Medium Resolution Imaging Spectrometer Data Research Articles

Retrieval of the seawater reflectance for suspended solids monitoring in the East China Sea using MODIS, MERIS and GOCI satellite data

The objectives of this study are to: (i) compare the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS), the Medium Resolution Imaging Spectrometer (MERIS) and the Geostationary Ocean Color Imager (GOCI) in retrieving the particulate backscattering coefficient, robust proxy of the concentration of suspended particulate matter (SPM), in the turbid waters of the East China Sea, then (ii) combine ocean color satellite data to field data recorded by an autonomous bio-optical profiling float to reconstitute and explain the dynamics of SPM within the whole water column in the middle of the East China Sea. MODIS, MERIS and GOCI seawater reflectance products at green, red and near-infrared (NIR) wavelengths are generated using atmospheric correction algorithms designed for turbid coastal waters then compared. Good agreement is obtained in the green and red while significant differences are observed in the NIR. The standard GOCI algorithm and a new one based on a regional empirical relationship established using MODIS satellite data are both proved to perform well over the whole range of water turbidity. Finally the dynamics of SPM is reconstituted along a three-month period to highlight a steady-state and stratified situation before the overpass of a large typhoon which mixed the water column, destroyed the deep-chlorophyll maximum and enhanced resuspension of bottom sediments varying according to tidal cycles.

Comparison between MERIS and regional high-level products in European seas

Standard ocean color data products from the Medium Resolution Imaging Spectrometer (MERIS) are compared with equivalent regional products in European seas exhibiting different bio-optical properties: the northern Adriatic Sea, the Baltic Sea and the Western Black Sea (ADRS, BLTS and BLKS, respectively). Investigated quantities are: 1) the algal-2 pigment index, alg2; 2) the composite absorption coefficient of yellow substance and non-pigmented particles at 442nm, adg; and 3) the concentration of the total suspended matter, TSM. Regional data products are created using ocean color inversion schemes based on MultiLayer Perceptron (MLP) neural nets trained with field measurements from the Coastal Atmosphere and Sea Time Series (CoASTS) and Bio-Optical mapping of Marine Properties (BiOMaP) programs. MLP input is the remote sensing reflectance RRS at MERIS center-wavelengths specifically selected for different water types in view of minimizing the perturbing effects of inaccurate atmospheric correction on the retrieval of regional data products. A new method is also proposed to define the applicability of regional MLPs to input RRS. Results indicate that MERIS alg2 values tend to overestimate the equivalent quantity computed with MLP regional algorithms. The agreement between MERIS and regional TSM data products is significantly better than that reported for alg2 and adg, especially for BLKS. Findings highlight the relevance of using regional inversion schemes to evaluate standard products over extended oceanographic regions as a complement to the analysis of match-ups between marine products measured in situ and derived from space-born data.

Monitoring blooms and surface accumulation of cyanobacteria in the Curonian Lagoon by combining MERIS and ASAR data

This study shows how different remote sensing techniques can be used to distinguish between surface accumulations (scum) and dense blooms of cyanobacteria in the Curonian Lagoon, the largest lagoon in Europe. Cyanobacteria blooms are a major concern in this region due to water quality issues interfering with the conservation of the whole ecosystem. Chlorophyll-a (Chl-a) concentrations can be extremely high (up to about 500mg/m3 in some cases) during cyanobacteria blooms, which are often associated with a surface accumulation of algae. The Medium Resolution Imaging Spectrometer (MERIS) was used to acquire 52 images covering the summers of 2004 to 2011. These images were analyzed to map Chl-a concentrations and the presence of scum using two different band ratio algorithms applied to atmospherically-corrected data. The results identify wind speed as the main driving factor in the surface accumulation of algae, as well as in the spatial distribution of Chl-a. The utility of microwave images was also assessed, as since any cloud cover obviously hampers the use of optical data. Advanced Synthetic Aperture Radar (ASAR) images were collected synchronously with the MERIS data and the normalized radar cross-section (NRCS) signal was corrected for the contribution of wind for the purposes of correlating the results with the MERIS-derived Chl-a concentrations. In general, there was a stepwise decrease in the NRCS for high values of Chl-a (>50mg/m3) with wind speeds in the range of 2 to 6m/s. Under these conditions, our results demonstrate that optical and microwave signals can be used in combination to improve our understanding of cyanobacteria blooming.

Remote sensing of diffuse attenuation coefficient of photosynthetically active radiation in Lake Taihu using MERIS data

Especially for a shallow and extremely turbid lake, light availability in the water column can determine the euphotic zone, and the horizontal and vertical distribution of algae species. Light attenuation is traditionally quantified as the diffuse attenuation coefficient of the photosynthetically available radiation (Kd(PAR)). Global coverage of Kd(PAR) at high spatial and temporal resolution can be provided by satellite measurements, and these data can be used to improve our understanding of the physical, chemical and biological processes in Lake Taihu, a large shallow lake in China, of considerable importance as a source of drinking water for several large cities.The primary dominated contributor to Kd(PAR) was determined firstly using linear regression. There was a significant positive correlation between Kd(PAR) and concentrations of total suspended matter (TSM). The determination coefficient between Kd(PAR) and TSM (R2=0.91) was significantly higher than that between Kd(PAR) and chlorophyll-a concentrations (Chl-a) (R2=0.11), and between Kd(PAR) and absorption of chromophoric dissolved organic matter (CDOM) (R2=0.01). Our results therefore could demonstrate that TSM usually plays a dominant role in the attenuation of light in Lake Taihu.A retrieval model of Kd(PAR) values for Lake Taihu was subsequently developed using top-of-atmosphere (TOA) radiance of Medium Resolution Imaging Spectrometer (MERIS) image data at band 10, which was strongly correlated with in situ Kd(PAR) (R2=0.74, p<0.005, N=48). In contrast, the atmospheric corrected reflectance of MERIS image data was not strongly correlated with in situ Kd(PAR). Thus, atmospheric correction was not a prerequisite for estimations of Kd(PAR) in this highly turbid and hyper-eutrophic lake, and a simple empirical model of the Kd(PAR) estimation for Lake Taihu was effective.With the simple model, the seasonal and spatial distributions of Kd(PAR) in Lake Taihu were studied using the MERIS measurements from 2003 to 2010. Our results show distinct seasonal, spatial, and wind driven, Kd(PAR) values in Lake Taihu. The highest and the lowest Kd(PAR) values were found in summer and in winter, respectively. The increase of Kd(PAR) in summer can be attributed to the phytoplankton blooms, and wind-driven sediment resuspension. Spatially, the Kd(PAR) values were high in the southern part and the lake center, and low Kd(PAR) in East Lake Taihu. Based on the MERIS derived Kd(PAR) values, a significant correlation was found between Kd(PAR) and wind speeds, suggesting a critical role of wind speeds in the Kd(PAR) variations in Lake Taihu.

Estimating Regional Winter Wheat Leaf N Concentration with MERIS by Integrating a Field Observation-Based Model and Histogram Matching

<abstract><title><italic>Abstract.</italic></title> Leaf nitrogen status is a key indicator for evaluating crop growth, yield potential, and grain quality. Non-destructive and rapid assessment of leaf nitrogen is required for improving nitrogen management in wheat production. The Medium-Resolution Imaging Spectrometer (MERIS), one of the payloads on ENVISAT, has an average 20 nm spectral resolution in the red edge region, a 300 m spatial resolution, and a 3-day repeat cycle, making MERIS a potentially valuable sensor for fast measurement and monitoring of crop N status at regional to global scales. The red edge can provide information related directly to the N concentration of vegetation. To develop a model for estimating crop N from field observations, winter wheat (Triticum aestivum L.) leaf nitrogen concentration (LNC) and spectral reflectance data in the North China Plain were collected. A comparison between spectral reflectance, its first derivative, and red edge parameters for estimating wheat LNC found that the red well position (RWP) is the best indicator of LNC (R² = 0.60). A model was developed for LNC estimation with RWP, and the model was calibrated to the satellite imagery using a histogram matching method. A geo-rectified LNC map of Shandong and Henan provinces in the North China Plain was then generated from MERIS data. Validation with independent field observation data showed that the accuracy was higher than 95%, with root mean square error (RMSE) of 0.11 g N g^-1 DW and a coefficient of determination (R²) of 0.90. This research will contribute to improving N fertilizer usage by mapping large-area winter wheat N status.

Estimation of particulate zinc using MERIS data of the Pearl River Estuary

Marine heavy metals in the particulate phase are considered to be undetectable from remote sensing since their minor spectral signal is masked by marine optical constitutes. However, over 80% of heavy metals adsorb to and complex with the suspended sediment and the quantity of adsorbed heavy metals strongly depends on suspended sediment load and size. Thus, the suspended sediment load and size could be useful information to estimate heavy metal levels through remote-sensing techniques. We investigate the potential to derive heavy metal concentration, using zinc as an example. Eight cruises in the Pearl River Estuary were carried out to measure zinc concentrations in the particulate phase (C zn) and remote-sensing reflectance (R rs) data from 2009 to 2010. The relationship of R rs ratio in the near infrared bands and the scattering spectral slope (which indicates particle size distribution (PSD)) is first proposed. According to a previously established suspended sediment algorithm for the study area and the newly proposed relationship of remote-sensing reflectance and suspended sediment size, a piecewise algorithm is developed to retrieve C zn. The accuracies of the algorithm developed here (correlation coefficient (r) = 0.88, mean relative error (MRE) = 24.7% and root mean square error (RMSE) = 5.51) are higher than those algorithms only considering suspended sediment load (r = 0.79, MRE = 35.3% and RMSE = 8.93) or size (r = 0.78, MRE = 30.0% and RMSE = 7.19). The algorithm is applied to Medium Resolution Imaging Spectrometer (MERIS) full resolution data to obtain C zn distribution in the Pearl River Estuary. The C zn from image is mainly in the range from 10 to 63 μg l−1, which is consistent with the in situ data ranges (5.67–86.62 μg l−1).

Regional Algorithms for European Seas: A Case Study Based on MERIS Data

Advances in satellite ocean color technologies and methodologies are expected to lead to the generation of coastal water bio-optical products with accuracies close to those targeted for oceanic regions. In view of contributing to such a progress, multilayer perceptron neural networks complying with standard Medium Resolution Imaging Spectrometer (MERIS) pigment indices were developed relying on regional highly accurate in situ data. This work illustrates and discusses the application to sample MERIS imagery of those neural networks trained to produce pigment indices in seas characterized by increased levels of bio-optical complexity: the Baltic, the Northern Adriatic, and the Western Black Seas.

Remote sensing retrieval of total absorption coefficient in the Bohai Sea

Temporal and spatial patterns of inherent optical properties in the Bohai Sea are very complex. In this paper, we used 77 groups of field data of AOPs (apparent optical properties) and IOPs (inherent optical properties) collected in June, August, and September of 2005 in the Bohai Sea, to retrieve the spectral total absorption coefficient a(λ) with the quasi-analytical algorithm (QAA). For QAA implementation, different bands in the region 680–730 nm (in 5 nm intervals) were selected and compared, to determine the optimal band domain of the reference wavelength. On this basis, we proposed a new algorithm (QAA-Com), a combination of QAA-685 and QAA-715, according to turbidity characterized by a(440). The percentage difference of model retrievals in the visible domain was between 4.5%–45.1%, in average of 18.8% for a(λ). The QAA model was then applied to Medium Resolution Imaging Spectrometer (MERIS) radiometric products, which were temporally and spatially matched with in-situ optical measurements. Differences between MERIS retrievals and in-situ values were in the range 9.2%–27.8% for a(λ) in the visible domain. Major errors in satellite retrieval are attributable to uncertainties of QAA model parameters and in-situ measurements, as well as imperfect atmospheric correction of MERIS data by the European Space Agency (ESA). During a storm surge in April 2009, time series of MERIS images together with the QAA model were used to analyze spatial and temporal variability of the total absorption coefficient pattern in the Bohai Sea. It is necessary to collect more independent field data to improve this algorithm.

Utility of Field Spectroradiometer Data in Chlorophyll-α Estimation

We present here results supporting the use of Medium Resolution Imaging Spectrometer (MERIS)-based Near Infrared-Red algorithms for estimating chlorophyll-a (chl-a) concentration in complex coastal waters. The objective of the study was to test the potential of universal applicability of NIR-Red algorithms, calibrated with (a) radiometric measurements and in situ data from inland waters in Nebraska, (b) MERIS data, acquired over Azov Sea in Russia, and (c) data synthetically generated using a radiative transfer model, to estimate chl-a concentration in the Hudson/Raritan Estuary of New York/New Jersey. We used a set of in situ reflectance and water samples collected in the Hudson/Raritan Estuary of New York/New Jersey for this validation. The NIR-Red algorithms produced consistently accurate estimates of chl-a concentration, ranging from 3.9 mg m -3 to 26.3 mg m 3 , with the root mean square error (RMSE) below 2 mg m -3 . The algorithms do not need re-parameterization and it presents a strong case for the use of NIR-Red algorithms for real-time quantitative monitoring of Hudson/Raritan Estuary, and potentially other inland and coastal waters.

Consistent assimilation of MERIS FAPAR and atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; into a terrestrial vegetation model and interactive mission benefit analysis

Abstract. The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2, the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty reflects uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR (fraction of absorbed photosynthetically active radiation) provided by the MERIS (ESA's Medium Resolution Imaging Spectrometer) sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network. We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial BETHY (Biosphere Energy Transfer Hydrology) model. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. The assimilation improves the match to independent observations. We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions. We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.

Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model

Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of grassland ecosystems. The Medium Resolution Imaging Spectrometer (MERIS) is a promising sensor for measuring and monitoring grasslands due to its high spectral resolution, medium spatial resolution and a two- to three-day repeat cycle. However, thus far the multi-biome MERIS land products have limited consistency with in-situ measurements of leaf area index (LAI), while the multi-biome canopy chlorophyll content (CCC) has not been validated yet with in-situ data. This study proposes a single-biome approach to estimate grassland LAI (a surrogate of grass quantity) and leaf chlorophyll content (LCC) and CCC (surrogates of grass quality) using the inversion of the PROSAIL model and MERIS reflectance. Both multi-biome and single-biome approaches were validated using two-season in-situ data sets and the temporal consistency was analyzed using time-series of MERIS data. The single-biome approach showed a consistently better performance for estimating LAI (R2=0.70, root mean square error (RMSE)=1.02, normalized RMSE (NRMSE)=16%) and CCC (R2=0.61, RMSE=0.36, NRMSE=23%) compared with the multi-biome approach (LAI: R2=0.36, RMSE=1.77, NRMSE=28%; CCC: R2=0.47, RMSE=1.33, NRMSE=84%). However, both single-biome and multi-biome approaches failed to retrieve LCC. The multi-biome LAI was overestimated at lower LAI values (<2) and saturated at higher LAI values (≥4), and the multi-biome CCC was consistently overestimated through the whole data range. Similar temporal trajectories of grassland LAI and CCC estimates were observed using these two approaches, but the multi-biome trajectory consistently produced larger values than the single-biome trajectory. The spatio-temporal variation of grassland LAI and CCC estimated by the single-biome approach was shown to be closely associated with agricultural practices. Our results underline the potential of mapping grassland LAI and CCC using the PROSAIL model and MERIS satellite data.

Variability in specific-absorption properties and their use in a semi-analytical ocean colour algorithm for MERIS in North Sea and Western English Channel Coastal Waters

Coastal areas of the North Sea are commercially important for fishing and tourism, and are subject to the increasingly adverse effects of harmful algal blooms, eutrophication and climate change. Monitoring phytoplankton in these areas using Ocean Colour Remote Sensing is hampered by the high spatial and temporal variations in absorption and scattering properties. In this paper we demonstrate a clustering method based on specific-absorption properties that gives accurate water quality products from the Medium Resolution Imaging Spectrometer (MERIS). A total of 468 measurements of Chlorophyll a (Chla), Total Suspended Material (TSM), specific- (sIOP) and inherent optical properties (IOP) were measured in the North Sea between April 1999 and September 2004. Chla varied from 0.2 to 35mgm−3, TSM from 0.2 to 75gm−3 and absorption properties of coloured dissolved organic material at 442nm (aCDOM(442)) was 0.02 to 0.26m−1. The variation in absorption properties of phytoplankton (aph) and non-algal particles (aNAP) were an order of magnitude greater than that for aph normalized to Chla (aph*) and aNAP normalized to TSM (aNAP*). Hierarchical cluster analysis on aph*, aNAP* and aCDOM reduced this large data set to three groups of high aNAP*–aCDOM, low aph* situated close to the coast, medium values further offshore and low aNAP*–aCDOM, high aph* in open ocean and Dutch coastal waters. The median sIOP of each cluster were used to parameterize a semi-analytical algorithm to retrieve concentrations of Chla, TSM and aCDOM(442) from MERIS data. A further 60 measurements of normalized water leaving radiance (nLw), Chla, TSM, aCDOM(442) and aNAP(442) collected between 2003 and 2006 were used to assess the accuracy of the satellite products. The regionalized MERIS algorithm showed improved performance in Chla and aCDOM(442) estimates with relative percentage differences of 29 and 8% compared to 34 and 134% for standard MERIS Chla and adg(442) products, and similar retrieval for TSM at concentrations >1g−3.

Atmospheric correction of ENVISAT/MERIS data over case II waters: the use of black pixel assumption in oxygen and water vapour absorption bands

The Medium Resolution Imaging Spectrometer (MERIS) sensor, with its good physical design, can provide excellent data for water colour monitoring. However, owing to the shortage of shortwave-infrared (SWIR) bands, the traditional near-infrared (NIR)–SWIR algorithm for atmospheric correction in inland turbid case II waters cannot be extended to the MERIS data directly, which limits its applications. In this study, we developed a modified NIR black pixel method for atmospheric correction of MERIS data in inland turbid case II waters. In the new method, two special NIR bands provided by MERIS data, an oxygen absorption band (O2 A-band, 761 nm) and a water vapour absorption band (vapour A-band, 900 nm), were introduced to keep the assumption of zero water-leaving reflectance valid according to the fact that both atmospheric transmittance and water-leaving reflectance are very small at these two bands. After addressing the aerosol wavelength dependence for the cases of single- and multiple-scattering conditions, we further validated the new method in two case lakes (Lake Dianchi in China and Lake Kasumigaura in Japan) by comparing the results with in situ measurements and other atmospheric correction algorithms, including Self-Contained Atmospheric Parameters Estimation for MERIS data (SCAPE-M) and the Basic ERS (European Remote Sensing Satellite) & ENVISAT (Environmental Satellite) (A)ATSR ((Advanced) Along-Track Scanning Radiometer) and MERIS (BEAM) processor. We found that the proposed method had acceptable accuracy in the bands within 560–754 nm (MERIS bands 5–10) (average absolute deviation (AAD) = 0.0081, average deviation (AD) = 0.0074), which are commonly used in the estimation models of chlorophyll-a (chl-a) concentrations. In addition, the performance of the new method was superior to that of the BEAM processor and only slightly worse than that of SCAPE-M in these bands. Considering its acceptable accuracy and simplicity both in principle and at implementation compared with the SCAPE-M method, the new method provides an option for atmospheric correction of MERIS data in inland turbid case II waters with applications aiming for chl-a estimation.

Validation of MERIS water quality products in Murchison Bay, Lake Victoria – Preliminary results

As a step forward to validating the retrieval algorithm associated with the MERIS (Medium Resolution Imaging Spectrometer) sensor onboard the European Space Agency's ENVISAT satellite, we compare scaling factors for (442) (pigment absorption coefficient at 442 nm) versus chl-a concentration, and for (scattering coefficient for particles at 440 nm) versus TSM (total suspended matter) concentration, as well as in situ measurements of chl-a and TSM concentrations in Murchison Bay, Lake Victoria, with MERIS level 2 products. Our comparisons show that MERIS chl-a data products underestimate the in situ chl-a concentration by a factor of about 0.6, and that MERIS level 2 products overestimate the in situ TSM concentration and the yellow substance absorption coefficient by a factor of about 2. Application of a local inherent optical property (IOP) scaling factor improves the retrieval provided by MERIS level 2 products. The MERIS level 2 products also underestimate the water-leaving reflectance. These discrepancies between retrievals from MERIS data and in situ measurements are due to the current imperfect atmospheric correction algorithm for inland waters. Therefore, a need arises to develop site-specific bio-optical models and to improve existing MERIS retrieval algorithms.

Remote sensing of water quality parameters over Alqueva Reservoir in the south of Portugal

In this study, the potential of MEdium Resolution Imaging Spectrometer (MERIS) to describe variations of optically active substances over Alqueva artificial lake is investigated. Limnological laboratory analyses of the water samples collected monthly, from 2003 to 2006, are used in combination with MERIS. The water surface spectral reflectance is derived from Level1b MERIS data, using radiative transfer calculations to account for the atmospheric effects. The lake water spectral surface reflectance is combined with laboratory analyses of cyanobacteria total densities as well as chlorophyll a concentrations and empirical algorithms for both quantities are derived. The results obtained are compared with independent laboratory analyses from 2007, with good correlation coefficients obtained both for cyanobacteria (R = 0.93) and chlorophyll a(R = 0.80). The methodology proposed here has been developed to inexpensively monitor Alqueva Reservoir water quality in terms of cyanobacteria and chlorophyll a on a regular basis, and to provide useful information to the authorities.

Assessing remotely sensed chlorophyll-a for the implementation of the Water Framework Directive in European perialpine lakes

The lakes of the European perialpine region constitute a large water reservoir, which is threatened by the anthropogenic pressure altering water quality. The Water Framework Directive of the European Commission aims to protect water resources and monitoring is seen as an essential step for achieving this goal. Remote sensing can provide frequent data for large scale studies of water quality parameters such as chlorophyll-a (chl-a). In this work we use a dataset of maps of chl-a derived from over 200 MERIS (MEdium Resolution Imaging Spectrometer) satellite images for comparing water quality of 12 perialpine lakes in the period 2003–2009. Besides the different trophic levels of the lakes, results confirm that the seasonal variability of chl-a concentration is particularly pronounced during spring and autumn especially for the more eutrophic lakes. We show that relying on only one sample for the assessment of lake water quality during the season might lead to misleading results and erroneous assignments to quality classes. Time series MERIS data represents a suitable and cost-effective technology to fill this gap, depicting the dynamics of the surface waters of lakes in agreement with the evolution of natural phenomena.

Using MERIS fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes

In this paper we evaluate the potential of ENVISAT–Medium Resolution Imaging Spectrometer (MERIS) fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes. A series of MERIS fused images (15 spectral bands; 25 m pixel size) is created using the linear mixing model and a Landsat Thematic Mapper (TM) image acquired over the Netherlands. First, the fused images are classified to produce a map of the eight main land-cover types of the Netherlands. Subsequently, the maps are validated using the Dutch land-cover/land-use database as a reference. Then, the fused image with the highest overall classification accuracy is selected as the best fused image. Finally, the best fused image is used to compute three vegetation indices: the normalized difference vegetation index (NDVI) and two indices specifically designed to monitor vegetation status using MERIS data: the MERIS terrestrial chlorophyll index (MTCI) and the MERIS global vegetation index (MGVI). Results indicate that the selected data fusion approach is able to downscale MERIS data to a Landsat-like spatial resolution. The spectral information in the fused images originates fully from MERIS and is not influenced by the TM data. Classification results for the TM and for the best fused image are similar and, when comparing spectrally similar images (i.e. TM with no short-wave infrared bands), the results of the fused image outperform those of TM. With respect to the vegetation indices, a good correlation was found between the NDVI computed from TM and from the best fused image (in spite of the spectral differences between these two sensors). In addition, results show the potential of using MERIS vegetation indices computed from fused images to monitor individual fields. This is not possible using the original MERIS full resolution image. Therefore, we conclude that MERIS–TM fused images are very useful to map heterogeneous landscapes.

Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables

Remote estimation of water constituent concentrations in case II waters has been a great challenge, primarily due to the complex interactions among the phytoplankton, tripton, colored dissolved organic matter (CDOM) and pure water. Semi-analytical algorithms for estimating constituent concentrations are effective and easy to implement, but two challenges remain. First, a dataset without a sampling bias is needed to calibrate estimation models; and second, the semi-analytical indices were developed based on several specific assumptions that may not be universally applicable. In this study, a semi-analytical model-optimizing and look-up-table (SAMO-LUT) method was proposed to address these two challenges. The SAMO-LUT method is based on three previous semi-analytical models to estimate chlorophyll a, tripton and CDOM. Look-up tables and an iterative searching strategy were used to obtain the most appropriate parameters in the models. Three datasets (i.e., noise-free simulation data, in situ data and Medium Resolution Imaging Spectrometer (MERIS) satellite data) were collected to validate the performance of the proposed method. The results show that the SAMO-LUT method yields error-free results for the ideal simulation dataset; and is able also to accurately estimate the water constituent concentrations with an average bias (mean normalized bias, MNB) lower than 9% and relative random uncertainty (normalized root mean square error, NRMS) lower than 34% even for in situ and MERIS data. These results demonstrate the potential of the proposed algorithm to accurately monitor inland and coastal waters based on satellite observations.

Recognizing harmful algal bloom based on remote sensing reflectance band ratio

We present a band ratio algorithm based on remote sensing reflectance (RRS) data to detect an algal bloom composed of cyanobacteria (Planktothrix spp.) and chrysophytes in Lake Idro, a small meso-eutrophic lake situated in the subalpine region (northern Italy). The bloom started around the first week of September 2010 and persisted for about 1 month, with highest mean chlorophyll-a concentrations (17.5 ± 1.6 mgm−3) and phytoplankton cellular density (7,250,000 cell·l−1) measured on September 14, 2010. RRS data obtained from in situ measurements were first investigated to select the diagnostic wavelengths (i.e., 560 and 620 nm) of both phycoerythrin (present in the Planktothrix spp.) and other pigments (e.g., fucoxanthin, common to several species of chrysophyte). Testing the algorithm on RRS data derived from atmospherically corrected image data showed the ability of the medium resolution imaging spectrometer (MERIS) to detect the bloom also. The results demonstrate that a combination of in situ and MERIS data is a valuable tool to monitor the extent and duration of phytoplankton blooms.

Medium resolution imaging spectrometer data for monitoring tropical coastal waters: a case study of Berau estuary, East Kalimantan, Indonesia

This article investigates the performance of MERIS reduced resolution data to monitor water quality parameters in the Berau estuary waters, Indonesia. Total suspended matter (TSM), Chlorophyll-a (Chl-a) concentration and diffuse attenuation coefficient (Kd ) were derived from MERIS data using three different algorithms for coastal waters: standard global processor (MERIS L2), C2R and FUB. The outcomes were compared to in situ measurements collected in 2007. MERIS data processed with C2R gave the best retrieval of Chl-a, while MERIS L2 performed the best for TSM retrieval, but large deviations from in situ data were observed, pointing at inversion problems over these tropical waters for all standard processors. Nevertheless, MERIS can be of use for monitoring equatorial coastal waters like the Berau estuary and reef system. Applying a Kd (490) local algorithm to the MERIS RR data over the study area showed a sufficient good correlation to the in situ measurements (R 2 = 0.77).