Search

Satellite-Derived Bathymetry (SDB) methods have found their way into the hydrographers’ toolbox and are part of integrated survey concepts, nautical charts and support global and European programs such as Seabed2030 or EMODnet Bathymetry. The concept of the ‘physics-based’ SDB describes the calculation of bathymetry by modelling the sunlight path from the sun to the seafloor to the satellite sensor. It is a highly sophisticated model which enables the calculation of shallow water depth in the absence of any other survey or ground-truth data. Thus, bathymetric data can also be retrieved for remote and inaccessible areas - in contrast to empirical SDB approaches. Key questions which arise for SDB results are vertical accuracy, potential and feasibility for different sites and the methods to upscale SDB solutions. These questions are addressed in the current European innovation project 4S. Within the project SDB-Online was developed, a fully physics-based SDB concept which is installed in a cloud and accessible via a web user interface. The backend is parallelised and can be accessed via application programming interface (API) which allows a fully scalable and automatic SDB processing. In this study SDB-Online results are validated at ten sites, ranging from the higher latitudes of Canada to turbid UK waters to the Caribbean. Furthermore, a relationship between the Secchi Disc Depth and the cutoff depth of the SDB results is established and a global map of water-clarity potential of the SDB solution is presented.

The EC HYPOS (HYdro-POwer-Suite) project (https://hypos-project.eu/) has the main goal of assessing the environmental impact of existing and future hydropower systems. The project will provide a suite of data analysis applications which integrates Earth Observation (EO) technologies and hydrological modelling. These include an online Decision Support Tool (DST) for investment planning and monitoring, as well as a subscription portal combining satellite data over time, current measurements and detailed estimates for present and near future assessments. A dedicated analysis on the “blue footprint” (i.e. the amount of water used to produce a service) of reservoirs is included for addressing sustainable monitoring solutions. Such analysis comprises the evaluation of the climate change effects on reservoirs management and hydropower production. For instance, extreme weather events like short-term heavy precipitations are connected with flooding and transport of large amounts of sediments in dammed reservoirs, with critical consequences for their management. Similarly, global warming can heat the surface of water bodies and induce higher evaporation rates, thus decreasing the amount of water available for energy production. In this study we present the first products from HYPOS project. These products are representative of what can be generated within the DST using elaboration techniques of EO data. Gridded products of water quality parameters (e.g. water turbidity, Chlorophyll-a concentration, suspended sediments concentration) are generated for the test sites of the project, which are small dammed reservoirs located in Switzerland, France, Albania and Georgia. These products are obtained using the Modular Inversion and Processing System (MIP), a sensor independent image processing chain based on radiative transfer models, which works in a multi-layer system, solving the light transfer in the atmosphere, at the water surface and inside the waterbody.For the assessment of the “blue footprint” of a reservoir, the water loss due to evaporation is computed by applying a consolidated mass transfer evaporation method to EO data. The resulting evaporation rates are first compared with the outputs of semi-automatic evapotranspiration EO-based models (e.g. SEBAL), and then with the estimates ​​obtained from two different numerical models: a hydrological model (E-Hype) and a 3D hydrodynamic model (Delft3D). The key parameters influencing water evaporation rates, their behavior and the issues related to each approach are analyzed. The first comparison results are made for lake Garda, where a complete set of data is available for the production of evaporation maps.

Phytoplankton indicated by its photosynthetic pigment chlorophyll-a is an important pointer on lake ecology and a regularly monitored parameter within the European Water Framework Directive. Along with eutrophication and global warming cyanobacteria gain increasing importance concerning human health aspects. Optical remote sensing may support both the monitoring of horizontal distribution of phytoplankton and cyanobacteria at the lake surface and the reduction of spatial uncertainties associated with limited water sample analyses. Temporal and spatial resolution of using only one satellite sensor, however, may constrain its information value. To discuss the advantages of a multi-sensor approach the sensor-independent, physically based model MIP (Modular Inversion and Processing System) was applied at Lake Kummerow, Germany, and lake surface chlorophyll-a was derived from 33 images of five different sensors (MODIS-Terra, MODIS-Aqua, Landsat 8, Landsat 7 and Sentinel-2A). Remotely sensed lake average chlorophyll-a concentration showed a reasonable development and varied between 2.3±0.4 and 35.8±2.0mg·m-3 from July to October 2015. Match-ups between in situ and satellite chlorophyll-a revealed varying performances of Landsat 8 (RMSE: 3.6 and 19.7mg·m-3), Landsat 7 (RMSE: 6.2mg·m-3), Sentinel-2A (RMSE: 5.1mg·m-3) and MODIS (RMSE: 12.8mg·m-3), whereas an in situ data uncertainty of 48% needs to be respected. The temporal development of an index on harmful algal blooms corresponded well with the cyanobacteria biomass development during summer months. Satellite chlorophyll-a maps allowed to follow spatial patterns of chlorophyll-a distribution during a phytoplankton bloom event. Wind conditions mainly explained spatial patterns. Integrating satellite chlorophyll-a into trophic state assessment resulted in different trophic classes. Our study endorsed a combined use of satellite and in situ chlorophyll-a data to alleviate weaknesses of both approaches and to better characterise and understand phytoplankton development in lakes.

Satellites that capture large areas with high spatial and temporal resolution allow extensive analyses of water bodies and thus represent an ideal supplement to existing in situ point measurements. In the joint project WasMon-CT (Water Monitoring of Chlorophyll and Turbidity) the usability of satellite data for official monitoring of flowing waters and lakes was examined. The subproject at the Institute for Lake Research of the LUBW focused on satellite-based monitoring of chlorophyll a, an important indicator for water quality, in lakes. Freely available data from spatially reasonable high-resolution satellites, e.g. Sentinel-2, open up new possibilities for monitoring the water quality of a larger number of small lakes. The aim of the comprehensive validation study presented here was to get information about applicability and potential limitations of remote sensing techniques for different types of lakes. EOMAP processed the satellite data used in the validation (Sentinel-2/3, Landsat 7/8 and MODIS) by applying its Modular Inversion and Processing System MIP. Results extracted from satellite data between 2000 and 2017 were compared with in situ measurement data of about 20 lakes in Baden-Wuerttemberg, including Lake Constance, for water quality parameters such as chlorophyll a and Secchi depth. First results of the validation study show that in general the statistical values, e.g. annual mean values of in situ and remote sensing retrieved chlorophyll a and Secchi depth data, agree well, but some systematic differences occur. Further validation and data interpretation steps take into account methodical differences as well as time differences between in situ and satellite measurements.

Freshwater as one of the most relevant resources for life is facing increasing human made pressures. Suitable information about the status of the water quality in lakes and rivers is sparse, although required for environmental assessments and impact monitoring: There is a vast demand on actual data in many countries, where water policies and management decisions are based on scarce and unreliable information. Satellite data with newest data analytics technologies can already contribute to this today with regular mapping and monitoring in freshwater systems: Consistent information of valuable water quality products are derived for single applications in small lakes, covering extended river basins or the whole world, as provided by the UNESCO World Water Quality Portal. A number of examples from this first global water quality portal is discussed, addressing ecological and economic issues in Africa. At the conceptual level, UNESCO and EOMAP advocate the long-term consistency of the data of these new measurement capabilities: Both satellite sensing and data processing technologies are rapidly evolving. Hence, nowadays concepts should already ensure that the data products are globally intercomparable and in future, even if the accuracy of information products become better and better. This ensures that the sustainable development goals can be supported with meaningful, comparable indicators over time.

The presented adjacency correction algorithm is based on the use of the point spread function (PSF) which allows calculating the contribution of reflections from the nearby pixels to the apparent radiance of the target. The analytical expression of the PSF for an arbitrary stratified atmosphere is obtained in the approximation of primary scattering, whereas the full equation of radiative transfer is used for the estimation of the radiance reflected from the surface. The algorithm is sensor independent and can be applied for processing images of water basins with arbitrary shape of the shore line and under different geometries of observation. The program using this algorithm is included in Modular Inversion Program — MIP (Heege et al., 2014) for processing of satellite images on a routine basis. Examples of processing results are presented in the paper.

By assessing how Earth Observation (EO) products are accurate is a fundamental task to develop a SPACE-O service meeting the user needs. To the aim, this report summarizes the validation activities that have been performed in order to measures how satellite products generated within the project agreed to field and modelled data. Satellite data for which the validation is presented are those acquired from Sentinel-2 and Landsat-8 being these the two sensors mainly used in the project. The satellite validation includes both radiometric measurements (i.e. to measure the uncertainty of the atmospheric correction) and water quality data (e.g. turbidity, lake surface water temperature).

An operational satellite-based approach was implemented to monitor turbidity and organic absorption in the Mekong river system. Using physics-based algorithms linked together in a fully automated processing chain, more than 300 Landsat Enhanced Thematic Mapper (ETM) scenes and 1000 MODIS scenes, representing five years of data, were used to produce standardized, quantitative time series of turbidity and organic absorption across Vietnam, Thailand, Cambodia, Laos, and China. To set up this system, the specific inherent optical properties (SIOPs) of the Mekong river system were determined through three separate field campaigns, laboratory analysis, and subsequent optical closure calculations. Following this, a range of satellite data types was tested using the derived Mekong-specific inherent optical properties, including Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m data, Landsat ETM, Medium Resolution Imaging Spectrometer (MERIS), Satellite Pour l’Observation de la Terre (SPOT) 5, RapidEye, ...

12The German Aerospace Center DLR - namely the Applied Remote Sensing Cluster CAF and the German Space Operations Center GSOC - is responsible for the establishment of the ground segment of the future German hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program). The Applied Remote Sensing Cluster has long lasting experiences with air- and spaceborne acquisition, processing, and analysis of hyperspectral image data. This paper mainly addresses the concept of the operational and automatic processing chain and the calibration / data quality to generate high quality data products. ©2010 IEEE.

Physics-based remote sensing in littoral environments for ecological monitoring and assessment is a challenging task that depends on adequate atmospheric conditions during data acquisition, sensor capabilities and correction of signal disturbances associated with water surface and water column. Airborne hyper-spectral scanners offer higher potential than satellite sensors for wetland monitoring and assessment. However, application in remote areas is often limited by national restrictions, time and high costs compared to satellite data. In this study, we tested the potential of the commercial, high-resolution multi-spectral satellite QuickBird for monitoring littoral zones of Lake Sevan (Armenia). We present a classification procedure that uses a physics-based image processing system (MIP) and GIS tools for calculating spatial metrics. We focused on classification of littoral sediment coverage over three consecutive years (2006–2008) to document changes in vegetation structure associated with a rise in water levels. We describe a spectral unmixing algorithm for basic classification and a supervised algorithm for mapping vegetation types. Atmospheric aerosol retrieval, lake-specific parameterisation and validation of classifications were supported by underwater spectral measurements in the respective seasons. Results revealed accurate classification of submersed aquatic vegetation and sediment structures in the littoral zone, documenting spatial vegetation dynamics induced by water level fluctuations and inter-annual variations in phytoplankton blooms. The data prove the cost-effective applicability of satellite remote-sensing approaches for high-resolution mapping in space and time of lake littoral zones playing a major role in lake ecosystem functioning. Such approaches could be used for monitoring wetlands anywhere in the world.

An accurate atmospheric correction (AC) of Earth remote-sensing data in the spectral region 450–800 nm has to account for the ozone gas absorption influence. Usual operational AC codes employ a fixed ozone concentration corresponding to a climatologic average for a certain region and season, e.g. the mid-latitude summer atmosphere of the Moderate Resolution Atmospheric Transmission (MODTRAN) code. The reasons for a fixed ozone column are that ozone does not vary rapidly on a spatial and temporal scale, and additionally, the look-up table (LUT) size for AC is already big. This means that another degree of freedom for the ozone parameter would dramatically increase the size of the LUT database and the time required for LUT interpolation. In order to account for this effect, we use already existing LUTs that were calculated for a certain ozone reference level, e.g. an ozone column of g = 330 Dobson Units (DU) for MODTRAN’s mid-latitude summer atmosphere. Then the deviation of the top-of-atmosphere (TOA) radi...

(2010). Mapping of water constituents in high mountainous Lake Sevan (Armenia) SIL Proceedings, 1922-2010: Vol. 30, No. 9, pp. 1453-1455.

(2009). Reviewing Lake Sevan's surface area using remote sensing & GIS techniques. SIL Proceedings, 1922-2010: Vol. 30, No. 8, pp. 1264-1266.

Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km(2) of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km(2)). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas.

We applied remote sensing techniques using WorldView-2 images with high spatial resolution and field verification to map the bathymetry and benthic habitats of the Puerto Morelos Reef National Park in the Mexican Caribbean. These images were processed using the standardized physics-based data processing of EOMAP's Modular Inversion and Processing System (MIP). To generate a detailed benthic habitat map we developed a two level-classification scheme based on biological and geomorphologic characteristics. These images showed high effectiveness for mapping benthic habitat. Further improvements will emphasize and focus on the selection of regional specific main spectral components and the automation of the radiometric fine tuning. The main contribution is the generation of a detailed benthic habitat map representing functional classification by combining both maps. These studies serve for the management of marine areas in Mexico and could be extended to the entire Mesoamerican Reef System.

A method to obtain underwater topography for coastal areas using state-of-the-art remote sensing data and techniques worldwide is presented. The data from the new Synthetic Aperture Radar (SAR) satellite TerraSAR-X with high resolution up to 1 m are used to render the ocean waves. As bathymetry is reflected by long swell wave refraction governed by underwater structures in shallow areas, it can be derived using the dispersion relation from observed swell properties. To complete the bathymetric maps, optical satellite data of the QuickBird satellite are fused to map extreme shallow waters, e.g., in near-coast areas. The algorithms for bathymetry estimation from optical and SAR data are combined and integrated in order to cover different depth domains. Both techniques make use of different physical phenomena and mathematical treatment. The optical methods based on sunlight reflection analysis provide depths in shallow water up to 20 m in preferably calm weather conditions. The depth estimation from SAR is based on the observation of long waves and covers the areas between about 70- and 10-m water depths depending on sea state and acquisition quality. The depths in the range of 20 m up to 10 m represent the domain where the synergy of data from both sources arises. Thus, the results derived from SAR and optical sensors complement each other. In this study, a bathymetry map near Rottnest Island, Australia, is derived. QuickBird satellite optical data and radar data from TerraSAR-X have been used. The depths estimated are aligned on two different grids. The first one is a uniform rectangular mesh with a horizontal resolution of 150 m, which corresponds to an average swell wavelength observed in the 10 × 10-km SAR image acquired. The second mesh has a resolution of 150 m for depths up to 20 m (deeper domain covered by SAR-based technique) and 2.4 m resolution for the shallow domain imaged by an optical sensor. This new technique provides a platform for mapping of coastal bathymetry over a broad area on a scale that is relevant to marine planners, managers, and offshore industry.

Multispectral satellite data (WordView-2, IKONOS, QuickBird) are used to map bathymetry and spectral sea floor classes in a range of coastal areas. The standardized physics-based data processing integrates MODIS satellite data for the radiometric intercalibration and estimates of turbidity. This process includes corrections for sunglitter, the adjacency and the atmospheric effect. The water depth is calculated iteratively in combination with the spectral unmixing of the respective bottom reflectance on base of the subsurface reflectance. The final step of the processing classifies the bottom reflectance due to the spectral signature of different bottom types and biota using a specific cluster and classification approach. The comparison with in situ data at different sites worldwide proves the approach, but also emphasizes the necessity of radiometric well calibrated satellite data.

Traditional methods for aerosol retrieval and atmospheric correction of remote sensing data over water surfaces are based on the assumption of zero water reflectance in the near-infrared. Another type of approach which is becoming very popular in atmospheric correction over water is based on the simultaneous retrieval of atmospheric and water parameters through the inversion of coupled atmospheric and bio-optical water models. Both types of approaches may lead to substantial errors over optically-complex water bodies, such as case II waters, in which a wide range of temporal and spatial variations in the concentration of water constituents is expected. This causes the water reflectance in the near-infrared to be non-negligible, and that the water reflectance response under extreme values of the water constituents cannot be described by the assumed bio-optical models. As an alternative to these methods, the SCAPE-M atmospheric processor is proposed in this paper for the automatic atmospheric correction of ENVISAT/MERIS data over inland waters. A-priori assumptions on the water composition and its spectral response are avoided by SCAPE-M by calculating reflectance of close-to-land water pixels through spatial extension of atmospheric parameters derived over neighboring land pixels. This approach is supported by the results obtained from the validation of SCAPE-M over a number of European inland water validation sites which is presented in this work. MERIS-derived aerosol optical thickness, water reflectance and water pigments are compared to in-situ data acquired concurrently to MERIS images in 20 validation match-ups. SCAPE-M has also been compared to specific processors designed for the retrieval of lake water constituents from MERIS data. The performance of SCAPE-M to reproduce ground-based measurements under a range of water types and the ability of MERIS data to monitor chlorophyll-a and phycocyanin pigments using semiempirical algorithms after SCAPE-M processing are discussed. It has been found that SCAPE-M is able to provide high accurate water reflectance over turbid waters, outperforming models based on site-specific bio-optical models, although problems of SCAPE-M to cope with clear waters in some cases have also been identified. © 2009 Elsevier Inc. All rights reserved.

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.

Earth observation sensors collect valuable data of aquatic systems, which are further used for the retrieval of concentrations of water constituents such as suspended matter and phytoplankton. Different sensors deliver data with various spatial and temporal resolution ranging from 1 day to approximately a month in time and from 1km to 3m in space, high spatial resolution being connected with low temporal resolution and vice versa. In order to have detailed information on both spatial distribution of water constituents and their temporal variability, that is especially important for small aquatic objects like rivers and lakes, it is necessary to combine the data from several sensors. This creates certain problems as also spectral and radiometric resolutions of the sensors can be different. The use of the modular image processing system MIP for the integration of data from different sensor is advantageous as it ensures standardized product outputs for a variety of satellite sensors such as MERIS, MODIS, SPOT, IKONOS, RapidEye. The processing chain of this system is automatically adapted to the sensor parameters as well as to the region specific inherent optical properties (SIOP) of the water basin. Various worldwide applications and time series for lakes, rivers and coastal areas are demonstrated and discussed.

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.

Mapping the submerse vegetation is of prime importance for the ecological evaluation of an entire lake. Remote sensing techniques are efficient for such mapping tasks, if the retrieval algorithms and processing methods are robust and mostly independent from additional ground truth measurements. The Modular Inversion Program (MIP) follows this concept. It is a processing tool designed for the recovery of hydro-biological parameters from multi- and hyper-spectral remote sensing data. The architecture of the program consists of physical inversion schemes that derive bio-physical parameters from the measured radiance signal at the sensor. Program modules exist for the retrieval of aerosols, sun glitter correction, atmospheric corrections, retrieval of water constituents among others. For the purpose of mapping the bottom coverage in optically shallow waters, two modules have been added to MIP. The first module calculates the bottom reflectance using the subsurface reflectance, the depth and an approximation of the water constituent concentrations as input. The second module fractionalizes the bottom reflectance to three endmembers of specific reflectance spectra by linear unmixing. The three endmembers are specific reflectance spectra of bottom sediments, small growing macrophytes (Characeae) and tall macrophytes such as Potamogeton perfoliatus & P. pectinatus. The processing system has been tested with data collected from the multi-spectral airborne scanner Daedalus AADS1268 at Lake Constance, Germany, for multi- temporal analysis.

A physically based method to determine water constituents in case II waters from imaging spectrometer data is presented. The processing scheme includes modules for sunglitter correction, aerosol retrieval, atmospheric correction, and the retrieval of water constituents from subsurface reflectance spectra in case II waters. For the aerosol retrieval, the sun glitter, and the atmospheric correction modules, an azimuthally resolved radiative transfer model for a multilayer atmosphere-ocean system with a flat water surface is used. The retrieval of water constituents is achieved by an iterative fitting algorithm to adjust modeled and measured underwater reflectances. The processing scheme is applied to airborne multispectral scanner Daedalus AADS1268 in ATM (airborne thematic mapper) modus. Owing to the sensor properties (reduced radiometric sensitivity of the blue channel), it was not possible to work completely without additional data; Gelbstoff absorption could not be retrieved. It had to be inputted and w...

The determination of phytoplankton primary production is one key future application of remote sensing data in Lake Constance. One of the main influencing factors of the maximum photosythetic rate in waters is the bulk temperature. From satellite data like NOAA-AVHRR, the skin temperature can be retrieved at high temporal resolution. However, as parameter for primary production, the bulk temperature in the mixed water layer is needed. In this study, an extensive multi-temporal set of AVHRR data is used to set up a relationship between lake skin temperature from satellite data and the bulk temperature in 0.1 m depth from in situ temperature profiles. Temperature profiles exist for a fixed station in Lake Constance in 20 min intervals over the last twelve years. From 245 cloudless AVHRR-scenes for the years 1995 until 1999, the skin temperature was derived using the split window technique. Different approaches of this technique are intercompared for their accuracy for Lake Constance. In a future step, the bulk temperature in the upper 4 m and the depth of the mixed layer will be determined for input in a primary production model. Multiple regression analysis as well as a neural network approach will be used for the prediction of the bulk temperature from the skin temperature for Lake Constance.

The applicability of MERIS data for the retrieval of water constituent concentrations in oligo- to mesotrophic perialpine lakes is demonstrated by means of two different algorithms. The C2R algorithm is an easily applicable neural network processor which is bound to MERIS data. The MIP algorithm is a complex, coupled inversion program, which can be used with a variety of remote sensing sensors, but requires extensive parameterization. Both algorithms were applied with the ICOL adjacency effect correction. C2R's potential for automatic processing of large data quantities is validated in a time series study with water quality monitoring data. Individual C2R image products are then compared to MIP, which will allow for an extended comparison with new APEX imaging spectrometry data in the near future.