Analyses of the Positive Bias of Remotely Sensed SST Retrievals in the Coastal Waters of Rio de Janeiro

Peres, Leonardo F.; França, Gutemberg Borges; Paes, Rosa Cristhyna; Sousa, Rodrigo Carvalho de; Oliveira-Jr, Antônio

doi:10.1109/tgrs.2017.2726344

Cited by 12 publications

(8 citation statements)

References 24 publications

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“…5) using exact a priori error is significantly lower than the DFR of the MTLS for ∼70% of cases; and 6) the values of OEM h are closer to the MTLS results, but "truth" is required to obtain such results from OEM. A similar finding is also reported in [27], and OEM can produce better retrieval than regression when truth is used as a priori.…”

Section: A Information Content and Error Analysissupporting

confidence: 83%

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

Koner

2021

IEEE Trans. Geosci. Remote Sensing

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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 physicalbased 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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Section: A Information Content and Error Analysissupporting

confidence: 83%

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

Koner

2021

IEEE Trans. Geosci. Remote Sensing

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“…Satellite data include the hourly sea surface temperatures (SSTs) from the Advanced Himawari Imager (AHI) of the Japanese Himawari‐8 with 2‐km spatial resolution (Bessho et al., 2016; Peres et al., 2017), and the hourly Chl‐a from the Geostationary Ocean Color Imager (GOCI) of the Republic of Korea's Communication, Ocean and Meteorological Satellite (COMS) with 500‐m spatial resolution (Ryu et al., 2012). Daily AHI SST and Chl‐a products were composited from the hourly data.…”

Section: Oceanic Sea Surface Cooling and High Biomass Blooms From Satmentioning

confidence: 99%

Dynamical Ocean Responses to Typhoon Malakas (2016) in the Vicinity of Taiwan

Shen

Wang

et al. 2021

JGR Oceans

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Five to ten typhoons pass Taiwan each year and often cause substantial losses of life and property. In Taiwan coastal areas, typhoon winds generate upper ocean Ekman transport and trigger coastal upwelling or downwelling, depending on the typhoon track orientation relative to the coastline. The storm wind field can mechanically drive the upper water column away from (toward) the coast, forming a divergence (convergence), which induces local coastal upwelling (downwelling) (

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“…To solve the differences among MODIS and multisource daily SST products caused by the sampling depths, it is necessary to consider the differences as results of the cool skin effect and diurnal heating (Luo et al, 2020). The General Ocean Turbulence Model (GOTM) can model the SST signal at different depths by simulating the hydrodynamic and thermodynamic processes of vertical mixing in one-dimensional water columns in natural waters which has been successfully used to model the near-surface variability of ocean tem-perature (Karagali et al, 2017;Pimentel et al, 2018). General ocean models typically simulate the surface layer of 5-10 m as a uniform layer, and simulating such thin sea surface skin layers and subskin layers takes a long time.…”

Section: Bias Adjustment Scheme For Multisource Remote Sensing Datamentioning

confidence: 99%

“…The temperature at the interface between the atmosphere and ocean, known as the sea surface temperature (SST), is an important indicator of Earth's ecosystem (Hosoda and Sakaida, 2016). SSTs are widely used in atmospheric and oceanographic studies, such as in atmospheric simulations, climate change monitoring, and studies of marine dynamic environments (Kawai and Wada, 2007;Martin et al, 2007;Peres et al, 2017;Reynolds and Smith, 1995). In addition, the oceans cover 70 % of Earth's surface.…”

Section: Introductionmentioning

confidence: 99%

A new global gridded sea surface temperature data product based on multisource data

Cao

Mao

Yan

et al. 2021

Earth Syst. Sci. Data

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Abstract. Sea surface temperature (SST) is an important geophysical parameter that is essential for studying global climate change. Although sea surface temperature can currently be obtained through a variety of sensors (MODIS, AVHRR, AMSR-E, AMSR2, WindSat, in situ sensors), the temperature values obtained by different sensors come from different ocean depths and different observation times, so different temperature products lack consistency. In addition, different thermal infrared temperature products have many invalid values due to the influence of clouds, and passive microwave temperature products have very low resolutions. These factors greatly limit the applications of ocean temperature products in practice. To overcome these shortcomings, this paper first took MODIS SST products as a reference benchmark and constructed a temperature depth and observation time correction model to correct the influences of the different sampling depths and observation times obtained by different sensors. Then, we built a reconstructed spatial model to overcome the effects of clouds, rainfall, and land interference that makes full use of the complementarities and advantages of SST data from different sensors. We applied these two models to generate a unique global 0.041∘ gridded monthly SST product covering the years 2002–2019. In this dataset, approximately 25 % of the invalid pixels in the original MODIS monthly images were effectively removed, and the accuracies of these reconstructed pixels were improved by more than 0.65 ∘C compared to the accuracies of the original pixels. The accuracy assessments indicate that the reconstructed dataset exhibits significant improvements and can be used for mesoscale ocean phenomenon analyses. The product will be of great use in research related to global change, disaster prevention, and mitigation and is available at https://doi.org/10.5281/zenodo.4419804 (Cao et al., 2021a).

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Analyses of the Positive Bias of Remotely Sensed SST Retrievals in the Coastal Waters of Rio de Janeiro

Cited by 12 publications

References 24 publications

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

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

Dynamical Ocean Responses to Typhoon Malakas (2016) in the Vicinity of Taiwan

A new global gridded sea surface temperature data product based on multisource data

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