The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958).

Atmospheric precipitable water vapor (PWV) is an essential geophysical parameter for weather forecasting, climate change research and hydrological studies. Spaceborne passive microwave sensors support all-weather PWV observation, yet accurate retrieval of land PWV still faces great difficulties owing to the spatiotemporal complexity and variability of land surface emissivity (LSE). This study proposes a novel all-weather land PWV retrieval approach using Fengyun-3D microwave radiation imager (MWRI) observations, which combines physical radiative transfer principles with machine learning techniques. By reorganizing the radiative transfer equation, this study constructs two characteristic factors that can effectively characterize LSE and PWV, and further incorporates them into the machine learning retrieval framework. Three typical machine learning algorithms, namely Random Forest (RF), Adaptive Kernel Extreme Learning Machine (AKELM), and Extreme Gradient Boosting (XGBoost), achieve the optimal retrieval accuracy, with an RMSE of 3.80 mm and an R² of 0.91. Multiple validation results indicate that (1) compared with conventional physically constrained machine learning retrieval methods, the proposed method reduces the root mean square error (RMSE) by 16.5%; (2) against ground-based SuomiNet site observations, the minimum retrieval RMSE is as low as 2.95 mm; (3) the proposed method exhibits stable superiority over the Moderate Resolution Imaging Spectroradiometer (MODIS) MYD05 water vapor products with lower RMSE in all experimental cases. This study offers an effective and reliable technical scheme for all-weather land PWV retrieval, and can facilitate high-precision atmospheric monitoring and related practical operational services.