Daytime Sea Surface Temperature Retrieval Incorporating Mid-Wave Imager Measurements: Algorithm Development and Validation

Incorporation of mid-wave infrared (MWIR) channel/s into the prevalent regression-based split-window technique (SWT) for operational daytime sea surface temperature (SST) retrieval is challenging. However, the MWIR channels are highly desirable to obtain unambiguous information from the surface sinc...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing 2021-04, Vol.59 (4), p.2833-2844
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description Incorporation of mid-wave infrared (MWIR) channel/s into the prevalent regression-based split-window technique (SWT) for operational daytime sea surface temperature (SST) retrieval is challenging. However, the MWIR channels are highly desirable to obtain unambiguous information from the surface since these channels offer high transparency with respect to the earth's atmosphere and are very sensitive to the thermal emission from the surface. On the other hand, the MWIR channel/s can be easily incorporated into any physical-based SST retrieval scheme. Daytime SST retrieval using various physical-based methods is studied and it is found that the physical deterministic sea surface temperature (PDSST) retrieval scheme is the best choice. This article discusses various scientific aspects of the daytime PDSST retrieval including MWIR channels from a theoretical point of view and its application on real data from Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua. Daytime SST retrievals from PDSST, including MWIR channels, are also compared with the currently operational SWT-based SSTs from MODIS-Aqua and MODIS-Terra by NASA, without MWIR channels. The root-mean-square differences in PDSST from the in situ buoys using the global matchup data for daytime MODIS-Aqua SSTs is ~0.28 K for complete cloud-free set and is ~0.38 K for MODIS-Aqua and MODIS-Terra when quasi-deterministic cloud and error masking algorithm is applied for cloud detection. The information gain is defined by combining the two metrics, quality improvement and the increase in cloud-free data. The PDSST suite rendered two to three times as much information as the NASA-produced daytime regression-based SST.
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The root-mean-square differences in PDSST from the in situ buoys using the global matchup data for daytime MODIS-Aqua SSTs is ~0.28 K for complete cloud-free set and is ~0.38 K for MODIS-Aqua and MODIS-Terra when quasi-deterministic cloud and error masking algorithm is applied for cloud detection. The information gain is defined by combining the two metrics, quality improvement and the increase in cloud-free data. 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subjects Algorithms
Atmospheric modeling
Buoys
Channels
Data models
Daytime
Information retrieval
infrared image sensor (Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua and MODIS-Terra)
inverse problem [physical deterministic (PD)]
MODIS
Ocean temperature
Predictive models
Quality control
radiative transfer (RT)
Regression analysis
remote sensing
Retrieval
Sea measurements
Sea surface
Sea surface temperature
sea surface temperature (SST)
Spectroradiometers
Temperature measurement
Thermal emission
title Daytime Sea Surface Temperature Retrieval Incorporating Mid-Wave Imager Measurements: Algorithm Development and Validation
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