Retrieval of atmospheric and oceanic properties from MERIS measurements: A new Case‐2 water processor for BEAM

Schroeder, T.; Schaale, M.; Fischer, J.

doi:10.1080/01431160701601774

Cited by 102 publications

(55 citation statements)

References 7 publications

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“…SCAPE-M (Self-Contained Atmospheric Parameters Estimation from MERIS data, [32]) is not included in the classification analysis, because of known problems with MERIS Band 2, which would have a large influence on the produced classes. Due to missing spectral bands, the output of the Freie Universität Berlin (FUB/WeW) Water Processor [33] cannot be fed into the OWT tool. The standard MERIS Ground Segment (MEGS) Processor is not included because of the extremely low number of valid pixels it produced in atmospheric correction tests in GLaSS (0-14%, depending on the lake [31]).…”

Section: Characteristics Of Remote Sensing Datamentioning

confidence: 99%

An Optical Classification Tool for Global Lake Waters

et al. 2017

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Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes.

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Section: Characteristics Of Remote Sensing Datamentioning

confidence: 99%

An Optical Classification Tool for Global Lake Waters

et al. 2017

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“…MERIS was an orbital sensor with 15 bands covering the spectral range from 400 to 950 nm and was succeeded by the Ocean and Land Color Instrument (OLCI) on board Sentinel-3 beyond 2015 [55]. The high sensitivity and large dynamic range of the MERIS sensor has been widely used for ocean and coastal water remote sensing [56][57][58][59]. In this study, ocean color data were obtained from ESA archive of MERIS images (full resolution: 300 m) covering the Wadden Sea during 2002-2012 (data provided by European Space Agency).…”

Section: Satellite Observationsmentioning

confidence: 99%

MOD2SEA: A Coupled Atmosphere-Hydro-Optical Model for the Retrieval of Chlorophyll-a from Remote Sensing Observations in Complex Turbid Waters

et al. 2016

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An accurate estimation of the chlorophyll-a (Chla) concentration is crucial for water quality monitoring and is highly desired by various government agencies and environmental groups. However, using satellite observations for Chla estimation remains problematic over coastal waters due to their optical complexity and the critical atmospheric correction. In this study, we coupled an atmospheric and a water optical model for the simultaneous atmospheric correction and retrieval of Chla in the complex waters of the Wadden Sea. This coupled model called MOD2SEA combines simulations from the MODerate resolution atmospheric TRANsmission model (MODTRAN) and the two-stream radiative transfer hydro-optical model 2SeaColor. The accuracy of the coupled MOD2SEA model was validated using a matchup data set of MERIS (MEdium Resolution Imaging SpectRometer) observations and four years of concurrent ground truth measurements (2007)(2008)(2009)(2010) at the NIOZ jetty location in the Dutch part of the Wadden Sea. The results showed that MERIS-derived Chla from MOD2SEA explained the variations of measured Chla with a determination coefficient of R 2 = 0.88 and a RMSE of 3.32 mg·m −3 , which means a significant improvement in comparison with the standard MERIS Case 2 regional (C2R) processor. The proposed coupled model might be used to generate a time series of reliable Chla maps, which is of profound importance for the assessment of causes and consequences of long-term phenological changes of Chla in the turbid Wadden Sea area.

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“…Schroeder et al [30] a pig (443), and a CDOM (443) denote the absorption coefficients of phytoplankton and colored dissolved organic matter at 443-nm; Chla and TSM represent the concentrations of chlorophyll-a and total suspended matter; and b TSM (443) refers to the total scattering coefficient of TSM at 443-nm band.…”

Section: Medium Resolution Imaging Spectrometer (Meris) Chlorophyll-amentioning

confidence: 99%

“…Schroeder et al [30] developed the FUB processor to retrieve the concentrations of Chla, TSM, and a CDOM along with the water leaving reflectance at eight bands (MERIS bands 1-7 and 9), from MERIS L1b TOA radiance using four separate NNs that were run simultaneously. The four NN models were trained using extensive radiative transfer simulations that covered a wide range of constituent concentrations in ranges of 0.05-50 mg·m −3 , 0.05-50 g·m −3 and 0.005-1 m −1 for Chla, TSM, and a CDOM (443), respectively.…”

Section: Medium Resolution Imaging Spectrometer (Meris) Chlorophyll-amentioning

confidence: 99%

“…Four algorithms based on the neural network have been developed for the MERIS sensor to retrieve water leaving reflectance as well as the water constituents' concentrations from MERIS L1b data. The four algorithms are Eutrophic Lake (EUL) [28], Boreal Lake (BOL) [28], Case 2 Regional (C2R) [29] and the Free University of Berlin (FUB) [30]. Numerous studies that adopted these neural network algorithms only performed atmospheric correction for the MERIS L1B data without performing an evaluation for the Chla produced from these algorithms [16,17,[31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of MERIS Chlorophyll-a Retrieval Processors in a Complex Turbid Lake Kasumigaura over a 10-Year Mission

et al. 2017

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Abstract:The chlorophyll-a (Chla) products of seven processors developed for the Medium Resolution Imaging Spectrometer (MERIS) sensor were evaluated. The seven processors, based on a neural network and band height, were assessed over an optically complex water body with Chla concentrations of 8.10-187.40 mg·m −3 using 10-year MERIS archival data. These processors were adopted for the Ocean and Land Color Instrument (OLCI) sensor. Results indicated that the four processors of band height (i.e., the Maximum Chlorophyll Index (MCI_L1); and Fluorescence Line Height (FLH_L1)); neural network (i.e., Eutrophic Lake (EUL); and Case 2 Regional (C2R)) possessed reasonable retrieval accuracy with root mean square error (R 2 ) in the range of 0.42-0.65. However, these processors underestimated the retrieved Chla > 100 mg·m −3 , reflecting the limitation of the band height processors to eliminate the influence of non-phytoplankton matter and highlighting the need to train the neural network for highly turbid waters. MCI_L1 outperformed other processors during the calibration and validation stages (R 2 = 0.65, Root mean square error (RMSE) = 22.18 mg·m −3 , the mean absolute relative error (MARE) = 36.88%). In contrast, the results from the Boreal Lake (BOL) and Free University of Berlin (FUB) processors demonstrated their inadequacy to accurately retrieve Chla concentration > 50 mg·m −3 , mainly due to the limitation of the training datasets that resulted in a high MARE for BOL (56.20%) and FUB (57.00%). Mapping the spatial distribution of Chla concentrations across Lake Kasumigaura using the seven processors showed that all processors-except for the BOL and FUB-were able to accurately capture the Chla distribution for moderate and high Chla concentrations. In addition, MCI_L1 and C2R processors were evaluated over 10-years of monthly measured Chla as they demonstrated the best retrieval accuracy from both groups (i.e., band height and neural network, respectively). The retrieved Chla of MCI_L1 was more accurate at tracking seasonal and annual variation in Chla than C2R, with only slight overestimation occurring during the springtime.

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Retrieval of atmospheric and oceanic properties from MERIS measurements: A new Case‐2 water processor for BEAM

Cited by 102 publications

References 7 publications

An Optical Classification Tool for Global Lake Waters

An Optical Classification Tool for Global Lake Waters

MOD2SEA: A Coupled Atmosphere-Hydro-Optical Model for the Retrieval of Chlorophyll-a from Remote Sensing Observations in Complex Turbid Waters

Evaluation of MERIS Chlorophyll-a Retrieval Processors in a Complex Turbid Lake Kasumigaura over a 10-Year Mission

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