Global Sea Surface Height Measurement From CYGNSS Based on Machine Learning

Abstract

Cyclone Global Navigation Satellite System (CYGNSS) launched in recent years, provides a large amount of spaceborne GNSS Reflectometry data with all-weather, global coverage, high space-time resolution, and multiple signal sources, which provides new opportunities for the machine learning (ML) study of sea surface height (SSH) inversion. This article proposes for the first time two different CYGNSS SSH inversion models based on two widely used ML methods, back propagation (BP) neural network and convolutional neural network (CNN). The SSH calculated by using Danmarks Tekniske Universitet (DTU) 18 ocean wide mean SSH (MSSH) model (DTU18) with DTU global ocean tide model is used for verification. According to the strategy of independent analysis of data from different signal sources, the mean absolute error (MAE) of the BP and CNN models’ inversion specular points’ results during 7 days is 1.04 m and 0.63 m, respectively. The CLS 2015 product and Jason-3 data were also used for further validation. In addition, the generalization ability of the model, for 6 days and 13 days training sets, was also evaluated. For 6 days training set, the prediction results’ MAE of the BP model is 11.59 m and 5.90 m for PRN2 and PRN4, and the MAE of the CNN model is 1.37 m and 0.97 m for PRN2 and PRN4, respectively. The results show that BP and CNN inversions are in high agreement with each product, and the CNN model has relatively higher accuracy and better generalization ability.

Keywords

• Back propagation (BP)
• convolution neural network (CNN)
• cyclone global navigation satellite system (CYGNSS)
• global navigation satellite system reflectometry (GNSS-R)
• sea surface height (SSH)