Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application

Abstract

Sea-surface salinity (SSS) is an essential climate variable connected to Earth's hydrological cycle and a dynamical component of ocean circulation, but its variability is not well-understood. Thanks to Argo floats, and the first decade of salinity remote sensing, this is changing. While satellites can retrieve salinity with some confidence, accuracy is regionally dependent and challenging within 500-1000 km offshore. The present work assesses the first four years of the National Aeronautics and Space Administration's Soil Moisture Active Passive (SMAP) satellite in the North Indian Ocean. SMAP's improved spatial resolution, better mitigation for radio-frequency interference, and land contamination make it particularly attractive to study coastal areas. Here, regions of interest are the Bay of Bengal, the Arabian Sea, and the extremely salty Red Sea (the last of which has not yet received attention). Six SMAP products, which include Levels 2 and 3 data, were statistically evaluated against in situ measurements collected by a variety of instruments. SMAP reproduced SSS well in both the Arabian Sea and the Bay of Bengal, and surprisingly well in the Red Sea. Correlations there were 0.81-0.93, and the root-mean-square difference was 0.38-0.67 for Level 3 data.

Correction of Satellite Sea Surface Salinity Products Using Ensemble Learning Method

“…However, the salinity variability is higher in the BOB than in other regions as shown by the SSS standard deviation of the products (Figure 1(b) and Table 1, line 7). As a consequence the two SMOS products are more correlated with in-situ data in the BOB (Corr, on the order of 0.85, refer to table 1 for precise statistics) than in the AS (Corr: on the order of 0.6), demonstrating a higher signal-to-noise ratio in BoB, which is consistent with the results of a previous study [34]. Moreover, two SMOS products are consistent with the in situ data in the southern tropical Indian Ocean with low RMSD (on the order of 0.28) and high correlation coefficients (on the order of 0.9).…”

“…The time range of two SMOS products considered in our study is Jan. ,2011-Dec., 2018. The SMAP SSS V4 8-day running averages data on the 0.25° × 0.25° grids is chosen to compare the SMOS SSS data after correction [33], because SMAP has less landsea contamination and better RFI mitigation than SMOS [28], [34], [35].…”

Impact of Remote Equatorial Winds and Local Mesoscale Eddies on the Existence of “River in the Sea” Along the East Coast of India Inferred From Satellite SMAP

“…The discarded values were 3%-18% for all boxes except CBOB. In the open ocean, in CBOB box, the salinity amplitude drop is relatively less, hence 2 psu criteria were followed for data filtration (Menezes, 2020) and discarded values are only 3%. Pairs of respective months of all 5 years were separated to calculate the monthly statistics such as RMSD and correlation based on the equations given under Table 2.…”

Application of Phenomena‐Resolving Assessment Methods to Satellite Sea Surface Salinity Products

“…However, there are only a few studies on the ability of SSS satellite data to depict oceanic phenomena. In assessing the performance of satellite data to reflect oceanic phenomena, some studies compare the time series of SSS between satellite data and in situ data (such as moored buoys) to analyze whether satellite data can capture the rapid salinity changes over short periods (Menezes, 2020; Tang et al., 2017). In addition, the wavenumber spectrum is also applied to determine the effective resolution of satellite products by analyzing the slope of the wavenumber spectrum and comparing the spectrum energy (Olmedo et al., 2016; Yan et al., 2019).…”