Algorithm and validation of sea surface temperature observation using MODIS sensors aboard terra and aqua in the western North Pacific

Hosoda, Kohtaro; Murakami, Hiroshi; Sakaida, Futoki; Kawamura, Hiroshi

doi:10.1007/s10872-007-0027-4

Cited by 63 publications

(57 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A comparison of Multi-Channel SST (MCSST) [8], Nonlinear SST (NLSST) [9], Water Vapor SST (WVSST) and Quadratic SST (QDSST) [10] algorithms which retrieve SST from MODIS L1B data showed that the NLSST method has both quality and high efficiency in the Western North Pacific [4]. Thus, the NLSST algorithm is used in our study for estimating SST in the China Seas.…”

Section: Sst Retrieval Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Ocean Eddies Automatically from MODIS L1B Data

Ding¹,

Zhang²

2017

Proceedings of the 3rd 2017 International Conference on Sustainable Development (ICSD 2017)

View full text Add to dashboard Cite

Abstract. This study aims at the automatic detection of the ocean eddies from Moderate Resolution Imaging Spectrometer (MODIS) L1B data in the China Seas. First, the sea surface temperature (SST) is estimated from MODIS Aqua/Terra L1B data using the nonlinear IMAPP algorithm. Based on the distribution of SST, the characteristics of ocean eddies such as the position, the size, and the elliptical parameters can be investigated using the multi-scale edge detection technique. The MODIS L1B data can be obtained in near real time, therefore, the automatic near-real-time detection of ocean eddies can be realized.

show abstract

Section: Sst Retrieval Methodsmentioning

confidence: 99%

“…Two bands are imaged at a resolution of 250 m at nadir, with five bands at 500 m; the resolution of the remaining 29 bands is 1 km. In this study, L1B data with spatial resolution of 1 km × 1 km are used to estimate SST [4] based on which the ocean eddies are detected.…”

Section: Datamentioning

confidence: 99%

Detecting Ocean Eddies Automatically from MODIS L1B Data

Ding¹,

Zhang²

2017

Proceedings of the 3rd 2017 International Conference on Sustainable Development (ICSD 2017)

View full text Add to dashboard Cite

show abstract

“…Unless stated otherwise, we use the 35 most dominant tidal constituents. Beside climatological Sea Surface Temperature (SST), TCS and near bottom temperature, TCB (Berx and Hughes, 2009), also the 8-day composite SST data from MODIS, TMS (MODerate resolution Imaging Spectrometer) L3 data product (http://oceandata.sci.gsfc.nasa.gov/ have an imaging footprint of ~1 km and were used in the analysis (Hosoda et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

Bubble momentum plume as a possible mechanism for an early breakdown of the seasonal stratification in the northern North Sea

Nauw

Линке

Leifer

2015

Marine and Petroleum Geology

View full text Add to dashboard Cite

The presence of a seasonal thermocline likely plays a key role in restraining methane released from a seabed source in the deeper water column, thereby inhibiting exchange to the atmosphere. The bubble plume itself, however, generates an upward motion of fluid, e.g. upwelling and may thereby be partially responsible for an early breakdown of the seasonal thermocline. Measurements at site 22/4b, located at (57 o 55'N, 1 o 38'E) in the UK Central North Sea, 200 km east of the Scottish mainland, where gas is still being released since a blow out in 1990, have been used to identify the generation of the seasonal thermocline, and thus, the depth of the upper mixed layer and its breakdown in autumn. Data derived from two landers, containing an Acoustic Doppler Current Profiler and a Conductivity Temperature Depth recorder, were used to determine the mixed layer depth and the breakdown of the thermocline. Mixing of upper layer fluid into the lower layer has been inferred from large amplitude variations in the near-bottom temperature. The ADCPs estimate velocity profiles in four beam directions using Doppler shifted frequency from acoustic pings sent out and received by four different transducers in a specific configuration. Besides that, the intensity of the backscattered sound per transducer is also recorded. Bubbles from the nearby plume contaminate the signal during part of the tidal cycle, but in bubble free periods, the mixed layer depth can be estimated using the acoustic backscatter signal as local maxima. Results show that the thermocline broke down between mid-October and early November, several weeks earlier than the breakdown of the thermocline in nearby/comparable areas, likely caused by bubble-induced downwelling at the site. The early breakdown of the thermocline was accompanied by multiple occurrence of a strong jet-like structure, associated with the seasonal tidal mixing front. 2

show abstract

“…Additionally, Francois et al [73] used seven daytime algorithms: linear, nonlinear-including a first-guess temperature term, nonlinear complete including more secant-dependent terms, a water vapor first version algorithm, a water vapor special version algorithm including quadratic terms in W, a quadratic algorithm, and a quadratic alternate version algorithm including a developed form of the quadratic term (T11_T12), and seven nighttime algorithms including T37 nonlinear algorithms (including T37 and Ts clim), a triple-window algorithm (TRI), T37 algorithm version 0-3 and a multiple-window algorithm (triple-window algorithm) to estimate SST from NOAA-14 and compared the results of each algorithm. Hosoda et al [74] investigated multiple SST algorithms for atmospheric correction: multi-channel SST (MCSST), water vapor SST (WVSST), quadratic SST (QDSST) and nonlinear SST (NLSST) algorithms. They showed that the NLSST method using high-resolution climatological SST as a first-guess had good quality and high efficiency.…”

Section: Sea Surface Temperaturementioning

confidence: 99%

A Review of Ocean/Sea Subsurface Water Temperature Studies from Remote Sensing and Non-Remote Sensing Methods

Akbari

Alavipanah

Jeihouni

et al. 2017

Water

View full text Add to dashboard Cite

Oceans/Seas are important components of Earth that are affected by global warming and climate change. Recent studies have indicated that the deeper oceans are responsible for climate variability by changing the Earth's ecosystem; therefore, assessing them has become more important. Remote sensing can provide sea surface data at high spatial/temporal resolution and with large spatial coverage, which allows for remarkable discoveries in the ocean sciences. The deep layers of the ocean/sea, however, cannot be directly detected by satellite remote sensors. Therefore, researchers have examined the relationships between salinity, height, and temperature of the oceans/Seas to estimate their subsurface water temperature using dynamical models and model-based data assimilation (numerical based and statistical) approaches, which simulate these parameters by employing remotely sensed data and in situ measurements. Due to the requirements of comprehensive perception and the importance of global warming in decision making and scientific studies, this review provides comprehensive information on the methods that are used to estimate ocean/sea subsurface water temperature from remotely and non-remotely sensed data. To clarify the subsurface processes, the challenges, limitations, and perspectives of the existing methods are also investigated.

show abstract

Algorithm and validation of sea surface temperature observation using MODIS sensors aboard terra and aqua in the western North Pacific

Cited by 63 publications

References 23 publications

Detecting Ocean Eddies Automatically from MODIS L1B Data

Detecting Ocean Eddies Automatically from MODIS L1B Data

Bubble momentum plume as a possible mechanism for an early breakdown of the seasonal stratification in the northern North Sea

A Review of Ocean/Sea Subsurface Water Temperature Studies from Remote Sensing and Non-Remote Sensing Methods

Contact Info

Product

Resources

About