Method and system for constructing a spaceborne gnss-r ocean significant wave height retrieval model under high sea conditions

A method for constructing an ocean significant wave height retrieval model under high sea conditions, comprising:

• S1, acquiring modeling data, auxiliary data and validating data, wherein the modeling data comprises:
• spaceborne global navigation satellite system reflectometry (GNSS-R) data and ERA5 significant wave height data;
• the auxiliary data comprises:
• ERA5 data, SMAP data and OSCAR data;
• and the validating data comprises:
• ERA5, WaveWatch III and Jason 3 significant wave height data;
• S2, extracting spaceborne GNSS-R characteristic variable parameters and auxiliary variable parameters, and matching the parameters of the extracted variables by space-time;
• S3, carrying out data quality control and data set division on the variable parameters after time-space matching;
• S4, using the divided data set to construct and train the WaveMambaFormer model, and using shapley additive explanations (SHAP) explainer to calculate that importance of feature and global interpretation, and to enhance the explainability of WaveMambaFormer model;
• S5, using the ERA5, WaveWatch III and Jason-3 significant wave height data as reference data to evaluate the retrieval performance of WaveMambaFormer model,
• wherein in the S4, the WaveMambaFormer model comprises three models:
• delayed doppler diagram (DDM) feature extraction model of CNN, the model of Transformer and the State Space Model, SSM model;
• wherein first, the local spatial information of the signal in bistatic radar cross section (BRCS) of a two-base radar scattering cross-section is extracted by a multilayer convolutional layer of CNN, the preset salient features are retained and the number of parameters is reduced using global average pooling after the convolution operation, and then the retained salient features are input into the Transformer to further capture long-range dependencies and temporal relations;
• and the information in eff_scatter is extracted from the reflected signals by capturing the long-range dependencies between different time points through the self-attention mechanism;
• finally, the features are extracted from the variable parameters through the dynamic multipath SSM model.