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HABs, more specifically red tides, have been responsible for massive shellfish kills in the Mar Piccolo (Gulf of Taranto, Ionian Sea) since 1787, but in the last years, the problem is worsening. This paper describes the history of HABs in the Mar Piccolo in relation to the management strategies of the shellfish farming up to the current use of remote sensing techniques. Based on in situ and remote sensing researches, future efforts should concentrate on building models and early-warning tools that will allow authorities to make timely decisions in order to minimize the sanitary and economic damages caused by HABs.

An at-sensor radiance simulation environment based on Hydrolight and MODTRAN-5 was set up for the evaluation of arbitrary combinations of sensors, methods and targets for the investigation of inland water quality. Each Ls simulation requires three MODTRAN- 5 runs, whereas two runs are needed for the calculation of the specular reflectance. Simulation results can be used in the preparation of specific algorithms for future sensors, e.g. the Airborne Prism Experiment (APEX), as well as for vicarious calibration, to estimate the noise sensitivity of a specific algorithm or in general project planning.

An intercomparison of a curve fitting and least squares approach is presented for the estimation of CHL, CDOM and TSM for two inland waters. In the inversion procedures two different bio-optical models were used, i.e. the Gordon model and the model of Albert and Mobley. The intercomparison is based on simulated APEX reflectance data. For these simulations two field campaigns were organised on the Scheldt river and Lake Constance to collect water samples. They were analysed for the optical properties and concentration values of the three optically active constituents. Reflectance data were then simulated using Hydrolight and the models of Gordon and Albert and Mobley. These simulations were used to test several inversion approaches. The curve fitting with the model of Albert and Mobley provided the best results for the Scheldt river. For Lake Constance no single procedure outperformed the others.

We present an analysis of the hemispherical-directional reflectance factor (HDRF) of snow, using 16 seasonal datasets of the spectral range from 400 to 2,500 nm. The data was measured under clear sky conditions in Davos Dorf (Grisons, Switzerland, 1,560 m a. s. l.). Fieldwork was carried out on seven days between February 5 and March 30 2004 with the Swiss Field Goniometer System (FIGOS). In addition to the HDRF measurements, snow stratigraphy, temperature and density were measured, and chemical and photomicroscopical analyses of snow samples were performed. Concentration of organic and elemental carbon was determined by chemical analysis. The grain size analyses through image processing of micrographs revealed relatively small differences of 0.21 to 0.33 mm mean radius in the top layers of snow cover. Seven datasets present HDRF of wet snow surfaces with similar anisotropy at smaller sun zenith angles (qI = 3.3 to 64.5°) compared to the nine surfaces measured at larger sun zenith angles (qI = 6.5 to 75.3°). Spectral albedo is relatively constant throughout datasets of different sun zenith angles of the same day, but has large variability among measurements of different days. With increasing wavelength, it decreases significantly faster for wet surfaces than for dry surfaces. The forward scattering peak was found to strengthen with increasing sun zenith angle and with increasing wavelength for both wet and dry surfaces at wavelengths above 700 nm. Finally, a spectral wet snow determination method is performed and the cross-sensitivity to HDRF variation could be derived. The best differentiability was found for 1,380 nm. This basis work increases the understanding of snow signatures for potential imaging spectroscopy applications in alpine regions.

Sun-induced fluorescence (SIF) retrieved from satellite measurements has been widely used as proxy for chlorophyll-a concentration and as indicator of phytoplankton physiological status in oceans. The practical use of this naturally occurring light signal in environmental research is, however, under-exploited, particularly in research focusing on optically complex waters such as inland and coastal waters. In this study, we investigated methodological and knowledge gaps in remote sensing of chlorophyll-a SIF in optically complex waters by reviewing the theory behind SIF occurrence, the availability of existing and upcoming instrumentation, the availability of SIF retrieval schemes, and the applications for aquatic research. Starting with an overview of factors that influence SIF leaving the water body, we further investigated available and upcoming observational capacity by in situ, airborne and satellite sensors. We discuss requirements for spatial, spectral, temporal, and radiometric resolution of observing systems in the context of SIF dynamics. We assessed viable retrieval techniques able to disentangle SIF from non-SIF contribution to the upwelling radiance, ranging from the established multispectral Fluorescence Line Height algorithm (FLH) approach to hyperspectral approaches including model inversion, spectral fitting methods and machine learning regression procedures. Finally, we provide an overview of applications, which could potentially benefit from improved SIF emission estimates such as biomass estimation, algal bloom investigation and primary productivity modelling.

article i nfo We provide a comprehensive overview of water constituent retrieval algorithms and underlying definitions and models for optically deep and complex (i.e. case 2) waters using earth observation data. The performance of constituent retrieval algorithms is assessed based on matchup validation experiments published between January 2006 and May 2011. Validation practices range from singular vicarious calibration experiments to comparisons using extensive in situ time series. Band arithmetic and spectral inversion algorithms for all water types are classified using a method based scheme that supports the interpretation of algorithm validity ranges. Based on these ranges we discuss groups of similar algorithms in view of their strengths and weak- nesses. Such quantitative literature analysis reveals clear application boundaries. With regard to chlorophyll retrieval, validation of blue-green band ratios in coastal waters is limited to oligotrophic, predominantly ocean waters, while red-NIR ratios apply only at more than 10 mg/m 3 . Spectral inversion techniques — although not validated to the same extent — are necessary to cover all other conditions. Suspended matter retrieval is the least critical, as long as the wavelengths used in empirical models are increased with concen- trations. The retrieval of dissolved organic matter however remains relatively inaccurate and inconsistent, with large differences in the accuracy of comparable methods in similar validation experiments. We conclude that substantial progress has been made in understanding and improving retrieval of constituents in optically deep and complex waters, enabling specific solutions to almost any type of optically complex water. Further validation and intercomparison of spectral inversion procedures are however needed to learn if solutions with a larger validity range are feasible.

Semi-analytical remote sensing applications for eutrophic waters are not applicable to oligo- and mesotrophic lakes in the perialpine area, since they are insensitive to chlorophyll concentration variations between 1-10 mg/m3. The neural network based Case-2-Regional algorithm for MERIS was developed to fill this gap, and a dedicated validation campaign on Lake Constance revealed promising results. For a more extensive validation, the algorithm is applied to a collection of 239 satellite images from 2003-2008, and the results are compared to experimental and official water quality data collected on seven perialpine lakes in the same period. It is shown that remote sensing estimates can provide an adequate supplementary data source to fluorescence probe monitoring data series of the top 5-10 m water layer, provided that a sufficient number of matchups for a site specific maximum temporal offset is available. Comparison with 20 m mixed profiles is less adequate. The supplemental correction of adjacency effects by the Improved Contrast between Ocean and Land algorithm performs well regarding the retrieval of accurate remote sensing reflectances, while its improvement to the accuracy of estimated chlorophyll concentrations is less significant.

A physically based algorithm is used for automatic processing of MERIS level 1B full resolution data. The algorithm is originally used with input variables for optimization with different sensors (i.e. channel recalibration and weighting), aquatic regions (i.e. specific inherent optical properties) or atmospheric conditions (i.e. aerosol models). For operational use, however, a lake-specific parameterization is required, representing an approximation of the spatio-temporal variation in atmospheric and hydrooptic conditions, and accounting for sensor properties. The algorithm performs atmospheric correction with a LUT for at-sensor radiance, and a downhill simplex inversion of chl-a, sm and y from subsurface irradiance reflectance. These outputs are enhanced by a selective filter, which makes use of the retrieval residuals. Regular chl-a sampling measurements by the Lake’s protection authority coinciding with MERIS acquisitions were used for parameterization, training and validation.

A physically based water constituent retrieval algorithm is used for the automatic processing of MERIS level 1B full resolution data. The algorithm requires several input variables for individual optimization with different sensors (i. e. channel calibration and weighting), aquatic regions (i. e. specific inherent optical properties) or atmospheric conditions (i. e. Aerosol models). The optical properties are derived from optical in situ measurements during concurrent MERIS data acquisition on 20 April 2007. Remaining parameters are iteratively optimized for best performance with 21 MERIS datasets of Lake Constance in the years 2003-2005, and validated with 11 datasets in 2006. Operational water quality sampling measurements acquired by local authorities serve as reference.