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

Physical Oceanography Department, Woods Hole Oceanographic Institution, 266 Woods Hole Road, MS 21, Woods Hole, MA 02543, USA
Remote Sens. 2020, 12(3), 447; https://doi.org/10.3390/rs12030447
Received: 28 November 2019 / Revised: 8 January 2020 / Accepted: 28 January 2020 / Published: 1 February 2020
(This article belongs to the Section Ocean Remote Sensing)
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. View Full-Text
Keywords: sea-surface salinity; satellite; validation; Red Sea; mesoscale; eddy sea-surface salinity; satellite; validation; Red Sea; mesoscale; eddy
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Menezes, V.V. Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application. Remote Sens. 2020, 12, 447.

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