Chl-a Algorithms Research Articles

Water quality monitoring in Basque coastal areas using local chlorophyll-<inline-formula><math display="inline" overflow="scroll"><mrow><mi>a</mi></mrow></math></inline-formula> algorithm and MERIS images

Accurate estimation of chlorophyll-a (chl-a), a proxy of the eutrophication risk, is necessary in coastal areas for the assessment of water quality in accordance with European Direc- tives. Local parameterization of remote sensing algorithms is useful to cope with the variability and specificity of optically-active in-water constituents. Using the Bay of Biscay coastal waters, affected by Basque river runoffs, as a case study, the objectives of this investigation are to: 1. develop an empirical algorithm to estimate water surface chl-a for the optically-complex Basque coastal waters; 2. explore the influence of suspended matter, phytoplankton species, and pigment content on the algorithm developed for medium resolution imaging spectrometer instrument (MERIS) imagery; 3. compare the local algorithm to three ocean color algorithms (OC4v6, Gitelson's algorithm, and the OC5); and 4. apply the local algorithm to the MERIS images. For this purpose, two surveys were undertaken within the study area, the Batel-1 survey in 2007, and the Batel-2, in 2009. The empirical algorithm was developed with remote sensing reflectances (Rrs), undertaken with a TriOS field spectrometer, and chl-a measured in situ from the Batel-2 survey. The algorithm was not affected by different concentrations of suspended matter in surface waters, within the range from 0.0 to 6.6 g · m −3 . There was no significant effect of 23 accessory pigments found in the area on the algorithm. Eighty-four Rrs and chl-a measure- ments from the Batel-1 survey were used to validate the local algorithm and to compare it with output of the other algorithms. The local algorithm provided the lowest root-mean-square differ- ence (RMS ¼ 1.7 mg · m −3 ), the best correlation with the observed data (R ¼ 0.8), together with the best slope-intercept combination between predicted and observed chl-a (slope ¼ 0.5, intercept ¼ 0.6). The chl-a algorithm developed here for MERIS imagery can assist in the assess- ment of water ecological status in the southeastern part of the Bay of Biscay, in a cost-effective manner. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). (DOI: 10.1117/ 1.JRS.6.063519)

Water quality assessment using satellite-derived chlorophyll-a within the European directives, in the southeastern Bay of Biscay

The implementation of water quality European Directives requires an intensification of water quality monitoring, within the limits of the Exclusive Economic Zone. Remote sensing technologies can provide a valuable tool for frequent, synoptic, water-quality observations, over large areas. The aim of this study is to assess the ecological status of Basque coastal water bodies using satellite imagery from MODIS sensor, together with optical and chlorophyll-ain situ measurements. Thus, sea surface satellite-derived chl-a algorithms, the OC3 M, OC5 and a Local empirical algorithm, were compared against in situ measurements using satellite in situ match-ups, 90th Percentile (P90) monthly values for the 2005–2010 period. The OC5 algorithm corresponded most accurately with in situ measurements performed in the area, hence, it was selected. A P90 chlorophyll-a map was created with this algorithm to apply the classification scheme required by the directives. The classification of water bodies, based upon satellite-derived chlorophyll-a, could improve considerably the assessment of water quality.

Empirical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas

A bio-optical dataset collected during the 1998–2007 period in the Yellow and East China Seas (YECS) was used to provide alternative empirical ocean-color algorithms in the retrieval of chlorophyll-a (Chl-a), total suspended matter (TSM), and colored dissolved organic matter (CDOM) absorption coefficients at 440 nm (ag440). Assuming that remote-sensing reflectance (Rrs) could be retrieved accurately, empirical algorithms for TChl (regionally tuned Tassan’s Chl-a algorithm) in case-1 waters (TChl2i in case-2 waters), TTSM (regionally tuned Tassan’s TSM algorithm), and Tag440 or Cag440 (regionally tuned Tassan’s or Carder’s ag440 algorithm) were able to retrieve Chl-a, TSM, and ag440 with uncertainties as high as 35, 46, and 35%, respectively. Applying the standard SeaWiFS Rrs, TChl was not viable in the eastern part of the YECS, which was associated with an inaccurate SeaWiFS Rrs retrieval because of improper atmospheric correction. TChl behaved better than other algorithms in the turbid case-2 waters, although overestimation was still observed. To retrieve more reliable Chl-a estimates with standard SeaWiFS Rrs in turbid water (a proxy for case-2 waters), we modified TChl for data with SeaWiFS normalized water-leaving radiance at 555 nm (nLw555) > 2 mW cm−2 μm−1 sr−1 (TChl2s). Finally, with standard SeaWiFS Rrs, we recommend switching algorithms from TChl2s (for case-2 waters) to MOCChl (SeaWiFS-modified NASA OC4v4 standard algorithm for case-1 waters) for retrieving Chl-a, which resulted in uncertainties as high as 49%. To retrieve TSM and ag440 using SeaWiFS Rrs, we recommend empirical algorithms for TTSM (pre-SeaWiFS-modified form) and MTag440 or MCag440 (SeaWiFS Rrs-modified forms of Tag440 or Cag440). These could retrieve with uncertainties as high as 82 and 52%, respectively.

Estimation of chlorophyll-a concentration in waters over the continental shelf of the Bay of Biscay: a comparison of remote sensing algorithms

Ocean colour imagery is used increasingly as a tool to assess water quality via chlorophyll-a concentration (chl-a) estimations in European waters. The Bay of Biscay is affected by major river discharges, which alter the constituents of the marine waters. Chlorophyll-a algorithms, designed for use at global scales, are less accurate due to the variability of optically active in-water constituents. Hence, regionally parameterized empirical algorithms are necessary. The main objective of the present study was to develop a regional algorithm to retrieve chl-a in surface water using in situ R rs, for a subsequent application to Medium Resolution Imaging Spectrometer (MERIS) satellite images. To address this objective, a platform was developed initially and a measurement procedure adapted for the field HR4000CG Spectrometer. Subsequently, the procedure was tested during a survey over the south-eastern Bay of Biscay (North-East Atlantic Ocean), to establish a MERIS chl-a algorithm for the area, by comparing different global remote sensing chl-a algorithms, with band ratios. Results validated with the jackknife resampling procedure show a satisfactory relationship between the R rs(510)/R r s(560) and chl-a (R 2 jac = 0.681). This ratio is better correlated to chl-a than those obtained with established chl-a remote sensing algorithms. High content in coloured dissolved organic matter (CDOM > 0.4 m−1) and suspended particulate matter (SPM > 2.8 mg l−1) influenced this relationship, with yellow substances having a stronger effect.

ADEOS-II/GLIの生物光学アルゴリズム

Empirical bio-optical algorithms developed for GLI are described. These algorithms can be used to retrieve in-water properties from GLI normalized water leaving radiances in temperate Case + waters. The OC. Chl-a algorithm uses the maximum band ratio switching procedure for bands that do not saturate over the ocean. GLI is the only ocean color sensor that has ultra-violet (UV) bands and we developed an experimental red tide UV index for the early detection of dinoflagellate blooms. The red tide index uses the increased absorption of UV light by mycosporine-like amino-acids (MAAs). A special version of the Chl-a algorithm was developed for the Southern Ocean Case + waters. The standard algorithms cannot be applied to turbid near-shore waters, e.g. o# Korea and Hong Kong where bio-optical characteristics deviate drastically from Case + characteristics. Developing new and improved algorithms for Case , waters is a major challenge in bio-optical oceanography.

Effect of bio-optical parameter variability and uncertainties in reflectance measurements on the remote estimation of chlorophyll-a concentration in turbid productive waters: modeling results

Most algorithms for retrieving chlorophyll-a concentration (Chla) from reflectance spectra assume that bio-optical parameters such as the phytoplankton specific absorption coefficient (aPhi*) or the chlorophyll-a fluorescence quantum yield (eta) are constant. Yet there exist experimental data showing large ranges of variability for these quantities. The main objective of this study was to analyze the sensitivity of two Chla algorithms to variations in bio-optical parameters and to uncertainties in reflectance measurements. These algorithms are specifically designed for turbid productive waters and are based on red and near-infrared reflectances. By means of simulated data, it is shown that the spectral regions where the algorithms are maximally sensitive to Chla overlap those of maximal sensitivity to variations in the above bio-optical parameters. Thus, to increase the accuracy of Chla retrieval, we suggest using spectral regions where the algorithms are less sensitive to Chla, but also less sensitive to these interferences. aPhi* appeared to be one of the most important sources of error for retrieving Chla. However, when the phytoplankton backscattering coefficient (bb,Phi) dominates the total backscattering, as is likely during algal blooms, variations in the specific bb,Phi may introduce large systematic uncertainties in Chla estimation. Also, uncertainties in reflectance measurements, which are due to incomplete atmospheric correction or reflected skylight removal, seem to affect considerably the accuracy of Chla estimation. Instead, variations in other bio-optical parameters, such as eta or the specific backscattering coefficient of total suspended particles, appear to have minor importance. Suggestions regarding the optimal band locations to be used in the above algorithms are finally provided.

Comparison of bio-physical marine products from SeaWiFS, MODIS and a bio-optical model with in situ measurements from Northern European waters

In this paper, we compare bio-physical marine products from SeaWiFS, MODIS and a novel bio-optical absorption model with in situ measurements of chlorophyll-a (Chla) concentrations, total suspended material (TSM) concentrations, normalized water-leaving radiances (nLw) and absorption coefficients of coloured dissolved organic matter (aCDOM), total particulate (atotal) and phytoplankton (aphy) for 26 satellite match-ups in three Northern European seas. Cruises were undertaken in 2002 and 2003 in phytoplankton dominated open ocean waters of the Celtic Sea and optically complex waters of the Western English Channel (WEC) and North Sea. For all environments, Chla concentrations varied from 0.4 to 7.8 mg m−3, TSM from 0.2 to 6.0 mg l−1 and aCDOM at 440 nm from 0.02 to 0.30 m−1. SeaWiFS OC4v4, with the Remote Sensing Data Analysis Service (RSDAS) atmospheric correction for turbid waters, showed the most accurate retrieval of in situ Chla (RMS = 0.24; n = 26), followed by MODIS chlor_a_3 (RMS = 0.40; n = 26). This suggested that improving the atmospheric correction over optically complex waters results in more accurate Chla concentrations compared to those obtained using more complicated Chla algorithms. We found that the SeaWiFS OC4v4 and the MODIS chlor_a_2 switching band ratio algorithms, which mainly use longer wavebands than 443 nm, were less affected by CDOM. They were both more accurate than chlor_MODIS in the higher CDOM waters of the North Sea. Compared to MODIS the absorption model was better at retrieving atotal (RMS = 0.39; n = 78) and aCDOM (RMS = 0.79; n = 12) in all study areas and TSM in the WEC (RMS = 0.04; n = 10) but it underestimated Chla concentrations (RMS = 0.45; n = 26). The results are discussed in terms of atmospheric correction, sensor characteristics and the functioning and performance of Chla algorithms.