A LIDAR-compatible approach to remote sensing of water temperature using Raman spectroscopy

The measurement of water temperature provides essential information for the understanding of the water column dynamics, being important for research fields including oceanography, climate change, marine ecology, fisheries and coastal management. Traditional in situ measurements provide accurate depth-resolved information at limited spatial and temporal scales. As an alternative suitable for large scales studies, researchers may rely on remote sensing tools such as passive satellite sensors and active LIDAR (Light Detection and Ranging) methods. However, satellite-derived sea surface data are restricted to the first micrometres of the water column, not providing information regarding vertical structure and stratification. LIDAR methods employ active excitation and fast time-resolved detectors, allowing for depth-resolved measurements performed from airborne or ship-based platforms and, when coupled to spectroscopic measurements, have the potential to assess subsurface water temperature. The aim of this research work is to develop LIDAR-compatible spectroscopic methods for monitoring water temperature based on the inelastic Raman scattering of photons in water. Raman scattering in water exhibits a temperature-dependent behaviour, which can be used to estimate temperature markers for remote sensing predictions. The analysis of Raman spectra from natural water samples, which were acquired by using a commercial Raman spectrometer (532 nm excitation) indicated that the presence of other optical signals in natural waters, such as fluorescence, may compromise the accuracy of Raman temperature sensing. In order to circumvent this issue, I proposed methods for spectral correction which resulted in temperature determination with improved accuracy. I designed and assembled multichannel LIDAR-compatible Raman spectrometers integrated to excitation lasers having green and blue wavelengths. The design allowed for simultaneous collection of unpolarised and polarised Raman signals, enabling the calculation of four temperature markers carrying different types of temperature information.Each marker was analysed in terms of accuracy of temperature predictions, sensitivities and percentage errors associated with signal-to-noise ratios. A novel linear combination method was employed to use all four temperature markers and was effective in enabling enhanced temperature predictions. The relative merits of using green and blue excitation were considered in the context of laboratory studies and proposed field implementation. The work presented in this thesis represents a major step forward in the quest for a LIDAR-based optical system to measure subsurface water temperature with an accuracy of ±0.5°C and depth resolution of 0.5 m in near real-time.