Search

Radiometric products determined from fixed-depth and continuous in-water profile data collected at a coastal site characterized by moderately complex waters were compared to investigate differences and limitations between the two measurement methods. The analysis focused on measurements performed with the same radiometer system sequentially deployed at discrete depths (i.e., 1 and 3 m) and successively used to profile the water column. Within the 412–683-nm spectral interval, comparisons show uncertainties of 2%–4%, 3%–5%, and 2% for the subsurface values of upwelling radiance, Lun, upward irradiance, Eun, and downward irradiance, Edn, all normalized with respect to the above-water downward irradiance. The related spectral biases vary from −2% to 1% for Lun, are in the range of 2%–3% for Eun, and are lower than 0.5% for Edn. Derived products like the irradiance reflectance, R, Q factor at nadir, Q, and normalized water leaving radiance, LWN, exhibit spectral uncertainties of 4%–6%, 2%–3%, and 2%–4%. The related spectral biases vary from 1% to 3%, 2% to 3%, and −2% to 1%, respectively. An analysis of these results indicates a general diminishing of uncertainties and biases with a decrease of the diffuse attenuation coefficient, Kd, determined at 490 nm, for most of the quantities investigated. Exceptions are Edn and Kd because an increase of Kd reduces the perturbations due to wave effects on downward irradiance measurements. An evaluation of the perturbing effects due to the presence of optical stratifications, which lead to a nonlinear decrease with depth of log-transformed radiometric measurements, shows an expected increase in uncertainty and bias specifically evident for Ku, Eun, Kl, and Lun, and derived quantities like R, Q, and LWN. Overall results, supported by a t-test analysis, indicate the possibility of using moorings in moderately complex coastal waters to determine LWN with a slightly higher uncertainty with respect to that achievable with continuous profiling systems.

Accurate merging of primary radiometric ocean color products like the normalized water-leaving radiance, requires combining data from various space missions which may be affected by different uncertainties as resulting from absolute calibration and minimization of the atmospheric effects. A statistical correction scheme based on a multi-linear regression algorithm is here used to remove systematic differences between in situ and remote sensing measurements. The application of the correction scheme to SeaWiFS and MODIS primary radiometric products has shown an increased convergence between remote sensing and in situ measurements, with the largest effects at 412 and 443 nm. Specifically, the scatter and bias of MODIS derived with respect to in situ Lwn at 412 nm has shown values of 12% and 2% for corrected with respect to values of 29% and -20% for uncorrected data, respectively. Similarly, the scatter and bias for SeaWiFS derived Lwn at 412 nm has shown values of 11% and 2% for corrected with respect to 27% and -11% for uncorrected data. Results at 667 nm for MODIS, and at 670 nm for SeaWiFS, although displaying a reduction in the scatter of data, have shown a significant residual bias of about 8% with respect to in situ values. Differently, the correction scheme does not improve Lwn values at the other center-wavelengths were the agreement between remote sensing and in situ data is already comparable to the intrinsic uncertainty of the in situ data. Finally, the study has shown the need of restricting the application of the correction scheme to data with spectral features represented within the reference dataset used for defining the correction parameters., JRC.H.3-Global environement monitoring

Water-type (WT) definition as a function of bio-optical properties has relevance to the exploitation of satellite ocean color data because it may provide a mean to optimize the atmospheric correction process or select the most appropriate bio-optical algorithm for the determination of optically significant constituents in seawater. Within such a framework, this paper defines a WT index assuming the existence of two extreme conditions. On one side, there are waters with optical properties totally depending on the phytoplankton component. On the other side, waters characterized by fully unrelated bio-optical quantities. The index of intermediate WTs is then defined on a statistical basis, and the resulting scheme is applied to a set of experimental data collected in an optically complex coastal region. Finally, in view of supporting remote sensing applications, a neural network algorithm is implemented to identify WTs using the normalized water leaving radiance.

This paper, addresses the problem of novelty detection in the case that the observed data is a mixture of a known 'background' process contaminated with an unknown other process, which generates the outliers, or novel observations. The framework we describe here is quite general, employing univariate classification with incomplete information, based on knowledge of the distribution (the 'probability density function', 'pdf') of the data generated by the 'background' process. The relative proportion of this 'background' component (the 'prior' 'background' 'probability), the 'pdf' and the 'prior' probabilities of all other components are all assumed unknown. The main contribution is a new classification scheme that identifies the maximum proportion of observed data following the known 'background' distribution. The method exploits the Kolmogorov-Smirnov test to estimate the proportions, and afterwards data are Bayes optimally separated. Results, demonstrated with synthetic data, show that this approach can produce more reliable results than a standard novelty detection scheme. The classification algorithm is then applied to the problem of identifying outliers in the SIC2004 data set, in order to detect the radioactive release simulated in the 'oker' data set. We propose this method as a reliable means of novelty detection in the emergency situation which can also be used to identify outliers prior to the application of a more general automatic mapping algorithm. © Springer-Verlag 2007.

In situ autonomous systems are commonly used for the collection of measurements for the vicarious calibration of satellite data and the successive validation of derived products. However, the use of autonomous systems creates the need of assessing the quality of the large volume of collected data. Within the framework of ocean color activities, this work investigates the consistency of normalized water leaving radiances spectra produced from measurements taken with an above-water autonomous system installed on an oceanographic tower. The study has shown the need of addressing the problem under two different levels of inference. The first level, so-called self-consistency, has demonstrated the capability of identifying spectra with a low statististical representativeness within the dataset itself. The second level, so-called relative-consistency, has provided the possibility of evaluating whether a spectrum is relatively consistent to a reference set of quality-assured data.

Wave perturbations induce uncertainties in subsurface quantities determined from the extrapolation of optical measurements taken at different depths. An analysis of these uncertainties was made using data collected in the northern Adriatic Sea coastal waters over a wide range of environmental conditions with a profiling system having a 6-Hz acquisition rate, ∼0.1 m s−1 deployment speed, radiance sensors with 20° full angle field of view, and irradiance collectors of ∼1-cm diameter. The uncertainties were quantified as a function of the depth resolution of radiance and irradiance profiles through the percent differences between the subsurface values computed from full and reduced resolution profiles (the latter synthetically created by removing data from the former). The applied method made the analysis independent from instrument calibration; from perturbations induced by instrument self-shading, deployment structure, and bottom effects; and from environmental variability caused by seawater and i...

The development and validation of ocean colour products require the availability of in situ atmospheric and marine data. Since 1995, the Coastal Atmosphere and Sea Time Series (CoASTS) Program ensures the collection of an in situ comprehensive set of measurements taken from an oceanographic tower in the northern Adriatic Sea. An example of a validation exercise is presented through the comparison of in situ and satellite-derived aerosol optical thickness at various wavelengths in the 443–865 nm spectral interval.

An operational system for autonomous above-water radiance measurements, called the SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM), was deployed at the Acqua Alta Oceanographic Tower in the northern Adriatic Sea and used for the validation of remote sensing radiometric products in coastal waters. The SeaPRISM data were compared with simultaneous data collected from an independent in-water system for a wide variety of sun elevations along with different atmospheric, seawater, and sea state conditions. The average absolute differences between the above- and in-water determinations of water-leaving radiances (computed linearly) were less than 4.5% in the 412-555-nm spectral interval. A similar comparison for normalized water-leaving radiances showed average absolute differences less than 5.1%. The comparison between normalized water-leaving radiances computed from remote sensing and SeaPRISM matchup data, showed absolute spectral average (linear) differences of 17.0%, 22.1%, and 20.8% for SeaWiFS, MODIS, and MERIS, respectively. The results, in keeping with those produced by independent in-water systems, suggest the feasibility of operational coastal networks of autonomous above-water radiometers deployed on fixed platforms (towers, lighthouses, navigation aids, etc.) to support ocean color validation activities.

Novelty detection is used to identify the range of applicability of empirical ocean color algorithms. This method is based on the assumption that the level of accuracy of the algorithm output depends on the representativeness of inputs in the training dataset. The effectiveness of the novelty detection method is assessed using two datasets: one representative of the northern Adriatic Sea coastal waters and the other representative of open sea waters. The two datasets are independently used to develop neural network algorithms for the retrieval of chlorophyll-a concentration (Chl-a). The range of applicability of the individual algorithms is presented using remote sensing data derived from the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS) for three selected regions: the central Mediterranean Sea, the North Sea, and the Baltic Sea. An extension of the novelty detection technique is also proposed to blend the individual algorithms and to avoid discontinuities in the resulting Chl-a maps.

Chlorophyll a concentration (Chl) product validation off theWestern Iberian coast is here undertaken by directly comparing remote sensing data with in situ surface reference values. Both standard and recently developed alternative algorithms are considered for match-up data analysis. The investigated standard products are those produced by the MERIS (algal 1 and algal 2) and MODIS (OC3M) algorithms. The alternative data products include those generatedwithin the CoastColour Project and Ocean Color Climate Change Initiative (OC-CCI) funded by ESA, as well as a neural net model trained with field measurements collected in the Atlantic off Portugal (MLPATLP). Statistical analyses showed that satellite Chl estimates tend to be larger than in situ reference values. The study also revealed that a non-uniform Chl distribution in the water column can be a concurring factor to the documented overestimation tendency when considering larger optical depth match-up stations. Among standard remote sensing products, MODIS OC3M and MERIS algal 2 yield the best agreement with in situ data. The performance of MLPATLP highlights the capability of regional solutions to further improve Chl retrieval by accounting for environmental specificities. Results also demonstrate the relevance of oceanographic regions such as the Nazaré area to evaluate how complex hydrodynamic conditions can influence the quality of Chl products. This studywas performed in the framework of HabSpot FCT Project, PTDC/MAR/100348/2008 and European Space Agency projects DUE CoastColour (ESRIN/AO/1-6141/09/l-EC) and Climate Change Iniciative — Ocean Color (AO-1/6207/09/I-LG). The work has been also partially supported by the European Space Agency within the framework of the MERIS Validation Activities under contract n. 12595/09/I-OL, and sampling activities benefited from European projects HERMES (GOCE-CT-2005-511234) and Hermione (EC contract 226354) support. We would like to thank NASA OBPG for the MODIS data and ESA Project AOPT-2423 for providing MERIS full resolution images. Ana C. Brito was funded by a Portuguese Post-doc grant from FCT (BPD/63017/2009) and by the Investigador FCT Program (IF/00331/2013). Davide D'Alimonte was funded by Investigador FCT Program (IF/00541/2013). info:eu-repo/semantics/publishedVersion

An autonomous system for making above-water radiance measurements has been produced by adding a new measurement scenario to a CIMEL CE-318 sun photometer. The new system, called the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Photometer Revision for Incident Surface Measurement (SeaPRISM), combines the normal CE-318 capability for measuring direct sun irradiance and sky radiance, with a new capability for measuring above-water radiance for the retrieval of water-leaving radiance. The system has been extensively tested during several measurement periods over a 1-yr time frame from August 1999 to July 2000 under various sun elevations along with different atmospheric, seawater, and sea-state conditions. The field assessment of the new instrument was conducted at an oceanographic tower located in the northern Adriatic Sea within the framework of measurement campaigns aimed at supporting ocean color calibration and validation activities. Sample data at 440, 500, 670, 870, and 1020 nm were collect...

The collection of high-quality in situ measurements is a key component for the calibration and validation of space-borne radiometric data and the development of inversion schemes for retrieving higher level product maps underpinning climate-change studies. In situ radiometric measurements to compute quantities such as the remote sensing reflectance are, however, subject to different perturbing factors. Scope of this chapter is to discuss perturbations due to large deployment structures, such as ships and oceanographic towers, as well as instrument self-shading and sea-surface light focusing. The radiance distribution induced by these perturbing elements cannot be evaluated analytically by directly solving the radiative transfer equation. Among numerical simulations, the use of deterministic approaches is partially limited by the absence of translational invariance in the perturbed light field. On the other hand, results show that Monte Carlo simulations of radiometric perturbations represent an effective mean for enhancing the quality of field measurements by offering the possibility to define correction schemes and refine measurement protocols.

Statistical performance tuning of a parallel Monte Carlo (MC) radiative transfer code for ocean color (OC) applications is presented. A low observed-to-peak performance ratio due to highly sparse computations is compensated by online and offline tuning techniques based on a statistical indicator of products accuracy. Run-time adaptive control employs the accuracy indicator to set up two complementary tuning criteria: one general to MC computations and the other specific to OC applications. The same accuracy indicator is also used for pre-execution tuning of a threshold parameter. Numerical simulations of real case scenarios showed that the proposed methods consistently led to faster runs, while satisfying application accuracy requirements. Specifically, speed-ups range from 2.17 to 7.44 times when compared with the un-optimized version of the MC code. The applied techniques are orthogonal to parallelization, so that the reported performance gains are further amplified by parallel speed-ups.

Motivation: Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods. Results: A newly developed software tool called MILVA (microarray latent visualization and analysis) is presented here to investigate microarray data without separating gene expression profiles into discrete classes. The underpinning of the MILVA software is the two-dimensional topographic representation of multidimensional microarray data. On this basis, the interactive MILVA functions allow a continuous exploration of microarray data driven by the direct supervision of the biologist in detecting activity patterns of co-regulated genes. Availability: The MILVA software is freely available. The software and the related documentation can be downloaded from http://www.ncrg.aston.ac.uk/Projects/milva. User 'surrey' as username and '3245' as password to login. The software is currently available for Windows platform only. Contact: d.lowe@aston.ac.uk

This study addresses the regression of in-water radiometric profile data with the objective of investigating solutions to minimize uncertainties of derived products like subsurface radiance and irradiance (L(u0) and E(d0)) and diffuse attenuation coefficients. Analyses are conducted using radiometric profiles generated through Monte Carlo simulations and field measurements. A nonlinear NL approach is presented as an alternative to the standard linear method LN. Results indicate that the LN method, relying on log-transformed data, tends to underestimate regression results with respect to NL operating on non-transformed data. The log-transformation is thus identified as the source of biases in data products. Observed differences between LN and NL regression results for L(u0) are of the order of 1-2%, that is well below the target uncertainty for data products from in situ measurements (i.e., 5%). For E(d0), instead, differences can easily exceed 5% as a result of more pronounced light focusing and defocusing effects due to wave perturbations. This work also remarks the importance of applying the multi-cast measurement scheme as a mean to increase the precision of data products.

A Monte Carlo code for ocean color simulations has been developed to model in-water radiometric fields of downward and upward irradiance (E(d) and E(u)), and upwelling radiance (L(u)) in a two-dimensional domain with a high spatial resolution. The efficiency of the code has been optimized by applying state-of-the-art computing solutions, while the accuracy of simulation results has been quantified through benchmark with the widely used Hydrolight code for various values of seawater inherent optical properties and different illumination conditions. Considering a seawater single scattering albedo of 0.9, as well as surface waves of 5 m width and 0.5 m height, the study has shown that the number of photons required to quantify uncertainties induced by wave focusing effects on E(d), E(u), and L(u) data products is of the order of 10(6), 10(9), and 10(10), respectively. On this basis, the effects of sea-surface geometries on radiometric quantities have been investigated for different surface gravity waves. Data products from simulated radiometric profiles have finally been analyzed as a function of the deployment speed and sampling frequency of current free-fall systems in view of providing recommendations to improve measurement protocols.