Journal Article

The SSR Brightness Temperature Increment Model Based on a Deep Neural Network

Zhang, Huan, Zhang, Qingjun, Wen, Zhongkai, Shu, Weiping, Zhou, Yashi, Ren, Jingjing, Li, Shuang, Zhang, Liqiang, Lu, Xiang, Liu, Lei

2023

Overview

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research.

• Publisher: MDPI AG
• Subject: Accuracy / Altitude / Approximation / Artificial intelligence / Artificial neural networks / Atmosphere / Atmospheric pressure / Brightness / Brightness temperature / carbon / Carbon cycle / climate / Climate change / Comparative analysis / Data processing / deep neural network / Experiments / Global climate / Hydrologic cycle / Integral equations / Marine ecology / Mathematical models / Measurement / Neural networks / Ocean / Ocean temperature / Optical properties / Pallets / Permittivity / Radiation / Radiometers / relative permittivity model / Remote observing / Remote sensing / Salinity / satellites / Sea level / sea surface brightness temperature forward model / sea surface salinity / Sea surface temperature / Seawater / SSR brightness temperature increment model / Surface brightness / Surface roughness / Temperature / Temperature requirements