MERIS Imagery Research Articles

Cyanobacterial Bloom Phenology in Green Bay Using MERIS Satellite Data and Comparisons with Western Lake Erie and Saginaw Bay

Cyanobacteria blooms have been reported to be increasing worldwide. In addition to potentially causing major economic and ecological damage, these blooms can threaten human health. Furthermore, these blooms can be exacerbated by a warming climate. One approach to monitoring and modeling cyanobacterial biomass is to use processed satellite imagery to obtain long-term data sets. In this paper, an existing algorithm for estimating cyanobacterial biomass previously developed for MERIS is validated for Green Bay using cyanobacteria biovolume estimates obtained from field samples. Once the algorithm was validated, the existing MERIS imagery was used to determine the bloom phenology of the cyanobacterial biomass in Green Bay. Modeled datasets of heat flux (as a proxy for stratification), wind speed, water temperature, and gelbstoff absorption along with in situ river discharge data were used to separate bloom seasons in Green Bay from bloom seasons in nearby cyanobacteria bloom hotspots including western Lake Erie and Saginaw Bay. Of the ten-year MERIS dataset used here, the highest five years were considered “high bloom” years, and the lowest five years from biomass were considered “low bloom” years and these definitions were used to separate Green Bay. Green Bay had a strong relationship with gelbstoff absorption making it unique among the water bodies, while western Lake Erie responded strongly with river discharge as previously reported. Saginaw Bay, which has low interannual bloom variability, did not exhibit a largely influential single parameter.

Sensitivity of a Satellite Algorithm for Harmful Algal Bloom Discrimination to the Use of Laboratory Bio-optical Data for Training

Early detection of dense harmful algal blooms (HABs) is possible using ocean colour remote sensing. Some algorithms require a training dataset, usually constructed from satellite images with a priori knowledge of the existence of the bloom. This approach can be limited if there is a lack of in situ observations, coincident with satellite images. A laboratory experiment collected biological and bio-optical data from a culture of Karenia mikimotoi, a harmful phytoplankton dinoflagellate. These data showed characteristic signals in chlorophyll-specific absorption and backscattering coefficients. The bio-optical data from the culture and a bio-optical model were used to construct a training dataset for an existing statistical classifier. MERIS imagery over the European continental shelf were processed with the classifier using different training datasets. The differences in positive rates of detection of K. mikimotoi between using an algorithm trained with purely manually selected areas on satellite images and using laboratory data as training was overall <1%. The difference was higher, <15%, when using modeled optical data rather than laboratory data, with potential for improvement if local average chlorophyll concentrations are used. Using a laboratory-derived training dataset improved the ability of the algorithm to distinguish high turbidity from high chlorophyll concentrations. However, additional in situ observations of non-harmful high chlorophyll blooms in the area would improve testing of the ability to distinguish harmful from non-harmful high chlorophyll blooms. This approach can be expanded to use additional wavelengths, different satellite sensors and different phytoplankton genera.

Benthic classification and IOP retrievals in shallow water environments using MERIS imagery

Deriving inherent optical properties (IOPs) from multispectral imagery of shallow water environments using physics-based inversion models require prior knowledge of the spectral reflectance of the bottom substrate. The use of an incorrect bottom reflectance adversely affects the IOPs and, in part, the depth derived from inversion models. To date, an operational approach that determines the bottom reflectance from multispectral imagery is lacking; development in this area is especially paramount for locations that exhibit temporal variability in the spatial distributions of submerged aquatic vegetation and benthic microalgae. In this work, we develop a multispectral implementation of the HOPE-LUT algorithm (Hyperspectral Optimization Processing Exemplar with benthic Look Up Table), and apply the approach to MERIS imagery of the Great Bahama Bank (GBB). Overall benthic classification accuracy of this approach was 80.0%, revealing the areal coverage of benthic flora can range from 1052.3 km2 to 6169.3 km2 between years in the Exumas, GBB. Comparison of HOPE-LUT IOP retrievals to common inversion model implementations (particularly HOPE, with its default sand endmember) shows that using an incorrect bottom reflectance can lead to over-estimations in aphy(443) (absorption coefficient of phytoplankton at 443 nm), of up to 95%, under-estimations of adg(443) (absorption coefficient of detritus and gelbstoff) up to 50%, and over-estimations of depth up to 20%. In addition, the HOPE-LUT parameterizations generate IOPs within the range of those measured in situ. We demonstrate that, at the scale of a MERIS pixel, the dominant substrates of seagrass, unattached bottom macroalgae and benthic microalgae are spectrally unresolvable at the depths that these classes occur in the GBB. Lastly, we evaluate the performance of commonly used atmospheric corrections algorithms for bathymetry estimation and benthic classification accuracy. The combined benthic classification and inversion scheme presented here is autonomous, i.e., it does not require scene-specific thresholds or modifications. Thus, it should be portable to Sentinel 3 OLCI and potentially MODIS Aqua imagery to obtain a continuous time series of changes in IOPs and benthic cover for the shallow waters over the Great Bahama Bank.

Retrieving Crop Leaf Chlorophyll Content Using an Improved Look-Up-Table Approach by Combining Multiple Canopy Structures and Soil Backgrounds

Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval consist of statistically- and physically-based models. The physical approaches for LCC estimation require the use of radiative transfer models (RTMs), which are more robust and transferrable than empirical models. However, the operational retrieval of LCC at large scales is affected by the large variability in canopy structures and soil backgrounds. In this study, we proposed an improved look-up-table (LUT) approach to retrieve LCC by combining multiple canopy structures and soil backgrounds to deal with the ill-posed inversion problem caused by the lack of prior knowledge on canopy structure and soil-background reflectance. Firstly, the PROSAIL-D model was used to simulate canopy spectra with diverse imaging gometrics, canopy structures, soil backgrounds and leaf biochemical contents, and the canopy spectra were resampled according to the spectral response functions of ENVISAT MERIS and Sentinel-3 OLCI instruments. Then, an LUT that included 25 sub-LUTs corresponding to five types of canopy structure and five types of soil background was generated for LCC estimation. The mean of the best eight solutions, rather than the single best solution with the smallest RMSE value, was selected as the retrieval of each sub-LUT. The final inversion result was obtained by calculating the mean value of the 25 sub-LUTs. Finally, the improved LUT approach was tested using simulations, field measurements and ENVISAT MERIS satellite data. A simulation using spectral bands from the MERIS and Sentinel-3 OLCI simulation datasets yielded an R2 value of 0.81 and an RMSE value of 10.1 μg cm−2. Validation performed well with field-measured canopy spectra and MERIS imagery giving RMSE values of 9.9 μg cm−2 for wheat and 9.6 μg cm−2 for soybean using canopy spectra and 8.6 μg cm−2 for soybean using MERIS data. The comparison with traditional chlorophyll-sensitive indices showed that our improved LUT approach gave the best performance for all cases. Therefore, these promising results are directly applicable to the use of ENVISAT MERIS and Sentinel-3 OLCI data for monitoring of crop LCC at a regional or global scale.

FRY, a global database of fire patch functional traits derived from space-borne burned area products

Vegetation fires are intrinsic ecosystem disturbances of the Earth system. Global burned area products have been delivered from several space-borne instruments, and have recently provided pixel-level information underpinning fire spread processes. Here we present FRY, a global database of fire patches with morphology-based functional traits reconstructed from pixel-based burned areas derived from the MODIS and MERIS imagery using a flood-fill algorithm. Each fire patch is characterized by the geo-location of its center, area, perimeter, the features of the ellipse fitted over its pixel’s spatial distribution, and different indices of patch complexity. We obtained a consistent spatial distribution of global fire patch functional traits between the MCD64A1 Collection 6 and the MERIS fire_cci v4.1 datasets during their overlap period (2005-2011), confirming the robustness of the applied algorithm and the consistency between both products. This database is relevant to a broad spectrum of fire-related applications such as local to global functional pyrodiversity, fire emissions quantification, and the benchmarking of fire modules embedded in dynamic global vegetation models.

An Optical Classification Tool for Global Lake Waters

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.

Satellite assessment of particulate matter and phytoplankton variations in the Santa Barbara Channel and its surrounding waters: Role of surface waves

AbstractSatellite observations of chlorophyll in coastal waters are often described in terms of changes in productivity in response to regional upwelling processes while optical backscattering coefficients are more often linked to episodic inputs of suspended sediments from storm runoff. Here we show that the surface gravity wave resuspension of sediments has a larger role in controlling backscatter than previously considered. Almost 18 years of SeaWiFS, MODIS, MERIS, and VIIRS satellite imagery of the Santa Barbara Channel, California and its surrounding waters spectrally merged with the Garver‐Siegel‐Maritorena bio‐optical model were used to assess the controls on suspended particle distributions. Analysis revealed that chlorophyll blooms in the warmer portions of the domain occur in phase with SST minima, usually in early spring, while blooms in the cooler regions lag SST minima and occur simultaneously to the strongest equatorward winds every year, often in the summer. Tight coupling between the optical variables was seen in offshore areas, as expected for productive waters. However, values of backscatter near the coast were primarily modulated by surface waves. This relationship holds throughout all seasons and is stronger within the 100 m isobath, but often extends tens of kilometers offshore. This forcing of particle resuspension by surface waves is likely a feature ubiquitous in all coastal oceans characterized by fine sediments. The implication of surface wave processes determining suspended particle loads far beyond the surf zone has large consequences for the interpretation of satellite ocean color signals in coastal waters and potentially redefines the extent of the littoral zone.

Global burned area mapping from ENVISAT-MERIS and MODIS active fire data

We present the development of a global burned area (BA) algorithm based on MERIS imagery along with the assessment of the global BA results for three years (2006–2008). This work was developed within the Fire Disturbance project under the European Space Agency Climate Change Initiative programme, which aimed to generate long-term BA information for climate models. Our algorithm combined temporal series of MERIS reflectances with thermal information from MODIS HS (hotspot) product. The algorithm included two-steps. Firstly, cumulative distribution functions were computed to discriminate the most clearly burned pixels using regionally-oriented near infrared reflectance thresholds. Secondly, a contextual criterion improved the spatial detection of the burned patches from the seed pixels. BA estimates for the three target years range from 3.6 to 3.8million km2. Results were validated from a statistically designed sample of fire perimeters generated from Landsat multi-temporal imagery. Intercomparison with existing BA products was also carried out. Results from global validation datasets provided an average overall accuracy higher than 0.95. The accuracy of the BA category was lower than the accuracy of the unburned one. Within the former, average omission and commission errors were lower for areas where the proportion of burned area was higher (for pixel-based error matrices, commission error (CE) was 0.52 and omission error (OE) was 0.51), than for those areas with very low BA proportion (CE=0.60 and OE=0.74). In terms of total BA estimation, errors were generally well balanced, with a tendency towards underestimation (34%). Relevant impact of temporal reporting accuracy was found for these validation datasets. Intercomparison with other existing BA datasets pointed out similar spatial and temporal trends, with high correlation with GFED v4 BA estimations for the three years (r2>0.974).

A new method to generate a high-resolution global distribution map of lake chlorophyll

A new method was developed, evaluated, and applied to generate a global dataset of growing-season chlorophyll-a (chl) concentrations in 2011 for freshwater lakes. Chl observations from freshwater lakes are valuable for estimating lake productivity as well as assessing the role that these lakes play in carbon budgets. The standard 4 km NASA OceanColor L3 chlorophyll concentration products generated from MODIS and MERIS sensor data are not sufficiently representative of global chl values because these can only resolve larger lakes, which generally have lower chl concentrations than lakes of smaller surface area. Our new methodology utilizes the 300 m-resolution MERIS full-resolution full-swath (FRS) global dataset as input and does not rely on the land mask used to generate standard NASA products, which masks many lakes that are otherwise resolvable in MERIS imagery. The new method produced chl concentration values for 78,938 and 1,074 lakes in the northern and southern hemispheres, respectively. The mean chl for lakes visible in the MERIS composite was 19.2 ± 19.2, the median was 13.3, and the interquartile range was 3.90–28.6 mg m−3. The accuracy of the MERIS-derived values was assessed by comparison with temporally near-coincident and globally distributed in situ measurements from the literature (n = 185, RMSE = 9.39, R2 = 0.72). This represents the first global-scale dataset of satellite-derived chl estimates for medium to large lakes.

Estimation of cyanobacteria biovolume in water reservoirs by MERIS sensor

Planktonic cyanobacteria primarily develop in lentic water bodies, such as lakes and water reservoirs. In certain instances, toxin-producing cyanobacterial populations might dominate the phytoplankton community. Satellite remote sensing is a useful tool for large spatial scale monitoring of cyanobacteria, and the MERIS sensor from the Envisat satellite has taken worldwide images at a high frequency for over 10 years. This short time lapse image collection has provided an extensive record of images for the analysis of variation in the cyanobacterial communities in water reservoirs for management and scientific purposes. The objective of this work is to determine the relationship between measured cyanobacterial biomass as biovolume and the estimations derived from MERIS imagery. This study encompasses two independent studies relying on data from 23 water reservoirs. First, a long-term global limnological research study was conducted that provided a field data collection that included cyanobacterial biovolume, among other variables. Second, a survey was conducted that applied the processed images derived from the Envisat MERIS sensor. The chlorophyll-a (Chl a) content and phycocyanin concentration (PC) were estimated from the MERIS images. The PC estimated by remote sensing and total cyanobacterial biovolume measured from the field samples were found to be significantly correlated (R2 = 0.6219; p < 0.001). No relevant differences were found among the taxonomical groups, which indicated that this tool provides accurate estimations irrespective of the cyanobacterial group. For validation, the algorithm derived from the entire dataset was applied to the MERIS image dataset of the Rosarito reservoir. An estimated cyanobacterial biovolume time series was performed and compared to the biovolume data collected in an extensive sampling schedule spanning 4 years. The results indicated a strong correlation (R2 = 0.72; p < 0.001) between the measured and estimated data acquired on the same day.

Multi-scale strategies for the monitoring of freshwater cyanobacteria: Reducing the sources of uncertainty

Cyanobacterial blooms are a frequent phenomenon in eutrophic freshwaters worldwide and are considered potential hazards to ecosystems and human health. Monitoring strategies based on conventional sampling often fail to cover the marked spatial and temporal variations in cyanobacterial distribution and fluctuating toxin concentrations inherent to cyanobacterial blooms. To deal with these problems, we employed a multi-scale approach for the study of a massive Microcystis bloom in Tajo River (Spain) utilizing 1) remote sensing techniques, 2) conventional water sampling and 3) analysis of chemotypical subpopulations. Tajo River at the study area is influenced by high temperatures waters diverted upstream from a nuclear power plant, the presence of a dam downstream and a high nutrient load, which provide optimal conditions for massive cyanobacterial proliferation. MERIS imagery revealed high Chl-a concentrations that rarely fell below 20 μg L−1 and moderate spatiotemporal variations throughout the study period (March–November 2009). Although the phytoplanktonic community was generally dominated by Microcystis, sampling points highly differed in cyanobacterial abundance and community composition. Microcystin (MC) concentrations were highly heterogeneous, varying up to 3 orders of magnitude among sampling points, exceeding in some cases WHO guideline values for drinking and also for recreational waters. The analysis of single colonies by MALDI-TOF MS revealed differences in the proportion of MC-producing colonies among points. The proportion of toxic colonies showed a highly significant linear correlation with total MC: biovolume ratio (r2 = 0.9; p < 0.001), evidencing that the variability in toxin concentrations can be efficiently addressed by simple analysis of subpopulations. We propose implementing a multi-scale monitoring strategy that allows covering the spatiotemporal heterogeneities in both cyanobacterial distribution (remote sensing) and MC concentrations (subpopulation analysis) and thereby reduce the main sources of uncertainty in the assessment of the risks associated to bloom events.

Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters — The Azov Sea case study

We present here results that strongly support the use of MERIS-based NIR-red algorithms as standard tools for estimating chlorophyll-a (chl-a) concentration in turbid productive waters. The study was carried out as one of the steps in testing the potential of the universal applicability of previously developed NIR-red algorithms, which were earlier calibrated using a limited set of MERIS imagery and in situ data from the Azov Sea and the Taganrog Bay, Russia, and data that were synthetically generated using a radiative transfer model. We used an extensive set of MERIS imagery and in situ data collected over a period of three years in the Azov Sea and the Taganrog Bay for this validation task. We found that the two-band and three-band NIR-red algorithms gave consistently highly accurate estimates of chl-a concentration, with a mean absolute error of 4.32mgm−3 and 4.71mgm−3, respectively, and a root mean square error as low as 5.92mgm−3, for data with chl-a concentrations ranging from 1.09mgm−3 to 107.82mgm−3. This obviates the need for case-specific reparameterization of the algorithms, as long as the specific absorption coefficient of phytoplankton in the water does not change drastically, and presents a strong case for the use of NIR-red algorithms as standard algorithms that can be routinely applied for near-real-time quantitative monitoring of chl-a concentration in the Azov Sea and the Taganrog Bay, and potentially elsewhere, which will be a real boon to ecologists, natural resource managers and environmental decision-makers.

Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors

A generalized coccolithophore bloom classifier has been developed for use with ocean color imagery. The bloom classifier was developed using extracted satellite reflectance data from SeaWiFS images screened by the default bloom detection mask. In the current application, we extend the optical water type (OWT) classification scheme by adding a new coccolithophore bloom class formed from these extracted reflectances. Based on an in situ coccolithophore data set from the North Atlantic, the detection levels with the new scheme were between 1,500 and 1,800 coccolithophore cells/mL and 43,000 and 78,000 liths/mL. The detected bloom area using the OWT method was an average of 1.75 times greater than the default bloom detector based on a collection of SeaWiFS 1 km imagery. The versatility of the scheme is shown with SeaWiFS, MODIS Aqua, CZCS and MERIS imagery at the 1 km scale. The OWT scheme was applied to the daily global SeaWiFS imagery mission data set (years 1997–2010). Based on our results, average annual coccolithophore bloom area was more than two times greater in the southern hemisphere compared to the northern hemisphere with values of 2.00 × 10 6 km 2 and 0.75 × 10 6 km 2, respectively. The new algorithm detects larger bloom areas in the Southern Ocean compared to the default algorithm, and our revised global annual average of 2.75 × 10 6 km 2 is dominated by contributions from the Southern Ocean.

Detection and tracking of oil slicks on sun-glittered visible and near infrared satellite imagery

The use of the visible and near infrared (VNIR) bands of MODIS and MERIS imaging sensors acquired in sunglint conditions to reveal smoothed regions such as those affected by oil pollution is investigated. The underlying physical mechanism that enables oil slick detection is based on the modification of the surface slope distribution composing the wind-roughened sea due to the action of mineral oils. The role of sunglint as the chief mechanism that allows the imaging of oil slick features with VNIR wavelengths is assessed for selected case studies in the Mediterranean Sea. The high rate of acquisition and the frequent occurrence of MODIS and MERIS imagery affected by sunglint, especially in low-latitude seas, can thus significantly contribute to increase the actual oil slick detection capability offered by synthetic aperture radar (SAR) systems. We also show how the combined observations from any of the microwave and optical sensors permit the slick to be followed during its movement. Finally, a simulation study specific to the Mediterranean Sea was carried out in order to demonstrate the feasibility of such an approach supporting SAR observations.

Object‐oriented and textural image classification of the Siberia GBFM radar mosaic combined with MERIS imagery for continental scale land cover mapping

Boreal forests and wetlands play an important role in the climate system, in particular through biosphere–atmosphere flux exchanges. They are an important pool of carbon and their role as sink or source of greenhouse gases is not fully understood. Accurate mapping of the vegetation of Siberia can therefore contribute to a better understanding of these processes at regional scale and of their effects on the climate through regional biosphere modeling. The potential of the combination of radar data with medium‐resolution optical data to obtain regional‐scale land cover mapping is investigated using multi‐spectral imagery from the MERIS sensor at 300 m resolution and a high resolution radar mosaic (pixel spacing of 100 m) covering Western and Eastern Siberia compiled in the framework of the Global Boreal Forest Mapping project. For this purpose, capabilities of oriented‐object image analysis associated to wavelet multi‐resolution techniques are investigated. Results show that wavelet multi‐resolution textures bring relevant additional information for land cover classification. Suggestions are made for the implementation of an object‐based wavelet multi‐resolution texture estimator.