Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for wind speed retrieval over inland waters, with relevance to wind energy assessment and lake–atmosphere exchange studies. Existing GNSS-R wind retrieval methods are well established for open oceans but face major challenges over inland waters, where coherent scattering dominates and traditional ocean models produce large systematic biases. Unlike open oceans, inland waters are dominated by coherent scattering due to limited fetch, resulting in Delay-Doppler Maps (DDM) with highly concentrated energy and minimal spreading. These characteristics render conventional ocean-based retrieval models—built on incoherent scattering assumptions—often inadequate. To overcome this, we develop a lightweight convolutional neural network (CNN) tailored to the coherent regime, using raw CYGNSS DDM as input for end-to-end wind speed regression. Cross-seasonal validation over Lake Victoria and Lake Hongze shows that the model robustly captures wind-driven spatiotemporal patterns aligned with ERA5. Notably, ERA5 reanalysis winds exhibit uncertainties over inland waters, with a root mean square error (RMSE) of 1.5–2.5 m/s against in situ buoys. The model yields a low RMSE (<0.7 m/s) in reconstructing ERA5-resolved wind patterns. This work extends GNSS-R to inland waters, offering a lightweight, deployable remote sensing solution for wind energy and lake–atmosphere research.
Ni et al. (Fri,) studied this question.