closely related to water cycle, weather, and climate. Passive microwave (MW) observations are powerful tools for retrieving LWP. An empirical relationship between the LWPs and MW brightness temperatures (BTs) can be obtained for conventional retrievals, which consider only the influence of LWP on BTs. However, besides LWP, the cloud vertical extent [e.g., cloud top height (CTH)] can affect MW emission, absorption, and corresponding channel BTs, but it is ignored in conventional retrievals. This study investigates the influences of CTH on MW LWP retrievals, and a CTH-dependent algorithm is developed using CTHs from infrared retrievals. Synthetic radiative transfer simulations are performed to quantify CTH effects on MW channel BTs and to establish the CTH-dependent retrieval coefficients. We use the Advanced MW Scanning Radiometer 2 (AMSR2) observations. Cloud products from Moderate Resolution Imaging Spectroradiometer (MODIS) are collocated to provide the necessary CTH information. Thus, we develop an LWP retrieval algorithm by combining AMSR2 BTs with MODIS CTHs. The results indicate that incorporating CTH information into LWP retrievals enhances the consistency between MW and visible/infrared retrievals. Specifically, the CTH-dependent algorithm showed an improvement in the intraclass correlation coefficient (ICC) and a reduction in mean relative differences (MRDs) by approximately 4% (from 18% to 14%) compared to AMSR2 operational retrievals. The CTH-dependent results are slightly more consistent with the MODIS results than the CTH-independent ones, though it remains important to note that the CTH-dependent retrievals introduce less differences compared to their CTH-independent retrievals.

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.

In recent decades, medium-resolution satellites have played a pivotal role in ecosystem research, providing global daily coverage. The Moderate-Resolution Spectral Imager (MERSI), a key component of Fengyun-3 (FY-3) series, exhibits excellent data continuity over three generations, delivering large-scale spatial-continuous documents. However, images from sensors with wide-view angles often exhibit considerable artifacts due to restriction of multi-detector parallel scanning system and multi-interferences from space. Furthermore, MERSI introduces known observation redundancy across pixels and tracks, presenting significant yet-untapped potential for image quality optimization and restoration from Level-1 to downstream products. We proposed a spatial quality enhancement algorithm (SPQE) for MERSI-like sensors. It has demonstrated effectiveness in generating detailed spatial information images without artifacts while retaining spectral information with four main procedures: (1) atmospheric correction inspired by Moderate-Resolution Imaging Spectroradiometer (MODIS); (2) optimization of geolocation sample methods through linear extrapolation, elimination of bow-tie effect through empirical screening; (3) utilization of observation redundancy from adjacent pixels through an restoration algorithm based on Thomas method; (4) exploitation of observation redundancy from adjacent tracks via relative registration and information stack sampling. To illustrate the effectiveness and robustness of SPQE, the widely-used MERSI-II images are tested. Examples of SPQE-derived surface reflectance imagery across global scenarios are provided. We evaluate the potential effect of SPQE on reflectance and ecological applications. Extensive validation demonstrates downstream products generated by SPQE-derived images exhibit a stronger correlation with classic products and public datasets compared to non-enhanced images. Given its performance and versatility, we anticipate the widespread use of SPQE for similar sensors.

The first C- and Ku-band dual-frequency scatterometer (WindRAD) onboard the Chinese FengYun-3E (FY-3E) satellite was successfully launched in July 2021. The WindRAD scatterometer uses an advanced fan-beam conical scanning mechanism to acquire wind vector data of global ocean surfaces and other geophysical parameters through backscattering measurements of the Earth. This article provides an introduction to the WindRAD instrument, an overview of the data preprocessing, and assessment of WindRAD measurements. The numerical weather prediction-based ocean calibration (NOC) approach, natural targets, and cross-calibration against the Ku-band scatterometer onboard the HY-2B satellite based on collocated backscatter measurements, were used to validate WindRAD backscatter results. The evaluation results revealed that the performance of the WindRAD data is generally in good agreement with other scatterometer data currently in use, while WindRAD backscatter data may contain nonlinear calibration issues that require further investigation. WindRAD has the ability to provide high-quality global backscattering measurements, which can be used for the inversion of various geophysical parameters and assimilation 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.

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.

Passive microwave (PMW) radiometers have been widely utilized for quantitative precipitation estimation (QPE) by leveraging the relationship between brightness temperature (Tb) and rain rate. Nevertheless, accurate precipitation estimation remains a challenge due to the intricate relationship between them, which is influenced by a diverse range of complex atmospheric and surface properties. In addition, the inherent skew distribution of rainfall values prevents models from correctly addressing extreme precipitation events, leading to a significant underestimation. This article presents a novel model called the multiscale and multilevel feature fusion network (MSMLNet), consisting of two essential components: a multiscale feature extractor and a multilevel regression predictor. The feature extractor is specifically designed to extract characteristics from multiple scales, enabling the model to incorporate various meteorological conditions, as well as atmospheric and surface information in the surrounding environment. The regression predictor first assesses the probabilities of multiple rainfall levels for each observed pixel and then extracts features of different levels separately. The multilevel features are fused according to the predicted probabilities. This approach allows each submodule only to focus on a specific range of precipitation, avoiding the undesirable effects of skew distributions. To evaluate the performance of MSMLNet, various deep learning methods are adapted for the precipitation retrieval task, and a PWM-based product from the global precipitation measurement (GPM) mission is also used for comparison. Extensive experiments show that MSMLNet surpasses GMI-based products and the most advanced deep learning approaches by 17.9% and 2.5% in root mean square error (RMSE), and 54.2% and 4.0% in CSI-10, respectively. Moreover, we demonstrate that MSMLNet significantly mitigates the propensity for underestimating heavy precipitation events and has a consistent and outstanding performance in estimating precipitation across various levels.

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.

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.

Cloud vertical structure detection is essential for understanding atmospheric dynamics. Currently, cloud boundaries can be effectively identified based on lidar and millimeter-wave radar. However, how to integrate the two observation methods and remove the interference of rainfall on cloud identification are crucial for precise detection of cloud boundaries. This study develops a robust cloud boundary detection method combining radar and lidar observations with ability to identify rainfall effectively. Consistency analysis at Sheyang meteorological station using radiosonde data showed that lidar detected 41.1% of clouds and radar detected 93.3% of clouds compared to composite detection. The composite method overestimated the cloud base by 855.1 m and underestimated the cloud top by 551.2 m compared to radiosonde, as radiosonde measurements are affected not only by drift but also by rainfall, which mainly affects cloud base detection. Utilizing Doppler velocity and the lidar-radar cloud base difference improved rainfall detection by 41.4% over Doppler velocity alone. Observations are consistent with ground-based rain gauge, with Doppler velocities providing good identification of significant rainfall. Also, different cloud bases detected by lidar and radar providing additional identification of drizzle. With the rainfall removed, the error of rainwater path offered by microwave radiometry during rainfall is reduced by 32.4%. Overall, this study proposes a threshold-insensitive lidar-radar composite cloud identification method. It has good robustness and more precise detection of cloud boundaries for its ability to identify vertical rainfall regions.

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.

In order to further improve the accuracy of the lunar radiation correction, an oversampling correction method based on the lunar phase function was firstly proposed in this paper. This method avoided the dependence of the MODIS algorithm on the relative space position and velocity accuracy, and overcame the limitation of the classical SeaWIFS algorithm only applicable to low lunar phase angles, in order to realize the full lunar phase observation of the sky, earth and space. Based on the least square fitting of the actual image data together with the analytic solution of the lunar phase function, the exact expression of the oversampling correction coefficient was given, and the spatial resolution of the remote sensor was improved to the sub-pixel level by the numerical difference calculation. The effectiveness of the novel method was validated through the Terra MODIS freemoon data with the lunar phase angle range of [55°,80°], and the lunar phase fitting function was highly consistent with the lunar phase shape observed by the remote sensor, together with the normalized calibration coefficient calculated by the novel method coinciding basically with the results of the MODIS maneuver calibration data with the lunar phase angle range of about 55°, which demonstrated the competitive efficiency and accuracy of the novel method. To evaluate the on-orbit stability of the FY3D/MERSI satellite, the long time series of the normalized calibration coefficient was obtained by the oversampling correction method, and the calibration uncertainty of the effective data was analyzed, showing no significant change for the gain of the FY3D/MERSI in most bands. This research had significance for the lunar observation together with the on-orbit calibration and deep space detection.

The third generation of MicroWave Temperature Sounder (MWTS-III) is a major payload of FengYun-3F (FY-3F) satellite, which was launched on August 3, 2023. It can provide valuable atmospheric temperature profiles in morning time for numerical weather prediction (NWP) applications. The prelaunch thermal vacuum (TVAC) calibration test was performed to examine the instrument quality. This paper briefly describes of the instrument design of MWTS-III and details the TVAC tests calibration methodology and radiometric performance, including sensitivity, striping, nonlinearity, and calibration accuracy. Channels 1 and 2, which are received by direct-detection receivers, have a noise equivalent delta temperature (NEDT) of less than 0.2K, weak correlations with other channels, striping indexes (SI) of 1.0 and 1.2, small peak nonlinearity and calibration residual errors within ±0.03K. The NEDT for Channels 3-17 is 0.16 K-1.38 K. The inter-channel correlation and striping phenomena are more significant for these 15 channels. Since the linear calibration deviations were well mitigated by the nonlinear calibration, the calibration residual errors for channels 3-11 are within ±0.05 K and for channels 12-17 are within ±0.17 K. The predicted on-orbit calibration accuracy for all channels are between 0.37 K and 1.48K. In summary, all channels of FY-3F MWTS-III meet the specifications, and the instrument has great application potential.

The radiometric properties of the lunar nearside have been used as a function of the observation geometry to generate disk-equivalent irradiance and as a reference for satellite measurements. However, lunar libration and non-Lambertian surface are among the factors that limit the accuracy of lunar irradiance models. Radiance knowledge of specific parts of the Moon also provides calibration standards. The regions can be identified in high-resolution lunar views from Moon orbiters and Earth-orbiting spacecraft, and very little work has been done to place the two-dimensional images from the latter under a designated frame. The lunar phase modifies grayscale or texture information, and image-matching algorithms are limited. The panchromatic and multispectral sensor (PMS) on the JiLin-1 GuangPu-02 (JL1GP02) operating in low Earth orbit can obtain spatially resolved images of the Moon by continuous sampling relying on maneuvers. In this work, we propose a geometric sensor model for PMS, construct geographic (or rather selenographic) positions in grid format, and then combine it with global lunar reference map to create simulated images, which are used to identify tie points with Wide Angle Camera (WAC) orthophoto map with similar geometry structure to characterize geometric errors. We also assume imaging as area charge-coupled device (CCD) for simplification and introduce two methods of instrument pointing correction based on image space residuals. The quality of the results is finally discussed. The results can be exploited to determine the geographic location of the observed targets and subdivision regions and to facilitate studies of the photometric properties of the targets in selected domains.

Radiative calibration of satellite spectral radiometers is essential for their downstream applications. The Medium Resolution Spectral Imager (MERSI-II) is a key instrument of the Chinese polar orbit Fengyun-3D (FY-3D) satellite. However, its calibration performance has not been sufficiently studied, which limits its broad application. This study revealed the feasibility of a cloud-target method for assessing the MERSI-II calibration performance in solar bands. The top-of-atmosphere (TOA) reflectances for six MERSI-II reflective solar bands (RSBs) were numerically simulated using a rigorous forward radiative transfer method and cloud properties from well-collocated and well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) operational cloud products with strict constraints. Only ice cloud targets were examined in the collocation due to their better homogeneity. The excellent agreement between our simulated reflectance and the MODIS reflectance (relative differences (RDs) of over 90% are within a 5% uncertainty range in six bands) validates our models. The simulated results in MERSI-II bands 1–4 showed reasonable agreements with the MERSI-II operational reflectance, i.e., mean RDs$\sim $15% and$\sim $12% (in the three years), respectively. More importantly, we removed these seasonal and degradation biases to improve the current calibration accuracy to a stable value within 3%. Due to its robust performance, our cloud-target-based calibration method can be applied to future MERSI-II sensors to monitor solar band stability.

In the above article[1], a difference in the definitions of our simulated reflectance (with respect to instantaneous TOA radiance) and the operational MERSI-II L1 reflectance (with respect to solar constant) causes errors in their direct comparisons.

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.

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.

Due to the degeneration problem of sensors, calibration becomes a prerequisite step to retrieve consistent satellite images, especially for the ones from long-term time series. Relative calibration is an economic manner to address the problem. Previous studies leverage the identified no-change pixels (NCPs) between two images for relative calibration. However, the identification of NCPs itself is a very hard task and the inferior detection quality affects the performances significantly. Inspired by the great success of deep learning techniques, in this article, we first develop a convolutional neural network (CNN)-based relative calibration method, which bypasses the NCP detection. In particular, the ratio of sensor sensitivity coefficients at two time points is directly estimated by feeding the corresponding image pair into our developed CNN regressor. A polynomial function is fitted upon the estimated ratios in time series. We train the CNN regressor based on the multisite calibration results and then conduct experiments on FengYun-3A (FY-3A), FengYun-3B (FY-3B), and FengYun-3C (FY-3C). The results validate the effectiveness of the proposed method, and it outperforms state-of-the-art NCP-based methods.

Geolocation accuracy is a critical issue for remote sensing applications. To achieve subpixel accuracy, geolocation errors need to be systematically identified and corrected. In this study, we propose a geometric sensor model for FengYun-3D (FY-3D) MERSI-II, a second-generation visible (VIS)/infrared (IR) spectroradiometer, to generate the geolocation lookup table (GLT). The geometric sensor model retrieves the imaging rays from the focal plane to the K-mirrors, 45° scanning mirrors, the platform, and the earth’s surface. After refining the attitude errors with ground control points (GCPs), the rigorous sensor model can achieve subpixel geolocation accuracy. However, significant systematic geolocation errors were identified from the residuals, especially for the area with large view angles. To study the errors of MERSI-II, we proposed a homogenous coordinate in the focal plane. As proven by both theory and experiments, the attitudes were adjusted to a wrong value and introduced systematic errors when there were principal point errors. The pitch angle error of K-mirrors caused the oscillation in the flight direction. The principal distance error introduced line coordinate-related error in the flight direction. Meanwhile, the initial phase angle error between the K-mirror and 45° scanning mirrors caused the line coordinate-related errors in the scanning direction. After correcting all the above-mentioned errors, the systematic geolocation errors of MERSI-II were removed. With 23 independent datasets, the root mean square errors (RMSEs) of 250 m bands were approximately 0.4 pixels, 100 m at nadir.

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).

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.

Satellite observation is an effective way of obtaining global aerosol information. The study focuses on developing a new scheme to apply the traditional dark target (DT) method to the advanced Medium Resolution Spectral Imager (MERSI II), which is a part of the Chinese Fengyun-3-D satellite. Compared with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI II shows higher ratios between red (0.65$\mu \text{m}$) and near-infrared ($2.13~\mu \text{m}$) bands in surface reflectance estimation and the green band ($0.55~\mu \text{m}$) that is more sensitive to cloud screening. Aerosol optical depth (AOD) is retrieved from earlier MERSI II observations by following the adapted DT method over land in Asia in 2018. Overall, AOD from MERSI II has a good performance compared with ground-based measurements with an expected error (EE%) of 66.38% and$R^{2}$of 0.834, which is close to the MODIS EE% of 70.59% and$R^{2}$of 0.829. Both sensors slightly overestimate the AOD over heavy aerosol loading regions, but MERSI-II has larger retrieval area covering a wider swath than MODIS in heavy hazy areas. On a spatial scale, the MERSI II effectively reflects the AOD distribution pattern but tends to overestimate and underestimate AOD at low and high latitudes, respectively, when compared with MODIS. The MERSI II sensor shows good aerosol detection potential, and the DT algorithm can be applied. MERSI II will provide important observation data on climate change and atmospheric pollution for the investigations in the future.

The High Spectral Infrared Atmospheric Sounder (HIRAS) is the first Chinese Fourier Transform Michelson interferometer onboard the FengYun 3D (FY-3D) polar-orbiting meteorological satellite launched on November 15, 2017. The FY-3D HIRAS provides infrared (IR) radiance spectra measurements in three spectral bands: the long-wave IR (LWIR) band from 650 to 1135 cm-1, middle-wave IR (MWIR) band from 1210 to 1750 cm-1, and short-wave IR (SWIR) band from 2155 to 2550 cm-1. The ground system processes the interferogram measurements into calibrated radiance spectra. In each cross-track scan, there are 29 observations, each with a field-of-regard (FOR) comprising an array of 2×2 field of views. In a six-month intensive campaign period, the HIRAS system was tuned, characterized, and validated. For the operational Level 1 product, the radiance noise levels meet the specifications. The spectral frequency accuracy was improved by maximizing the spectral correlation between the measured and simulated spectra by tuning the instrument-line-shape parameters. The absolute spectral frequency biases are less than 3 part per million (ppm) for all the three bands, and spectral bias standard deviations are less than 3 ppm in the LWIR and MWIR bands, and are about 3-5 ppm in the SWIR band. The radiometric calibration uncertainties were assessed by the comparisons of the radiance spectra between HIRAS and other IR hyperspectral sensors on different satellites. The radiance differences of the cross-sensor comparisons are in general less than 0.3, 0.7, and 1.0 K in the LWIR, MWIR, and SWIR bands, respectively. The HIRAS spectra were also compared with the spectra simulated with a fast radiative transfer model. Some remaining issues for the FY-3D HIRAS are also discussed.

The High-Spectral Infrared Atmospheric Sounder (HIRAS) is a Fourier transform spectrometer onboard the fourth polar-orbiting FengYun 3D satellite (FY-3D). The FY-3D HIRAS provides interferogram measurements of Earth view radiance spectra in three infrared spectral bands at 29 cross-track positions, each with a 2 × 2 array of field of views (FOVs). The HIRAS level 1 radiance data cover the spectral bands from 650 to 1135 cm-1[long-wave (LW) band], 1210 to 1750 cm-1[mid-wave (MW) band], and 2155 to 2550 cm-1[short-wave (SW) band] with a spectral resolution of 0.625 cm-1. The radiometric calibration algorithm and the methods of refining the nonlinearity (NL) and the polarization correction coefficients on orbit are summarized in this article. The NL correction coefficients are derived by minimizing the spread of the responsivity functions derived from the measurements of the internal calibration target with varying temperatures. The polarization correction coefficients are derived from the cold space observations and the routine Earth scene measurements. The radiometric accuracy is assessed by comparing the HIRAS measurements to the collocated Cross-track Infrared Sounder (CrIS) observations and radiance simulations. The results show that, compared to CrIS, the radiometric differences are about 0.3 and 0.7 K for the LW and MW bands, respectively, and 0.5 K for the CO absorption and window regions in the SW band. The consistency of the radiometric calibration among the four FOVs is estimated to be within 0.2 K for most of the spectral domain. Some remaining issues for the FY-3D HIRAS are also discussed.

Near-infrared (NIR) channels, such as the 1.6- and 2.13-μm channels of Moderate Resolution Imaging Spectroradiometer (MODIS), play an important role in inferring cloud properties because of their sensitivity to cloud amount and particle size. Instead of the 2.13-μm channel, which has shown great success on MODIS, the central wavelength of the Visible Infrared Imaging Radiometer Suite (VIIRS) is shifted to 2.25 μm. This paper investigates the influences of NIR channels (i.e., 2.13 and $2.25 μm) on cloud optical and microphysical property retrievals and reveals the potential applications of the 2.25-μm channel to cloud thermodynamic phase and multilayer cloud detections by combining with the 1.6-μm channel. Rigorous radiative transfer simulations are performed to provide theoretical reflectance at the channels of interest, and MODIS and VIIRS observations are used for case studies. Our results indicate a minor influence of the 2.25-μm channel on cloud optical depth and effective particle size retrievals. In combination with the 1.6-μm channel, the 2.25-μm channel provides additional information indicating cloud phases. However, the 1.6- and 2.13-μm channels do not show any sensitivity to cloud phase. Furthermore, by considering the infrared-based cloud phase results, the 1.6- and 2.25-μm channel combination becomes possible to infer multilayer clouds. Case studies based on simultaneous MODIS and VIIRS observations demonstrate the capability of the 1.6-2.25-μm channel combination for determining cloud phase and multilayer clouds. Collocated satellite-based active lidar observations further validate these advantages of the 2.25-μmu channel over the original 2.13-μm channel.

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.

The first products of the Global Space-based Inter-Calibration System (GSICS) include bias monitoring and calibration corrections for the thermal infrared (IR) channels of current meteorological sensors on geostationary satellites. These use the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) on the low Earth orbit (LEO) Metop satellite as a common cross-calibration reference. This paper describes the algorithm, which uses a weighted linear regression, to compare collocated radiances observed from each pair of geostationary-LEO instruments. The regression coefficients define the GSICS Correction, and their uncertainties provide quality indicators, ensuring traceability to the selected community reference, IASI. Examples are given for the Meteosat, GOES, MTSAT, Fengyun-2, and COMS imagers. Some channels of these instruments show biases that vary with time due to variations in the thermal environment, stray light, and optical contamination. These results demonstrate how inter-calibration can be a powerful tool to monitor and correct biases, and help diagnose their root causes.

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.

FY1C is a polar meteorological satellite of China, which had been worked on orbit about 5 years. In this paper, time series calibration method based on pseudo-invariant target site is applied to monitor the variance of FY1C instrument. Dunhuang test site is chose as the pseudo-invariant site and the FY1C images over this site are processed with some standard. Then the time series calibration result of FY1C seven channels at visible and near-infrared range has been calculated. In order to validate the result, apply the time series calibration coefficients to recalibrate the images of Wuwei test site from 1999 to 2003. The validation result shows that the time series calibration coefficients are efficient and can monitor the radiance status of FY1C instrument.

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.

In this paper, the reflectance-base method was adopted for the absolute radiometric calibration of HY-1 COCTS at China Dunhuang Calibration Test Site on July 16th, 20th and 23rd of 2002. and the background information was provided about the site condition of Dunhuang test site. At the same time, the BRDF correction due to the zenith angle of satellite was computed. Comparing the calibration results of this test with those of the pre-launch calibration, a good agreement at six channels is achieved except channel 1 (412 nm) and channel 2 (443 nm).