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.

Snow water equivalent (SWE) quantitatively describes water storage in snowpack. Satellite-based passive microwave (PMW) remote sensing is a valid tool for monitoring SWE in the Northern Hemisphere. However, the current operational SWE retrieval methods, especially those without assimilating near real-time station snow depth, still utilize globally-constant coefficients to build regression-based retrieval algorithms. In the context of the successful launch of the FY-3F satellites in 2023, we are attempting to work out a better Northern Hemisphere algorithm for the Micro-Wave Radiation Imager-II (FY-3F/MWRI-II), using a set of pixel-sensitive dynamic coefficients regressed based on a spatiotemporally continuous reference SWE dataset. We first utilize a method that couples random forest with the HUT snow emission model to calculate a high-accuracy SWE reference dataset. Then the linear-regression equations are used to fit the reference SWE data and satellite brightness temperature observations at each pixel to build the new operational FY-3F algorithm. Finally, the proposed FY-3F algorithm is validated extensively using four spatially independent datasets. The proposed FY-3F algorithm will improve the global monitoring capabilities for snow cover and enhance a complete and timely understanding of changes in SWE.

Multilayer clouds have a significant importance on cloud climate effects and remote sensing retrieval. In this study, a multilayer cloud detection algorithm is developed for the Advanced Geostationary Radiation Imager (AGRI) onboard the FY-4A geostationary satellite. The algorithm is based on the basic physical assumptions that are also employed for Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) to identify ice overlaying water multilayer clouds. Synchronous observation of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) has been collected and acknowledged as a reliable reference dataset for determination of thresholds. The algorithm used the long-wave infrared bands (8.5 and$10.8~\mu \text{m}$) to determine the phase of the upper layer cloud. Then, the difference between solar reflectance band pairs (1.375 and$1.61~\mu \text{m}$) is used to identify ice overlayer water multilayer clouds when the upper layer is ice cloud. When the upper layer cloud is water, the infrared band ($7.1~\mu \text{m}$) is applied to find misclassified multilayer clouds. The algorithm demonstrates a notable improvement of approximately 0.146 in the probability of detection (POD) compared to MODIS while using CALIOP products as a reference, specifically for cases when the cloud optical depth (COD) surpasses 4. Nevertheless, it does result in a slightly elevated false alarm rate (FAR), around 0.042. In the future, it is necessary to conduct a more comprehensive validation of the algorithm, with particular emphasis on its limits in scenarios where the upper cloud layer is too thin (thick).

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

Satellite passive microwave (PMW) remote sensing is widely used for monitoring the snow water equivalent (SWE) in the Northern Hemisphere. Existing operational SWE retrieval methods, especially those without assimilating ground-based snow depth priors, still utilize globally constant coefficients to construct regression-based retrieval algorithms. The current Fengyun-3 (FY-3) series of SWE product algorithms has made improvements in China, where biases have been significantly reduced locally but not in other regions. Within the context of the successful launch of the FY-3F satellites in 2023, we developed a better Northern Hemisphere algorithm for the Microwave Radiation Imager-II (FY-3F/MWRI-II) using pixel-sensitive coefficients regressed on a reference SWE dataset. We utilized the random forest model coupled with the snow emission model (HUT-RF) to obtain a high-accuracy SWE reference dataset. Then, we employed linear regression equations to fit the reference HUT-RF dataset at each pixel to construct the new operational FY-3F algorithms. We innovatively introduced the brightness temperature differences between 18.7 and 89 GHz and the polarization differences at 10.65 GHz in the regression after noting their sensitivity in deep snow estimation. The proposed FY-3F algorithm was extensively validated via four spatially independent datasets. The results demonstrated that the proposed FY-3F algorithm performed well in non-mountainous and sparsely forested areas, e.g., the overall unbiased root mean square error (unRMSE) values were 27.15 mm over Russia and 13.70 mm over China. High uncertainties still occurred in complex terrains and densely forested areas, e.g., the overall unRMSE values were 75.30 mm over Canada and 129.06 mm over western North America. The proposed FY-3F algorithm could improve global snow cover monitoring capabilities and enhance the complete and timely understanding of SWE changes.

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

Lead is an important feature of the Arctic ice cover, with possible contents of thin ice /or open water. In this letter, we present an algorithm for lead detection based on brightness temperature observations from a single thermal infrared channel of MERSI-II onboard the Chinese FY-3D satellite. Lead contents is classified into open water and thin ice with support information from Sentinel-1 SAR data. Results are evaluated based on visual interpretation of MERSI-II TIR (Thermal infrared) and Sentinel-2 NIR (Near Infrared) data. The accuracy is found to be 85.6% for lead detection and 67% and 52% for thin ice and open water within the lead, respectively.

As one of the major components in the hydrological system, seasonal snow cover in Northeast China has drawn much attention recently. Because of the coarse spatial resolution of the passive microwave (PMW), heterogeneity of snowpack, and forest cover, it is difficult for existing snow products to achieve high precision snow parameters (e.g. snow depth (SD) or snow water equivalent (SWE)) assessment and hydrological research in fine scale. In this study, a novel SD retrieval algorithm that considered both the spatiotemporal dynamic of snow characteristics and forest attenuation was developed by combining the Calibrated Enhanced Resolution Brightness Temperature (TB) data and other auxiliary information, and produced a fine resolution (i.e., 6.25 km × 6.25 km) and high accuracy SD data in Northeast China. Instead of complex physical models, the machine learning was used to untangle the nonlinear complex relationship between SD and the enhanced resolution TB, forest fraction (FF), and snow characteristics. The verification results at ground weather stations showed that the retrieved SD by the proposed algorithm had high consistency with the observed SD, its RMSE, bias, and correlation coefficient (R) of 6.32 cm, -0.23 cm, and 0.63, respectively. Compared with the existing SD products (WESTDC and AMSR2), the developed model greatly improved both in spatial resolution and retrieval accuracy. In general, the fine-resolution SD inversion model achieved satisfactory accuracy and stability, and it will be used to generate long-term SD dataset service for climate change and hydrological research in the future.

Sensitivity analysis (SA) of model parameters is of great importance for understanding, development, and application of models. However, the influence of snow microstructure variability on snow water equivalent retrieval from passive microwave measurements is still unclear. This article explores the parameter sensitivity of the microwave emission model of layered snowpacks (MEMLS) with improved born approximation (IBA) by using a quantitative global SA method, the extended Fourier amplitude sensitivity test (EFAST) algorithm. A deep analysis is conducted, including the sensitivity of passive microwave emission to snow parameters, the sensitivity variation analysis for different snow conditions, and the temporal properties of the parameter sensitivity. The results show the exponential correlation length, snow depth, and snow density are the three most sensitive parameters for snow without salt in the MEMLS model for the brightness temperature gradient at 18.7 and 36.5 GHz. For snow with a small salt content, the exponential correlation length, snow depth, snow temperature, and snow density are the four most sensitive parameters. Second, snow parameter variability highly affects the microwave radiation. The sensitivity values of microwave brightness temperature to snow depth gradually increase when the exponential correlation length is less than 0.25 mm and then slightly decreases with the increase of exponential correlation length and decreases along with the increase of snow density. Finally, our analysis highlights the importance to include the snow density, especially for deep snow depth, in the combination of sensitive factors in future multiparameter retrievals.

Due to the harsh environment, observation networks over the Tibetan Plateau (TP) are very sparse, which makes satellite data valuable for spatially explicit analyses. This study focuses on satellite-based estimations of minimum temperature (Tn) for the Tibet Autonomous Region (TAR). First, seasonal variation in the relationship between nighttime land surface temperature (LSTnight) and Tn at meteorological stations was investigated and winter time was found to be the period when LSTnight has the strongest predictive power for Tn. Then, a thin-plate spline interpolation method incorporating LSTnight as a covariate was implemented to obtain 1 km winter Tn over the TAR. Finally, the trend in winter Tn over 2000-2013 and the impacts of snow cover and elevation on the trend were explored. The main findings are as follows. (1) The introduction of LSTnight can greatly increase Tn estimation accuracy. (2) Negative Tn trends were prevalent over western and northern TAR regions, and a decreasing trend was detected in the regional mean Tn (-0.18 °C/decade). (3) The southeastern TAR, which has lower elevations (<; 4000 m) and higher snow coverage than other areas, showed the most significant warming trend in Tn (0.84 °C/decade). Temperature in this area was close to the melting point, indicating that snow-albedo feedback can be easily triggered. (4) There was a stronger correlation between LSTnight and Tn and an enhanced cold bias in LSTnight over higher elevations or areas with larger snow coverage.

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