scholarly journals Using empirical orthogonal functions derived from remote sensing reflectance for the prediction of concentrations of phytoplankton pigments

2014 ◽  
Vol 11 (5) ◽  
pp. 2073-2117 ◽  
Author(s):  
A. Bracher ◽  
M. H. Taylor ◽  
B. Taylor ◽  
T. Dinter ◽  
R. Röttgers ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Samples ◽  
Empirical Orthogonal Functions ◽  
Optical Measurements ◽  
Small Data ◽  
Tropical Atlantic ◽  
Phytoplankton Pigments ◽  
Remote Sensing Reflectance ◽  
Pigment Concentrations ◽  
Reflectance Data

Abstract. The composition and abundance of algal pigments provide information on characteristics of a phytoplankton community in respect to its photoacclimation, overall biomass, and taxonomic composition. Particularly, these pigments play a major role in photoprotection and in the light-driven part of photosynthesis. Most phytoplankton pigments can be measured by High Performance Liquid Chromatography (HPLC) techniques to filtered water samples. This method, like others when water samples have to be analysed in the laboratory, is time consuming and therefore only a limited number of data points can be obtained. In order to receive information on phytoplankton pigment composition with a higher temporal and spatial resolution, we have developed a method to assess pigment concentrations from continuous optical measurements. The method applies an Empirical Orthogonal Function (EOF) analysis to remote sensing reflectance data derived from ship-based hyper-spectral underwater radiometric and from multispectral satellite data (using the MERIS Polymer product developed by Steinmetz et al., 2011) measured in the Eastern Tropical Atlantic. Subsequently we developed statistically linear models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables. The model results, show that surface concentrations of a suite of pigments and pigment groups can be well predicted from the ship-based reflectance measurements, even when only a multi-spectral resolution is chosen (i.e. eight bands similar to those used by MERIS). Based on the MERIS reflectance data, concentrations of total and monovinyl chlorophyll a and the groups of photoprotective and photosynthetic carotenoids can be predicted with high quality. The fitted statistical model constructed on the satellite reflectance data as input was applied to one month of MERIS Polymer data to predict the concentration of those pigment groups for the whole Eastern Tropical Atlantic area. Bootstrapping explorations of cross-validation error indicate that the method can produce reliable predictions with relatively small data sets (e.g., < 50 collocated values of reflectance and pigment concentration). The method allows for the derivation of time series from continuous reflectance data of various pigment groups at various regions, which can be used to study variability and change of phytoplankton composition and photo-physiology.

scholarly journals Using empirical orthogonal functions derived from remote-sensing reflectance for the prediction of phytoplankton pigment concentrations

Ocean Science ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 139-158 ◽  
Author(s):  
A. Bracher ◽  
M. H. Taylor ◽  
B. Taylor ◽  
T. Dinter ◽  
R. Röttgers ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Empirical Orthogonal Functions ◽  
Optical Measurements ◽  
Small Data ◽  
Linear Regression Models ◽  
Orthogonal Functions ◽  
Phytoplankton Pigment ◽  
Remote Sensing Reflectance ◽  
Pigment Concentrations ◽  
Reflectance Data

Abstract. The composition and abundance of algal pigments provide information on phytoplankton community characteristics such as photoacclimation, overall biomass and taxonomic composition. In particular, pigments play a major role in photoprotection and in the light-driven part of photosynthesis. Most phytoplankton pigments can be measured by high-performance liquid chromatography (HPLC) techniques applied to filtered water samples. This method, as well as other laboratory analyses, is time consuming and therefore limits the number of samples that can be processed in a given time. In order to receive information on phytoplankton pigment composition with a higher temporal and spatial resolution, we have developed a method to assess pigment concentrations from continuous optical measurements. The method applies an empirical orthogonal function (EOF) analysis to remote-sensing reflectance data derived from ship-based hyperspectral underwater radiometry and from multispectral satellite data (using the Medium Resolution Imaging Spectrometer – MERIS – Polymer product developed by Steinmetz et al., 2011) measured in the Atlantic Ocean. Subsequently we developed multiple linear regression models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables. The model results show that surface concentrations of a suite of pigments and pigment groups can be well predicted from the ship-based reflectance measurements, even when only a multispectral resolution is chosen (i.e., eight bands, similar to those used by MERIS). Based on the MERIS reflectance data, concentrations of total and monovinyl chlorophyll a and the groups of photoprotective and photosynthetic carotenoids can be predicted with high quality. As a demonstration of the utility of the approach, the fitted model based on satellite reflectance data as input was applied to 1 month of MERIS Polymer data to predict the concentration of those pigment groups for the whole eastern tropical Atlantic area. Bootstrapping explorations of cross-validation error indicate that the method can produce reliable predictions with relatively small data sets (e.g., < 50 collocated values of reflectance and pigment concentration). The method allows for the derivation of time series from continuous reflectance data of various pigment groups at various regions, which can be used to study variability and change of phytoplankton composition and photophysiology.

Application of the active learning method to the retrieval of pigment from spectral remote sensing reflectance data

2009 ◽  
Vol 30 (4) ◽  
pp. 1045-1065 ◽  
Author(s):  
H. Taheri Shahraiyni ◽  
S. Bagheri Shouraki ◽  
F. Fell ◽  
M. Schaale ◽  
J. Fischer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Active Learning ◽  
Learning Method ◽  
Remote Sensing Reflectance ◽  
Active Learning Method ◽  
Reflectance Data

scholarly journals An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4285 ◽  
Author(s):  
Shubha Sathyendranath ◽  
Robert Brewin ◽  
Carsten Brockmann ◽  
Vanda Brotas ◽  
Ben Calton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Marine Ecosystem ◽  
Ocean Colour ◽  
Remote Sensing Reflectance ◽  
Resolution Imaging ◽  
In Situ Observations ◽  
Climate Studies ◽  
Reflectance Data

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

scholarly journals Monitoring Changes in the Transparency of the Largest Reservoir in Eastern China in the Past Decade, 2013–2020

2021 ◽  
Vol 13 (13) ◽  
pp. 2570
Author(s):  
Teng Li ◽  
Bozhong Zhu ◽  
Fei Cao ◽  
Hao Sun ◽  
Xianqiang He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mean Squared Error ◽  
Eastern China ◽  
Water Environment ◽  
Relative Difference ◽  
Boreal Summer ◽  
Support Vector ◽  
Lake Area ◽  
The Past ◽  
Remote Sensing Reflectance

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs.

scholarly journals Meta-classification of remote sensing reflectance to estimate trophic status of inland and nearshore waters

2021 ◽  
Vol 176 ◽  
pp. 109-126
Author(s):  
Mortimer Werther ◽  
Evangelos Spyrakos ◽  
Stefan G.H. Simis ◽  
Daniel Odermatt ◽  
Kerstin Stelzer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Trophic Status ◽  
Remote Sensing Reflectance
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