Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets.

The 4 km/15 min downward surface shortwave radiation (DSSR) product from Fengyun-4B (FY-4B)/AGRI shows great potential for solar energy assessment in China, but its applicability requires further validation. This study conducts a comprehensive evaluation of the FY-4B DSSR product over Guangxi for 2025, using ground-observed irradiance from a dense network of 101 photovoltaic (PV) power stations. The overall comparison shows a correlation coefficient (R) of 0.84, a root-mean-square error (RMSE) of 161.78 W/m², a relative prediction error (RPE) of 51.02%, and a mean bias error (MBE) of 56.23 W/m², indicating systematic overestimation. Seasonally, the largest discrepancies occur in spring (MBE = 85.27 W/m², RPE = 53.12%) and summer (R = 0.82, RMSE = 184.10 W/m²). Diurnally, retrievals are most reliable around 09:00–13:00 local time, deteriorating notably in the early morning and, especially, the afternoon and evening. Spatially, errors are larger in the hilly, elevated terrain of northwestern Guangxi (e.g., Hechi) than in flatter southern and coastal cities, with RPE rising from roughly 40–60% at lower elevations to around 80% above 600–700 m. Sky-condition classification confirms that data quality follows clear > cloudy > overcast sky, while AOD-binned analysis shows aerosol loading playing a secondary but non-negligible role, especially under high-AOD pollution events. Solar zenith angle (SZA) also strongly affects accuracy: R peaks around 0.75–0.8 in the 30–50° SZA range and drops below 0.4 beyond about 75°. This study offers the most spatially and dimensionally comprehensive validation of FY-4B DSSR over Guangxi to date, characterizing accuracy across seasonal, diurnal, spatial, cloud, aerosol, solar-geometry, and elevation dimensions using a denser ground-truth network than previously available, with direct implications for photovoltaic resource assessment and power forecasting in subtropical hilly regions.

Gaofen-1/Gaofen-6 (GF1/GF6) wide-field-view (WFV) imagery provides repeated wide-area observations for regional surface-water monitoring, but its use is complicated by fragmented water patterns, vegetation–water mixtures, terrain and cloud shadows, and cross-scene radiometric variability. This study presents a seven-channel nested U-Net (UNet++) framework with a residual logit refinement branch (RLRB) for binary surface-water extraction. The input combines Blue, Green, Red, near-infrared, normalized difference water index, normalized difference vegetation index, and digital-elevation-model-derived slope channels. Five complete GF1/GF6 scenes were locked before dataset partitioning, and an 8192 m exclusion buffer was used to exclude spatially overlapping or adjacent non-test tiles from model development, thereby reducing the risk of spatial leakage. The resulting quantitative test set contained 2783 valid 512 by 512 pixel tiles derived from the five scenes withheld at scene level. The UNet++ model with RLRB and edge-consistency supervision achieved an mIoU of 0.941, Water IoU of 0.889, and Water F1 of 0.941. A controlled $2\times 2$ experiment separated the effects of residual refinement and edge-consistency supervision, while feature, loss-component, random-seed, rule-based-baseline, and complete-scene analyses further characterized the framework. Complete-scene sliding-window inference required approximately 3.7 min per scene on a single NVIDIA GeForce RTX 5060 Laptop GPU, demonstrating the feasibility of georeferenced water-mask generation under the specified computational setting.