Wind effects on coastal zone color scanner chlorophyll patterns in the U.S. Mid‐Atlantic Bight during spring 1979

Eslinger, David L.; Iverson, Richard L.

doi:10.1029/jc091ic11p12985

Cited by 12 publications

(4 citation statements)

References 34 publications

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“…This suggests that on the temporal and spatial scales considered here, the spatial variability of phytoplankton pigment is predominantly controlled by factors operating on large spatial scales and over long time periods, such as nutrient input to the euphotic zone and water column mixed layer depth. The effects of forcing variables that operate on shorter spatial and temporal scales, such as increased chlorophyll concentration due to wind effects [Walsh et al, 1978;Eslinger and Iverson, 1986], are present in the second mode (Plate 5). In contrast, temporal variability varies through space as well as time.…”

Section: Discussionmentioning

confidence: 99%

Empirical orthogonal function analysis of cloud‐containing coastal zone color scanner images of northeastern North American coastal waters

Eslinger

O’Brien²,

Iverson

1989

J. Geophys. Res.

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Empirical orthogonal function (EOF) analyses were performed on 36 coastal zone color scanner (CZCS) images of the Mid‐Atlantic Bight and Gulf of Maine taken from February 28, 1979, through July 9, 1979. The EOF procedure was modified to allow images with significant portions of data missing because of clouds to be included in the analysis. Analyses performed both with the temporal and with the spatial means removed from the data produced similar results. A strong gradient existed in the temporal mean from high pigment concentrations nearshore to much lower pigment concentrations offshore. The first mode of the temporally averaged analysis explained 21.5% of the variance and showed a south‐to‐north pattern corresponding to an April Mid‐Atlantic Bight bloom and a June bloom over Nantucket Shoals and Platts Bank. The second temporally averaged mode explained 18.3% of the variance and corresponded to a shelf‐wide phytoplankton bloom that resulted in increased pigment concentrations through the spring. The first mode of the spatially averaged analysis explained 59.1% of the variance and had a pattern similar to the temporal mean. The second mode explained an additional 13.2% of the variance and was almost identical to the first mode in the temporally averaged analysis. The inclusion of cloud‐containing images introduced some small‐scale spatial noise into the EOF analyses. However, the inclusion of these images in the EOF analysis allowed postanalysis image reconstruction that provided estimates of pigment concentrations beneath the clouds.

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Section: Discussionmentioning

confidence: 99%

Empirical orthogonal function analysis of cloud‐containing coastal zone color scanner images of northeastern North American coastal waters

Eslinger

O’Brien²,

Iverson

1989

J. Geophys. Res.

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“… Walsh et al [1987] use CZCS imagery to analyze the cross‐shelf transport of the spring bloom. The relationship between wind forcing and phytoplankton abundance is investigated by Eslinger and Iverson [1986] using data from the Mid‐Atlantic Bight. As with SST time series, one may use EOF analysis to decompose the sequence into its temporal and spatial components.…”

Section: Visible Sensorsmentioning

confidence: 99%

Advances in Passive Remote Sensing of the Ocean

Abbott

Chelton

1991

Reviews of Geophysics

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Remote sensing of ocean processes using infrared, visible, and passive microwave data has advanced considerably over the last eight years. Although the basic sensors remained the same, techniques for processing and deriving geophysical and biological data have improved substantially. Infrared data are used routinely to derive high quality sea surface temperature estimates, and visible data are used to produce estimates of near‐surface phytoplankton pigment concentrations. Passive microwave data are used to estimate sea surface temperature, water vapor, wind speed, and rain rate. These basic data sets are then in turn used in estimating upper ocean velocities, water column primary production, and air/sea fluxes of latent heat and freshwater. Challenges remain in the area of algorithm development, but improvements in data processing and access has expanded the volume and range of oceanographic applications of these satellite data sets.

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“…Ocean color data has been exploited to help identify the scales associated with these features, and attempts have been made to correlate this data with in situ observations to identify the processes contributing to spatial and temporal variability. Examples where CZCS observations have been used for these purposes include studies of boundary current systems [ Peláez and McGowan , 1986; Smith et al , 1988; Denman and Abbott , 1988; Thomas et al , 1994], coastal upwelling regions [ McClain et al , 1984; Abbott and Zion , 1985], and other dynamic coastal environments [ Eslinger and Iverson , 1986; Yoder et al , 1987; Abbott and Zion , 1987; McClain et al , 1990], including areas dominated by river plumes [ Müller‐Karger et al , 1989; Hochman et al , 1994]. These types of studies provide fundamental information about upper ocean processes and can also be useful for improving the accuracy with which satellite data is interpreted, as is the case in high CDOM shelf waters [e.g., Hochman et al , 1994].…”

Section: Introductionmentioning

confidence: 99%

Feature‐based classification of optical water types in the Northwest Atlantic based on satellite ocean color data

Traykovski

Sosik

2003

J. Geophys. Res.

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[1] We have developed an optical water type classification approach based on remotely sensed water leaving radiance, for application to the study of spatial and temporal dynamics of ecologically and biogeochemically important properties of the upper ocean. For CZCS and SeaWiFS imagery of the Northwest Atlantic region, pixels from several different locations projected into distinct clusters in water-leaving radiance feature space, suggesting that these waters can be distinguished using a few spectral bands of ocean color data. Based on these clusters, we constructed a Northwest Atlantic Training Set and developed two different classification techniques. The Euclidean Distance Classifier minimizes the raw distance between each pixel and the centroid of the class to which it is assigned, whereas the Eigenvector Classifier is based on scaling the raw distances by the variance of each class, thereby accounting for the shape of each class in feature space. We conducted an initial evaluation of these two classification techniques by constructing water type classes based on only half of the pixels of each water type (randomly selected) in the Northwest Atlantic Training Set; classification was then carried out on the remaining half of the training set data. Applying the Euclidean Distance Classifier resulted in an average of 97.4% correctly classified pixels over 20 trials; even higher success rates were achieved with the Eigenvector Classifier, which gave an average of 99.1% correctly classified pixels. The Euclidean Distance Classifier performed well with spherical classes, but with more ellipsoidal classes, classification success improved considerably using the Eigenvector Classifier. We then applied these classifiers to ocean color images of the Northwest Atlantic to elucidate the geographical location and extent of each water type. We interpreted classifier results based on our Classification Goodness of Fit measure, which indicates how closely a given pixel is associated with its assigned class. This revealed that sharp boundaries exist between water masses of different optical types, with pixels on either side of the boundaries being strongly associated with their water type class. We anticipate that our classification techniques will facilitate long-term time series studies by tracking optical water types through seasonal and interannual changes.

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Wind effects on coastal zone color scanner chlorophyll patterns in the U.S. Mid‐Atlantic Bight during spring 1979

Cited by 12 publications

References 34 publications

Empirical orthogonal function analysis of cloud‐containing coastal zone color scanner images of northeastern North American coastal waters

Empirical orthogonal function analysis of cloud‐containing coastal zone color scanner images of northeastern North American coastal waters

Advances in Passive Remote Sensing of the Ocean

Feature‐based classification of optical water types in the Northwest Atlantic based on satellite ocean color data

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