Using empirical orthogonal functions derived from remote sensing reflectance for the prediction of concentrations of phytoplankton pigments.

The composition and abundance of algal pigments provide information on characteristics of a phytoplankton community in respect to its photoacclimation, overall biomass, and taxonomic composition. Particularly, these pigments play a major role in photoprotection and in the light-driven part of photosynthesis. Most phytoplankton pigments can be measured by High Performance Liquid Chromatography (HPLC) techniques to filtered water samples. This method, like others when water samples have to be analysed in the laboratory, is time consuming and therefore only a limited number of data points can be obtained. In order to receive information on phytoplankton pigment composition with a higher temporal and spatial resolution, we have developed a method to assess pigment concentrations from continuous optical measurements. The method applies an Empirical Orthogonal Function (EOF) analysis to remote sensing reflectance data derived from ship-based hyper-spectral underwater radiometric and from multispectral satellite data (using the MERIS Polymer product developed by Steinmetz et al., 2011) measured in the Eastern Tropical Atlantic. Subsequently we developed statistically linear models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables. The model results, show that surface concentrations of a suite of pigments and pigment groups can be well predicted from the ship-based reflectance measurements, even when only a multi-spectral resolution is chosen (i.e. eight bands similar to those used by MERIS). Based on the MERIS reflectance data, concentrations of total and monovinyl chlorophyll a and the groups of photoprotective and photosynthetic carotenoids can be predicted with high quality. The fitted statistical model constructed on the satellite reflectance data as input was applied to one month of MERIS Polymer data to predict the concentration of those pigment groups for the whole Eastern Tropical Atlantic area. Bootstrapping explorations of cross-validation error indicate that the method can produce reliable predictions with relatively small data sets (e.g., < 50 collocated values of reflectance and pigment concentration). The method allows for the derivation of time series from continuous reflectance data of various pigment groups at various regions, which can be used to study variability and change of phytoplankton composition and photo-physiology

A Fast Vector Radiative Transfer Model for Polarimetric Remote Sensing

Polarimetric remote sensing technologies have been demonstrated to be irreplaceable and effective for inferring cloud, aerosol, and ocean properties. To infer atmospheric and oceanic constituent properties from observational data, an efficient and accurate retrieval algorithm is needed. The accuracy and efficiency of the retrieval algorithm depends on the radiative transfer model (RTM) used in the forward calculations involved in implementing the retrieval algorithm. If a radiative transfer calculation is implemented in-line as part of a retrieval algorithm, rather than simply generating and interpolating from a look-up table, the atmospheric profiles and surface properties can be directly incorporated into the retrieval system to improve accuracy. Some interpolation errors can also be avoided. However, an in-line radiative transfer calculation usually does not satisfy computational efficiency requirements for an operational remote sensing application. To fully exploit the capability of satellite polarimetric instruments, it is imperative to develop an accurate and fast vector RTM. The reported research develops a fast vector RTM in support of atmospheric and oceanic polarimetric remote sensing. This model is capable of simulating the Stokes vector observed at the top of the atmosphere and at the terrestrial surface by considering absorption, scattering, and emission in the atmosphere and ocean. Gas absorption is parameterized in terms of gas concentration, temperature, and pressure. The parameterization scheme uses a regression method and can be easily applied to an inhomogeneous atmospheric path. An efficient two-component approach combining the small-angle approximation and the adding-doubling method is utilized to solve the vector radiative transfer equation (RTE). The thermal emission source is approximated as a linear function of optical thickness in homogeneous layers. Based on this approximation, the thermal emission component of the RTE solution can be obtained by an efficient doubling process. The air-sea interface is treated as a wind-ruffled rough surface in the model to mimic a realistic ocean surface. Several bio-optical models are introduced to model ocean inherent optical properties. It is shown that the developed RTM can be used in a retrieval algorithm by comparing the simulation results with observations by POLDER and MODIS satellite instruments

Hyperspectral sub-pixel target detection using hybrid algorithms and Physics Based Modeling

This thesis develops a new hybrid target detection algorithm called the Physics Based-Structured InFeasibility Target-detector (PB-SIFT) which incorporates Physics Based Modeling (PBM) along with a new Structured Infeasibility Projector (SIP) metric. Traditional matched filters are susceptible to leakage or false alarms due to bright or saturated pixels that appear target-like to hyperspectral detection algorithms but are not truly target. This detector mitigates against such false alarms. More often than not, detection algorithms are applied to atmospherically compensated hyperspectral imagery. Rather than compensate the imagery, we take the opposite approach by using a physics based model to generate permutations of what the target might look like as seen by the sensor in radiance space. The development and status of such a method is presented as applied to the generation of target spaces. The generated target spaces are designed to fully encompass image target pixels while using a limited number of input model parameters. Evaluation of such target spaces shows that they can reproduce a HYDICE image target pixel spectrum to less than 1% RMS error (equivalent reflectance) in the visible and less than 6% in the near IR. Background spaces are modeled using a linear subspace (structured) approach characterized by basis vectors found by using the maximum distance method (MaxD). The SIP is developed along with a Physics Based Orthogonal Projection Operator (PBosp) which produces a 2 dimensional decision space. Results from the HYDICE FR I data set show that the physics based approach, along with the PB-SIFT algorithm, can out perform the Spectral Angle Mapper (SAM) and Spectral Matched Filter (SMF) on both exposed and fully concealed man-made targets found in hyperspectral imagery. Furthermore, the PB-SIFT algorithm performs as good (if not better) than the Mixture Tuned Matched Filter (MTMF)

Towards high fidelity mapping of global inland water quality using earth observation data

This body of work aims to contribute advancements towards developing globally applicable water quality retrieval models using Earth Observation data for freshwater systems. Eutrophication and increasing prevalence of potentially toxic algal blooms among global inland water bodies have become a major ecological concersn and require direct attention. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products. This study provides one of the first assessments of the state-ofthe-art for trophic status (chlorophyll-a) retrievals for small water bodies using Sentinel-3 Ocean and Land Color Imager (OLCI). Multiple fieldwork campaigns were undertaken for the collection of common aquatic biogeophysical and bio-optical parameters that were used to validate current atmospheric correction and chlorophyll-a retrieval algorithms. The study highlighted the difficulties of obtaining robust retrieval estimates from a coarse spatial resolution sensor from highly variable eutrophic water bodies. Atmospheric correction remains a difficult challenge to operational freshwater monitoring, however, the study further validated previous work confirming applicability of simple, empirically derived retrieval algorithms using top-of-atmosphere data. The apparent scarcity of paired in-situ optical and biogeophysical data for productive inland waters also hinders our capability to develop and validate robust retrieval algorithms. Radiative transfer modeling was used to fill this gap through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which attempts to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size and type specific phytoplankton IOPs for mixed eukaryotic/cyanobacteria 6 assemblages, 2) calculations of mixed assemblage chl-a fluorescence, 3) modeled phycocyanin concentration derived from assemblage based phycocyanin absorption, 4) and paired sensor-specific TOA reflectances which include optically extreme cases and contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships and ranges of concentrations and inherent optical properties of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, as well as first order calculations of the signal-to-noise-ratio (SNR) for the various optical water types, a first for productive inland waters, as well as conduct a sensitivity analysis of cyanobacteria detection from top-of-atmosphere. Finally, the synthetic dataset was used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and inherent optical properties over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. It is hoped the results of this work incrementally improves inland water Earth observation on multiple aspects of the forward and inverse modelling process, and provides an improvement in our capabilities for routine, global monitoring of inland water quality

Towards CO2 emission monitoring with passive air- and space-borne sensors

Coal-fueled power plants are responsible for 30 % of anthropogenic carbon dioxide (CO2) emissions and can therefore be considered important drivers of climate warming. The 2015 Paris Climate Accord has established a global stock take mechanism, which will assess the progress of global carbon emission reduction policies in five-yearly tallies of worldwide emissions. However, there exists no independent monitoring network, which could verify such stock takes. Remote sensing of atmospheric CO2 concentrations from air- and space-borne sensors could provide the means of monitoring localized carbon sources, if their ground sampling distance is sufficiently fine (i.e. below the kilometer scale). Increased spatial resolution can be achieved at the expense of decreasing the spectral resolution of the instrument, which in turn complicates CO2 retrieval techniques due to the reduced information content of the spectra. The present thesis aims to add to the methodology of remote CO2 monitoring approaches by studying the compromise between spectral and spatial resolution with CO2 retrievals from three different sensors. First, the trade-off between coarse spectral resolution and retrieval performance is discussed for a hypothetical imaging spectrometer which could reach a spatial resolution of ~50×50 m2 by measuring backscattered sunlight in the short wave infrared spectral range at a resolution of ∆λ ~ 1 nm. To this end, measurements of the Greenhouse gases Observing SATellite (GOSAT) at ∆λ = 0.1 nm are artificially degraded to coarser spectral resolutions to emulate the proposed sensor. CO2 column retrievals are carried out with the native and degraded spectra and the results are compared with each other, while data from the ground based Total Carbon Column Observing Network (TCCON) serve as independent reference data. This study identifies suitable retrieval windows in the short wave infrared spectral range and a favorable spectral resolution for a CO2 monitoring mission. Second, CO2 column retrievals are carried out with measurements of the air-borne AVIRIS-NG sensor at a spectral resolution of ∆λ = 5 nm. This case study identifies advantageous CO2 retrieval configurations, which minimize correlations between retrieval parameters, near two coal-fired power plants. A bias correction method is proposed for the retrievals and a plume mask is applied to the retrieved CO2 enhancements to separate the CO2 emission signal from the atmospheric background. Emission rates of the two facilities are calculated under consideration of the local wind speed, compared to a public inventory and discussed in terms of their uncertainties. Third, CO2 retrievals are extended to spectral resolutions on the order of ∆λ ~ 10 nm by analyzing spectra of the specMACS imager near a small power plant. Retrieval effects that hamper the detection of the source signal are discussed

Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

Numerical Prediction of Dust

Covers the whole breadth of mineral dust research, from a scientific perspective Presents interdisciplinary work including results from field campaigns, satellite observations, laboratory studies, computer modelling and theoretical studies Explores the role of dust as a player and recorder of environmental change This volume presents state-of-the-art research about mineral dust, including results from field campaigns, satellite observations, laboratory studies, computer modelling and theoretical studies. Dust research is a new, dynamic and fast-growing area of science and due to its multiple roles in the Earth system, dust has become a fascinating topic for many scientific disciplines. Aspects of dust research covered in this book reach from timescales of minutes (as with dust devils, cloud processes, and radiation) to millennia (as with loess formation and oceanic sediments), making dust both a player and recorder of environmental change. The book is structured in four main parts that explore characteristics of dust, the global dust cycle, impacts of dust on the Earth system, and dust as a climate indicator. The chapters in these parts provide a comprehensive, detailed overview of this highly interdisciplinary subject. The contributions presented here cover dust from source to sink and describe all the processes dust particles undergo while travelling through the atmosphere. Chapters explore how dust is lifted and transported, how it affects radiation, clouds, regional circulations, precipitation and chemical processes in the atmosphere, and how it deteriorates air quality. The book explores how dust is removed from the atmosphere by gravitational settling, turbulence or precipitation, how iron contained in dust fertilizes terrestrial and marine ecosystems, and about the role that dust plays in human health. We learn how dust is observed, simulated using computer models and forecast. The book also details the role of dust deposits for climate reconstructions. Scientific observations and results are presented, along with numerous illustrations. This work has an interdisciplinary appeal and will engage scholars in geology, geography, chemistry, meteorology and physics, amongst others with an interest in the Earth system and environmental change