MERIS Bands Research Articles

Pseudo-nitzschia Blooms in a Coastal Upwelling System: Remote Sensing Detection, Toxicity and Environmental Variables

The NW coast of the Iberian Peninsula is dominated by extensive shellfish farming, which places this region as a world leader in mussel production. Harmful algal blooms in the area frequent lead to lengthy harvesting closures threatening food security. This study developed a framework for the detection of Pseudo-nitzschia blooms in the Galician rias from satellite data (MERIS full-resolution images) and identified key variables that affect their abundance and toxicity. Two events of toxin-containing Pseudo-nitzschia were detected (up to 2.5 μg L−1 pDA) in the area. This study suggests that even moderate densities of Pseudo-nitzschia in this area might indicate high toxin content. Empirical models for particulate domoic acid (pDA) were developed based on MERIS FR data. The resulting remote-sensing model, including MERIS bands centered around 510, 560, and 620 nm explain 73% of the pDA variance (R2 = 0.73, p < 0.001). The results show that higher salinity values and lower Si(OH)4/N ratios favour higher Pseudo-nitzschia spp. abundances. High pDA values seem to be associated with relatively high PO43, low NO3− concentrations, and low Si(OH)4/N. While MERIS FR data and regionally specific algorithms can be useful for detecting Pseudo-nitzschia blooms, nutrient relationships are crucial for predicting the toxicity of these blooms.

Developing a two-step method for retrieving cyanobacteria abundance from inland eutrophic lakes using MERIS data

Cyanobacteria are the main dominant species in inland eutrophic lakes during algae blooms, and measures of cyanobacteria abundance can be used for monitoring and early detection of algal blooms by remote sensing. During May 2013 and August 2016, a total 137 water samples were collected from Lake Taihu and Lake Chaohu. Remote-sensing reflectance was measured, surface water was collected in the field, and chlorophyll-a concentration, phycocyanin concentration, suspended-matter concentration and phytoplankton pigment absorption parameters were measured in the laboratory. The composition and density of planktonic algae were also detected by microscope examination. The remote-sensing reflectance at 15 MERIS bands was simulated based on our measured spectral data, and a two-step method for detecting cyanobacteria abundance using the partial least squares model based on 5 MERIS bands was developed. The results showed that the estimation algorithm can predict cyanobacteria abundance in inland eutrophic lakes with satisfactory accuracy, with RMSE of 7.56 and MAPE of 13.44 %. This algorithm was successfully applied to the MERIS image acquired on August 12, 2010, and showed a reasonable spatial distribution of cyanobacteria abundance in Lake Taihu. It demonstrated that the developed estimation method was an effective way to monitor cyanobacteria abundance in water with a potential to be successfully applied to Sentinel-3 images.

Developing a semi-analytical algorithm to estimate particulate organic carbon (POC) levels in inland eutrophic turbid water based on MERIS images: A case study of Lake Taihu

Particulate organic carbon (POC) plays an important role in the carbon cycle in water due to its biological pump process. In the open ocean, algorithms can accurately estimate the surface POC concentration. However, no suitable POC-estimation algorithm based on MERIS bands is available for inland turbid eutrophic water. A total of 228 field samples were collected from Lake Taihu in different seasons between 2013 and 2015. At each site, the optical parameters and water quality were analyzed. Using in situ data, it was found that POC-estimation algorithms developed for the open ocean and coastal waters using remote sensing reflectance were not suitable for inland turbid eutrophic water. The organic suspended matter (OSM) concentration was found to be the best indicator of the POC concentration, and POC has an exponential relationship with the OSM concentration. Through an analysis of the POC concentration and optical parameters, it was found that the absorption peak of total suspended matter (TSM) at 665nm was the optimum parameter to estimate POC. As a result, MERIS band 7, MERIS band 10 and MERIS band 12 were used to derive the absorption coefficient of TSM at 665nm, and then, a semi-analytical algorithm was used to estimate the POC concentration for inland turbid eutrophic water. An accuracy assessment showed that the developed semi-analytical algorithm could be successfully applied with a MAPE of 31.82% and RMSE of 2.68mg/L. The developed algorithm was successfully applied to a MERIS image, and two full-resolution MERIS images, acquired on August 13, 2010, and December 7, 2010, were used to map the POC spatial distribution in Lake Taihu in summer and winter.

Evaluation of chlorophyll-a retrieval algorithms based on MERIS bands for optically varying eutrophic inland lakes

Fourteen field campaigns were conducted in five inland lakes during different seasons between 2006 and 2013, and a total of 398 water samples with varying optical characteristics were collected. The characteristics were analyzed based on remote sensing reflectance, and an automatic cluster two-step method was applied for water classification. The inland waters could be clustered into three types, which we labeled water types I, II and III. From water types I to III, the effect of the phytoplankton on the optical characteristics gradually decreased. Four chlorophyll-a retrieval algorithms for Case II water, a two-band, three-band, four-band and SCI (Synthetic Chlorophyll Index) algorithm were evaluated for three water types based on the MERIS bands. Different MERIS bands were used for the three water types in each of the four algorithms. The four algorithms had different levels of retrieval accuracy for each water type, and no single algorithm could be successfully applied to all water types. For water types I and III, the three-band algorithm performed the best, while the four-band algorithm had the highest retrieval accuracy for water type II. However, the three-band algorithm is preferable to the two-band algorithm for turbid eutrophic inland waters. The SCI algorithm is recommended for highly turbid water with a higher concentration of total suspended solids. Our research indicates that the chlorophyll-a concentration retrieval by remote sensing for optically contrasted inland water requires a specific algorithm that is based on the optical characteristics of inland water bodies to obtain higher estimation accuracy.

Reconstruction of hyperspectral reflectance for optically complex turbid inland lakes: test of a new scheme and implications for inversion algorithms.

A new scheme has been proposed by Lee et al. (2014) to reconstruct hyperspectral (400 - 700 nm, 5 nm resolution) remote sensing reflectance (Rrs(λ), sr-1) of representative global waters using measurements at 15 spectral bands. This study tested its applicability to optically complex turbid inland waters in China, where Rrs(λ) are typically much higher than those used in Lee et al. (2014). Strong interdependence of Rrs(λ) between neighboring bands (≤ 10 nm interval) was confirmed, with Pearson correlation coefficient (PCC) mostly above 0.98. The scheme of Lee et al. (2014) for Rrs(λ) re-construction with its original global parameterization worked well with this data set, while new parameterization showed improvement in reducing uncertainties in the reconstructed Rrs(λ). Mean absolute error (MAERrs(λi)) in the reconstructed Rrs(λ) was mostly < 0.0002 sr-1 between 400 and 700nm, and mean relative error (MRERrs(λi)) was < 1% when the comparison was made between reconstructed and measured Rrs(λ) spectra. When Rrs(λ) at the MODIS bands were used to reconstruct the hyperspectral Rrs(λ), MAERrs(λi) was < 0.001 sr-1 and MRERrs(λi) was < 3%. When Rrs(λ) at the MERIS bands were used, MAERrs(λi) in the reconstructed hyperspectral Rrs(λ) was < 0.0004 sr-1 and MRERrs(λi) was < 1%. These results have significant implications for inversion algorithms to retrieve concentrations of phytoplankton pigments (e.g., chlorophyll-a or Chla, and phycocyanin or PC) and total suspended materials (TSM) as well as absorption coefficient of colored dissolved organic matter (CDOM), as some of the algorithms were developed from in situ Rrs(λ) data using spectral bands that may not exist on satellite sensors.

Robust remote sensing algorithms to derive the diffuse attenuation coefficient for lakes and coastal waters

In this study, empirical and semianalytical algorithms are developed and compared for optically complex waters to retrieve the diffuse attenuation coefficient of downwelling irradiance (Kd(λ)) from satellite data. In the first approach, a band ratio algorithm was used. Various sets of MERIS band ratios were tested to achieve the best estimates for Kd(490) based on the in situ dataset which was measured in Nordic lakes (oligotrophic to eutrophic conditions). In the second approach, Kd(490) was expressed as a function of inherent optical properties which were retrieved from MERIS standard products. The algorithms from both approaches were tested against an independent data set and validated in optically complex coastal waters in the Baltic Sea and in Nordic lakes with high concentrations of coloured dissolved organic matter (0.3 < acdom(442) m−1 < 4.5), chlorophyll a (Chl a) (0.7 < CChl a(mg m−3) < 67.5) and total suspended matter (TSM) (0.5 < CTSM(g m−3) < 26.4). MERIS-derived Kd(490) values showed reliable estimates in case of both methods. The results indicate that for band ratio algorithms, the root mean square error (RMSE) decreases and the coefficient of determination (R2) increases when using longer wavelengths in the visible spectrum as a reference band. It was found that the best estimates were retrieved from MERIS data when using the ratio of Rrs(490)/Rrs(709) for coastal waters (Kd(490) < 2.5 m−1) and the ratio Rrs(560)/Rrs(709) for more turbid inland waters (Kd(490) > 2.5 m−1). As a result, a combined band ratio algorithm was developed, which provides a promising approach (R2 = 0.98, RMSE=17%, N = 34, p < 0.05) for estimating Kd(490) over a wide range of values (0.3–6.1 m−1).

Validation of MERIS spectral inversion processors using reflectance, IOP and water quality measurements in boreal lakes

We evaluated MERIS spectral inversion processors in the estimation of chlorophyll a (Chl-a), total suspended matter (TSM), absorption of colored dissolved organic matter (aCDOM at 443nm) and Secchi disk transparency (ZSD) in four lakes located in southern Finland. The lakes represent oligotrophic, mesotrophic and humic (high aCDOM, low TSM) lake types. Our validation data was extensive, consisting of remote sensing reflectance (Rrs) measured in the field, detailed measurements of (specific) inherent optical properties ((S)IOP) and water quality measurements. Validation measurements were collected at stations and as spatially intensive transects. The tested processors were Boreal lakes (BOR) and Free University of Berlin WeW WATER processor (FUB). In the case of BOR we included two versions, the original one available in 2008 and the most recent version. The processing chains of these versions differed in terms of MERIS archive reprocessing (2nd reprocessing (RP2) and 3rd reprocessing (RP3) were used), version of adjacency effect correction (ICOL) and rectification method. However, all these had only minor effect on the estimated Rrs when compared to the effect caused by the differences in the atmospheric correction versions. The use of ICOL improved the estimation of Rrs and therefore all IOP and water quality validations were made with ICOL pre-processed data. Rrs was best estimated with RP2 BOR, although it somewhat overestimated Rrs in blue in the humic lake. RP3 BOR greatly overestimated the Rrs in blue. FUB was outside its aCDOM training range in most of the studied lakes. RP2 BOR estimated water quality with the best accuracy in mesotrophic lakes, but failed in humic and clear water lakes. BOR overestimated Chl-a, particularly with RP3. The best TSM correlation was for RP2 BOR. All processors underestimated aCDOM. The SIOP measurements showed that overestimation of Chl-a was not due to incorrect conversion factors, and that underestimation of TSM was to some extent explained by lower average conversion factor than that measured in the validation lakes. The inaccuracies in Chl-a and aCDOM(443) estimation were related to the partition of atot to phytoplankton and CDOM absorption, which emphasizes the importance of including IOP measurements in the validation studies. ZSD was best estimated on the basis of atot and btot, which were separately calculated from BOR output and the bio-optical model assumed in the processor. For the needs of transparency estimation the processors should produce atot and btot for all MERIS bands as standard output. Atmospheric correction over humic lakes is another problem to be solved in future versions of the processors. The 2008 version of BOR provided better atmospheric correction results in humic lakes and is thus more applicable in the boreal zone where these lakes are common. This suggests that before a new processor version is published it should be validated with existing in situ data.

MERIS-based ocean colour classification with the discrete Forel–Ule scale

Abstract. Multispectral information from satellite borne ocean colour sensors is at present used to characterize natural waters via the retrieval of concentrations of the three dominant optical constituents; pigments of phytoplankton, non-algal particles and coloured dissolved organic matter. A limitation of this approach is that accurate retrieval of these constituents requires detailed local knowledge of the specific absorption and scattering properties. In addition, the retrieval algorithms generally use only a limited part of the collected spectral information. In this paper we present an additional new algorithm that has the merit of using the full spectral information in the visible domain to characterize natural waters in a simple and globally valid way. This Forel–Ule MERIS (FUME) algorithm converts the normalized multiband reflectance information into a discrete set of numbers using uniform colourimetric functions. The Forel–Ule (FU) scale is a sea colour comparator scale that has been developed to cover all possible natural sea colours, ranging from indigo blue (the open ocean) to brownish-green (coastal water) and even brown (humic-acid dominated) waters. Data using this scale have been collected since the late nineteenth century, and therefore, this algorithm creates the possibility to compare historic ocean colour data with present-day satellite ocean colour observations. The FUME algorithm was tested by transforming a number of MERIS satellite images into Forel–Ule colour index images and comparing in situ observed FU numbers with FU numbers modelled from in situ radiometer measurements. Similar patterns and FU numbers were observed when comparing MERIS ocean colour distribution maps with ground truth Forel–Ule observations. The FU numbers modelled from in situ radiometer measurements showed a good correlation with observed FU numbers (R2 = 0.81 when full spectra are used and R2 = 0.71 when MERIS bands are used).

Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands

Remote sensing algorithms that use red and NIR bands for the estimation of chlorophyll-a concentration [Chl] can be more effective in inland and coastal waters than algorithms that use blue and green bands. We tested such two-band and three-band red-NIR algorithms using comprehensive synthetic data sets of reflectance spectra and inherent optical properties related to various water parameters and a very consistent in situ data set from several lakes in Nebraska, USA. The two-band algorithms tested with MERIS bands were Rrs(708)/Rrs(665) and Rrs(753)/Rrs(665). The three-band algorithm with MERIS bands was in the form R3=[Rrs(-1)(665)-Rrs(-1)(708)]×Rrs(753). It is shown that the relationships of both Rrs(708)/Rrs(665) and R3 with [Chl] do not depend much on the absorption by CDOM and non-algal particles, or the backscattering properties of water constituents, and can be defined in terms of water absorption coefficients at the respective bands as well as the phytoplankton specific absorption coefficient at 665 nm. The relationship of the latter with [Chl] was established for [Chl]>1 mg/m3 and then further used to develop algorithms which showed a very good match with field data and should not require regional tuning.

Seasonal-spatial variation and remote sensing of phytoplankton absorption in Lake Taihu, a large eutrophic and shallow lake in China

Shallow water, strong sediment resuspension, complex river inputs and frequent cyanobacterial blooms characterize the waters of Lake Taihu. In such shallow, eutrophic lakes, the remote sensing of phycocyanin (PC), a characteristic pigment of cyanobacteria, is dependent on the estimation precision of phytoplankton absorption. For Lake Taihu, we monitored the seasonal–spatial variation of phytoplankton absorption, and a three-band model was calibrated and validated to estimate phytoplankton absorption (aph(665)) from a dataset of the spatial and temporal patterns of the bio-optical properties collected, during five cruises in January (winter), April (spring), July (summer), and October (autumn) in 2006 to 2007. Two distinct situations prevailed; in winter tripton strongly predominated over particulate matter, and in spring-summer-autumn phytoplankton made an important contribution. In winter, meteorology mainly determined the bio-optical properties of the water column, whereas in the spring-summer-autumn the biological activity was an additional active factor. The three-band remote sensing model 1⁄2R 1 rs ð673Þ R 1 rs ð698Þ Rrsð731ÞðRrs: remote sensing reflectance) of aph(665) was calibrated and validated, and its performance was compared and assessed with the published band-ratio method. With the three-band model, the root mean square error and mean relative error were 0.150 m (50.5% accounting for the mean value) and 45.7% respectively; with the published band-ratio method, the values were 0.290 m (97.3% accounting for the mean value) and 213.0% respectively, based on an independent validation dataset. Furthermore, the three-band and band-ratio models worked well in estimating phytoplankton absorption with simulated MERIS bands data with higher precision for the three-band model in Lake Taihu. The result showed that the three-band model was superior to the published band-ratio method, and thus the former can be used to improve the estimation precision of remote sensing of PC.

Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters

Mapping of total suspended matter concentration (TSM) can be achieved from space-based optical sensors and has growing applications related to sediment transport. A TSM algorithm is developed here for turbid waters, suitable for any ocean colour sensor including MERIS, MODIS and SeaWiFS. Theory shows that use of a single band provides a robust and TSM-sensitive algorithm provided the band is chosen appropriately. Hyperspectral calibration is made using seaborne TSM and reflectance spectra collected in the southern North Sea. Two versions of the algorithm are considered: one which gives directly TSM from reflectance, the other uses the reflectance model of Park and Ruddick (2005) to take account of bidirectional effects. Applying a non-linear regression analysis to the calibration data set gave relative errors in TSM estimation less than 30% in the spectral range 670–750 nm. Validation of this algorithm for MODIS and MERIS retrieved reflectances with concurrent in situ measurements gave the lowest relative errors in TSM estimates, less than 40%, for MODIS bands 667 nm and 678 nm and for MERIS bands 665 nm and 681 nm. Consistency of the approach in a multisensor context (SeaWiFS, MERIS, and MODIS) is demonstrated both for single point time series and for individual images.

Using MERIS on Envisat for land cover mapping in the Netherlands

This paper describes the results of a feasibility study to test the usefulness of MERIS for land cover mapping. The Netherlands was used as a test site because of its highly fragmented landscape. Results showed that the geometric and radiometric properties of the studied MERIS images of the Netherlands are suitable for land applications. Calculation of principal components and correlation coefficients revealed that the 15 MERIS bands provided a lot of redundant spectral information. For land applications, information came from the visible part of the spectrum on the one hand and from the near‐infrared part on the other hand. In addition, the red‐edge slope of the reflectance curve (in particular MERIS band 9 at about 708 nm) provided supplementary information. The Dutch land use database LGN5 was used as a reference for classifications in this study after aggregation from 25 m to 300 m and recoding to 7 relevant land cover classes. For land cover classification best results in terms of classification accuracies were obtained for the image of 14 July 2003. For the seven land cover classes selected the overall classification accuracy was 67.2%. A multitemporal classification did not improve the overall classification accuracy.

MERIS in‐flight spectral calibration

This paper will describe the spectral calibration activities conducted during the MERIS commissioning phase and during operation since orbit 12000. MERIS is a medium resolution (300–1200m) push‐broom imaging spectrometer covering the spectral domain 390–1040nm with 15 bands, programmable in position and width down to steps of 1.25nm. The onboard spectral calibration hardware is based on the use of an Erbium doped‐diffuser panel presenting well‐defined absorption peaks. In the spectral calibration mode, MERIS is configured with narrow bands centred on an Erbium absorption feature (two are used). The first orbit, the instrument is calibrated by viewing the “white” radiometric diffuser plate and the following orbit the “pink” Erbium diffuser plate is deployed. This method allows each of the MERIS detectors involved to be characterized in wavelength. The Fraunhofer absorption lines were used to complement these results by providing additional measurements in the violet and near infrared part of the spectrum. For this method, MERIS was configured both for Earth and diffuser observations and acquired data for only a limited number of orbits. This procedure was repeated for different band settings covering a number of Fraunhofer absorption lines. Finally, using Oxygen (O2A) absorption Earth observation data, two different approaches were developed, one based on the retrieval of surface pressure and one based on the shape of the O2A absorption band. Both methods were developed for clear sky land observations, but their performances are improved over bright land targets. Both methods agree to within an accuracy of 0.02 nm. The results from the different methods are analyzed in order to propose a spectral model for the MERIS instrument. Preliminary results of the spectral variation with time are reported. Except camera 4, the instrument is quite stable with time. Camera 4 needs further investigations to better understand its behaviour. Except for the use of the MERIS oxygen band, the spectral characterization of the other MERIS bands is achieved within the nominal accuracy (1 nm).

A method for aerosol correction from the spectral variation in the visible and near infrared: application to the MERIS sensor

The signal recorded by the sensor at the satellite level contains information relative both to the atmosphere and the surface. Atmospheric correction is therefore necessary to extract the surface reflectance required within biophysical algorithms used to estimate canopy attributes. Aerosol characteristics are difficult to evaluate because they vary rapidly with time and space. The objective of this study is to develop an autonomous aerosol correction method exploiting the information content in MERIS images. A dedicated neural network was trained to retrieve aerosol optical thickness (AOT) from the top of atmosphere signal recorded in 13 MERIS bands. The training database was made of radiative transfer model simulations, SMAC code coupled to SAIL and PROSPECT models. In this preliminary study, aerosol were characterized only by their optical thickness at 550nm, considering only the continental aerosol type. Theoretical results derived from model simulations demonstrate the pertinence of the method, with a 0.058 Root Mean Square Error associated to the estimation of the AOT, inducing a RMSE on the estimated top of canopy reflectance better than 0.005. The method was then validated using 61 actual MERIS images acquired over AERONET sites. Results show RMSE values of 0.116 associated to the AOT estimation. Comparison with MODIS achieved over 31 among the 61 scenes show a RMSE value of 0.11 similar to the 0.10 value observed for MERIS with our algorithm. Comparison between atmospheric correction achieved with our algorithm and with those based on actual AERONET measurements show RMSE values on top of canopy reflectance close to 0.005.

Monitoring cyanobacterial blooms by satellite remote sensing

Cyanobacterial blooms are attracting the increasing attention of environment agencies, water authorities, and human and animal health organizations, since they can present a range of amenity, water quality ant treatment problems, and hazards to human and animal health. The problem is especially acute in the Baltic Sea where cyanobacterial blooms occur every summer covering areas of more than 100 000 km 2. It has been shown that quantitative mapping of cyanobacteria during bloom conditions is possible with hyperspectral instruments. These sensors, however, cannot provide synoptic spatial coverage and high revisit times needed for near real-time monitoring of potentially harmful blooms. The aim was to estimate whether spectral resolution of multispectral sensors, which can provide needed coverage, is adequate for quantitative mapping of cyanobacteria and whether it is possible to separate potentially harmful blooms of cyanobacteria from waters dominated by algae using ocean colour satellites. The modelling results show that multispectral sensors like ALI, Landsat or MODIS are not capable of separating waters dominated by cyanobacteria from waters dominated by other algae species, as their spectral band configuration does not allow detecting absorption features caused by phycocyanin (present primarily in cyanobacteria) or any other spectral features that are characteristic to cyanobacteria only. MERIS bands 6 and 7 allow detecting phycocyanin absorption feature near 630 nm and a small peak in reflectance spectra near 650 nm characteristic to only cyanobacteria. Thus, MERIS can be used in detecting cyanobacteria if they are present in relatively high quantities. Unfortunately it is not possible to use MERIS for early warning of emerging potentially harmful blooms as the minimum biomass needed to cause features in reflectance spectra typical to cyanobacteria is higher than the biomass already considered as a bloom in the Baltic Sea.

Mapping of the water quality of Lake Erken, Sweden, from Imaging Spectrometry and Landsat Thematic Mapper

Hyperspectral data have been collected by the Compact Airborne Spectrographic Imager (CASI) and multispectral data by the Landsat Thematic Mapper (TM) instrument for the purpose of mapping lake water quality. Field campaigns have been performed on Lake Erken in Sweden during the summer of 1997. Water samples have been collected and analysed in laboratory. Continuously measured variables from a boat have added a spatial dimension to the ground truth dataset. The data have been used to construct algorithms, based on remotely sensed data, for the retrieval of water quality parameters. The correlation between the continuous data and the collected CASI data has been investigated. Algorithms using both the point sampling results and the continuous data have been developed. Maps based on data from each instrument, showing the distribution of chlorophyll, are presented. Problems of having few water sampling stations, and the potential of using sub-water optics models are addressed as well. Tests were performed on MERIS bands and found useful for mapping chlorophyll and turbidity, and algorithms have been suggested for future use with MERIS.

Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland

The aim of the study was to test the feasibility of the band combination of the TERRA MODIS and ENVISAT MERIS instruments for operational monitoring of lakes and coastal waters in Finland. Also simulated LANDSAT TM data were tested. Satellite bands were simulated using airborne measurements with AISA imaging spectrometer. Semi-empirical algorithms with simulated satellite data were tested against field observations using regression analysis. Interpretation of chlorophyll a, suspended matter, turbidity and secchi-disk depth was included in the analyses. The data for this study were gathered in campaigns carried out in May and August 1997 and August 1998 both for lakes in southern Finland and coastal waters of the Baltic Sea. The data set included 85 in situ observations for lakes and 107 for coastal waters. Our results show that the band combination to be included in the ENVISAT MERIS instrument enables the interpretation of water quality, including chlorophyll a concentration using semi-empirical algorithms both for lakes and coastal waters. MERIS band 9 centred at 705 nm is proven to be of vital importance for the detection of chlorophyll a in local surface waters.