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Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures
Article

Optimal Estimation of Sea Surface Temperature from AMSR-E

1
Danish Meteorological Institute, Lyngbyvej 100, DK-2100 Copenhagen Ø, Denmark
2
DTU-Space, Technical University of Denmark, DK-2800 Lyngby, Denmark
3
Earth and Space Research, Seattle, WA 98121, USA
4
Brockmann Consult GmbH, Max-Planck-Str. 2, 21502 Geesthacht, Germany
5
European Space Agency/European Space Research and Technology Centre (ESA/ESTEC), 2201 AZ Noordwijk, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 229; https://doi.org/10.3390/rs10020229
Received: 26 November 2017 / Revised: 19 January 2018 / Accepted: 30 January 2018 / Published: 2 February 2018
(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)
The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA’s Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST. View Full-Text
Keywords: remote sensing; sea surface temperature (SST); microwave; optimal estimation remote sensing; sea surface temperature (SST); microwave; optimal estimation
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MDPI and ACS Style

Nielsen-Englyst, P.; L. Høyer, J.; Toudal Pedersen, L.; L. Gentemann, C.; Alerskans, E.; Block, T.; Donlon, C. Optimal Estimation of Sea Surface Temperature from AMSR-E. Remote Sens. 2018, 10, 229. https://doi.org/10.3390/rs10020229

AMA Style

Nielsen-Englyst P, L. Høyer J, Toudal Pedersen L, L. Gentemann C, Alerskans E, Block T, Donlon C. Optimal Estimation of Sea Surface Temperature from AMSR-E. Remote Sensing. 2018; 10(2):229. https://doi.org/10.3390/rs10020229

Chicago/Turabian Style

Nielsen-Englyst, Pia; L. Høyer, Jacob; Toudal Pedersen, Leif; L. Gentemann, Chelle; Alerskans, Emy; Block, Tom; Donlon, Craig. 2018. "Optimal Estimation of Sea Surface Temperature from AMSR-E" Remote Sens. 10, no. 2: 229. https://doi.org/10.3390/rs10020229

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