GPI RAS 1 Remote Sensing of Ice and Snow by Compact Raman Lidar V.N. Lednev, S.M. Pershin, R.N. Yulmetov, A.F. Bunkin Prokhorov General Physics Institute Russian Academy of Sciences June 10, 2013 Espoo, Finland

GPI RAS 2 Prokhorov General Physics Institute A.M. Prokhorov Nobel prize winner (1964) for maser-laser principles the lasers beginning open resonator build by A.M. Prokhorov and his Ph.D. student N.G. Basov for microwave amplification (1952) from left to right: C.H. Townes, A.M. Prokhorov, N.G. Basov General Physics Institute, RAS (Moscow, Russia)

GPI RAS 3 Outline Why we need remote sensing Why we need laser remote sensing Remote temperature measurements: pro and con Laser remote sending: principals and capabilities Aircraft vehicles for laser remote sensing How to determine ice thickness Ice thickness measurements by Raman spectroscopy Snow classification by Raman spectroscopy

GPI RAS 4 Remote sensing for Arctic region (Why we need remote sensing?) Arctic region: Global climate changes Oil/gas production Temperature map of Arctic region Parameters 1.seawater temperature 2.seawater salinity 3.seawater phytoplankton concentration 4.seawater organic substances concentration 5.seawater optical properties 6.seawater contamination 7.ice temperature 8.ice surface roughness 9.ice optical properties 10.snow cover optical and thermodynamic properties automatic monitoring of ocean surface by remote sensing Satellite image of Arctic region Oil spills image Global climate modeling

GPI RAS 5 Remote measurements of temperature (Why we need laser remote sensing?) Remote temperature measurements: Spaceborne radiometer radars Airborne microwave scatterometers Airborne laser scatterometers * A. V. Soloviev and R. Lukas, Deep-Sea Res. Part I, 44, 1055–1076 (1997) ! 1 0 С low-speed wind* temperature detection in 30 um surface layer 5 definitions of temperature Alternatives: 1. thermocouple measurements 2. temperature measurements by Raman spectroscopy (laser remote sensing) 30 um vs m

GPI RAS 6 Temperature dependence of Raman OH-band Raman spectra for distilled water from C to C А B Hare D.E. and Sorensen C.M., J. Chem. Phys. 1990, 93(10), 6954 water molecule Raman spectroscopy

GPI RAS 7 Temperature measurements by Raman OH-band profile S. M. Pershin, et.al., Quantum Electron. 40, 1146 (2010) OH-band center vs temperaureOH-band peak fitting vs temperature A B R= BABA Becucci M., et.al., Appl.Opt., 38, 928 (1999)

GPI RAS 8 LIDAR: principles and signals LIght Detecting And Ranging Laser matter interaction: elastic scattering (surface, particles or bubbles, sea floor) Raman scattering (H 2 O molecules, dissolved salts ) fluorescence (distributed organic material, chlorophyll a)

GPI RAS 9 Laser Remote Sensing Applications: 1. Bathymetry 2. Temperature measurements 3. Ecology monitoring (oil leaks detection or other contaminations) 4. Biology applications (chlorophyll concentrations)

GPI RAS 10 Bathymetry by LIDAR LIDAR bathymetry 1. express mapping 2. ground and underwater mapping 3. no needs for water contact

GPI RAS 11 Laser remote sensing in s mirror telescope Helicopter Kamov-32 for laser remote sensing Result * no systematic expeditions * a few flights per year Laser remote sensing drawbacks: heavy equipment (>300 kg) high power consumption (> 3 kW) expansive vehicle rental fee Lidar system installed in helicopter (developed at GPI RAS in 1992)

GPI RAS 12 Compact LIDAR for UAV Future of remote sensing: micro UAV with installed lidar? Unmanned aircraft vehicles (UAV) facilities for remote sensing: payload mass power heavy (>1000 kg, 24 h) <100 kg <800 W midi ( kg, <12 h )<20 kg <100 W mini (5-50 kg, 1-4 h)<2 kg <10 W micro (<1 kg, 1 h)<100 g <0.1 W

GPI RAS 13 Compact Raman LIDAR Specification* Mass: ~20 kg Dimensions: 60x40x20 cm Power: 300 W Details Laser: DPSSL laser Nd:YVO 4 (Laser Compact), 527 nm, 5 ns, 1 kHz, 200 J/pulse Detection system: Spectrograph (Spectra Physics MS127i) Spectral range 500 – 750 nm Spectral resolution 0.1 nm + ICCD detector (Andor iStar) Gate 5 ns Delay s with step 0.25 ns *A.F. Bunkin et. al., Appl. Opt. 51, (2012)

GPI RAS 14 Seawater echo-spectrum water laser beam

GPI RAS 15 Ice mechanics Ice properties: thickness temperature density age and history ice mechanical properties port and ocean engineering

GPI RAS 16 Ice thickness measurements Conventional techniques : 1. drilling (a, b) - human costs - long duration for measurements ( 10 holes for 1 m depth equals >1 hour ) - low accuracy of temperature measurements 2. electro-magnetic antenna (c) - expansive equipment - poor accuracy for remote sensing - no temperature measurements 3. thermistor chain (d) - long duration - difficult to remove from ice - high precision of temperature measurements Ice Water TmTm 0 T a)a) b)b) c)c) d)d)

GPI RAS 17 Ice and Water Raman spectra Raman spectrum of OH-band profile (stretching vibration) in water and in ice. water ice

GPI RAS 18 Ice thickness measurements (experiment setup)

GPI RAS 19 Raman Spectra for Ice and Water water ice elastic scattering Raman scattering

GPI RAS 20 Ice thickness measurement by elastic scattering ice water laser beam Conclusion: elastic scattering is not convenient for ice–water interface detection n water = n ice = Elastic scattering (ice bathymetry)

GPI RAS 21 Ice thickness measurements by Raman scattering a) Raman spectra of OH-band for ice (black triangles) and water (red circus) and corresponding fitting curves b) Ice thickness measurements by Raman OH-band center (blue circus) Raman scattering a) ice water laser beam b) Conclusion: Raman OH-band center shift is convenient for ice–water interface detection

GPI RAS 22 Future plans compact LIDAR for ice thickness and temperature measurements mass: < 2 kg power: < 20 W conditions: -50 to C Capabilities Ice thickness measurements: distance to object 0 – 600 m thickness range 0 – 100 m thickness accuracy ± 1 cm Ice temperature profile: sample dimensions 0 – 100 m temperature accuracy ± 0.5 o C spatial resolution 5 cm key features: single photon counting and time-of-flight measurements 0 – 450 m with accuracy 1 cm ice LIDAR water eye-safe LIDAR

GPI RAS 23 Snow Characterization by Raman spectra Raman spectra for different snow layers (a) and detailed OH-band profile (b): fresh snow (black), snow cover (red) and old snow (blue)

GPI RAS 24 Snow Profile by Raman spectroscopy Snow depth profile measurements by Raman spectroscopy: a) the sampled points; b) elastic signal; c) Raman OH-band intensity; d) Raman OH-band center. Sample was taken in GPIs courtyard on 23 March of min after strong snowfall

GPI RAS 25 Conclusions Compact Raman LIDAR has been developed in GPI RAS for remote sensing of ocean Light weight and low power consumption make possible to install the device on any vehicle like unmanned aircraft or submarine A new optic technique for express ice thickness measurements by Raman spectroscopy was suggested Snow classification by Raman spectroscopy was performed Ultracompact Raman LIDAR for ice thickness and temperature measurements

GPI RAS 26 Thank you for your attention