학술논문

Daytime Sea Surface Temperature Retrieval Incorporating Mid-Wave Imager Measurements: Algorithm Development and Validation
Document Type
Periodical
Author
Source
IEEE Transactions on Geoscience and Remote Sensing IEEE Trans. Geosci. Remote Sensing Geoscience and Remote Sensing, IEEE Transactions on. 59(4):2833-2844 Apr, 2021
Subject
Geoscience
Signal Processing and Analysis
Ocean temperature
Temperature measurement
Sea measurements
Sea surface
Data models
Atmospheric modeling
Predictive models
Information retrieval
infrared image sensor (Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua and MODIS-Terra)
inverse problem [physical deterministic (PD)]
radiative transfer (RT)
remote sensing
sea surface temperature (SST)
Language
ISSN
0196-2892
1558-0644
Abstract
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.