Medium Resolution Spectral Imager Research Articles

Research and Application of Destriping Algorithm based on Weighted Block Sparse Representation to FengYun Satellite Imager Data

Abstract In remote sensing imaging systems, stripe noise is a pervasive issue primarily caused by the inconsistent response of multiple detectors. Stripe noise not only affects image quality but also severely hinders subsequent quantitative derived products and applications. Therefore, it is crucial to eliminate stripe noise while preserving detailed structure information in order to enhance image quality. Although existing destriping methods have achieved certain effects to some extent, they still face problems such as loss of image details, image blur, and ringing artifacts. To address these issues, this study proposes an image stripe correction algorithm based on weighted block sparse representation. This research applies techniques such as differential low-rank constraint and edge weight factor to remove stripe noise while retaining image detail information. The algorithm also uses the alternating direction method of multipliers (ADMM) to solve the MCP regularized least squares optimization problem model, improving the processing efficiency of the model. The results of this study have been applied and validated in imager data from the Medium Resolution Spectral Imager (MERSI-II) onboard FengYun-3D satellite, the Multi-channel Scanning Radiometer (AGRI) onboard FengYun-4A satellite and Precipitation Microwave Radiometer (MWRI–RM) onboard FengYun-3G. Compared to typical stripe correction methods, the proposed method achieves better stripe removal while preserving image detail information. The destriped image data can be used to generate high-quality quantitative products for various applications. Overall, by combining insights from prior research and innovative techniques, this study provides a more effective and robust solution to the stripe noise problem in remote sensing and weather forecast.

High-accuracy FY-3A/MERSI precipitable water vapour (PWV) data over the continental United States

ABSTRACT Water vapour is an important atmospheric gas with a direct impact on climate and weather. Given the significant variations of water vapour distribution in the tridimensional and temporal space, it is of great significance to use remote sensing data to monitor it on a large scale. One of the main missions of the Medium Resolution Spectral Imager (MERSI) onboard FengYun (FY)-3A satellite is to monitor the distribution of precipitable water vapour (PWV). However, the official MERSI PWV data is not widely used due to the limitation of its accuracy. The primary objective of this study is to develop FY-3A/MERSI PWV data with higher accuracy. The study area of this paper is the continental United States (125°W–65°W, 25°–50°N), and the selected period is the year 2010. Three PWV retrieval models for three near-infrared (NIR) channels (0.905, 0.940 and 0.980 μm) of MERSI are constructed based on the matching results between MERSI data and SuomiNET PWV data. Compared with the existing semi-empirical PWV retrieval algorithms, the algorithm developed in this work has two main changes: (1) MERSI PWV retrieval models are constructed using data after removing outliers rather than all data. (2) The algorithm can identify abnormal retrieval results. The validation results based on PWV data derived from Aerosol Robotic NETwork (AERONET) indicate that these two changes both contribute positively to enhancing the accuracy of MERSI PWV data. The root mean square errors (RMSE) of MERSI PWV data developed using the three channels at 0.905, 0.940 and 0.980 μm are 0.28 cm, 0.22 cm and 0.24 cm, respectively. The relative errors (RE) of these PWV data are 0.14, 0.10 and 0.12, respectively. In comparison to the official MERSI PWV data, the RMSE of MERSI PWV data retrieved in this study is reduced by at least 44%, and RE is reduced by at least 42%.

Comprehensive evaluation of the precipitable water vapor products of Fengyun satellites via GNSS data over mainland China

The Chinese meteorological satellites Fengyun (FY) have recently developed rapidly, resulting in a substantial wealth of precipitable water vapor (PWV) products. However, these latest operational PWV products still need to be systematically validated. In this paper, we focus on the PWV products derived from the FY-3D/Medium Resolution Spectral Imager II (MERSI-II), FY-2H/Visible and Infrared Spin Scan Radiometer (VISSR), and FY-4A/Advanced Geostationary Radiation Imager (AGRI), and test their performance by using the global navigation satellite system (GNSS) data. We find that FY-4A/AGRI performs best in various regions of China in 2021. Compared to GNSS-PWV, FY-4A/AGRI PWV has a correlation coefficient 0.95, a root mean square error (RMSE) of 4.67 mm, and a mean deviation (MB) of −0.73 mm, respectively. Significant discrepancies between FY-PWV and GNSS-PWV are observed in the Qinghai-Tibetan Plateau (MB: −4.86 mm for FY-2H/VISSR and − 5.12 mm for FY-4A/AGRI) and the Sichuan Basin (MB: 5.52 mm for FY-2H/VISSR, and 3.82 mm FY-4A/AGRI). All FY-PWVs exhibit season-dependent variations, and their discrepancies with GNSS-PWV during winter are smaller than in summer. Finally, we analyze the distribution and changes of water vapor in China from 2019 to 2022 using the preferred FY-4A/AGRI PWV. The results reveal a gradual decrease in the annual mean water vapor content, diminishing by approximately 0.33 mm during this period. Notably, this decrease occurs in southern China, and the water vapor content spikes in summer, culminating at 23.59 mm in 2022. Our findings strongly correlate with the precipitation changes documented in the China Climate Bulletin (2022).

Stability Analysis of Radiometric Properties in Visible On-Board Calibration System of FY-3B Satellite Medium-Resolution Spectral Imager

Stability Analysis of Radiometric Properties in Visible On-Board Calibration System of FY-3B Satellite Medium-Resolution Spectral Imager

Simultaneous estimation of five temporally regular land variables at seven spatial resolutions from seven satellite data using a multi-scale and multi-depth convolutional neural network

Various satellite sensors have provided a huge amount of observations of Earth's environment at variable spatial and temporal resolutions. Many global coarse-resolution land products have been generated from single-satellite data, but global temporally regular land products at fine spatial resolutions (say 10-30 m) are scarce because of infrequent observations. An ideal inversion framework can estimate multiple global continuous land variables at different spatial resolutions by combining all sources of satellite data. This paper proposes a new framework that can estimate five land variables simultaneously from the top-of-atmosphere (TOA) reflectance acquired by seven satellite sensors based on a multi-scale and multi-depth convolutional neural network (MSDCNN). This framework enables us to estimate temporally regular land variables at as fine as 10 m spatial resolution by transforming information from satellite data at coarser spatial resolutions. The estimated land variables include Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), shortwave albedo, visible albedo, and spectral reflectance. Seven sensors that acquire data at different spatial resolutions, include Visible Infrared Imaging Radiometer Suite (VIIRS) (750 m), Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m), Fengyun-3 (FY-3) Medium Resolution Spectral Imager (MERSI) (250 m), China-Brazil Earth Resources Satellite 04 (CBERS-04) Wide Field Imager (WFI) (73 m), Landsat 8 Operational Land Imager (OLI) (30 m), Gaofen-1 (GF-1) Wide-Field-of-View (16 m) and Sentinel 2 A/B Multispectral Imager (MSI) (10 m). This framework is mainly composed of four steps. First, a Shuffled Complex Evolution (SCE) optimization method is adopted to estimate these variables from VIIRS TOA reference. Second, a joint-output random forest regression (RF) method is used to link the satellite observations and the estimated values from step 1. Third, the six-type multi-resolution satellite observations are used to downscale the VIIRS TOA reflectance to six different spatial resolutions by using the MSDCNN. Finally, the downscaled six-resolution VIIRS TOA reflectance is fed into the multiple-variable RF model to estimate land variables. The framework was validated, and the results based on high-resolution reference maps from ImagineS network and time series shortwave albedo field values from Surface Radiation (SURFRAD) and Integrated Carbon Observation System (ICOS) network show that the retrieved variables had high validation accuracy, with root mean square error (RMSE) ranges of 0.361–0.489 (LAI), 0.023–0.120 (FAPAR), 0.013–0.026 (snow-free shortwave albedo), respectively. Comparison results of the retrieved multi-scale variables with the Sentinel 2 A/B (10 m), Landsat 8 (30 m), Global LAnd Surface Satellite (GLASS, 250 m, 500 m), MODIS (500 m), and VIIRS (750 m) products show that their values were close, with RMSE ranges of 0.107–0.273 (LAI), 0.015–0.027 (FAPAR), 0.003–0.007 (shortwave albedo), 0.001–0.007 (visible albedo), and 0.003–0.025 (spectral reflectance), respectively. The results of the direct validation as well as the product intercomparison show that this novel framework has the potential to be used in estimating global land variables at various spatial scales using a variety of satellite data sources.

Polar Cloud Detection of FengYun-3D Medium Resolution Spectral Imager II Imagery Based on the Radiative Transfer Model

The extensive existence of high-brightness ice and snow underlying surfaces in polar regions presents notable complexities for cloud detection in remote sensing imagery. To elevate the accuracy of cloud detection in polar regions, a novel polar cloud detection algorithm is proposed in this paper. Employing the MOD09 surface reflectance product, we compiled a database of monthly composite surface reflectance in the shortwave infrared bands specific to polar regions. Through the forward simulation of the correlation between the apparent reflectance and surface reflectance across diverse conditions using the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer model, we established a dynamic cloud detection model for the shortwave infrared channels. In contrast to a machine learning algorithm and the widely used MOD35 cloud product, the algorithm introduced in this study demonstrates enhanced congruence with the authentic cloud distribution within cloud products. It precisely distinguishes between the cloudy and clear-sky pixels, achieving rates surpassing 90% for both, while maintaining an error rate and a missing rate each under 10%. The algorithm yields positive results for cloud detection in polar regions, effectively distinguishing between ice, snow, and clouds. It provides robust support for comprehensive and long-term cloud detection efforts in polar regions.

Study of the Application of FY-3D/MERSI-II Far-Infrared Data in Wildfire Monitoring

In general, the far-infrared channel in the wavelength range of 10.5–12.0 µm plays an auxiliary role in wildfire detection as its sensitivity to high-temperature targets is far lower than the mid-infrared channel in the wavelength range of 3.5–4.0 µm at the same spatial resolution (1 km, which is the spatial resolution of infrared channels in most satellites used for wildfire monitoring in daily operational mode). The Medium-Resolution Spectral Imager II onboard the Fengyun-3D polar orbiting meteorological satellite (FY-3D/MERSI-II) contains far-infrared channels with a spatial resolution of 250 m at the wavelengths of 10.8 μm and 12.0 μm, which promotes the application of far-infrared channels in wildfire monitoring. In this study, the features of FY-3D/MERSI-II far-infrared channels in fire monitoring are discussed. The sensitivity of 10.8 μm (250 m) to fire spots and the influence of solar radiation reflection on the infrared channels are quantitatively analyzed. The method of using 10.8 μm (250 m) as a major data source to detect fire spots is proposed, and several typical wildfire cases are used to verify the proposed method. The results show that the 10.8 μm (250 m) far-infrared channel has the same advantages as the existing method in wildfire monitoring in terms of a more precise positioning of the detected fire pixel, avoiding interference by solar radiation reflections, and reflecting stronger fire regions in large fire fields.

Change assessment of the Tonga island surface temperature before and after volcanic eruption based on FY-3D time series dataset

Taking advantage of the 250 m resolution and daily observation of dual thermal infrared channels data of the Medium Resolution Spectral Imager (MERSI) onboard the Fengyun 3D (FY-3D) satellite, a refined thermal infrared island surface temperature (ILST) inversion model is established through radiation transmission simulation. And the Tonga ILST change maps before and after the volcanic eruption are produced and analyzed based on ILST time series dataset. The results show that the ILST decreases by 2 K –3 K within the five days after the eruption due to the amount of volcanic ash deposition, but increases significantly by 2 K –6 K within the fifth to tenth days after the eruption. The trend of time-limited cooling, warming, and then gradual recovery after volcanic eruption is very obvious. Through the manual cleaning and the scouring by seawater or rainwater to the volcanic ash covered ground, more parts of the island surface restored its original state. Hence, the Tonga ILST also increased gradually within a week thereafter. However, in regions with local vegetation, the recovery time of ILST would be longer. The temporal and spatial resolution of FY-3D MERSI meets the need for monitoring the changes of the island surface states before and after the volcanic eruption.

The Uncertainty of SNO Cross-Calibration for Satellite Infrared Channels

The on-orbit radiometric calibration is a fundamental task in quantitative remote sensing applications. A widely used calibration method is the cross-calibration based on Simultaneous Nadir Observation (SNO), which involves using high-precision reference instruments to calibrate lower-precision onboard instruments. However, despite efforts to match the observation time, spatial location, field geometry, and instrument spectra, errors can still be introduced during the matching processes and linear regression analysis. This paper focuses on the error generated by sample matching and the error fitting method generated by the sample fitting method. An error propagation analysis is performed to develop a generic model for assessing the uncertainty of the SNO cross-calibration method itself in meteorological satellite infrared channels. The model is validated using the payload parameters of the Hyperspectral Infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) instruments aboard the FengYun-3D (FY-3D). Simulation experiments are performed considering typical bright temperatures, different background fields, and varying matching threshold conditions. The results demonstrate the effectiveness of the proposed model in capturing the error propagation chain in the SNO cross-calibration process. The model provides valuable insight into error analysis in the SNO cross-calibration method and can assist in determining the optimal sample matching threshold for achieving radiometric calibration accuracy.

Retrieval and validation of the dawn-dusk land surface temperature from FY-3E MERSI-LL

ABSTRACT Land surface temperature (LST) is an important physical parameter that reflects changes in surface processes. Remote sensing makes it possible to obtain global or regional LSTs with high spatial and temporal resolution. Feng-Yun 3E (FY-3E) is the world’s first civilian dawn-dusk orbiting meteorological satellite on board the Medium Resolution Spectral Imager – Low-Light (MERSI-LL) sensor capable of acquiring LST at 250 m spatial resolution at dawn and dusk. By complementing with existing polar-orbiting satellites, FY-3E can fill the satellite observation gap within the 6-hour assimilation window and further enhance the observation capability of LST. To retrieve accurate LST from MERSI-LL, the generalized split-window (GSW) method was developed. The GSW coefficients were derived from the simulation dataset produced by Moderate Resolution Transmittance Code 5.2 with an atmospheric profile database. As a key input to the GSW algorithm, the land surface emissivity was dynamically estimated by the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset in combination with the fractional vegetation cover. The water vapour content is calculated in real time by the split-window covariance/variance ratio method. The comparison between retrieved LSTs and in situ LSTs shows that the GSW algorithm can effectively retrieve the LSTs of vegetation cover surfaces. The root mean square error ranged from 0.79 to 1.79 K at dawn and from 1.32 to 1.92 K at dusk.

Retrieving leaf area index from FY-3D MERSI-II data using a sensor-adaptive algorithm

ABSTRACT Leaf area index (LAI), officially listed as one of essential climate variables, quantifies the structure and amount of vegetation and characterizes the interaction between vegetation and climate. The advanced MEdium Resolution Spectral Imager (MERSI-II) onboard FengYun-3D (FY-3D) can provide twice-daily global observations of earth at a spatial resolution of 250 m. Therefore, it has great potential for promoting the improvement of global LAI products and boosting the development of earth system modelling. However, the existing methods are mostly sensor-specific, they can not be directly applied to MERSI-II data for LAI generation. In this paper, we proposed a sensor-adaptive approach for LAI estimation based on MERSI-II observations. This method is composed of an LAI retrieval look-up table based on radiative transfer theory, which was calibrated by a global optimizing algorithm to adapt MERSI-II characteristics, and a backup algorithm training neural networks with MERSI-II Normalized Difference Vegetation Index (NDVI) and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. We evaluated the MERSI-II LAI retrievals by intercomparison to MODIS products over mainland China and direct validation using ground-based upscaled LAI reference maps. The assessments demonstrate that (1) the derived MERSI-II LAI products agree well with the MODIS benchmarks in both spatial and temporal respects; (2) compared to the MODIS LAI, the MERSI-II LAI retrievals with less gaps show great potential in improving spatiotemporal continuity; (3) validation versus in-situ measurements reveals acceptable accuracy of the MERSI-II LAI products with a R2 of 0.85 and RMSE of 0.82; (4) the proposed parametric optimization strategy could successfully transplant MODIS LAI algorithm to MERSI-II data.

Water vapour products from ERA5, MERSI‐II/FY‐3D, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra in Australia: A comparison against in situGPS water vapour data

AbstractAustralia is a region of high sensitivity to the El Niño–Southern Oscillation events in association with atmospheric water vapour, which is an essential atmospheric parameter in hydrological cycle, energy transport and climate monitoring. In this article, we thoroughly validated the quality of multisource precipitable water vapour (PWV) products sourced from ERA5 reanalysis, advanced Medium Resolution Spectral Imager (MERSI‐II) sensor on the FY‐3D satellite, Ocean and Land Colour Instrument (OLCI) sensor on the Sentinel‐3A and Sentinel‐3B satellites, and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua and Terra satellites. The PWV estimates measured by 453 in situ Global Positioning System (GPS) sites from 1 June 2019 to 31 May 2020 over Australia were employed as the common reference. The validation results show that all the reanalysis‐based and satellite‐based PWV products had a good agreement with reference GPS‐based PWV data. ERA5 reanalysis had the highest PWV accuracy with a root‐mean‐square error (RMSE) of 2.016 mm and a relative RMSE (RRMSE) of 12.038%, while the MODIS/Terra instrument had the lowest PWV accuracy (RMSE = 4.903 mm and RRMSE = 34.187%). In contrast to the MERSI‐II/FY‐3D instrument that underestimated PWV, the PWV data from ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra overestimated the water vapour values. The quality of reanalysis‐based and satellite‐based PWV measurements showed significant dependencies on several variables – location, PWV, solar zenith angle, season, elevation, land surface cover and latitude. It is expected that this research can help enhance the understanding of the quality of reanalysis and satellite water vapour products in Australia, that is, ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra. This work could provide an insight into improving the accuracy of reanalysis‐based and satellite‐based PWV data products after exploring the dependency factors that affect PWV performance as presented in our work.

Cloud-Target Calibration for Fengyun-3D MERSI-II Solar Reflectance Bands: Model Development and Instrument Stability

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 satellite. However, its calibration performance has not been sufficiently studied, which limits its broad application. This study revealed the feasibility of an cloud-target method for assessing the MERSI-II calibration performance in solar bands. The top-of-atmosphere reflectances for six MERSI-II reflective solar bands were numerically simulated using a rigorous forward radiative transfer method and cloud properties from well-collocated and well-calibrated 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 <3%, while the RDs in bands 6 and 7 reaches 12% and 6%, respectively. Our systematic cloud-target-calibration results over three years (2019-2021) indicated clear seasonal calibration biases and signal degradation of the MERSI-II solar bands, and those in the two cloud-absorbing bands, which reached ~15% and ~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 the solar band stability.

Retrieval of land surface temperature from FY3D MERSI-II based on re-fitting Split Window Algorithm

ABSTRACT Medium Resolution Spectral Imager II (MERSI-II) is one of the core sensors mounted on the FengYun-3D (FY3D) satellite. Two adjacent 250 m long-wave thermal infrared (TIR) channels provide a considerable opportunity for retrieving Land Surface Temperature (LST) with high spatiotemporal resolution. In this paper, Thermodynamic Initial Guess Retrieval (TIGR) dataset and MODTRAN 4.0 model were used to re-fit the parameters of the Split-Window (SW) algorithm suitable for MERSI-II TIR channels, and then the daily 250 m resolution MERSI-II LST product was retrieved. The Radiance-based (R-based) method results showed that the bias value between simulated by MODTRAN4.0 and the input is 0.16 K, and the MAE value is 0.38 K. Inter-comparison method results showed that the MERSI-II LST and MODIS LST products were consistent in spatial distribution, but there were certain differences between MODIS LST and MERSI-II LST at different land cover types. T-based method results showed that R values between the site-observed LST and MERSI-II LST retrieved by SW algorithm exceeded 0.92, the bias value was between 3.6 K and 4.4 K, and the MAE value was between 2.6 K and 4.5 K. The above results indicating that the SW algorithm proposed in this study has good accuracy and applicability.

Determining pseudo-invariant calibration sites for comparing inter-mission ocean color data

Comparing inter-mission space instrument performance is crucial to accurate satellite measurements which guarantees the quality of ocean color products. However, comparing inter-mission instrument performance is limited by strong dispersion, which clearly originates from the spatial and temporal variability inside the oceanic sampling sites. We designed a novel comprehensive score metric (CSM) to quantitatively identify the candidate pseudo-invariant calibration sites (PICS) for inter-mission comparisons. We calculated the CSM from a year of ocean color and meteorological products from 2018 using a pixel-by-pixel method with a simple average of a temporal meteorological metric, a spatial aerosol metric, a temporal optical metric, a spatial optical metric, a data quality metric, a spectral shape metric, and a directional homogeneity metric. When we filtered the data with the threshold CSM > 0.6, we found that atmospheric and oceanic conditions from two smooth belts in the low latitude open ocean were more clear, stable, and homogeneous than other regions, and we suggested these smooth belts as candidate PICS. With image data from the Visible Infrared Imaging Radiometer (VIIRS) and Medium Resolution Spectral Imager II (MERSI II), we found that our candidate PICS were more effective than regions with low CSM in providing stable synchronous data for inter-mission comparisons. The MERSI II instrument, however, experienced significant degradation in radiance measurements from January to April in 2021, while the VIIRS instrument performed well. These results suggested that the CSM values were “experimental” but, under restrictive conditions, were sufficient for an inter-mission comparison and calibration application.

Assessing FY-3E HIRAS-II Radiance Accuracy Using AHI and MERSI-LL

The FY-3E/HIRAS-II (Hyperspectral Infrared Atmospheric Sounder-II), as an infrared hyperspectral instrument onboard the world’s first early morning polar-orbiting satellite, plays a major role in improving the accuracy and timeliness of global numerical weather predictions. In order to assess its observation quality, the geometrically, temporally, and spatially matched scene homogeneous HIRAS-II hyperspectral observations were convolved to the channels corresponding to the Himawari-8/AHI (Advanced Himawari Imager) and FY-3E/MERSI-LL (Medium-Resolution Spectral Imager) imagers from 15 March to 21 April 2022, and their brightness temperature deviation characteristics were statistically calculated in this paper. The results show that the HIRAS-II in-orbit observed brightness temperatures are slightly warmer than the AHI observations in all the matched AHI channels (long wave infrared channel 8 to channel 16) with a mean brightness temperature bias less than 0.65 K. The bias of the atmospheric absorption channel is slightly larger than that of the window channel. A standard deviation less than 0.31 K and a correlation coefficient higher than 0.98 in all channels means that the quality of the observation is satisfactory. The thresholds chosen for the colocation approximation factors (e.g., observation geometry angle, scene uniformity, observation azimuth, and observation time) for matching the HIRAS-II with AHI contribute little and negligible uncertainty to the bias assessment, so the difference between the two observed radiations is considered to be mainly from the systematic bias of the two-instrument measurement. Compared with MERSI-LL window channel 5, the observations of both instruments are very close, with a mean bias of 0.002 K and a standard deviation of 0.31 K. The mean brightness temperature bias (HIRAS-II minus MERSI-LL) of the MERSI-LL water vapor channel 4 is 0.66 K with a standard deviation of 0.22 K. The mean brightness temperature bias of channel 6 and channel 7 is 0.63 K (the standard deviation is 0.36 K) and 0.5 K (the standard deviation is 0.3 K), respectively. The biases of channel 4 are significantly and positively correlated with the target scene temperature, and the biases of channel 6 and 7 show a U-shaped change with the increase in the scene temperature, and the biases are smallest (close to 0 K) when the scene temperature is between 250 K and 280 K. The statistical characteristics of the HIRAS-II–MERSI-LL difference vary minimally and almost constantly over a period of time, indicating that the performance of the HIRAS-II instrument is stable.

Assessment and correction of radiometric calibration for the medium resolution spectral imager on FY-3C

As a keystone instrument on the Fengyun-3C (FY-3C) satellite, the medium resolution spectral imager (MERSI) provides global coverage of top-of-atmosphere radiances for a broad range of scientific studies of the earth system. Based on the onboard measurements of the blackbody (BB) and deep space view (SV), the modified radiometric calibration equation was first given for the MERSI thermal emission band. The accuracy of the radiometric calibration was evaluated by comparing the average irradiance corresponding to the spectral response function and the irradiance corresponding to the wavelength, whose relative error was <0.40 % . Moreover, the BB-derived brightness temperature TR calculated by the BB irradiance shows a systematic error relative to the BB equivalent temperature TE obtained by the voltage code values of seven platinum resistors, and the value of TR was lower 0.25 K than that of TE. To further verify the validity of the calibration coefficient, the influences of the BB temperature, wavelength, and emissivity offsets on the derived irradiance and temperature of the earth were analyzed in detail. For the BB temperature changing by 0.10 K, the absolute values of the earth irradiance together with its equivalent temperature were 0.01 Wm − 2 sr − 1 μm − 1 and 0.10 K, respectively, whose relative errors were about 0.16% and 0.40%. When the BB wavelength and emissivity changing by 0.1 μm and 0.005, the variations of the earth irradiance were 0.02 and 0.04 Wm − 2 sr − 1 μm − 1, respectively, whereas the relative errors of the equivalent temperature were 0.07% and 0.11%. These works provided an important theoretical guidance for the satellite data assimilations along with the quantitative remote sensing applications.

Cross-Comparison of channel parameters between FY-3E/MERSI-LL and Himawari-8/AHI in China

ABSTRACT Fengyun-3E (FY-3E) is the world’s first operational meteorological satellite on dawn-dusk orbit. In order to realize the generality of algorithms for different satellites and provide a basis for subsequent inversion studies on remote sensing parameters, such as land surface temperature, fire, and atmospheric water vapour, we perform a cross-comparison of the brightness temperature (TB) of the six infrared (IR) channels between the Medium Resolution Spectral Imager Level L onboard the FY-3E satellite (FY-3E/MERSI-LL) and the Advanced Himawari Imager onboard the Himawari-8 satellite (Himawari-8/AHI). The statistical indicators, correlation coefficient, root mean square error (RMSE) and mean bias, are used to analyse the consistency and differences of the channel parameters between the MERSI-LL and AHI in terms of overall data, different regions and different TB. The results show that the TB data observed by the FY-3E/MERSI-LL and Himawari-8/AHI are generally consistent, with correlation coefficients of more than 0.99, mean biases of less than 1.60 K and RMSEs of less than 1.90 K. In terms of different channels, the TB in channel IR 038 has the best consistency between the two sensors, while the results from the MERSI-LL are higher than those of the AHI for channels IR038, IR086, IR108 and IR120, and lower than those of the AHI for channel IR041. Analysing the situation in different regions, we find that the MERSI-LL and AHI data are highly consistent and relatively stable in the Huang-Huai Region, with low RMSEs, low mean biases and high correlation coefficients (more than 0.95) at all channels. For the different TB ranges, the RMSEs and mean biases for the TB ranges of 270–280 K and 280–290 K between the MERSI-LL and AHI are small, and the data in the 290–300 K TB range has great differences and fluctuations between the two sensors.

Comprehensive Precipitable Water Vapor Retrieval and Application Platform Based on Various Water Vapor Detection Techniques

Atmospheric water vapor is one of the important parameters for weather and climate studies. Generally, atmospheric water vapor can be monitored by some techniques, such as the Global Navigation Satellite System (GNSS), radiosonde (RS), remote sensing and numerical weather forecast (NWF). However, the comprehensive retrieval and application of precipitable water vapor (PWV) using multi techniques has been hardly performed before, which becomes the focus of this study. A comprehensive PWV retrieval and application platform (CPRAP) is first established by combing the ground-based (GNSS), space-based (Fengyun-3A, Sentinel-3A) and reanalysis-based (the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting, ERA5) techniques. Additionally, its applications are then extended to drought and rainfall monitoring using the CPRAP-derived PWV. The statistical result shows that PWV derived from ground-based GNSS has high accuracy in China, with the root mean square (RMS), Bias and mean absolute error (MAE) of 2.15, 0.05 and 1.65 mm, respectively, when the RS-derived PWV is regarded as the reference. In addition, the accuracy of PWV derived from the space-based (FY-3A and Sentinel-3A) techniques technique is also validated and the RMS, Bias and MAE of a Medium Resolution Spectral Imager (MERSI) onboard Fengyun-3A (FY-3A) and an Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A are 4.46/0.56/3.61 mm and 2.95/0.01/1.37 mm, respectively. Then, the performance of ERA5-derived PWV is evaluated based on GNSS-derived and RS-derived PWV. The result also shows good accuracy of ERA5-provided PWV with the averaged RMS, Bias and MAE of 1.86/0.11/1.48 mm and 0.90/−0.05/1.51 mm, respectively. Finally, the PWV data derived from the established CPRAP are further used for drought and rainfall monitoring. The applied results reveal that the calculated the standardized precipitation evapotranspiration index (SPEI) using the CPRAP-derived PWV can monitor the drought and the correlation coefficient ranges from 0.83 to 0.9 when compared with the SPEI. Furthermore, in this paper correlation analysis between PWV derived from the CPRAP and rainfall, and its potential for rainfall monitoring was also validated. Such results verify the significance of the established CPRAP for weather and climate studies.

Estimating the Clear-Sky Longwave Downward Radiation in the Arctic from FengYun-3D MERSI-2 Data

Surface longwave downward radiation (LWDR) plays a key role in determining the Arctic surface energy budget, especially in insolation-absent boreal winter. A reliable LWDR product is essential for understanding the intrinsic physical mechanisms of the rapid changes in the Arctic climate. The Medium-Resolution Spectral Imager (MERSI-2), a major payload of the Chinese second-generation polar-orbiting meteorological satellite, FengYun-3D (FY-3D), was designed similar to the NASA Moderate-Resolution Imaging Spectroradiometer (MODIS) in terms of the spectral bands. Although significant progress has been made in estimating clear-sky LWDR from MODIS observations using a variety of methods, few studies have focused on the retrieval of clear-sky LWDR from FY-3D MERSI-2 observations. In this study, we propose an advanced method to directly estimate the clear-sky LWDR in the Arctic from the FY-3D MERSI-2 thermal infrared (TIR) top-of-atmosphere (TOA) radiances and auxiliary information using the extremely randomized trees (ERT) machine learning algorithm. The retrieval accuracy of RMSE and bias, validated with the Baseline Surface Radiation Network (BSRN) in situ measurements, are 14.14 W/m2 and 4.36 W/m2, respectively, which is comparable and even better than previous studies. The scale effect in retrieval accuracy evaluation was further analyzed and showed that the validating window size could significantly influence the retrieval accuracy of the MERSI-2 clear-sky LWDR dataset. After aggregating to a spatial resolution of 9 km, the RMSE and bias of MERSI-2 retrievals can be reduced to 9.43 W/m2 and −0.14 W/m2, respectively. The retrieval accuracy of MERSI-2 clear-sky LWDR at the CERES SSF FOV spatial scale (approximately 20 km) can be further reduced to 8.64 W/m2, which is much higher than the reported accuracy of the CERES SSF products. This study demonstrates the feasibility of producing LWDR datasets from Chinese FY-3D MERSI-2 observations using machine learning methods.