A decade of sea surface temperature from MODIS

Kilpatrick, K. A.; Podestá, Guillermo; Walsh, Susan; Williams, E.; Halliwell, Vicki; Szczodrak, M.; Brown, Otis B.; Minnett, Peter J.; Evans, Robert H.

doi:10.1016/j.rse.2015.04.023

Cited by 205 publications

(174 citation statements)

References 40 publications

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“…SST skin data were compared with SST1 (radiosonde based) , SST1 (ECMWF based) , SST1 (collection 5) , and SST2 (collection 6) measurements retrieved from MODIS bands 31 and 32 with the operational algorithms for retrieving the global data of SST proposed by [16][17][18] (Table 2). The SST skin data were compared with the SST3, SST4, SST5, and SST6 measurements retrieved from MODIS bands 31 and 32 with the algorithms for retrieving SST data proposed by Sobrino et al [37] and Niclòs et al [41] (Table 3).…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, σ values of LST1 and SST4 data were smaller than the one from SST2(collection 6) measurements, i.e., 0.35 K, 0.39 K, and 0.40 K, respectively; and the maximum bias of LST1 data was smaller than the one from SST2(collection 6) measurements, i.e., −1.37 K and −1.90 K, respectively. It should be noted that the operation algorithms do not perform well in coastal situations since they were developed to produce global measurement of SST and the atmospheric correction algorithms were optimized for oceanic conditions [16][17][18][19].…”

Section: Resultsmentioning

confidence: 99%

“…The authors [16] provided two distinct sets of coefficients: one was retrieved from a global dataset of 1200 quality controlled radiosondes, and another was derived from European Center Medium Weather Forecast (ECMWF) assimilation model marine atmospheres. Collection 5 introduced new coefficients of the operational algorithm (Equation (1)), which were estimated for each month of any year from training data collected in 2004 [18]. Throughout this paper, SST measurements (named SST1 (radiosonde based) , SST1 (ECMWF based) , and SST1 (collection 5) ) were used to identify the data obtained with Equation (1) using the coefficients derived from radiosondes and the ECMWF assimilation model, and the coefficients of collection 5, respectively.…”

Section: Operational Algorithms For Retrieving Global Data Of Sea Surmentioning

confidence: 99%

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Comparison of Split Window Algorithms for Retrieving Measurements of Sea Surface Temperature from MODIS Data in Near-Land Coastal Waters

Cavalli

2018

IJGI

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Split window (SW) methods, which have been successfully used to retrieve measurements of land surface temperature (LST) and sea surface temperature (SST) from MODIS images, were exploited to evaluate the SST data of three sections of Italian coastal waters. For this purpose, sea surface emissivity (SSE) values were estimated by adding the effects of salinity and total suspended particulate matter (SPM) concentrations, sea surface wind speed, and zenith observation angle. The total column atmospheric water vapor contents were retrieved from MODIS data. SST data retrieved from MODIS images using these algorithms were compared with SST skin measurements evaluated from in situ data. The comparison showed that the algorithms for retrieving LST measurements minimized the error in SST data in near-land coastal waters with respect to the algorithms for retrieving SST measurements: a method for retrieving LST measurements highlighted the smallest root-mean-square deviation (RMSD) value (0.48 K) and values of maximum bias and standard deviation (σ) equal to −3.45 K and 0.41 K; the current operation algorithm for retrieving LST data highlighted the smallest values of maximum bias and σ (−1.37 K and 0.35 K) and an RMSD value of 0.66 K; and the current operation algorithm for retrieving global measurements of SST showed values of RMSD, maximum bias, and σ equal to 0.68 K, −1.90 K, and 0.40 K, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Operational Algorithms For Retrieving Global Data Of Sea Surmentioning

confidence: 99%

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Comparison of Split Window Algorithms for Retrieving Measurements of Sea Surface Temperature from MODIS Data in Near-Land Coastal Waters

Cavalli

2018

IJGI

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“…To assess the effects of anomalous atmospheres, Szczodrak et al [77] applied the MODIS SST retrieval split window algorithm versions 5 and 6 to obtain the SST and the retrieval errors. Furthermore, Kilpatrick et al [78] described the evolution of, motivation for, and improvements to the MODIS SST global products using their split window NLSST and LWIR NLSST with different estimation coefficients for over a decade.…”

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

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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.

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“…The visibility mask produced by IceMap250 is obtained using a spectral ratio between brightness temperatures at 3.7 µm (Band 20) and 12 µm (Band 32), which are known to be sensitive respectively to surface temperature and to cloud temperature [50,51]. These two bands are available only at a 1 km grid resolution; a resampling is done on the generated mask to bring it to 250 m, in agreement with the IceMap250 actual map resolution.…”

Section: Improving the Mapped Extent: The Visibility Maskmentioning

confidence: 99%

IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

et al. 2017

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Abstract:The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3-7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps.

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A decade of sea surface temperature from MODIS

Cited by 205 publications

References 40 publications

Comparison of Split Window Algorithms for Retrieving Measurements of Sea Surface Temperature from MODIS Data in Near-Land Coastal Waters

Comparison of Split Window Algorithms for Retrieving Measurements of Sea Surface Temperature from MODIS Data in Near-Land Coastal Waters

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

IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

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