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Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 22 — Aug. 1, 2012
  • pp: 5477–5485
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Remote sensing of seawater and drifting ice in Svalbard fjords by compact Raman lidar

Alexey F. Bunkin, Vladimir K. Klinkov, Vasily N. Lednev, Dmitry L. Lushnikov, Aleksey V. Marchenko, Eugene G. Morozov, Sergey M. Pershin, and Renat N. Yulmetov  »View Author Affiliations


Applied Optics, Vol. 51, Issue 22, pp. 5477-5485 (2012)
http://dx.doi.org/10.1364/AO.51.005477


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Abstract

A compact Raman lidar system for remote sensing of sea and drifting ice was developed at the Wave Research Center at the Prokhorov General Physics Institute of the Russian Academy of Sciences. The developed system is based on a diode-pumped solid-state YVO4:Nd laser combined with a compact spectrograph equipped with a gated detector. The system exhibits high sensitivity and can be used for mapping or depth profiling of different parameters within many oceanographic problems. Light weight (20kg) and low power consumption (300 W) make it possible to install the device on any vehicle, including unmanned aircraft or submarine systems. The Raman lidar presented was used for study and analysis of the different influence of the open sea and glaciers on water properties in Svalbard fjords. Temperature, phytoplankton, and dissolved organic matter distributions in the seawater were studied in the Ice Fjord, Van Mijen Fjord, and Rinders Fjord. Drifting ice and seawater in the Rinders Fjord were characterized by the Raman spectroscopy and fluorescence. It was found that the Paula Glacier strongly influences the water temperature and chlorophyll distributions in the Van Mijen Fjord and Rinders Fjord. Possible applications of compact lidar systems for express monitoring of seawater in places with high concentrations of floating ice or near cold streams in the Arctic Ocean are discussed.

© 2012 Optical Society of America

1. Introduction

The interest in research in the Arctic Ocean and polar areas has been increasing in the last decades due to the growing need for oil and gas production and the fact that the Arctic region is a sensitive global climate indicator. Continuous diagnostics of human activity’s influence on the ecology and global climate changes are the most pressing goals for research in all countries located in the Arctic region. Depending on the research goals, different techniques can be used for laboratory measurements, while monitoring of vast areas can be carried out only by remote sensing [1

1. R. M. Measures, Laser Remote Sensing: Fundamentals and Applications (Wiley, 1985).

3

3. S. Haykin, E. O. Lewis, R. K. Raney, and J. R. Rossiter, Remote Sensing of Sea Ice and Icebergs (Wiley, 1994).

]. Human activity’s influence on ecology can be traced by pollution detection: oil [4

4. F. Nirchio, M. Sorgente, A. Giancaspro, W. Biamino, E. Parisato, R. Ravera, and P. Trivero, “Automatic detection of oil spills from SAR images,” Int. J. Remote Sens. 26, 1157–1174 (2005). [CrossRef]

6

6. M. Fingas and C. E. Brown, “Oil spill remote sensing,” in Oil Spill Environmental Forensics, Z. Wang and S. Stout, eds. (Academic, 2000), pp. 419–448.

], chemicals, or heavy metals in the ocean water [7

7. S. D. Richardson and T. A. Ternes, “Water analysis: emerging contaminations and current issues,” Anal. Chem. 83, 4614 (2011). [CrossRef]

,8

8. A. Chaulk, G. A. Stern, D. Armstrong, D. G. Barber, and F. Wang, “Mercury distribution and transport across the ocean—sea-ice—atmosphere interface in the arctic ocean,” Environ. Sci. Technol. 45, 1866– 1872 (2011). [CrossRef]

]. If chemical contamination of the sea is of primary interest, such as for ecology applications, then remote detection by laser fluorescence [9

9. M. Lennon, S. Babichenko, N. Thomas, V. Mariette, G. Mercier, and A. Lisin, “Detection and mapping of oil slicks in the sea by combined use of hyperspectral imagery and laser induced fluorescence,” EARSeL eProceedings 5, 120–128 (2006).

] or by laser-induced breakdown spectroscopy can be carried out [10

10. A. F. Bunkin, M. A. Davydov, A. V. Rezov, A. L. Surovegin, and Yu. D. Tsipenyuk, “Helicopter-based lidar complex for emission and fluorescence remote sensing of terrain surfaces,” Laser Phys. 4, 1198–1201 (1994).

13

13. J. J. Laserna, R. Fernández Reyes, R. González, L. Tobaria, and P. Lucena, “Study on the effect of beam propagation through atmospheric turbulence on standoff nanosecond laser induced breakdown spectroscopy measurements,” Opt. Express 17, 10265–10276 (2009). [CrossRef]

]. Global climate change is studied on the basis of the temperature and chlorophyll distribution in the Arctic Ocean using remote methods: spaceborne radars [1

1. R. M. Measures, Laser Remote Sensing: Fundamentals and Applications (Wiley, 1985).

,14

14. O. M. Johannessen, M. Miles, and E. Bjørgo, “The arctic’s shrinking sea ice,” Nature 376, 126–127 (1995). [CrossRef]

,15

15. O. M. Johannessen, E. V. Shalina, and M. W. Miles, “Satellite evidence for an Arctic sea ice cover in transformation,” Science 286, 1937–1939 (1999). [CrossRef]

] and airborne microwave and laser scatterometers [16

16. G. M. Krekov and G. G. Matvienko, “Laser technology development in the remote sensing of atmosphere,” Atmos. Oceanic Opt. 23, 835–844 (2010).

19

19. S. Pershin, A. Lyash, and V. Makarov, “Atmosphere remote sensing by microjoule pulses of diode-laser,” Phys. Vib. 9, 256–260 (2001).

].

The Arctic Ocean is an excellent region for revealing climate changes, and at the same time the Arctic Ocean has a strong influence on the climate. In the northern regions, ice study is of great interest due to the fact that ice serves as an effective interface between the ocean and the atmosphere, restricting exchange of heat, mass, momentum, and chemical constituents. Detailed study of the global climate system and prediction of future climate changes can be made only if reliable data on different physical parameters of water and ice are available. These parameters include water and ice temperature, salinity, ice surface roughness, optical and thermodynamic properties of the snow cover, and the presence of liquid water above ice, which frequently occurs in summer when ice and snow melt at the ice surface.

Conventional techniques for temperature detection include spaceborne radiometer radars [1

1. R. M. Measures, Laser Remote Sensing: Fundamentals and Applications (Wiley, 1985).

] and airborne microwave and laser scatterometers. However, temperature detection by satellite radiometer is performed in a thin 30 μm surface layer of seawater. It was found that under low-speed wind conditions the temperature of the surface layer was 0.5 to 1 °C lower than in the water column [20

20. A. V. Soloviev and R. Lukas, “Observation of large diurnal warming events in the near-surface layer of the western equatorial Pacific warm pool,” Deep-Sea Res. Part I 44, 1055–1076 (1997). [CrossRef]

]. The temperature detected by radar can be strongly influenced by surface waves. Frazil or grease ice can significantly change the results of measurements with such techniques in the Arctic region. Specifically, in the first stage of freezing, small ice crystals are formed at the surface, and as the freezing continues the crystals coagulate and form frazil ice. This frazil ice damps the short gravity waves at the sea surface, which has significant impact on radar remote sensing of the open ocean water. It was suggested to coordinate all data obtained by different techniques under one project to improve the reliability of satellite data [21

21. C. J. Donlon, I. S. Robinson, K. S. Casey, J. Vazquez-Cuervo, E. Armstrong, O. Arino, C. L. Gentemann, D. May, P. LeBorgne, J. Piollé, I. J. Barton, H. Beggs, D. J. S. Poulter, C. J. Merchant, A. Bingham, S. Heinz, A. Harris, G. A. Wick, W. J. Emery, P. J. Minnett, R. Evans, D. T. Llewellyn-Jones, C. T. Mutlow, R. W. Reynolds, H. Kawamura, and N. A. Rayner, “The global ocean data assimilation experiment high-resolution sea surface temperature pilot project,” Bull. Am. Meteorol. Soc. 88, 1197–1213 (2007). [CrossRef]

].

Raman spectroscopy is a promising method for remote detection of water temperature. This method has been successfully used by different scientific groups both in the laboratory [22

22. S. M. Pershin, A. F. Bunkin, and V. A. Luk’yanchenko, “Evolution of the spectral component of ice in the OH band of water at temperatures from 13 to 99 °C,” Quantum Electron. 40, 1146–1148 (2010). [CrossRef]

26

26. Q. Sun, “The Raman OH stretching bands of liquid water,” Vib. Spectrosc. 51, 213–217 (2009). [CrossRef]

] and field experiments [27

27. K.-J. Lee, Y. Park, A. Bunkin, R. Nunes, S. Pershin, and K. Voliak, “Helicopter-based lidar system for monitoring the upper ocean and terrain surface,” Appl. Opt. 41, 401–406 (2002). [CrossRef]

33

33. A. Wang, L. A. Haskin, A. L. Lane, T. J. Wdowiak, S. W. Squyres, R. J. Wilson, L. E. Hovland, K. S. Manatt, N. Raouf, and C. D. Smith, “Development of the Mars microbeam Raman spectrometer,” J. Geophys. Res. 108, 5005–5008(2003). [CrossRef]

]. Additionally, any system for Raman spectroscopy is based on the laser source that can be used as a source of excitation for chlorophyll or organic matter fluorescence. The Raman systems for remote sensing can be used for monitoring several parameters simultaneously: water temperature, chlorophyll and distributed organic matter (DOM), and sea water contaminations by gas or oil production activity [34

34. R. Barbini, F. Colao, R. Fantoni, L. Fiorani, A. Palucci, E. Artamonov, and M. Galli, “Remotely sensed primary production in the western Ross Sea: results of in situ tuned models,” Antarct. Sci. 15, 77–84 (2003). [CrossRef]

,35

35. R. Barbini, F. Colao, R. Fantoni, L. Fiorani, and A. Palucci, “Lidar fluorosensor calibration of the SeaWiFS chlorophyll algorithm in the Ross Sea,” Int. J. Remote Sens. 24, 3205–3218 (2003). [CrossRef]

]. Two approaches can be used for remote detection of water temperature by the Raman spectra, namely OH-band profile fitting [25

25. M. Becucci, S. Cavalieri, R. Eramo, L. Fini, and M. Materazzi, “Raman spectroscopy for water temperature sensing,” Laser Phys. 9, 422–425 (1999). [CrossRef]

,26

26. Q. Sun, “The Raman OH stretching bands of liquid water,” Vib. Spectrosc. 51, 213–217 (2009). [CrossRef]

] and OH-band profile center correlation on temperature [22

22. S. M. Pershin, A. F. Bunkin, and V. A. Luk’yanchenko, “Evolution of the spectral component of ice in the OH band of water at temperatures from 13 to 99 °C,” Quantum Electron. 40, 1146–1148 (2010). [CrossRef]

,24

24. N. P. Andreeva, A. F. Bunkin, and S. M. Pershin, “Deformation of the raman scattering spectrum of Ih ice under local laser heating near 0 °C,” Opt. Spectrosc. 93, 252–256 (2002). [CrossRef]

]. According to the first method, the Raman OH-band profile is fitted with five components attributed to different influence of the surrounding medium on a water molecule [26

26. Q. Sun, “The Raman OH stretching bands of liquid water,” Vib. Spectrosc. 51, 213–217 (2009). [CrossRef]

]. Different components of the OH profile depend differently on temperature; therefore the intensity ratio of these components can be used to detect water temperature. The accuracy of temperature detection by the method is generally about 1 °C [25

25. M. Becucci, S. Cavalieri, R. Eramo, L. Fini, and M. Materazzi, “Raman spectroscopy for water temperature sensing,” Laser Phys. 9, 422–425 (1999). [CrossRef]

]. The second procedure was developed by our group; it can be characterized by improved temperature accuracy detection that is better than 0.5 °C [22

22. S. M. Pershin, A. F. Bunkin, and V. A. Luk’yanchenko, “Evolution of the spectral component of ice in the OH band of water at temperatures from 13 to 99 °C,” Quantum Electron. 40, 1146–1148 (2010). [CrossRef]

]. Ice temperature can be also detected by Raman spectroscopy, but the error will be higher [18

18. A. F. Bunkin and K. I. Voliak, Laser Remote Sensing of the Ocean: Methods and Applications (Wiley, 2001).

]. Raman systems can be installed on airborne platforms, which would be especially useful for express diagnosis of large areas in any region including hardly accessible areas like the Arctic Ocean or mountain lakes. If a gated detector is used for signal digitizing, then range-resolved study can be carried out including depth profiling of temperature, chlorophyll, or organic matter distribution. Recent progress in laser technologies and high-speed electronics give opportunity to develop compact systems for ranged Raman measurements. The small mass and compact size of such systems provide a possibility to install these devices on aircraft or submarines including remotely driven platforms that can be extremely useful for Arctic exploration.

In this study, we have carried out field experiments on laser remote sensing of drifting ice and seawater in Svalbard fjords by compact Raman lidar. This lidar is a prototype of the system that was developed for installation on unmanned aircraft or underwater vehicles. The main goals of the research were to test the system in field experiment conditions and to perform remote sensing of fjords: (1) to characterize floating ice in seawater; (2) to determine temperature spatial distribution and depth profiles for different types of fjords; and (3) to detect chlorophyll and organic matter in different sea environments.

2. Experiment

A. Location

The experiment was organized by the Department of Arctic Technology of the University Centre in Svalbard (UNIS) within the joint project of the General Physics Institute (Russia) and UNIS (Norway). The expedition took place on 29 August through 1 September 2011, in the Ice Fjord and Van Mijen Fjord and also in the Rinders Fjord close to the Paula Glacier. The lidar was installed on the research vessel Viking Explorer (length 15 m, deadweight 25 tons) owned by UNIS. The expedition route based on the global positioning system (GPS) data is shown in Fig. 1. The study regions included three different fjords. The Ice Fjord, Van Mijen Fjord, and Rinders Fjord are good examples of the different influence of the open sea and glaciers on water mass transport and temperature distribution in Svalbard fjords. The Ice Fjord is strongly influenced by the Greenland Sea while glacier influence is not high. The Van Mijen Fjord opening to the ocean is blocked by Akseloya Island, and consequently the ocean impact is suppressed by the glacier influence. The Rinders Fjord is an example of a strong impact of glacier and almost no influence of the sea. The fjord depth at Akseloya Island reaches 50 m while the depths in the open sea and fjord exceed 200 m and 80 m, respectively. The three locations of the study are presented in Fig. 1: (1) Ice Fjord, (2) Akseloya Island, and (3) Van Mijen Fjord and Rinders Fjord. All the measurements were made during the Arctic daytime.

Fig. 1. Expedition map. The GPS record of the ship route (red line on map). Remote sensing was carried out in the regions of interest that are marked with grey color: 1, remote sensing of “open” Ice Fjord; 2, near Akseloya Island at depths shallower than 20 m; 3, seawater and floating ice study in “closed” fjords, i.e., Van Mijen Fjord and Rinders Fjord.

B. Lidar

The lidar system was developed at the Wave Research Centre of the General Physics Institute of the RAS and is presented in Fig. 2. The lidar is based on a compact diode-pumped solid-state YVO4:Nd laser (Laser Compact, Ltd., model LCM-DTL-319QT: 527 nm, 5 ns, 1 kHz, 200μJ/pulse). Beam diameter at 1/e2 level at the laser output is estimated at 1.8 mm; the beam divergence is 0.8 mrad. Two prisms were used to align the probe laser beam and optical path of the receiving channel. Signals from a remote object were collected by a quartz lens (F=21cm, diameter 9 cm) and directed by an aluminum-coated mirror (l×h4×3cm) to the input slit of the spectrograph. Signal-collecting efficiency was estimated in the air by elastic scattering from a concrete wall at 12 m distance by the spot diameter at the spectrograph slit, and its value was 250μm. The bandpass glass filters were used to suppress scattered laser irradiation. The detection system consists of a compact spectrograph (Spectra Physics, MS127i) equipped with an intensified CCD (ICCD) detector (Andor iStar). We used a low-resolution grating (150lines/mm and slit width 250 μm) to increase the signal-to-noise ratio. Spectral resolution was 120cm1 and was detected by measuring the FWHM for the scattered laser line. The chosen spectral window was 500 to 750 nm, which made it possible to register several signals simultaneously. The echo signal is a sum of Mie, Rayleigh (527 nm), and Raman (OH-bond center 650 nm) scattering and fluorescence from organic components (600 nm) and chlorophyll a (685 nm). The signals for elastic and Raman scattering and for fluorescence were determined as the integral of the corresponding band with background correction. The ICCD detector allowed us to obtain gated images with 5 ns duration and 0.25 ns step. The time jitter between the laser pulse and detection gate was less than 3 ns, which provided a depth resolution of 0.85 m (the column high was 0.55 m with an error of 0.31 m) for underwater measurements. In all experiments we used a gate of 5 ns. Time-of-flight spectrum measurements were carried out by varying the delay between the laser pulse and exposure gate. The gated detection effectively suppressed background sun irradiance scattered by seawater. During the whole period of our experiments there was cloudy weather and there was no direct sunlight. A fast gate and the absence of direct sunlight resulted in a very low level of background emission that was comparable to the noise of the detector. The system was limited to a low repetition rate, 23 Hz, due to the maximum detector digitizing rate. The experimental setup was controlled by a PC, and the software allowed us to perform automatic measurements depending on the goal: sea depth profiling or mapping study. Light weight (20kg), small dimensions (60×40×20cm) and low power consumption (300W) make it possible to install such a system on any vehicle including an unmanned aircraft system.

Fig. 2. Compact Raman lidar system developed at WRC GPI. Two prisms were used to guide and turn the laser beam (DPSS Nd:YAG) to remote object. Quartz lens collected the signal from the remote object and with aluminum coated mirror focused on spectrograph slit. Band pass glass filter was used to suppress laser irradiation. System dimensions were 60×40×20cm with a mass of about 20 kg.

The compact Raman lidar system was installed in the ship’s bridge. Owing to the favorable weather conditions, the signals were detected through an opened illuminator of the bridge. An additional aluminum-coated mirror with a diameter of 30 cm was placed at a distance of 2 m from the ship board and 2 m above the seawater to guide the laser beam and receive signals to and from the studied object. The position of the sensor relative to the sea level was determined by varying the delay between the laser pulse and detected Raman scattering from seawater. The maximum value of the Raman signal indicates that the upper edge of the detected column was at the sea level. The measured distance between the detector and sea level was about 6 m. The laser beam was directed to the sea surface at 90°, and the beam waist was placed at a distance of 2 m from ship board to prevent possible influence of wake waves.

3. Results

Several points of interest for remote sensing in the Svalbard fjords were selected according to the expedition goals discussed above. A typical spectrum of ice-free seawater in the Ice Fjord is presented in Fig. 3. The spectrum in Fig. 3(a) is a sum of the Mie scattering, Rayleigh scattering, Raman scattering, and fluorescence from DOM and chlorophyll. Drifting ice and sea water spectra that were detected in the Van Mijen Fjord 20 km away from Paula glacier are presented in Fig. 3. A detailed profile of the OH band and characteristic spectrum profile changes for ice and seawater can be observed in the inset in Fig. 3. Fluorescence of chlorophyll a and organic matter were not detected in seawater in the fjord because the ice has a glacier origin and fjord seawater is efficiently mixed with the water from the glacier.

Fig. 3. Typical spectrum of seawater and floating ice. (a) Spectrum of seawater in the Ice Fjord (region 1 in Fig. 1); the spectrum is a sum of Mie, Rayleigh scattering, Raman scattering, fluorescence from dissolved organic material (DOM) and chlorophyll. Detection was made with a delay starting 1–5 cm below the sea surface; the altitude of detection was 0.8 m (gate 5 ns); spectrum was a sum of 1000 pulses; repetition rate was 23 Hz; ship speed was 7 mph, and the presented graph was spatially averaged over the intervals of 150 m. (b) Spectrum of drifting ice and seawater in the Van Mijen Fjord (region 3 in Fig. 1). In the left inset, a view of floating ice and seawater is presented. Spectra were normalized by elastic scattering for a better view.

A. Laboratory Experiments

Before field experiments, we have carried out laboratory experiments under controlled conditions. The first objective was to calibrate our equipment for remote temperature detection by Raman spectroscopy. The second goal was to study the freezing process in detail to describe floating ice in fjords.

Previously we have proposed a method for remote temperature detecting based on the Raman spectrum of water OH band [22

22. S. M. Pershin, A. F. Bunkin, and V. A. Luk’yanchenko, “Evolution of the spectral component of ice in the OH band of water at temperatures from 13 to 99 °C,” Quantum Electron. 40, 1146–1148 (2010). [CrossRef]

]. A tank with a volume of 1.5m3 was filled with seawater from the Ice Fjord, and under controlled temperature conditions, the Raman spectra were detected. The laser beam was directed vertically into the tank by an aluminum-coated mirror placed 1 m above the water surface. The detection system was placed in another room, and the spectra were collected through a glass window. The length of the optical path from laser output to water surface was about 5 m. The Raman spectra were detected at different temperatures of both seawater and distilled water. According to the suggested procedure [22

22. S. M. Pershin, A. F. Bunkin, and V. A. Luk’yanchenko, “Evolution of the spectral component of ice in the OH band of water at temperatures from 13 to 99 °C,” Quantum Electron. 40, 1146–1148 (2010). [CrossRef]

], every spectrum was fitted with the Gaussian profile [Fig. 4(a)

4. F. Nirchio, M. Sorgente, A. Giancaspro, W. Biamino, E. Parisato, R. Ravera, and P. Trivero, “Automatic detection of oil spills from SAR images,” Int. J. Remote Sens. 26, 1157–1174 (2005). [CrossRef]

] for different temperatures. The centers of the fitted curves were plotted versus temperature for calibration of the lidar (Fig. 4). It should be noted that the suggested procedure is very sensitive to profile variations and can be effectively used for remote temperature study. In our first paper [22

22. S. M. Pershin, A. F. Bunkin, and V. A. Luk’yanchenko, “Evolution of the spectral component of ice in the OH band of water at temperatures from 13 to 99 °C,” Quantum Electron. 40, 1146–1148 (2010). [CrossRef]

] we have compared the temperature detected by our system and by a thermocouple. The differences were within a range of 0.5 °C that was estimated as the error of this technique. In the case of compact lidar, the results were poorer and the accuracy was about 1 °C (Fig. 4). The slopes of the curves for seawater and distilled water differed by 18%. The water salinity should be known for reliable detection of water temperature by Raman spectroscopy. The same problem arises in the detection of temperature by fitting of the OH-band profile [25

25. M. Becucci, S. Cavalieri, R. Eramo, L. Fini, and M. Materazzi, “Raman spectroscopy for water temperature sensing,” Laser Phys. 9, 422–425 (1999). [CrossRef]

].

Fig. 4. System calibration for temperature detection by Raman spectroscopy. (a) Raman OH-band fitted with the Gaussian profile for seawater at different temperatures. (b) Raman OH-band centers versus temperature in distilled water and seawater.

The capabilities of the Raman spectroscopy for ice thickness detection by remote sensing were demonstrated for the ice formed in seawater. The previously used tank was exposed to 8°C for different time periods to obtain layers of ice with different thicknesses. The Raman spectra were obtained for a spatial column of 0.8 m for seawater covered with ice. The ice thickness was estimated by drilling holes in the ice with further measurements by a sliding meter. The ice surface was polished by melting before measurements. For several different ice thicknesses, spectra were determined, and the results are presented in Fig. 5. Ice absorption is low in the spectral range of our interest [36

36. S. G. Warren, “Optical constants of ice from the ultraviolet to the microwave,” Appl. Opt. 23, 1206–1225 (1984). [CrossRef]

] (absorption coefficient is 0.28m1 for wavelength equal to 650 nm) so ice absorption could be ignored for ice thickness below 0.5 m. In our experiments, the layers’ thickness was below 10 cm, and ice can be assumed to be optically thin for the Raman spectra of seawater. In the case when the ice was only a few millimeters thick, the spectra can be described as a combination of ice and seawater spectra. For the ice thickness greater than 1.5 cm, the Raman spectra were nearly the same, representing a characteristic spectrum for ice.

Fig. 5. Seawater spectra without and with the ice layer of different thickness. Spectra were averaged over 1000 pulses and the detection gate (5 ns) gives spatial averaging for 0.8 m.

B. Field Experiments in Fjords

Two types of remote sensing profiling can be used in seawater: mapping and sea depth profiling. During the measurements over the entire ship route in the Ice Fjord (region 1 in Fig. 1), we did not detect any significant changes of signals. The signal reproducibility (signal standard deviation) during the route in the Ice Fjord was 3 times greater compared to our laboratory measurements, and the amplitude deviations did not exceed 10%. These facts were explained by intense mass and heat transport of seawater from the Greenland Sea to the Ice Fjord that resulted in the uniform distribution of temperature, chlorophyll, and organic material.

Different echo-signal spectra were obtained during the measurements in the Rinders Fjord closer to the Paula Glacier (region 3, Fig. 1), and the corresponding spectra are shown in Fig. 6. It can be seen that the fluorescence signal from chlorophyll and DOM disappeared when the ship approached the glacier front. The Raman spectra profile also changed and became more similar to the profile that is characteristic of ice. This is explained by the strong impact of melted water from the Paula Glacier on fjord water that resulted in a temperature decrease and the absence of plankton.

Fig. 6. Spectra for seawater in the Rinders Fjord at different distances from the Paula glacier. Spectra were normalized by the elastic scattering signal for a better view.

The surface water temperature, chlorophyll, and organic material were automatically mapped during the ship route back from the Paula Glacier to the Rinders Fjord and Van Mijen Fjord as shown in Fig. 7. Data were collected automatically; each spectrum was summed from 1000 pulses during 43 s (150 m spatial averaging). The spectra were collected during 6 h (from 1130 to 1755 GMT+1, August 31, 2011) with a period of 450 s, and corresponding points are marked with red squares on the map. Detected signals were normalized by the Raman signal for the following reasons. The Raman signal is proportional to excitation laser energy and concentration of water molecules. Water molecule concentration is constant, and normalization by the Raman signal will correct all fluctuations of the incident laser irradiation due to the ship’s rocking or the influence of wind waves on the detected elastic signal or chlorophyll fluorescence. Additionally, we have observed by eye that there was a high concentration of solid particles near the glacier front. These particles originate from the erosion processes and are transported to the fjord with melted water. When the ship was approaching the glacier front, the elastic scattering enlarged due to increased concentration of dissolved solid particles. The Raman intensity was decreasing because laser irradiation was effectively scattered by these solid particles. This was the second reason for normalization by the Raman signal. The elastic-to-Raman-signals ratio decreased with increasing distance from the glacier, which correlates with the decreased concentration of solid particles in water. The chlorophyll-to-Raman ratio was used to correct chlorophyll distribution in water. Chlorophyll signal was increasing while the ship was moving closer to Akseloya Island. This dependence is explained by the influence of melted water from the Paula Glacier, which did not contain any organic material. Consequently, all chlorophyll in the Van Mijen Fjord was transported from the open sea while this influence was suppressed by Akseloya Island, which blocked the interaction between the fjord water and the seawater from the open ocean. The temperature of the surface layer of water (thickness 0.8 m) was detected by the Raman spectra and by a conductivity, temperature, and depth (CTD) profiler (Sea Bird, SBE-19). Because CTD measurements need a ship stop and work of the operator, the seawater parameters were determined only at the first eight points while moving from the glacier front. The difference between temperature measurements by the Raman spectra and direct CTD results did not exceed 1 °C. The profile of temperature was rather smooth for the surface layer of seawater at a distance greater than 20 km from the glacier front.

Fig. 7. (a) Mapping of elastic scattering (open triangles) and chlorophyll (blue circles) distribution and (b) temperature (black squares) in the Van Mijen Fjord and Rinders Fjord by Raman system. The temperature distribution detected by the CTD-profiler is presented with red triangles in (b). (c) Expedition route and mapping points are marked with red squares.

Additional experiments with depth profiling as well as the chlorophyll and temperature distribution were carried out near Akseloya Island to verify the capabilities of the compact lidar; the results are presented in Fig. 8. Signals were detected in a wide spectral range so all spectra detected from various depths were corrected according to absorption coefficients for different wavelengths with literature data [37

37. R. C. Smith and K. S. Baker, “Optical properties of the clearest natural waters (200–800 nm),” Appl. Opt. 20, 177–183 (1981). [CrossRef]

].

Fig. 8. Depth profiling of chlorophyll and temperature in seawater.

Chlorophyll fluorescence delay should be taken into account for depth profile measurements. It is recognized in literature that chlorophyll fluorescence delay depends on different environments and is generally less than 4 ns [38

38. N. G. Bukhov, U. Heber, C. Wiese, and V. A. Shuvalov, “Energy dissipation in photosynthesis: does the quenching of chlorophyll fluorescence originate from antenna complexes of photosystem II or from the reaction center?,” Planta 212, 749–758 (2001). [CrossRef]

,39

39. I. A. Stepanenko, V. O. Kompanets, S. V. Chekalin, Z. K. Makhneva, A. A. Moskalenko, R. Y. Pishchainikov, and A. P. Razjivin, “Two-photon excitation spectrum of fluorescence of the light-harvesting complex B800–850 from allochromatium minutissimum within 1200–1500 (600–750) nm spectral range is not carotenoid mediated,” Biol. Membr. 26, 180–187(2009).

]. Effective temporal resolution of the compact Raman lidar was 8 ns; hence, fluorescence delay could be assumed insignificant for the presented results. We have observed a small maximum in chlorophyll distribution at depth 2 m with a slow decrease for greater altitudes.

The Raman OH-band signal was detected at 5 m depth but the temperature measurement procedure (described above) needs a good-quality spectrum with high signal-to-noise ratio. Eventually, the developed compact Raman lidar can measure seawater temperature at depths up to 3 m only. The seawater temperature was greater by 1 °C at the surface compared to layers at 2 to 3 m depth.

Fig. 9. Detection of the fjord depth by elastic scattering. The additional possibility of detecting depth by the developed system was tested in field experiments. The detection accuracy of depth is in a good agreement with the echo sounder.

A compact Raman system for remote sensing of ocean is of growing interest because these systems can give reliable information about water temperature, water contaminations, and chlorophyll concentration in locations that can hardly be accessed (Arctic regions or mountain lakes).

One of the possible applications is to study an iceberg’s evolution during its motion in the open ocean. Direct measurements of iceberg parameters, such as temperature, salinity, mechanical properties of ice, and dimensions, are difficult and dangerous to perform. If a compact system will be installed on remotely operated aircraft, then properties of iceberg and surrounding water can be traced automatically and safely. Laser ranging with the excitation laser from the Raman system can be used to measure iceberg form and size for the above-water and underwater parts with a resolution below 5 cm. The temperature of the iceberg and surrounding water can be remotely detected by Raman spectroscopy of the OH-band profile and are of great importance for the study of iceberg thermodynamics. The Rayleigh, Mie, and Raman scattering can reveal such different ice properties as porosity, salinity, and density. Additionally, iceberg melting evolution and its influence on nearby seawater can be studied in detail with remotely driven lidar systems. This study can indicate prospective procedures for express simple detection of icebergs for safe sailing in the northern seas.

A compact system can be installed on a stationary platform in the Arctic Ocean as a real-time automatic alarm service to detect approaching icebergs. Laser ranging can be used for mapping the sea surface and automatic detection of floating objects that can be dangerous for the platform. Raman spectroscopy can be used to validate that a floating object is ice. Additionally, such a system can be also used for express diagnostics of oil leaks at early stages in the immediate vicinity of the platform.

The combination of a compact Raman system and remotely operated ship or submarine can be used for automatic detection of water properties along an arbitrary path in any place of interest. These data can improve the satellite data reliability for the temperature distribution for detecting global climate changes.

4. Conclusions

Remote sensing in the Svalbard fjords was carried out by a compact lidar system. The system was installed on a small ship but can be installed on any vehicle including remotely operated aircraft because it has small weight (20kg) and low power consumption (<300W). The evaluation of water properties in fjords with different influence of the Arctic Ocean and Svalbard glaciers were carried out by the compact Raman system: Raman spectroscopy and fluorescence of chlorophyll and DOM. The influence of glaciers and open sea was determined by the mapping of surface water temperature, chlorophyll, and organic matter distribution. The Raman spectroscopy was used for remote temperature detection of water. The temperature profiles for depth and mapping applications were detected in the Ice Fjord, Van Mijen Fjord, and Rinders Fjord. Possible applications of the compact Raman lidar for express monitoring of seawater properties in places with high concentrations of floating ice in the ocean are discussed.

This work was partially supported by the Russian Foundation for Basic Research (RFBR) under projects 11-02-00034, 11-02-01202, 11-05-00448, and 11-08-00076.

References

1.

R. M. Measures, Laser Remote Sensing: Fundamentals and Applications (Wiley, 1985).

2.

C. L. Parkinson, D. J. Cavalieri, P. Gloersen, H. J. Zwally, and J. C. Comiso, “Arctic sea ice extents, areas and trends, 1978–1996,” J. Geophys. Res. 104, 20837–20856 (1999). [CrossRef]

3.

S. Haykin, E. O. Lewis, R. K. Raney, and J. R. Rossiter, Remote Sensing of Sea Ice and Icebergs (Wiley, 1994).

4.

F. Nirchio, M. Sorgente, A. Giancaspro, W. Biamino, E. Parisato, R. Ravera, and P. Trivero, “Automatic detection of oil spills from SAR images,” Int. J. Remote Sens. 26, 1157–1174 (2005). [CrossRef]

5.

C. E. Brown and M. F. Fingas, “Review of development of laser fluorosensors for oil spill applications,” Mar. Pollut. Bull. 47, 477–484 (2003). [CrossRef]

6.

M. Fingas and C. E. Brown, “Oil spill remote sensing,” in Oil Spill Environmental Forensics, Z. Wang and S. Stout, eds. (Academic, 2000), pp. 419–448.

7.

S. D. Richardson and T. A. Ternes, “Water analysis: emerging contaminations and current issues,” Anal. Chem. 83, 4614 (2011). [CrossRef]

8.

A. Chaulk, G. A. Stern, D. Armstrong, D. G. Barber, and F. Wang, “Mercury distribution and transport across the ocean—sea-ice—atmosphere interface in the arctic ocean,” Environ. Sci. Technol. 45, 1866– 1872 (2011). [CrossRef]

9.

M. Lennon, S. Babichenko, N. Thomas, V. Mariette, G. Mercier, and A. Lisin, “Detection and mapping of oil slicks in the sea by combined use of hyperspectral imagery and laser induced fluorescence,” EARSeL eProceedings 5, 120–128 (2006).

10.

A. F. Bunkin, M. A. Davydov, A. V. Rezov, A. L. Surovegin, and Yu. D. Tsipenyuk, “Helicopter-based lidar complex for emission and fluorescence remote sensing of terrain surfaces,” Laser Phys. 4, 1198–1201 (1994).

11.

V. Lednev, S. M. Pershin, and A. F. Bunkin, “Laser beam profile influence on LIBS analytical capabilities: single vs. multimode beam,” J. Anal. At. Spectrom. 25, 1745–1757 (2010). [CrossRef]

12.

S. M. Pershin, V. N. Lednev, and A. F. Bunkin, “Laser ablation of alloys: selective evaporation model,” Phys. Wave Phenom. 19, 261–274 (2011). [CrossRef]

13.

J. J. Laserna, R. Fernández Reyes, R. González, L. Tobaria, and P. Lucena, “Study on the effect of beam propagation through atmospheric turbulence on standoff nanosecond laser induced breakdown spectroscopy measurements,” Opt. Express 17, 10265–10276 (2009). [CrossRef]

14.

O. M. Johannessen, M. Miles, and E. Bjørgo, “The arctic’s shrinking sea ice,” Nature 376, 126–127 (1995). [CrossRef]

15.

O. M. Johannessen, E. V. Shalina, and M. W. Miles, “Satellite evidence for an Arctic sea ice cover in transformation,” Science 286, 1937–1939 (1999). [CrossRef]

16.

G. M. Krekov and G. G. Matvienko, “Laser technology development in the remote sensing of atmosphere,” Atmos. Oceanic Opt. 23, 835–844 (2010).

17.

Q. P. Remund and D. G. Long, “Sea ice extent mapping using Ku-band scatterometer data,” J. Geophys. Res. 104, 11515–11527 (1999). [CrossRef]

18.

A. F. Bunkin and K. I. Voliak, Laser Remote Sensing of the Ocean: Methods and Applications (Wiley, 2001).

19.

S. Pershin, A. Lyash, and V. Makarov, “Atmosphere remote sensing by microjoule pulses of diode-laser,” Phys. Vib. 9, 256–260 (2001).

20.

A. V. Soloviev and R. Lukas, “Observation of large diurnal warming events in the near-surface layer of the western equatorial Pacific warm pool,” Deep-Sea Res. Part I 44, 1055–1076 (1997). [CrossRef]

21.

C. J. Donlon, I. S. Robinson, K. S. Casey, J. Vazquez-Cuervo, E. Armstrong, O. Arino, C. L. Gentemann, D. May, P. LeBorgne, J. Piollé, I. J. Barton, H. Beggs, D. J. S. Poulter, C. J. Merchant, A. Bingham, S. Heinz, A. Harris, G. A. Wick, W. J. Emery, P. J. Minnett, R. Evans, D. T. Llewellyn-Jones, C. T. Mutlow, R. W. Reynolds, H. Kawamura, and N. A. Rayner, “The global ocean data assimilation experiment high-resolution sea surface temperature pilot project,” Bull. Am. Meteorol. Soc. 88, 1197–1213 (2007). [CrossRef]

22.

S. M. Pershin, A. F. Bunkin, and V. A. Luk’yanchenko, “Evolution of the spectral component of ice in the OH band of water at temperatures from 13 to 99 °C,” Quantum Electron. 40, 1146–1148 (2010). [CrossRef]

23.

S. M. Pershin and A. F. Bunkin, “‘A jump’ in the position and width of the raman band envelope of O-H valence vibrations upon phase transitions of the first and second kinds in water,” Opt. Spectrosc. 85, 190–193 (1998).

24.

N. P. Andreeva, A. F. Bunkin, and S. M. Pershin, “Deformation of the raman scattering spectrum of Ih ice under local laser heating near 0 °C,” Opt. Spectrosc. 93, 252–256 (2002). [CrossRef]

25.

M. Becucci, S. Cavalieri, R. Eramo, L. Fini, and M. Materazzi, “Raman spectroscopy for water temperature sensing,” Laser Phys. 9, 422–425 (1999). [CrossRef]

26.

Q. Sun, “The Raman OH stretching bands of liquid water,” Vib. Spectrosc. 51, 213–217 (2009). [CrossRef]

27.

K.-J. Lee, Y. Park, A. Bunkin, R. Nunes, S. Pershin, and K. Voliak, “Helicopter-based lidar system for monitoring the upper ocean and terrain surface,” Appl. Opt. 41, 401–406 (2002). [CrossRef]

28.

G. E. Walrafen, M. R. Fisher, M. S. Hokmabadi, and W.-H. Yang, “Temperature dependence of the low- and high-frequency Raman scattering from liquid water,” J. Chem. Phys. 85, 6970–6983 (1986). [CrossRef]

29.

A. F. Bunkin, V. K. Klinkov, V. A. Lukyanchenko, and S. M. Pershin, “Ship wakes detection by Raman lidar,” Appl. Opt. 50, A86–A89 (2011). [CrossRef]

30.

J. N. Porter, C. E. Helsley, S. K. Sharma, A. K. Misra, D. E. Bates, and B. R. Lienert, “Two-dimensional standoff Raman measurements of distant samples,” J. Raman Spectrosc. 43, 165–166 (2012). [CrossRef]

31.

S.-H. Park, Y.-G. Kim, D. Kim, H.-D. Cheong, W.-S. Choi, and J.-I. Lee, “Selecting characteristic Raman wavelengths to distinguish liquid water, water vapor, and ice water,” J. Opt. Soc. Korea 14, 209–214 (2010). [CrossRef]

32.

V. Fadeev, S. Burikov, P. Volkov, V. Lapshin, and A. Syroeshkin, “Raman scattering and fluorescence spectra of water from the sea surface microlayer,” Oceanology 49, 205–210 (2009). [CrossRef]

33.

A. Wang, L. A. Haskin, A. L. Lane, T. J. Wdowiak, S. W. Squyres, R. J. Wilson, L. E. Hovland, K. S. Manatt, N. Raouf, and C. D. Smith, “Development of the Mars microbeam Raman spectrometer,” J. Geophys. Res. 108, 5005–5008(2003). [CrossRef]

34.

R. Barbini, F. Colao, R. Fantoni, L. Fiorani, A. Palucci, E. Artamonov, and M. Galli, “Remotely sensed primary production in the western Ross Sea: results of in situ tuned models,” Antarct. Sci. 15, 77–84 (2003). [CrossRef]

35.

R. Barbini, F. Colao, R. Fantoni, L. Fiorani, and A. Palucci, “Lidar fluorosensor calibration of the SeaWiFS chlorophyll algorithm in the Ross Sea,” Int. J. Remote Sens. 24, 3205–3218 (2003). [CrossRef]

36.

S. G. Warren, “Optical constants of ice from the ultraviolet to the microwave,” Appl. Opt. 23, 1206–1225 (1984). [CrossRef]

37.

R. C. Smith and K. S. Baker, “Optical properties of the clearest natural waters (200–800 nm),” Appl. Opt. 20, 177–183 (1981). [CrossRef]

38.

N. G. Bukhov, U. Heber, C. Wiese, and V. A. Shuvalov, “Energy dissipation in photosynthesis: does the quenching of chlorophyll fluorescence originate from antenna complexes of photosystem II or from the reaction center?,” Planta 212, 749–758 (2001). [CrossRef]

39.

I. A. Stepanenko, V. O. Kompanets, S. V. Chekalin, Z. K. Makhneva, A. A. Moskalenko, R. Y. Pishchainikov, and A. P. Razjivin, “Two-photon excitation spectrum of fluorescence of the light-harvesting complex B800–850 from allochromatium minutissimum within 1200–1500 (600–750) nm spectral range is not carotenoid mediated,” Biol. Membr. 26, 180–187(2009).

OCIS Codes
(280.3640) Remote sensing and sensors : Lidar
(300.6450) Spectroscopy : Spectroscopy, Raman
(280.4788) Remote sensing and sensors : Optical sensing and sensors

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: February 27, 2012
Revised Manuscript: June 20, 2012
Manuscript Accepted: June 23, 2012
Published: July 27, 2012

Virtual Issues
Vol. 7, Iss. 10 Virtual Journal for Biomedical Optics
September 4, 2012 Spotlight on Optics

Citation
Alexey F. Bunkin, Vladimir K. Klinkov, Vasily N. Lednev, Dmitry L. Lushnikov, Aleksey V. Marchenko, Eugene G. Morozov, Sergey M. Pershin, and Renat N. Yulmetov, "Remote sensing of seawater and drifting ice in Svalbard fjords by compact Raman lidar," Appl. Opt. 51, 5477-5485 (2012)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-51-22-5477


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References

  1. R. M. Measures, Laser Remote Sensing: Fundamentals and Applications (Wiley, 1985).
  2. C. L. Parkinson, D. J. Cavalieri, P. Gloersen, H. J. Zwally, and J. C. Comiso, “Arctic sea ice extents, areas and trends, 1978–1996,” J. Geophys. Res. 104, 20837–20856 (1999). [CrossRef]
  3. S. Haykin, E. O. Lewis, R. K. Raney, and J. R. Rossiter, Remote Sensing of Sea Ice and Icebergs (Wiley, 1994).
  4. F. Nirchio, M. Sorgente, A. Giancaspro, W. Biamino, E. Parisato, R. Ravera, and P. Trivero, “Automatic detection of oil spills from SAR images,” Int. J. Remote Sens. 26, 1157–1174 (2005). [CrossRef]
  5. C. E. Brown and M. F. Fingas, “Review of development of laser fluorosensors for oil spill applications,” Mar. Pollut. Bull. 47, 477–484 (2003). [CrossRef]
  6. M. Fingas and C. E. Brown, “Oil spill remote sensing,” in Oil Spill Environmental Forensics, Z. Wang and S. Stout, eds. (Academic, 2000), pp. 419–448.
  7. S. D. Richardson and T. A. Ternes, “Water analysis: emerging contaminations and current issues,” Anal. Chem. 83, 4614 (2011). [CrossRef]
  8. A. Chaulk, G. A. Stern, D. Armstrong, D. G. Barber, and F. Wang, “Mercury distribution and transport across the ocean—sea-ice—atmosphere interface in the arctic ocean,” Environ. Sci. Technol. 45, 1866– 1872 (2011). [CrossRef]
  9. M. Lennon, S. Babichenko, N. Thomas, V. Mariette, G. Mercier, and A. Lisin, “Detection and mapping of oil slicks in the sea by combined use of hyperspectral imagery and laser induced fluorescence,” EARSeL eProceedings 5, 120–128 (2006).
  10. A. F. Bunkin, M. A. Davydov, A. V. Rezov, A. L. Surovegin, and Yu. D. Tsipenyuk, “Helicopter-based lidar complex for emission and fluorescence remote sensing of terrain surfaces,” Laser Phys. 4, 1198–1201 (1994).
  11. V. Lednev, S. M. Pershin, and A. F. Bunkin, “Laser beam profile influence on LIBS analytical capabilities: single vs. multimode beam,” J. Anal. At. Spectrom. 25, 1745–1757 (2010). [CrossRef]
  12. S. M. Pershin, V. N. Lednev, and A. F. Bunkin, “Laser ablation of alloys: selective evaporation model,” Phys. Wave Phenom. 19, 261–274 (2011). [CrossRef]
  13. J. J. Laserna, R. Fernández Reyes, R. González, L. Tobaria, and P. Lucena, “Study on the effect of beam propagation through atmospheric turbulence on standoff nanosecond laser induced breakdown spectroscopy measurements,” Opt. Express 17, 10265–10276 (2009). [CrossRef]
  14. O. M. Johannessen, M. Miles, and E. Bjørgo, “The arctic’s shrinking sea ice,” Nature 376, 126–127 (1995). [CrossRef]
  15. O. M. Johannessen, E. V. Shalina, and M. W. Miles, “Satellite evidence for an Arctic sea ice cover in transformation,” Science 286, 1937–1939 (1999). [CrossRef]
  16. G. M. Krekov and G. G. Matvienko, “Laser technology development in the remote sensing of atmosphere,” Atmos. Oceanic Opt. 23, 835–844 (2010).
  17. Q. P. Remund and D. G. Long, “Sea ice extent mapping using Ku-band scatterometer data,” J. Geophys. Res. 104, 11515–11527 (1999). [CrossRef]
  18. A. F. Bunkin and K. I. Voliak, Laser Remote Sensing of the Ocean: Methods and Applications (Wiley, 2001).
  19. S. Pershin, A. Lyash, and V. Makarov, “Atmosphere remote sensing by microjoule pulses of diode-laser,” Phys. Vib. 9, 256–260 (2001).
  20. A. V. Soloviev and R. Lukas, “Observation of large diurnal warming events in the near-surface layer of the western equatorial Pacific warm pool,” Deep-Sea Res. Part I 44, 1055–1076 (1997). [CrossRef]
  21. C. J. Donlon, I. S. Robinson, K. S. Casey, J. Vazquez-Cuervo, E. Armstrong, O. Arino, C. L. Gentemann, D. May, P. LeBorgne, J. Piollé, I. J. Barton, H. Beggs, D. J. S. Poulter, C. J. Merchant, A. Bingham, S. Heinz, A. Harris, G. A. Wick, W. J. Emery, P. J. Minnett, R. Evans, D. T. Llewellyn-Jones, C. T. Mutlow, R. W. Reynolds, H. Kawamura, and N. A. Rayner, “The global ocean data assimilation experiment high-resolution sea surface temperature pilot project,” Bull. Am. Meteorol. Soc. 88, 1197–1213 (2007). [CrossRef]
  22. S. M. Pershin, A. F. Bunkin, and V. A. Luk’yanchenko, “Evolution of the spectral component of ice in the OH band of water at temperatures from 13 to 99 °C,” Quantum Electron. 40, 1146–1148 (2010). [CrossRef]
  23. S. M. Pershin and A. F. Bunkin, “‘A jump’ in the position and width of the raman band envelope of O-H valence vibrations upon phase transitions of the first and second kinds in water,” Opt. Spectrosc. 85, 190–193 (1998).
  24. N. P. Andreeva, A. F. Bunkin, and S. M. Pershin, “Deformation of the raman scattering spectrum of Ih ice under local laser heating near 0 °C,” Opt. Spectrosc. 93, 252–256 (2002). [CrossRef]
  25. M. Becucci, S. Cavalieri, R. Eramo, L. Fini, and M. Materazzi, “Raman spectroscopy for water temperature sensing,” Laser Phys. 9, 422–425 (1999). [CrossRef]
  26. Q. Sun, “The Raman OH stretching bands of liquid water,” Vib. Spectrosc. 51, 213–217 (2009). [CrossRef]
  27. K.-J. Lee, Y. Park, A. Bunkin, R. Nunes, S. Pershin, and K. Voliak, “Helicopter-based lidar system for monitoring the upper ocean and terrain surface,” Appl. Opt. 41, 401–406 (2002). [CrossRef]
  28. G. E. Walrafen, M. R. Fisher, M. S. Hokmabadi, and W.-H. Yang, “Temperature dependence of the low- and high-frequency Raman scattering from liquid water,” J. Chem. Phys. 85, 6970–6983 (1986). [CrossRef]
  29. A. F. Bunkin, V. K. Klinkov, V. A. Lukyanchenko, and S. M. Pershin, “Ship wakes detection by Raman lidar,” Appl. Opt. 50, A86–A89 (2011). [CrossRef]
  30. J. N. Porter, C. E. Helsley, S. K. Sharma, A. K. Misra, D. E. Bates, and B. R. Lienert, “Two-dimensional standoff Raman measurements of distant samples,” J. Raman Spectrosc. 43, 165–166 (2012). [CrossRef]
  31. S.-H. Park, Y.-G. Kim, D. Kim, H.-D. Cheong, W.-S. Choi, and J.-I. Lee, “Selecting characteristic Raman wavelengths to distinguish liquid water, water vapor, and ice water,” J. Opt. Soc. Korea 14, 209–214 (2010). [CrossRef]
  32. V. Fadeev, S. Burikov, P. Volkov, V. Lapshin, and A. Syroeshkin, “Raman scattering and fluorescence spectra of water from the sea surface microlayer,” Oceanology 49, 205–210 (2009). [CrossRef]
  33. A. Wang, L. A. Haskin, A. L. Lane, T. J. Wdowiak, S. W. Squyres, R. J. Wilson, L. E. Hovland, K. S. Manatt, N. Raouf, and C. D. Smith, “Development of the Mars microbeam Raman spectrometer,” J. Geophys. Res. 108, 5005–5008(2003). [CrossRef]
  34. R. Barbini, F. Colao, R. Fantoni, L. Fiorani, A. Palucci, E. Artamonov, and M. Galli, “Remotely sensed primary production in the western Ross Sea: results of in situ tuned models,” Antarct. Sci. 15, 77–84 (2003). [CrossRef]
  35. R. Barbini, F. Colao, R. Fantoni, L. Fiorani, and A. Palucci, “Lidar fluorosensor calibration of the SeaWiFS chlorophyll algorithm in the Ross Sea,” Int. J. Remote Sens. 24, 3205–3218 (2003). [CrossRef]
  36. S. G. Warren, “Optical constants of ice from the ultraviolet to the microwave,” Appl. Opt. 23, 1206–1225 (1984). [CrossRef]
  37. R. C. Smith, and K. S. Baker, “Optical properties of the clearest natural waters (200–800 nm),” Appl. Opt. 20, 177–183 (1981). [CrossRef]
  38. N. G. Bukhov, U. Heber, C. Wiese, and V. A. Shuvalov, “Energy dissipation in photosynthesis: does the quenching of chlorophyll fluorescence originate from antenna complexes of photosystem II or from the reaction center?,” Planta 212, 749–758 (2001). [CrossRef]
  39. I. A. Stepanenko, V. O. Kompanets, S. V. Chekalin, Z. K. Makhneva, A. A. Moskalenko, R. Y. Pishchainikov, and A. P. Razjivin, “Two-photon excitation spectrum of fluorescence of the light-harvesting complex B800–850 from allochromatium minutissimum within 1200–1500 (600–750) nm spectral range is not carotenoid mediated,” Biol. Membr. 26, 180–187(2009).

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