The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD.

The new-generation FengYun geostationary meteorological satellite has a high spatial and temporal resolution, which is advantageous in environmental assessments and air pollution monitoring. This study researched the ground-level particulate matter concentration estimation, based on satellite-observed radiations. The radiation of ground-level particulate matter is separate from the apparent radiation observed by satellites. The positive correlation between PM2.5 and PM10 is also considered to improve the accuracy of inversion results and the interpretability of the estimation model. Then, PM2.5 and PM10 concentrations were estimated synchronously every 5 min in mainland China based on FY-4A satellite directly observed radiations. The validation results showed that the improved model estimated results were close to the ground site measured results, with a high determination coefficient (R²) (0.89 for PM2.5, and 0.90 for PM10), and a small Root Mean Squared Error (RMSE) (4.69 μg/m³ for PM2.5 concentrations, and 13.77 μg/m³ for PM10 concentrations). The estimation model presented a good performance in PM2.5 and PM10 concentrations during typical haze and dust storm cases, indicating that it is applicable in different weather conditions and regions.

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.

Water transparency is one of the important parameters to describe the optical properties of seawater. It's a visual reflection of the degree of turbidity of seawater and the degree of absorption and scattering of light by seawater. Firstly, this paper verified and analyzed the suitability of the water transparency retrieval method proposed by Lee in 2015 (denoted as Zsd_lee_2015) for Jiaozhou Bay and Qingdao offshore waters by using the in-situ data. Secondly, the water transparency estimation results retrieved from GOCI (Geostationary Ocean Color Imager) using four atmospheric correction algorithms were compared and analyzed with in-situ data in study area. The four atmospheric correction algorithms are the standard atmospheric correction algorithms of KOSC GOCI GDPS1.3 and GDPS2.0, the standard near-infrared atmospheric correction algorithm of NASA, and MUMM (Management Unit of the North Sea Mathematical Models). At last, GOCI data on Mar 09, 2018 were used to analyze the differences of hourly changes of transparency caused by different atmospheric correction algorithms in detail. The results show that the Zsd_lee_2015 model is suitable for the study area (R ² >0.8, MAPE = 16.4%). The difference of transparency value caused by atmospheric correction algorithm cannot be ignored completely. Moreover, there are great differences in the moment when the maximum value of transparency presented based on different atmospheric correction algorithms, while the moment when the minimum value of transparency presented is basically the same, mostly occurring at 00:16. The results cast doubt on the previous conclusions about the hourly variations of biochemical parameters using GOCI because the hourly variations obtained under different atmospheric correction algorithms are not completely consistent.

长江冲淡水向外海扩展对于黄、东海水文生物环境以及物质循环有重大影响。通过分析1998－2014年连续17年卫星观测结果，研究夏季长江冲淡水向外海扩展的年际间变化规律，分析径流量和风场对其变化的影响。结果显示，夏季长江冲淡水向外海的扩展路径主要有东北偏北型、东北型和偶见型三类，以东北型为主；冲淡水向东扩展位置主要分布在123－126°E区间，年际间差异显著。扩展过程的年际变化主要体现在扩展路径是否转向、向东扩展的态势呈现7弱8强还是7盛8衰。长江径流量的多寡对冲淡水向外海扩展的远近起到重要影响作用，径流量较大时，冲淡水向东扩展较远，径流量较小时，冲淡水向东扩展的距离会受到明显的限制。风向的变化是导致冲淡水扩展过程呈现多样性的一个重要原因。东南风、东风对于冲淡水向东北方向扩展起到促进作用，对于向东南方向扩展起到一定的阻碍作用，东北风对冲淡水向外海的扩展起到阻碍作用，迫使冲淡水盘踞在长江口近岸海域。