Hourly Sea Level Prediction‐Based GNSS‐IR Inversions by Combining the Least Squares Learning Cross‐Checking Method With the Gaussian Kernel Model L2 Constraint and LSTM

Abstract

Multisatellite systems and multi‐signal‐to‐noise ratio types provide a more prosperous data basis for the inversion of sea level by GNSS‐IR technology. However, there are few studies on data reconstruction of the retrieved sea level height. This study takes the SPBY station as an example and introduces the least squares learning cross‐checking method with the Gaussian kernel model (GKM) L2 constraint. Furthermore, research on hourly sea level reconstruction based on GNSS‐IR is carried out. Compared with the measured sea level, the sea level height in 2021 obtained after the fusion of multisource data has an R of 0.905 and an RMSE of 0.144 m. And the result obtained after data reconstruction has an R of 0.958 and an RMSE of 0.090 m. Compared with the multisource data fusion results before reconstruction, R is increased by 5.9%, and RMSE is decreased by 37.5%. Finally, using the reconstructed sea level data based on Long Short‐Term Memory (LSTM) artificial neural network to carry out the research on sea level prediction, which verifies the conclusion that more reliable forecast values can be obtained based on 5 months of training data. Among them, the R from 1 to 24 hr is 0.905, and the RMSE is 0.145 m. Compared with the inversion accuracy of GNSS‐IR, the R is increased by 2.0%, and the RMSE is decreased by 21.4%. This study demonstrates the feasibility of GNSS‐IR technology and L2‐constrained least squares learning cross‐checking method based on the GKM to reconstruct sea level data with high temporal resolution and high accuracy. The reliability of the sea level prediction based on GKM reconstruction and LSTM is verified in the sea level forecast of the next 24 epochs, which has essential applications in sea level data recovery and forecasting.

Preliminary inquiry on the linear relationship between the height of the station and the ground height error retrieved by GNSS-IR with low-cost smart electronic equipment

“…Figure 3 takes Honor 60 as an example to show the diagram of GNSS-IR altimetry technology. To compare the measured height h and the retrieved height formula for effect during satellite signal propagation can be expressed as (Zhang et al 2017, Zheng et al 2022b, 2023:…”

Research on GNSS-IR soil moisture retrieval based on random forest algorithm

“…GNSS-IR is a technique that uses the GNSS signals for interferometric measurement to obtain the relevant parameters of the Earth's surface. This technique was originally used for water level inversion, and accurate information on water level can be obtained by interferometry of GNSS signals [4][5][6]. Later, GNSS-IR technology was gradually extended to other fields, such as SM [7], snow depth [8][9][10], vegetation index [11,12], sea ice thickness [13,14] and ground height [15].…”