Sea state from monoscopic ocean video in real environments

Video of the ocean surface is used as a means for estimating useful information about the scene. A methodology is first introduced for approximating the pixel to metre scale from high-scale videos of the ocean, such as from an aeroplane. Radar images are used for testing. The temporal and spatial domains are associated through the phase modulation of waves, and a process is introduced that selects the waves with the highest energy to be used for estimating the pixel scale. The spatial information is then used with the calculated pixel scale for approximating the sea state. Due to the difficulty of obtaining high-scale videos, a methodology is then introduced that uses the temporal variation from video, and specifically time series of pixel intensities. It aims to isolate and utilise the temporal variation of the wave field from all other video elements, such as environmental brightness fluctuations. The methodology utilises the Kalman filter and the least squares approximate solution for providing an uncalibrated video amplitude spectrum. A method is proposed for scaling this spectrum to metres with the use of an empirical model of the ocean. The significant wave height is estimated from the calibrated video amplitude spectrum. Videos of the ocean in real environments from a shipborne camera and a tower are used for testing. In both sets of data, in situ buoy information is used solely for validation. The next technique aims to approximate the sea state from the same kind of data, namely videos of the ocean in real environments, without calibrating a video amplitude spectrum. The proposed methodology tracks the principal component of the movement of water in the video, which is speculated to be associated with the dominant frequency of the ocean. To accomplish this, the singular spectrum analysis algorithm and the extended Kalman filter are used. Then, the shape of an empirical spectrum is utilised in order to translate the dominant frequency output into a significant wave height estimation. The problem of not using ocean theory associated with a particular empirical energy spectrum for calibration is examined in the next methodology. A secondary oscillatory component from the singular spectrum analysis algorithm is identified with the incorporation of the extended Kalman filter. Ocean theory involving the equilibrium range of oceans is used for calibration. The shipborne videos are used for testing the behaviour of the techniques for approximately the same sea state of 3.1m to 3.4m of significant wave height. The tower videos are used for testing the techniques for a variety of sea states ranging between 0.5m and 3.6m of significant wave height. From all methodologies, the maximum observed values of root mean square error 0.37m and of mean absolute percentage error 18% suggest that the work is promising at estimating these states

Optical remote sensing of water quality parameters retrieval in the Barents Sea

This thesis addresses various aspects of monitoring water quality indicators (WQIs) using optical remote sensing technologies. The dynamic nature of aquatic systems necessitate frequent monitoring at high spatial resolution. Machine learning (ML)-based algorithms are becoming increasingly common for these applications. ML algorithms are required to be trained by a significant amount of training data, and their accuracy depends on the performance of the atmospheric correction (AC) algorithm being used for correcting atmospheric effects. AC over open oceanic waters generally performs reasonably well; however, limitations still exist over inland and coastal waters. AC becomes more challenging in the high north waters, such as the Barents Sea, due to the unique in-water optical properties at high latitudes, long ray pathways, as well as the scattering of light from neighboring sea ice into the sensors’ field of view adjacent to ice-infested waters. To address these challenges, we evaluated the performances of state-of-the-art AC algorithms applied to the high-resolution satellite sensors Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI), both for high-north (Paper II) and for global inland and coastal waters (Paper III). Using atmospherically corrected remote sensing reflectance (Rrs ) products, estimated after applying the top performing AC algorithm, we present a new bandpass adjustment (BA) method for spectral harmonization of Rrs products from OLI and MSI. This harmonization will enable an increased number of ocean color (OC) observations and, hence, a larger amount of training data. The BA model is based on neural networks (NNs), which perform a pixel-by-pixel transformation of MSI-derived Rrs to that of OLI equivalent for their common bands. In addition, to accurately retrieve concentrations of Chlorophyll-a (Chl-a) and Color Dissolved Organic Matter (CDOM) from remotely sensed data, we propose in the thesis (Paper 1) an NN-based WQI retrieval model dubbed Ocean Color Net (OCN). Our results indicate that Rrs retrieved via the Acolite Dark Spectrum Fitting (DSF) method is in best agreement with in-situ Rrs observations in the Barents Sea compared to the other methods. The median absolute percentage difference (MAPD) in the blue-green bands ranges from 9% to 25%. In the case of inland and coastal waters (globally), we found that OC-SMART is the top performer, with MAPD Rrs products for varying optical regimes than previously presented methods. Additionally, to improve the analysis of remote sensing spectral data, we introduce a new spatial window-based match-up data set creation method which increases the training data set and allows for better tuning of regression models. Based on comparisons with in-water measured Chl-a profiles in the Barents Sea, our analysis indicates that the MSI-derived Rrs products are more sensitive to the depth-integrated Chl-a contents than near-surface Chl-a values (Paper I). In the case of inland and coastal waters, our study shows that using combined OLI and BA MSI-derived Rrs match-ups results in considerable improvement in the retrieval of WQIs (Paper III). The obtained results for the datasets used in this thesis illustrates that the proposed OCN algorithm shows better performance in retrieving WQIs than other semi-empirical algorithms such as the band ratio-based algorithm, the ML-based Gaussian Process Regression (GPR), as well as the globally trained Case-2 Regional/Coast Colour (C2RCC) processing chain model C2RCC-networks, and OC-SMART. The work in this thesis contributes to ongoing research in developing new methods for merging data products from multiple OC missions for increased coverage and the number of optical observations. The developed algorithms are validated in various environmental and aquatic conditions and have the potential to contribute to accurate and consistent retrievals of in-water constituents from high-resolution satellite sensors

Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize

This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornitz (1991) and enhanced using Machine learning (ML) clustering. ML has been employed to divide the coastline based on the geotechnical conditions observed to establish relative vulnerability. This has been demonstrated to alleviate bias and enhanced the scalability of the approach – especially in areas with poor data coverage – a known hinderance to the CVI approach (Koroglu et al., 2019).Belize provides a demonstrator for this novel methodology due to limited existing data coverage and the recent removal of the Mesoamerican Reef from the International Union for Conservation of Nature (IUCN) List of World Heritage In Danger. A strong characterization of the coastal zone and associated pressures is paramount to support effective management and enhance resilience to ensure this status is retained.Areas of consistent vulnerability have been identified using the KMeans classifier; predominantly Caye Caulker and San Pedro. The ability to automatically scale to conditions in Belize has demonstrated disparities to vulnerability along the coastline and has provided more realistic estimates than the traditional CVI groups. Resulting vulnerability assessments have indicated that 19% of the coastline at the highest risk with a seaward distribution to high risk observed. Using data derived using Sentinel-2, this study has also increased the accuracy of existing habitat maps and enhanced survey coverage of uncharted areas.Results from this investigation have been situated within the ability to enhance community resilience through supporting regional policies. Further research should be completed to test the robust nature of this model through an application in regions with different geographic conditions and with higher resolution input datasets

A new spectral harmonization algorithm for Landsat-8 and Sentinel-2 remote sensing reflectance products using machine learning: a case study for the Barents Sea (European Arctic)

The synergistic use of Landsat-8 operational land imager (OLI) and Sentinel-2 multispectral instrument (MSI) data products provides an excellent opportunity to monitor the dynamics of aquatic ecosystems. However, the merging of data products from multisensors is often adversely affected by the difference in their spectral characteristics. In addition, the errors in the atmospheric correction (AC) methods further increase the inconsistencies in downstream products. This work proposes an improved spectral harmonization method for OLI and MSI-derived remote sensing reflectance ( Rrs ) products, which significantly reduces uncertainties compared to those in the literature. We compared Rrs retrieved via state-of-the-art AC processors, i.e., Acolite, C2RCC, and Polymer, against ship-based in situ Rrs observations obtained from the Barents Sea waters, including a wide range of optical properties. Results suggest that the Acolite-derived Rrs has a minimum bias for our study area with median absolute percentage difference (MAPD) varying from 9% to 25% in the blue–green bands. To spectrally merge OLI and MSI, we develop and apply a new machine learning-based bandpass adjustment (BA) model to near-simultaneous OLI and MSI images acquired in the years from 2018 to 2020. Compared to a conventional linear adjustment, we demonstrate that the spectral difference is significantly reduced from ∼6 % to 12% to ∼2 % to Rrs products for water quality monitoring applications. The proposed method has the potential to be applied to other waters

A review on the current Status of Numerical Weather Prediction in Portugal 2021: surface–atmosphere interactions

Earth system modelling is currently playing an increasing role in weather forecasting and understanding climate change, however, the operation, deployment and development of numerical Earth system models are extremely demanding in terms of computational resources and human effort. Merging synergies has become a natural process by which national meteorological services assess and contribute to the development of such systems. With the advent of joining synergies at the national level, the second edition of the workshop on Numerical Weather Prediction in Portugal was promoted by the Portuguese Institute for the Sea and Atmosphere, I.P. (IPMA), in cooperation with several Portuguese Universities. The event was hosted by the University of Évora, during the period of 11–12 of November 2021. It was dedicated to surface–atmosphere interactions and allowed the exchange of experiences between experts, students and newcomers. The workshop provided a refreshed overview of ongoing research and development topics in Portugal on surface–atmosphere interaction modelling and its applications and an opportunity to revisit some of the concepts associated with this area of atmospheric sciences. This article reports on the main aspects discussed and offers guidance on the many technical and scientific modelling platforms currently under study.info:eu-repo/semantics/publishedVersio

Detection of Marine Plastic Debris in the North Pacific Ocean using Optical Satellite Imagery

Plastic pollution is ubiquitous across marine environments, yet detection of anthropogenic debris in the global oceans is in its infancy. Here, we exploit high-resolution multispectral satellite imagery over the North Pacific Ocean and information from GPS-tracked floating plastic conglomerates to explore the potential for detecting marine plastic debris via spaceborne remote sensing platforms. Through an innovative method of estimating material abundance in mixed pixels, combined with an inverse spectral unmixing calculation, a spectral signature of aggregated plastic litter was derived from an 8-band WorldView-2 image. By leveraging the spectral characteristics of marine plastic debris in a real environment, plastic detectability was demonstrated and evaluated utilising a Spectral Angle Mapper (SAM) classification, Mixture Tuned Matched Filtering (MTMF), the Reed-Xiaoli Detector (RXD) algorithm, and spectral indices in a three-variable feature space. Results indicate that floating aggregations are detectable on sub-pixel scales, but as reliable ground truth information was restricted to a single confirmed target, detections were only validated by means of their respective spectral responses. Effects of atmospheric correction algorithms were evaluated using ACOLITE, ACOMP, and FLAASH, in which derived unbiased percentage differences ranged from 1% to 81% following a pairwise comparison. Building first steps towards an integrated marine monitoring system, the strengths and limitations of current remote sensing technology are identified and adopted to make suggestions for future improvements

Arctic Thin Sea Ice Thickness Regression Models for Sentinel-2

Sea ice thickness is an important parameter for modelling the sea ice mass balance, momentum and gas exchanges, and global energy budget. The interest of studies into thin sea ice has increased as trends in recent years show a increasing abundance in thin first year ice. Existing thin sea ice thickness products operate at resolutions down to 750 meters. Very high resolution (less than 100 meters) retrieval of sea ice parameters is of particular interest due to maritime navigation and model parametrization of physical processes at meter-scaled resolutions that usually requires in-situ measurements. The Norwegian Meteorological Institute provided a 500 meter resolution thin sea ice thickness product developed by the Norwegian Computing Centre for the Norwegian Space Agency’s "Sentinel4ThinIce" project. The product is derived from Sentinel-3’s SLSTR sensor. Using overlapping multispectral optical data from Sentinel-2’s MultiSpectral Instrument at metre-scaled resolutions, we retrieved multiple regression models for thin sea ice thickness for Sentinel-2 data. The models included three univariate models for three different spectral band combinations using non-linear least squares method, and one multivariate model for three different band reflectance data-sets using a gradient boosting regression tree. The optical band reflectance data increased monotonically with sea ice thickness and saturated for thicker ice, proving a clear correlation between thin sea ice thickness and Sentinel-2’s band reflectance. The multivariate model produces overall best results compared to the univariate models. The reliability of the models couldn’t be trusted due to inaccurate atmospheric correction procedures and not enough temporal and geographical variance in the data-set. Proper calibration of Sentinel-2 data is of high priority in order to extend Sentinel-2’s platform further into Arctic research

Sea State Estimation from Uncalibrated, Monoscopic Video

This is the final version. Available on open access from Springer via the DOI in this recordVideo of the ocean surface is used as a means for estimating the sea state. Time series of pixel intensity values are given as input to a method that uses the Kalman filter and the least squares approximate solution for estimating the uncalibrated video amplitude spectrum. A method is proposed for scaling this spectrum to metres with the use of an empirical model of the ocean. The significant wave height is estimated from the calibrated video amplitude spectrum. The results are tested against two sets of video data, and buoy measurements in both cases are solely used for indicating the true state. For significant wave height values between 0.5 and 3.6 m, the maximum observed value of root mean square error is 0.37 m and of mean absolute percentage error 16%