An evaluation of algorithms for the remote sensing of cyanobacterial biomass

Ruiz‐Verdú, Antonio; Simis, Stefan; Hoyos, Caridad de; Gons, Herman J.; Peña-Martinez, R.

doi:10.1016/j.rse.2007.11.019

Cited by 143 publications

(91 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To achieve this, we assembled a dataset of in situ hyperspectral R rs (λ) with co-measured Chl-a and Total Suspended Matter (TSM) concentrations and absorption of CDOM at 443 nm (aCDOM) from multiple sources covering a wide dynamic range in optical and environmental conditions. This dataset includes the 'lake only' dataset portion (N = 320) from [3], which consists of measurements from the northeast U.S., the Great Salt Lake [16] and across Spain [17]. We refer the readers to these references for further information on the data collection protocols.…”

Section: In Situ Data Sourcesmentioning

confidence: 99%

An Optical Classification Tool for Global Lake Waters

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

Section: In Situ Data Sourcesmentioning

confidence: 99%

An Optical Classification Tool for Global Lake Waters

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Some pigments that occur in particular phytoplankton groups or taxa have been recognised as signatures for those groups or taxa, and their concentrations are used as indicators for the presence of these organisms in the water column [2,[24][25][26]. The summertime blooms of filamentous cyanobacteria in the Baltic Sea are known to contain large quantities of phycocyanin that may provide a more specific indicator of cyanobacterial biomass than chlorophyll a, which is present in all phytoplankton species [27,28]. Phycocyanin absorbs light strongly around 620 nm [29], allowing for its quantification from remotely sensed data [28,30,31].…”

Section: Introductionmentioning

confidence: 99%

A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters

et al. 2017

View full text Add to dashboard Cite

Abstract:Eutrophication is an increasing problem in coastal waters of the Baltic Sea. Moreover, algal blooms, which occur every summer in the Gulf of Gdansk can deleteriously impact human health, the aquatic environment, and economically important fisheries, tourism, and recreation industries. Traditional laboratory-based techniques for water monitoring are expensive and time consuming, which usually results in limited numbers of observations and discontinuity in space and time. The use of hyperspectral radiometers for coastal water observation provides the potential for more detailed remote optical monitoring. A statistical approach to develop local models for the estimation of optically significant components from in situ measured hyperspectral remote sensing reflectance in case 2 waters is presented in this study. The models, which are based on empirical orthogonal function (EOF) analysis and stepwise multilinear regression, allow for the estimation of parameters strongly correlated with phytoplankton (pigment concentration, absorption coefficient) and coloured detrital matter abundance (absorption coefficient) directly from reflectance spectra measured in situ. Chlorophyll a concentration, which is commonly used as a proxy for phytoplankton biomass, was retrieved with low error (median percent difference, MPD = 17%, root mean square error RMSE = 0.14 in log 10 space) and showed a high correlation with chlorophyll a measured in situ (R = 0.84). Furthermore, phycocyanin and phycoerythrin, both characteristic pigments for cyanobacteria species, were also retrieved reliably from reflectance with MPD = 23%, RMSE = 0.23, R 2 = 0.77 and MPD = 24%, RMSE = 0.15, R 2 = 0.74, respectively. The EOF technique proved to be accurate in the derivation of the absorption spectra of phytoplankton and coloured detrital matter (CDM), with R 2 (λ) above 0.83 and RMSE around 0.10. The approach was also applied to satellite multispectral remote sensing reflectance data, thus allowing for improved temporal and spatial resolution compared with the in situ measurements. The EOF method tested on simulated Medium Resolution Imaging Spectrometer (MERIS) or Ocean and Land Colour Instrument (OLCI) data resulted in RMSE = 0.16 for chl-a and RMSE = 0.29 for phycocyanin. The presented methods, applied to both in situ and satellite data, provide a powerful tool for coastal monitoring and management.

show abstract

“…1), reduced through averaging. All reference datasets are from continuous monitoring programmes according to the enclosed acknowledgements, the only exception being the 2001-2005 dataset collected in Spanish reservoirs (Ruiz-Verdú et al, 2008;Simis et al, 2007), which was sampled in reservoirs Almendra, Cuerda del Pozo, Iznajar, Rosarito and Talarn. Due to the small number of samples per reservoir, and for their methodological consistency, the data from Spain are used as if they represented a single water body.…”

Section: Performance Metrics and Reference Datamentioning

confidence: 99%

Diversity II water quality parameters from ENVISAT (2002–2012): a new global information source for lakes

Odermatt

Danne

Philipson

et al. 2018

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. The use of ground sampled water quality information for global studies is limited due to practical and financial constraints. Remote sensing is a valuable means to overcome such limitations and to provide synoptic views of ambient water quality at appropriate spatio-temporal scales. In past years several large data processing efforts were initiated to provide corresponding data sources. The Diversity II water quality dataset consists of several monthly, yearly and 9-year averaged water quality parameters for 340 lakes worldwide and is based on data from the full ENVISAT MERIS operation period (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). Existing retrieval methods and datasets were selected after an extensive algorithm intercomparison exercise. Chlorophyll-a, total suspended matter, turbidity, coloured dissolved organic matter, lake surface water temperature, cyanobacteria and floating vegetation maps, as well as several auxiliary data layers, provide a generically specified database that can be used for assessing a variety of locally relevant ecosystem properties and environmental problems. For validation and accuracy assessment, we provide matchup comparisons for 24 lakes and a group of reservoirs representing a wide range of bio-optical conditions. Matchup comparisons for chlorophyll-a concentrations indicate mean absolute errors and bias in the order of median concentrations for individual lakes, while total suspended matter and turbidity retrieval achieve significantly better performance metrics across several lake-specific datasets. We demonstrate the use of the products by illustrating and discussing remotely sensed evidence of lake-specific processes and prominent regime shifts documented in the literature. The Diversity II data are available from https://doi.pangaea.de/10.1594/PANGAEA.871462, and Python scripts for their analysis and visualization are provided at https://github.com/odermatt/diversity/.

show abstract

An evaluation of algorithms for the remote sensing of cyanobacterial biomass

Cited by 143 publications

References 27 publications

An Optical Classification Tool for Global Lake Waters

An Optical Classification Tool for Global Lake Waters

A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters

Diversity II water quality parameters from ENVISAT (2002–2012): a new global information source for lakes

Contact Info

Product

Resources

About