Remote Sensing

High-resolution bathymetry is critical for nearshore reefs, navigation, and island engineering. This paper proposes an Enhanced Mixture Density Network (EMDN) using dual-stream feature disentanglement and mixed total variance regularization to improve both accuracy and reliability of satellite-derived bathymetry (SDB). Validated with ICESat-2 and Sentinel-2 data over island reefs in the South China Sea and Hawaii, EMDN achieves RMSEs of 1.20 m, 1.24 m, and 3.71 m at Ganquan, Guangjin, and West Molokai, with the lowest mean uncertainties (0.76 m, 0.75 m, 2.46 m). Comparisons with the evaluated baselines demonstrate improved bathymetric accuracy and lower CRPS values, together with sharper prediction intervals and competitive calibration, highlighting the potential of EMDN for nearshore reef bathymetric mapping.

Spaceborne Global Navigation Satellite System reflectometry (GNSS-R) sea surface wind speed retrieval commonly relies on the normalized bistatic radar cross section (NBRCS), whose response depends on observation geometry, link conditions, signal quality, and sea state. Under low-wind, high-swell conditions, local wind speed alone cannot fully explain variations in reflected signals. We use SMAP-R hybrid compact polarimetric observations and ERA5 wind and wave data to assess whether polarimetric features improve sea surface wind speed retrieval. A dataset of 2,067,971 observations is divided by complete months into training, validation, calibration, and test sets. With total significant wave height included in every baseline, paired non-polarimetric and polarimetric configurations are evaluated on identical samples using a geophysical model function with minimum-variance fusion (GMF/MV), extreme gradient boosting (XGBoost), and a fully connected network trained by back-propagation (BP-FCN). Adding the degree of polarization and normalized third Stokes component reduces the overall root mean square error (RMSE) by 7.83%, 5.49%, and 5.31%, respectively, with the polarimetric BP-FCN configuration achieving the lowest RMSE of 1.666 m s⁻¹. Wind speed-stratified analysis shows that the relative error reduction from polarimetric information varies non-monotonically across wind speed intervals. Across all signal-to-noise ratio (SNR) intervals represented in this study, all three methods achieve positive polarimetric gains, with paired month-block bootstrap 95% intervals lying above zero, indicating benefits across different SNR conditions. Under low-wind, high-swell conditions, RMSE decreases by 10.58%, 11.26%, and 9.82%, respectively, exceeding the overall relative gains. Error decomposition indicates that these improvements mainly reflect reduced systematic overestimation. Comparisons against ASCAT scatterometer and NDBC buoy winds using all collocated samples also show positive polarimetric gains of 4.83–5.35% and 4.80–5.78%, respectively. These results demonstrate that polarimetric observations can further improve GNSS-R wind speed retrieval accuracy beyond the information already included in the baselines, with more pronounced gains under the low-wind, high-swell conditions sampled in this study, providing useful complementary information for sea surface wind speed retrieval under complex sea states.

Understanding and estimating the impact of atmospheric turbulence on the growth of precipitation particles in freezing weather can help improve the prediction of freezing rain precipitation. In this study, the turbulent kinetic energy dissipation rate (ε) during a freezing weather event was retrieved using the power spectral analysis method, based on observational data from a Ka-band millimeter-wave cloud radar, radiosondes, and ground automatic weather stations, and the influence of atmospheric turbulence on the growth of freezing rain precipitation particles was investigated using nonlinear causal methods such as transfer entropy (TE), convergent cross-mapping (CCM), the phase-randomized surrogate test, and partial CCM. Causality tests confirmed a significant bidirectional causal relationship between ε and Z_e, with the information transfer from ε to Z_e systematically dominating throughout the entire height range of precipitation particles. The bidirectional original ρ was far higher than the 99% quantile of the surrogate distribution, and the coupling was enhanced after removing the influence of temperature and humidity, indicating that it was independent of the thermodynamic covariant background. The causal intensity in the ice crystal growth zone above 6 km was approximately 3 times the average in the liquid zone, indicating that atmospheric turbulence is one of the key factors driving the growth of precipitation particles in freezing weather and that the effect is concentrated in the dominant layer of ice crystals. This finding provides a practical basis for improving the parameterization scheme and numerical prediction of the precipitation particle microphysical processes in freezing weather.