Estimating advective near-surface currents from ocean color satellite images

Yang, Haoping; Arnone, Robert; Jolliff, Jason K.

doi:10.1016/j.rse.2014.11.010

Cited by 40 publications

(34 citation statements)

References 35 publications

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“…The underlying idea is quite simple: given a template of N x × N y grid points in an image at time t 0 , it consists of searching which subwindow of size N x × N y has the maximum cross-correlation within a larger search window in an image at time t 0 + t and taking the displacement vector between images as the velocity field. This approach has been mainly applied to SST (e.g., Dransfeld et al, 2006;Castellanos et al, 2013;Doronzo et al, 2015), although recently it has been also applied successfully to ocean color data (e.g., Yang et al, 2015;Warren et al, 2016;Hu et al, 2017). …”

Section: Currents From a Sequence Of Tracer Imagesmentioning

confidence: 99%

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

Isern‐Fontanet

Ballabrera-Poy

Turiel

et al. 2017

Nonlin. Processes Geophys.

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Abstract. Ocean currents play a key role in Earth's climate -they impact almost any process taking place in the ocean and are of major importance for navigation and human activities at sea. Nevertheless, their observation and forecasting are still difficult. First, no observing system is able to provide direct measurements of global ocean currents on synoptic scales. Consequently, it has been necessary to use sea surface height and sea surface temperature measurements and refer to dynamical frameworks to derive the velocity field. Second, the assimilation of the velocity field into numerical models of ocean circulation is difficult mainly due to lack of data. Recent experiments that assimilate coastal-based radar data have shown that ocean currents will contribute to increasing the forecast skill of surface currents, but require application in multidata assimilation approaches to better identify the thermohaline structure of the ocean. In this paper we review the current knowledge in these fields and provide a global and systematic view of the technologies to retrieve ocean velocities in the upper ocean and the available approaches to assimilate this information into ocean models.

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Section: Currents From a Sequence Of Tracer Imagesmentioning

confidence: 99%

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

Isern‐Fontanet

Ballabrera-Poy

Turiel

et al. 2017

Nonlin. Processes Geophys.

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“…This method has been used to measure a variety of ocean surface currents over the past several decades [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Its effectiveness for retrieving ocean surface currents from Advanced Very High Resolution Radiometer (AVHRR) thermal infrared (IR) images has been repeatedly demonstrated [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Its effectiveness for retrieving ocean surface currents from Advanced Very High Resolution Radiometer (AVHRR) thermal infrared (IR) images has been repeatedly demonstrated [6][7][8][9][10][11]. Also, ocean color (OC) images collected from Coastal Zone Color Scanner (CZCS) [12], Moderate-Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) [9], and Geostationary Ocean Color Imager (GOCI) [13][14][15] have been used successfully to compute the space-time variability of the surface currents. More recently, sequential synthetic aperture radar (SAR) images derived from ERS-2, Envisat [16], TerraSAR-X (TSX) [17], TanDEM-X (TDX) and COSMO-SkyMed (CSK) [18] have also been used with the MCC method to map the space-time variations of coastal currents.…”

Section: Introductionmentioning

confidence: 99%

“…By merging the two types of MCC fields, the spatial coverage increased by ∼25%. Yang et al [13] applied a correlation coefficient based method to merge the individual MCC fields inferred from several OC products. In their study, they only showed one example of merging MCC fields retrieved from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite images of chlorophyll-a (Chl) concentration and remote sensing reflectance (Rrs) at 551 nm (Rrs551).…”

Section: Introductionmentioning

confidence: 99%

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Computing Coastal Ocean Surface Currents from MODIS and VIIRS Satellite Imagery

Liu

Emery

et al. 2017

Remote Sensing

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Abstract:We explore the potential of computing coastal ocean surface currents from ModerateResolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery using the maximum cross-correlation (MCC) method. To improve on past versions of this method, we evaluate combining MODIS and VIIRS thermal infrared (IR) and ocean color (OC) imagery to map the coastal surface currents and discuss the benefits of this combination of sensors and optical channels. By combining these two sensors, the total number of vectors increases by 58.3%. In addition, we also make use of the different surface patterns of IR and OC imagery to improve the tracking performance of the MCC method. By merging the MCC velocity fields inferred from IR and OC products, the spatial coverage of each individual MCC field is increased by 65.8% relative to the vectors derived from OC images. The root mean square (RMS) error of the merged currents is 18 cm · s −1 compared with coincident HF radar surface currents. A 5-year long time serious of merged MCC computed currents was used to investigate the current structure of the California Current (CC). Weekly, seasonal, and 5-year mean flows provide a unique space-time picture of the oceanographic variability of the CC.

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“…Sequences of time-ordered images allow for the estimation of two-dimensional image motion as either instantaneous image velocities or discrete image displacements. Primary approaches to estimating velocity fields from image sequences can be divided into two groups [38]: differential methods, which are mostly based on heat or optical flow equations inversion [39][40][41][42][43][44], and the Maximum Cross Correlation (MCC) technique [45][46][47][48][49][50][51][52][53][54][55]. Modern algorithms are complex, sometimes combining several approaches or in situ measurements, e.g., [52,56].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Large- and Mesoscale Dynamics in the Black Sea Region from Satellite Imagery and Altimetry Data—A Comparison of Two Methods

2018

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Two remote sensing methods, satellite altimetry and 4D-Var assimilation of satellite imagery, are used to compute surface velocity fields in the Black Sea region. Surface currents derived from the two methods are compared for several cases with intense mesoscale and large-scale dynamics during low wind conditions. Comparison shows that the obtained results coincide well quantitatively and qualitatively. However, satellite imagery provides more reasonable results on the spatial variability of coastal dynamics than altimetry data. In particular, this is related to the reconstruction of eddy coastal dynamics, such as Black Sea near-shore anticyclones. Current streamlines in these eddies are not closed near the coast in altimetry data, which we relate to the extrapolation during mapping procedure in the absence of coastal along-track measurements. On the other hand, in offshore areas, imagery-derived currents can be underestimated due to the absence of thermal contrasts and smoothing during the procedure of the 4D-Var assimilation. Wind drift currents are another source of inconsistency, as their impact is directly observed in satellite imagery but absent in altimetry data. The advantage of the 4D-Var method for reconstructing coastal dynamics is used to compute surface currents in the Marmara Sea on the base of 250 m resolution Modis optical data. The results reveal the very complex dynamics of the basin, with a large number of mesoscale and sub-mesoscale eddies. 4D-Var assimilation of Modis imagery is used to obtain information about dynamic characteristics of these small eddies with radiuses of 4-10 km.

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Estimating advective near-surface currents from ocean color satellite images

Cited by 40 publications

References 35 publications

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

Computing Coastal Ocean Surface Currents from MODIS and VIIRS Satellite Imagery

Reconstructing Large- and Mesoscale Dynamics in the Black Sea Region from Satellite Imagery and Altimetry Data—A Comparison of Two Methods

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