Investigation on an inversion method of ocean salinity by lidar based on a neural network.

• Raman spectra and Brillouin frequency shift can be combined for inversion of ocean salinity. • This paper proposes an inversion method of ocean salinity based on back propagation neural networks. • Compared with the physics-based algorithm model results, the salinity inversion accuracy of ocean salinity is improved. • This model can achieve high-speed and high-precision inversion of seawater salinity. To achieve high-precision and high-speed inversion of salinity using lidar data, this paper proposes a method for inversion of salinity based on back propagation neural network. Both the Raman spectra and the Brillouin frequency shift are related to temperature and salinity of ocean. However, pressure, refractive index, sound velocity, suspended particles, colored dissolved organic matter and ocean currents will affect the detection results when using lidar detection. Therefore, we establish a neural network model with 8 input parameters (NN-8), 5 input parameters (NN-5), and 2 input parameters (NN-2) and compares the inversion results of three models, NN-8 and NN-5 with higher accuracy, analyzes the correlation between the measurement results and the inversion results. The correlation coefficients of both NN-8 and NN-5 are greater than 0.999. Their RMSEs are 0.203‰ and 0.205‰, and REs are 0.09% and 0.105%, respectively. NN-5 can achieve higher inversion precision with fewer parameters. The influence of detection parameters on salinity inversion is evaluated using connection weights. Brillouin frequency shift and Raman spectra play a leading role in the salinity inversion, and other influencing factors can help to improve the inversion accuracy. Raman spectra at different salinities were detected experimentally, and the results are verified using the established model. The model results are consistent with the experimental results, and the error is less than 0.4‰. Compared with other classical regression methods, the results showed that the inversion results have high accuracy, and their errors are all less than 0.4‰. The research can offer data support for the global climates and ecosystems. [ABSTRACT FROM AUTHOR]