Calibration of CFOSAT Off‐Nadir SWIM SWH Product Based on CNN‐LSTM Model.

High‐precision observation of significant wave height (SWH) is crucial for marine research. The Surface Waves Investigation and Monitoring (SWIM) aboard the China France Oceanography Satellite (CFOSAT) provides the ocean wave spectrum that allows for the calculation of the off‐nadir SWH parameters, but there exists a certain bias with the in‐situ SWH values. To improve the accuracy of the SWH calculation bias from the off‐nadir 6°, 8°, 10° wave spectra and the whole combined spectrum, this paper establishes a spatio‐temporal hybrid model that combines convolutional neural network (CNN) and long short‐term memory network (LSTM). Additionally, to further correct bias exhibited under high sea state, we introduce a bias correction module based on deep neural network (DNN) to adjust the SWIM off‐nadir SWH greater than 4 m. The experimental results demonstrate a significant enhancement in the accuracy of corrected SWIM off‐nadir SWH, and the best calibration result is 10° with 0.267 m root mean square error (RMSE), and 0.979 correlation coefficient (R) compared with the ERA5 value. We conducted a comprehensive study and analysis on the performance of the proposed model under different wave heights, extreme sea states, and wind and swell regions. Meanwhile, the buoy and altimeters are leveraged to render further evaluation the RMSE of the corrected SWH is less than 0.5 m. Plain Language Summary: The Surface Waves Investigation and Monitoring (SWIM) on the China France Oceanography Satellite (CFOSAT) provides wave data support for ocean research. However, the significant wave height (SWH) parameter calculated from the SWIM off‐nadir spectra has a certain bias from the real value due to the influence of speckle noise. In order to improve the accuracy of the SWIM off‐nadir SWH parameter, we developed a CNN‐LSTM model and introduced a high sea state bias correction network to improve the accuracy of SWH and achieve the purpose of correction. The analysis evaluated the correction performance across various sea conditions, comparing the corrected SWH data with measurements from altimeters and buoys. The findings demonstrated that the root mean square error of the corrected SWH was consistently below 0.5 m, with a correlation coefficient exceeding 0.940. This underscores the effectiveness of our model in enhancing the precision of nadir SWH data bias from SWIM instruments. Key Points: A model of convolutional and long short‐term memory neural network is developed to calibrate the surface waves investigation and monitoring (SWIM) off‐nadir significant wave heightThe corrected SWIM off‐nadir significant wave height achieves comparable accuracy with the Jason‐3, HY‐2B, and buoysThe proposed model performs well in the calibration of the wind wave region and the swell region [ABSTRACT FROM AUTHOR]