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Open AccessArticle

Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature

1
Department of Meteorology, University of Reading, Reading RG6 6BB, UK
2
National Centre for Earth Observation, University of Reading, Reading RG6 6BB, UK
3
Brockmann Consult GmbH, 21029 Hamburg, Germany
4
EUMETSAT, 1 Eumetsat Allee, 64295 Darmstadt, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1048; https://doi.org/10.3390/rs12061048
Received: 28 February 2020 / Revised: 20 March 2020 / Accepted: 21 March 2020 / Published: 24 March 2020
(This article belongs to the Section Ocean Remote Sensing)
Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing. View Full-Text
Keywords: optimal estimation; satellite inter-calibration; harmonization; sea surface temperature; infra-red remote sensing; parameter estimation; retrieval theory; error covariance optimal estimation; satellite inter-calibration; harmonization; sea surface temperature; infra-red remote sensing; parameter estimation; retrieval theory; error covariance
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Merchant, C.J.; Block, T.; Corlett, G.K.; Embury, O.; Mittaz, J.P.D.; Mollard, J.D.P. Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature. Remote Sens. 2020, 12, 1048.

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