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Letter

A Deep Learning Network to Retrieve Ocean Hydrographic Profiles from Combined Satellite and In Situ Measurements

Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 80133 Naples, Italy
Remote Sens. 2020, 12(19), 3151; https://doi.org/10.3390/rs12193151
Received: 5 September 2020 / Revised: 23 September 2020 / Accepted: 24 September 2020 / Published: 25 September 2020
(This article belongs to the Section Ocean Remote Sensing)
An efficient combination of remotely-sensed data and in situ measurements is needed to obtain accurate 3D ocean state estimates, representing a fundamental step to describe ocean dynamics and its role in the Earth climate system and marine ecosystems. Observations can either be assimilated in ocean general circulation models or used to feed data-driven reconstructions and diagnostic models. Here we describe an innovative deep learning algorithm that projects sea surface satellite data at depth after training with sparse co-located in situ vertical profiles. The technique is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and is applied here to the measurements collected between 2010 and 2018 over the North Atlantic Ocean. The model provides hydrographic vertical profiles and associated uncertainties from corresponding remotely sensed surface estimates, outperforming similar reconstructions from simpler statistical algorithms and feed-forward networks. View Full-Text
Keywords: artificial intelligence; machine learning; deep learning; neural networks; Earth observations; ocean dynamics; sea surface temperature; sea surface salinity; altimetry; hydrography artificial intelligence; machine learning; deep learning; neural networks; Earth observations; ocean dynamics; sea surface temperature; sea surface salinity; altimetry; hydrography
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MDPI and ACS Style

Buongiorno Nardelli, B. A Deep Learning Network to Retrieve Ocean Hydrographic Profiles from Combined Satellite and In Situ Measurements. Remote Sens. 2020, 12, 3151. https://doi.org/10.3390/rs12193151

AMA Style

Buongiorno Nardelli B. A Deep Learning Network to Retrieve Ocean Hydrographic Profiles from Combined Satellite and In Situ Measurements. Remote Sensing. 2020; 12(19):3151. https://doi.org/10.3390/rs12193151

Chicago/Turabian Style

Buongiorno Nardelli, Bruno. 2020. "A Deep Learning Network to Retrieve Ocean Hydrographic Profiles from Combined Satellite and In Situ Measurements" Remote Sensing 12, no. 19: 3151. https://doi.org/10.3390/rs12193151

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