Measurement of sea foam

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Introduction

Marine physics is a subject which based on experiment and measurement. Therefore there are many important parameters, which can be achieved by using remote sensing technology, can be described such as ocean salinity. The ocean salinity is an important parameter for measure the marine ecosystems, climate system of ocean and also weather prediction.

With the development of satellite remote sensing technology and application, the long term observation of oceanographic satellite can provide large number kinds of information such as satellite images and they should be accurately observed and analyzed. However, the sea foam coverage affects the results accuracy of satellite images for sea observation. Therefore, it is necessary to research that the reason and principle of effect by analyzing the sea foam images and do some modeling to observe the transformation of foam models when time past.

The passive microwave radiometer of foam on the ocean surface increases the emissivity and brightness temperature measured. It is a key component of the wind speed signal measured by a linearly polarized radiometer. Therefore, the accuracy of the sea foam model affects the accuracy of many calculations of physical natural resources such as physical wind speed retrieval algorithms. To Understand the effect of sea foam should be more important for polarimetric radiometric observations of wind direction because the azimuthal variations in brightness temperature are small.

Foam

Foam is basically composed by a mixture of air bubbles and water which is generated by the sea breaking. The brightness temperature increase depends on the fractional foam-covered area collected at any instant by the radiometer, the volumetric foam distribution, and the types of foam formations. These can be divided into two groups attending to the types of foam formations: a thick part formed by whitecaps, and a thin part called foam streaks. Whitecaps are very poor reflectors of radiation, so that the emissivity of them is large. Streaks are generated by Langmuir circulation. However the emissivity it is not so high because of the poor volumetric foam content. In addition, it is interesting to classify the foam formations attending to its lifetime or stability. The lifetimes of whitecaps are very short, only few seconds, and are unstable. On the other hand, streak and thin foam generated by Langmuir circulation are stable, with a lifetime of several minutes, and sometimes hours.

Normally, foam is modeled by a several layers structure. Air bubbles and water shells are geometrically organized within the layer. The factors of contribution for the total emissivity are: the shape of bubbles (the spherical approximation simplifies the computation of the electromagnetic problem), thickness of foam layer, air bubbles bellow the foam layers, the physical temperature of sea surface and the stickiness parameter. It is inversely proportional to the strength of the attractive force between bubbles.

The salinity of Sea surface could be measured by microwave radiometry at a frequency range of 1400~1427MHz which is called L-band. In this frequency range, it was compromised between sensitivity to the salinity and also the small atmospheric perturbation and reasonable pixel resolution. The description of the ocean emission depends on two key factors:

The first one is the permittivity of sea water. It is a function which is related to salinity, temperature and frequency.

The second one is the sea surface state. it depends on many aspects such as the wind-induced wave spectrum, swell, rain-induced roughness spectrum and by the foam coverage and its emissivity.

During the weather prediction, there are much knowledge is required such as soil moisture (SM) and sea surface salinity (SSS) for analyzing and calculating if there are bad weather conditions such as natural catastrophes. The knowledge of the SSS distribution at a global scale by using a moderate revisit time is important to climate predictions. In a sense, SSS is as tracer of sea surface currents and also could be an indicator to measure the difference between evaporation and precipitation (E-P). Water density is determined by temperature and salinity. Then the thermo-haline circulation can also be observed by SSS measurements.

The SSS retrieval from microwave radiometric measurements is based on the fact that the dielectric constant of seawater is a function of salinity and temperature. The sensitivity of brightness temperature (TB) to SSS is maximum at low microwave frequencies, and the optimum conditions for salinity retrieval are found at L-band, where there is a protected band for passive observations (1400-1427 MHz). However, even at this frequency the sensitivity of brightness temperature(TB) to SSS is low: 0.5 K per psu for a sea surface temperature (SST) of 20 C, decreasing to 0.25 K per psu for an SST of 0°C. Since other variables influence the TB signal (polarization, incidence angle, sea surface temperature, roughness and foam), unless they are properly accounted for, the SSS determination will be erroneous.

Goals

To understand the effect of foam in remote sensing more clearly, it is necessary to measure and analyze the size distribution of sea foam, the transformation of foam models when time past by analyzing the images of foam in different situations.

Plan

For many features of foam such as size and transformation of models are related to the viscosity, density, surface tension of sea water and wind speed, it is better to collect various images in different situation. Using the mathematical model and formulas which had already been worked out by scientists, we can use the data which come from some peculiar experiments to calculate and analyze them to describe the details of foam effect. Then MATLAB should be used for modeling the foam's size and transformation.

Background of relevant experiment

Distribution of relevant experiment FORG

(This is a part to distribute what people have done.)

There are two planned space missions to measure global sea surface salinity maps as followed:

The Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Opportunity mission from the European Space Agency (ESA) and the Aquarius Earth System Science Pathfinder (ESSP) mission from the National Aeronautics and Space Administration (NASA).

Location of the WISE and FROG field experiments.

During SMOS Phase a two field experiments named the Wind and Salinity Experiment (WISE) were sponsored by the ESA in the fall of 2000 and 2001 to better understand the wind and sea state effects on the L-band brightness temperatures. They consisted of acquiring long time series of TB from an oil rig in the northern Mediterranean Sea to relate it to the sea surface roughness (wind speed, significant wave height) and the instantaneous foam coverage. Several effects were not completely understood during the WISE field experiments: the emissivity of foam and the impact of rain and oil slicks on the TB variations. Under Spanish National funds, the Foam, Rain, Oil Slicks and GPS Reflectometry (FROG) field experiment was carried out at the Institut de Recerca i Tecnologia Agroalimentàries (IRTA) facilities at the Ebro River delta.

This work is organized into two well-defined parts. In the first one, a two-layer sea foam emission model at L-band is presented. In the second one, the FROG 2003 field experiment is described, and the foam emissivity measurements at vertical and horizontal polarizations are presented in a wide range of water salinities from 0-37 psu. These measurements are then compared to the model presented in the first part using the measured foam parameters: bubble radii histograms, bubbles' water coating thickness, derived bubbles' packing coefficient, foam layer thickness, and the void fraction beneath the foam layer.

Suite of instruments deployed to acquire ancillary data.

  1. Six subsurface temperature sensors.
  2. Metal bars of the instantaneous surface level sensor.
  3. Array of 16 gold electrodes of the air fraction measurement system.
  4. Periscope with video camera to acquire vertical foam profiles.
  5. Infrared radiometer to measure surface emission with foam.
  6. Video camera to derive surface foam coverage [as in (h)].

Detail of foam and bubbles vertical profiles.

  1. Fresh water
  2. 15 psu. Corresponding bubble radius histograms.
  3. Fresh water: mean radius = 802 µm, median radius = 789 µm and (d) 15 psu (mean radius = 437 µm, median radius = 423 µm).

REFERENCES

  1. P. S. Chang, P. W. Gaiser, L. Li, and K. M. St. Germain, "Multi-frequency polarimetric microwave ocean wind direction retrievals," in Proc. IGARSS, Singapore, 1997, pp. 1009-1011.
  2. S. Yueh, W. J.Wilson, F. K. Li, W. Ricketts, and S. V. Ngheim, "Polarimetric brightness temperatures of sea surfaces measured with K-band and Ka-band radiometers," IEEE Trans. Geosci. Remote Sensing, vol. 35, pp. 1177-1187, Sept. 1997.
  3. S. Yueh, W. J. Wilson, S. J. Dinardo, and F. K. Li, "Polarimetric microwave brightness signatures of ocean wind directions," IEEE Trans. Geosci. Remote Sensing, vol. 37, pp. 949-959, Mar. 1999.
  4. J. R. Piepmeier and A. J. Gasiewski, "High-resolution passive polarimetric microwave mapping of ocean surface wind vector fields," IEEE Trans. Geosci. Remote Sensing, vol. 39, pp. 606-622, Mar. 2001.
  5. "Digital correlation microwave polarimetry," IEEE Trans. Geosci. Remote Sensing, vol. 39, pp. 2392-2410, Nov. 2001.
  6. S. Yueh, "Modeling of wind direction signals in polarimetric sea surface brightness temperatures," IEEE Trans. Geosci. Remote Sensing, vol. 35, pp. 1400-1418, Nov. 1997.
  7. D. B.Kunkee, "Ocean surface foam modeling for passive remote sensing of ocean surface wind vectors," in Proc. IGARSS, vol. 5, Seattle, WA, July 1998, pp. 2336-2338.
  8. P. M. Smith, "The emissivity of sea foam at 19 and 37 GHz," IEEE Trans. Geosci. Remote Sensing, vol. 26, pp. 541-547, Sept. 1988.
  9. N. N. Vorsin, A. A. Glotov, V. G. Mirovskii, V.Yu. Raizer, I. A. Troitskii, E. A. Sharkov, and V. S. Etkin, "Direct radiometric measurements of sea foam," Sov. J. Remote Sens., vol. 2, no. 3, pp. 520-525, 1984.
  10. W. Asher, Q. Wang, E. C. Monahan, and P. M. Smith, "Estimation of air-sea gas transfer velocities from apparent microwave brightness temperature," Marine Tech. Soc. J., vol. 32, no. 2, pp. 32-40, 1998.

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