Simulation Framework and Case Studies for the Design of Sea Surface Salinity Remote Sensing Missions

Abstract

L-band microwave radiometers have now been used to measure sea surface salinity (SSS) from space for over a decade with the SMOS, Aquarius, and SMAP missions, and it is expected that the launch of the CIMR mission in the later half of this decade will ensure measurement continuity in the near future. Beyond these missions, it is useful to consider how future missions can be designed to meet different scientific objectives and performance requirements as well as to fit within different cost spaces. In this article, we present a software simulator for remote sensing measurements of ocean state capable of generating L1-and L2-equivalent data products for an arbitrary spacecraft mission including multifrequency fixed-pointing or scanning microwave radiometers.This simulator is then applied to case studies of SSS measurement over selected areas of interest, including the Gulf Stream, Southern Ocean, and Pacific tropical instability wave regions. These simulations illustrate how different design choices concerning receiver bandwidth and revisit time can improve the detection of SSS features in these regions from the mesoscale to the seasonal scale.

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

“…The SSS is one of the three fundamental components of physical oceanography, and also an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. The SMOS satellite [1], Aquarius satellite [2], and SMAP satellite [3] have all demonstrated the feasibility of measuring SSS from space using an L-band microwave radiometer [4,5], and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model [6]. However, the sea surface brightness temperatures calculated with different forward models, which are composed of different relative permittivity models and SSR brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion [7,8].…”