Advances in satellite sea surface temperature measurement and oceanographic applications

Njoku, E. G.; Barnett, T. P.; Laurs, R. Michael; Vastano, Andrew C.

doi:10.1029/jc090ic06p11573

Cited by 33 publications

(10 citation statements)

References 36 publications

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“…This type of algorithm forms the basis of the MCSST (multichannel sea surface temperature) approach used operationally by NOAA (Strong and McClain 1984), which has the advantage of simplicity and under ideal conditions (if a very dry atmosphere and a blackbody behaviour for the sea surface emissivity is assumed) performs surprisingly well (Njoku et al 1985),…”

Section: Sst Algorithmsmentioning

confidence: 99%

Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data

Sobrino

Stoll

et al. 1996

International Journal of Remote Sensing

275

122

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Section: Sst Algorithmsmentioning

confidence: 99%

Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data

Sobrino

Stoll

et al. 1996

International Journal of Remote Sensing

275

122

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“…The compositing of five successive daily images conserving for each pixel the warmest temperature observed during those 5 days reduces cloud coverage. It is generally considered that satellite-derived sea surface temperatures are now approaching 0.5øC in accuracy [Bernstein and Chelton, 1985;Njoku, 1985;Njoku et al, 1985]. However, an additional problem here is the accuracy of the mosaics.…”

Section: Data Source and Data Accuracymentioning

confidence: 99%

Analysis of satellite sea surface temperature time series in the Brazil‐Malvinas Current Confluence region: Dominance of the annual and semiannual periods

Provost¹,

García²,

Garçon³

1992

J. Geophys. Res.

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We study the dominant periodic variations of sea surface temperature (SST) in the Brazil‐Malvinas Confluence region from a satellite‐derived data set compiled by Olson et al. (1988). This data set is composed of 202 sea surface temperature images with a 4 × 4 km resolution and extends over 3 years (from July 1984 to July 1987). Each image is a 5‐day composite. The dominant signal, as already observed by Podesta et al. (1991), has a 1‐year period. We first fit a single‐frequency sinusoidal model of the annual cycle in order to estimate mean temperature, amplitude, and phase at 159 points uniformly distributed over the region. The residuals are generally small (less than 2°C). The largest departures from this cycle are located either in the Brazil‐Malvinas frontal region or in the southeastern part of the region. Other periods in SST variations are identified by means of periodograms of the 159 residual time series in which the annual cycle has been substracted. The periodograms show that a semiannual frequency signal is present at almost every location. The ratio of the semiannual amplitude to the annual amplitude increases southward from 0% at 30°S to reach up to 45% at 50°S. In the south the semiannual signal creates an asymmetry, and the resulting (total) annual cycle has a cold period (winter) longer than the warm one (summer). In the frontal region the annual and semiannual signals have an important interannual variation. This semiannual frequency is associated with the semiannual wave present in the atmospheric forcing of the southern hemisphere. Differential heating over the mid‐latitude oceans and the high‐latitude ice‐covered Antarctic Continent has been suggested as the cause of this semiannual wave (Van Loon, 1967).

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“…Satellite imagery has promised (and in most cases provided) information on the ocean over a range of scales not attainable previously [e.g., Brown and Cheney, 1983;Maul, 1985;Robinson, 1985;Stewart, 1985]. We have learned much about joint physical and biological processes in the ocean from satellite measurements of sea surface temperature [e.g., Njoku et al, 1985] and of phytoplankton plant pigment derived from the coastal zone color scanner (CZCS) on Nimbus 7 [e.g., Gordon et al, 1983a]. However, despite the capability of satellites to The description and explanation of spatial patterns in plankton communities is a topic of intense interest to biological oceanographers •e.g., Steele, 1978].…”

Section: Introductionmentioning

confidence: 99%

Time evolution of surface chlorophyll patterns from cross‐spectrum analysis of satellite color images

Denman¹,

Abbott²

1988

J. Geophys. Res.

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Sequences of coastal zone color scanner (CZCS) images from the offshore region adjacent to Vancouver Island, Canada, have been analyzed to estimate the time rate of decorrelation of surface phytoplankton chlorophyll pigment patterns. In these high‐latitude, high‐pigment areas, CZCS‐derived pigment estimates were lower than those obtained from ship samples by about a factor of 3, their frequency distributions were skewed in opposite directions, and subareas of the images often showed a discontinuity in the frequency distribution at a concentration of 1.5 mg m−3, where the algorithm changes CZCS bands. We selected cloud‐free subareas that were common to several images separated in time by 1–17 days. Image pairs were subjected to two‐dimensional auto spectrum and cross‐spectrum analysis in an array processor, and spectra of squared coherence were formed. The squared coherence estimates for several wave bands were plotted against time separation, in analogy with a time‐lagged cross correlation function. Threshold levels for significant coherence were estimated from many realizations of squared coherence calculated for pairs of synthetic random uncorrelated fields with specified power law behavior κ−1.5, near the observed range κ−1.5–κ−2. For wavelengths of 50–150 km, significant coherence is lost after 7–10 days, and for wavelengths of 25–50 km, significant coherence is lost after 5–7 days; in both cases offshore regions maintain coherence longer than coastal regions. For wavelengths of 12.5–25 km, only the offshore regions maintained coherence after 1 day, but that was clearly lost after the next time separation of 6 days. The implication for the formation of monthly average large‐scale surface maps to estimate open ocean productivity (e.g., Esaias et al., 1986) is that all mesoscale patterns (<150‐km length scale) will not be resolved.

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Advances in satellite sea surface temperature measurement and oceanographic applications

Cited by 33 publications

References 36 publications

Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data

Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data

Analysis of satellite sea surface temperature time series in the Brazil‐Malvinas Current Confluence region: Dominance of the annual and semiannual periods

Time evolution of surface chlorophyll patterns from cross‐spectrum analysis of satellite color images

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