Medium Resolution Imaging Spectrometer Research Articles

Spatio-temporal dynamics of inherent optical properties in oligotrophic northern Gulf of Mexico estuaries

Coastal and estuarine ecosystems provide numerous economic and environmental benefits to society. However, increasing anthropogenic activities and developmental pressure may stress these areas and hamper their ecosystem services. Satellite remote sensing could be used as a tool for monitoring water quality parameters, including inherent optical properties (IOP) in coastal regions. Spatio-temporal information on IOP variability will help in understanding the dynamics of the water quality of estuaries. The objective of this research was to develop a novel hybrid model by combining and parameterizing existing quasi analytical and semi-analytical algorithms to estimate IOPs in four oligotrophic northern Gulf of Mexico Florida estuaries. The hybrid model was applied to above surface remote sensing reflectance data representing the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3's Ocean and Land Colour Instrument (OCLI) bands. The hybrid model produced a root means squared error (RMSE) of 0.32 m−1 (13.95% NRMSE) for total absorption (at), 0.21 m−1 (7.61% NRMSE) for detritus-gelbstoff absorption (adg), and 0.09 m−1 (22.77% NRMSE) for phytoplankton pigment absorption (aphi). Results showed that absorption by detritus and gelbstoff (adg) dominates the water in these estuaries. Monthly IOP variability in 2010 revealed that compared to other estuaries, magnitudes of IOPs was the highest in Pensacola Bay and therefore the highest attenuation. Findings also indicated that river discharge and precipitation predominantly govern the IOP variations in all four estuaries, showing an increase in IOP values following the high flow period. The hybrid model improved IOP retrieval in these low chlorophyll-a (Chl-a) estuaries where the existing spectral decomposition models did not perform satisfactorily.

Estimación de la cosecha de trigo en Andalucía usando series temporales de MERIS Terrestrial Chlorophyll Index (MTCI)

<p>There is a relationship between net primary production of wheat and vegetation indices obtained from satellite imaging. Most wheat production studies use the Normalised Difference Vegetation Index (NDVI) to estimate the production and yield of wheat and other crops. On the one hand, few studies use the MERIS Terrestrial Chlorophyll Index (MTCI) to determine crop yield and production on a regional level. This is possibly due to a lack of continuity of MERIS. On the other hand, the emergence of Sentinel 2 open new possibilities for the research and application of MTCI. This study has built two empirical models to estimate wheat production and yield in Andalusia. To this end, the study used the complete times series (weekly images from 2006–2011) of the MTCI vegetation index from the Medium Resolution Imaging Spectrometer (MERIS) sensor associated with the Andalusian yearbook for agricultural and fishing statistics (AEAP—Anuario de estadísticas agrarias y pesqueras de Andalucía). In order to build these models, the optimal development period for the plant needed to be identified, as did the time-based aggregation of MTCI values using said optimal period as a reference, and relation with the index, with direct observations of production and yield through spatial aggregation using coverage from the Geographic Information System for Agricultural Parcels (SIGPAC—Sistema de información geográfica de parcelas agrícolas) and requests for common agricultural policy (CAP) assistance. The obtained results indicate a significant association between the MTCI index and the production and yield data collected by AEAP at the 95% confidence level (R<sup>2</sup> =0.81 and R<sup>2</sup> =0.57, respectively).</p>

Application of extended full resolution MERIS imagery to assist coastal management of the area adjacent to the Guadalquivir estuary

High concentrations of suspended solids in coastal waters directly affect numerous processes. In the case of the marine region influenced by the Guadalquivir estuary, turbidity is one of the primary factors affecting this complex ecosystem and its adjacent coastal region. Ten-year (2002–2012) ocean color observations from the MEdium Resolution Imaging Spectrometer (MERIS) at 300 m are used to assess the variability of turbidity and chlorophyll-a concentration (Chl) in this region. The combination of high-frequency data from automated sensors in addition to synoptic imagery results in an excellent complement to better study water quality parameters. Turbidity plumes detected using the backscattering characteristics of the surface waters with Remote Sensing Reflectance at wavelength 665 nm (Rrs665) reveal the effects of physical processes governing both the spatial and temporal turbidity distribution. The results exhibit seasonal and interannual fluctuations driven by rainfall, freshwater discharges from Alcalá del Río dam, and the North Atlantic Oscillation index (NAO), with maximum turbidity levels in late autumn and winter. In addition, the temporal 1-month lag of Chl maximum and peak turbidity suggests a coupling between sediment resuspension and blooms, whereas extreme turbidity is limiting light availability for photosynthesis. Furthermore, turbidity varies on fortnightly scales, presenting lower intensity during neap tide and vice versa during spring tide. An asymmetric behavior of plume signals in connection with the semidiurnal tidal cycle is found, with maximum values during low tide. Both fortnightly and semidiurnal regimes appear to be superimposed on the large time-scale variability and occur throughout the year. Chl concentrations are dominated by the fortnightly-driven turbidity features throughout the year with minimum Chl during spring tides. This study evidences the main mechanisms influencing plume patterns and Chl, characterizing for the first time their variability in relation with tidal forcing, and therefore advancing the understanding of sediment dynamics and its relevant impact on the regulation of the pelagic ecosystem of the basin. The information obtained is critical for supporting scientists, stakeholders and ecosystem policy-makers for the challenging sustainable coastal management of the Eastern Gulf of Cádiz and its resources.

MERIS observations of phytoplankton phenology in the Baltic Sea

The historical data from the MEdium Resolution Imaging Spectrometer (MERIS) is an invaluable archive for studying global waters from inland lakes to open oceans. Although the MERIS sensor ceased to operate in April 2012, the data capacities are now re-established through the recently launched Sentinel-3 Ocean and Land Colour Instrument (OLCI). The development of a consistent time series for investigating phytoplankton phenology features is crucial if the potential of MERIS and OLCI data is to be fully exploited for inland water monitoring. This study presents a time series of phytoplankton abundance and bloom spatial extent for the highly eutrophic inland water of the Baltic Sea using the 10-year MERIS archive (2002–2011) and a chlorophyll-a based Summed Positive Peaks (SPP) algorithm. A gradient approach in conjunction with the histogram analysis was used to determine a global threshold from the entire collection of SPP images for identifying phytoplankton blooms. This allows spatio-temporal dynamics of daily bloom coverage, timing, phytoplankton abundance and spatial extent to be investigated for each Baltic basin. Furthermore, a number of meteorological and hydrological variables, including spring excess phosphate, summer sea surface temperature and photosynthetically active radiation, were explored using boosted regression trees and generalised additive models to understand the ecological response of phytoplankton assemblages to environmental perturbations and potential predictor variables of summer blooms. The results indicate that the surface layer excess phosphate available in February and March had paramount importance over all other variables considered in governing summer bloom abundance in the major Baltic basins. This finding allows new insights into the development of early warning systems for summer phytoplankton blooms in the Baltic Sea and elsewhere.

Applicability of Earth Observation chlorophyll-a data in assessment of water status via MERIS — With implications for the use of OLCI sensors

Earth Observation (EO) offers spatially and temporally unique data for generating information required under various environmental regulations for assessing the status of surface waters. These requirements, which are laid down in, for example, European Union directives and the Clean Water Act in the United States, share two core elements with respect to status assessments: 1) the status assessment is done using discrete classes, typically for water bodies, sub-areas or critical sites representative for certain area of interest, and 2) phytoplankton chlorophyll a (chl-a) is one of the main variables considered. We analysed the benefits of using chl-a concentrations derived from EO data for the status assessments specified in the EU Water Framework Directive (WFD). Our study focused especially on EO observations' ability to capture extreme and transient events (such as instances of cyanobacteria blooms) more frequently than the monitoring-station data conventionally employs. The accuracy of EO-based chl-a assessment was studied for, in all, 129 Finnish water bodies in the area of the Baltic Sea, in Northern Europe. Natural conditions in this coastal area – particularly its multitude of small bays, numerous estuaries, and mosaic of islands – impose exceptionally strict requirements for an EO instrument's spatial resolution. The analysis revealed that an instrument with a 300 m resolution, such as the MEdium Resolution Imaging Spectrometer (MERIS) or Ocean and Land Colour Instrument (OLCI), can be used to estimate the water quality in 62% of these water bodies. Processing of MERIS data into chl-a concentrations by means of a FUB inversion model demonstrated good accuracy relative to monitoring stations' measurements for the open-water season in 2003–2011. This extensive dataset showed a 23% difference in modal values between EO- and station-sampling-based chl-a concentrations. The bias in EO chl-a estimates was found to increase with low Secchi disk depth, elevated turbidity, and the presence of intensive phytoplankton blooms. The monitoring-station and EO data showed similar distributions of chl-a observations for a given day and location, a finding that supports the comprehensive use of EO-derived chl-a concentrations in assessment. For determination of a water body's status, the EO data required but also allowed for statistical analysis that differs from what has typically been utilised with sparse measurements from monitoring-station data. The geometric mean or the mode of the EO observations was found to represent the main bulk of the chl-a concentrations well statistically. In contrast, the arithmetic mean of EO observations yields chl-a concentrations that are roughly 1.1–1.6 μg/l higher and hence can lead to over-estimation in the associated status assessment. This paper also presents a new approach applicable for evaluating the validity of EO-based algorithms for any coastal water area requiring assessment. With this quality-grade (QG) method, the EO chl-a estimation accuracy is rated in terms of three grades, with water bodies taken as the evaluation units. For this, the method utilises statistical differences between EO and station-sampling chl-a concentrations and applies background information on optical properties obtained from measurements at routine-monitoring stations. The QG method showed the EO-based chl-a accuracy to suffice for assessing the status of 65% of the coastal water bodies examined. At concentrations representing the threshold for the target of “good status” under the WFD, the EO approach produced 0.6 μg/l higher chl-a values than the stations' sampling did. The MERIS results point to clear benefits of using OLCI-based status assessment throughout the Sentinel-3 era.

Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations

This study evaluated the performances of twenty-nine algorithms that use satellite-based spectral imager data to derive estimates of chlorophyll-a concentrations that, in turn, can be used as an indicator of the general status of algal cell densities and the potential for a harmful algal bloom (HAB). The performance assessment was based on making relative comparisons between two temperate inland lakes: Harsha Lake (7.99 km2) in Southwest Ohio and Taylorsville Lake (11.88 km2) in central Kentucky. Of interest was identifying algorithm-imager combinations that had high correlation with coincident chlorophyll-a surface observations for both lakes, as this suggests portability for regional HAB monitoring. The spectral data utilized to estimate surface water chlorophyll-a concentrations were derived from the airborne Compact Airborne Spectral Imager (CASI) 1500 hyperspectral imager, that was then used to derive synthetic versions of currently operational satellite-based imagers using spatial resampling and spectral binning. The synthetic data mimics the configurations of spectral imagers on current satellites in earth’s orbit including, WorldView-2/3, Sentinel-2, Landsat-8, Moderate-resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS). High correlations were found between the direct measurement and the imagery-estimated chlorophyll-a concentrations at both lakes. The results determined that eleven out of the twenty-nine algorithms were considered portable, with r2 values greater than 0.5 for both lakes. Even though the two lakes are different in terms of background water quality, size and shape, with Taylorsville being generally less impaired, larger, but much narrower throughout, the results support the portability of utilizing a suite of certain algorithms across multiple sensors to detect potential algal blooms through the use of chlorophyll-a as a proxy. Furthermore, the strong performance of the Sentinel-2 algorithms is exceptionally promising, due to the recent launch of the second satellite in the constellation, which will provide higher temporal resolution for temperate inland water bodies. Additionally, scripts were written for the open-source statistical software R that automate much of the spectral data processing steps. This allows for the simultaneous consideration of numerous algorithms across multiple imagers over an expedited time frame for the near real-time monitoring required for detecting algal blooms and mitigating their adverse impacts.

Remote sensing of canopy nitrogen at regional scale in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Abstract. Canopy nitrogen (N) concentration and content are linked to several vegetation processes. Therefore, canopy N concentration is a state variable in global vegetation models with coupled carbon (C) and N cycles. While there are ample C data available to constrain the models, widespread N data are lacking. Remotely sensed vegetation indices have been used to detect canopy N concentration and canopy N content at the local scale in grasslands and forests. Vegetation indices could be a valuable tool to detect canopy N concentration and canopy N content at larger scale. In this paper, we conducted a regional case-study analysis to investigate the relationship between the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) time series from European Space Agency (ESA) Envisat satellite at 1 km spatial resolution and both canopy N concentration (%N) and canopy N content (N g m−2, of ground area) from a Mediterranean forest inventory in the region of Catalonia, in the northeast of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20, 15, 10 and 5 km) and at the original spatial resolution of 1 km. The results at higher spatial resolution (1 km) yielded significant log–linear relationships between MTCI and both canopy N concentration and content: r2 = 0.32 and r2 = 0.17, respectively. We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N concentration was strongest for deciduous broadleaf and mixed plots (r2 = 0.24 and r2 = 0.44, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r2 = 0.19). At the species level, canopy N concentration was strongly related to MTCI for European beech plots (r2 = 0.69). These results present a new perspective on the application of MTCI time series for canopy N detection.

Functional regression on remote sensing data in oceanography

The aim of this study is to propose the use of a functional data analysis approach as an alternative to the classical statistical methods most commonly used in oceanography and water quality management. In particular we consider the prediction of total suspended solids (TSS) based on remote sensing (RS) data. For this purpose several functional linear regression models and classical non-functional regression models are applied to 10 years of RS data obtained from medium resolution imaging spectrometer sensor to predict the TSS concentration in the coastal zone of the Guadalquivir estuary. The results of functional and classical approaches are compared in terms of their mean square prediction error values and the superiority of the functional models is established. A simulation study has been designed in order to support these findings and to determine the best prediction model for the TSS parameter in more general contexts.

Phenotypic Variation of Cold Stress Resistance‐Related Traits of White Clover Populations Naturalized in Patagonian Cold Environments

White clover (Trifolium repens L.) is the main forage legume species in grazing pastures in central, central‐southern, and southern regions of Chile and many other temperate regions of the world. The selection of cold stress resistance genotypes is an important step for breeding cultivars to improve the clover growth curve and extend the grazing season in early spring and autumn. The objective of this work was to phenotypically characterize a population of 192 white clover genotypes selected for cold stress resistance under three contrasting regional winter‐cold conditions (mean daily minimum air temperatures of 2.5, 1.6, and −1.4°C) during the coldest month. An α‐lattice experimental field design was used, with 24 incomplete blocks and two replicates. Morphological, physiological and biochemical traits were evaluated at different dates over three growing seasons from 2013 to 2016. A high degree of variability was found among the genotypes for all the characteristics studied. There was no effect of environment when the experimental sites were considered as a source of variation, but comparison of measurements taken at dates separated by a winter showed that there was a decrease in stolon length and diameter, petiole length, leaflet size, and dry matter accumulation in response to cold conditions. The normalized difference and medium resolution imaging spectrometer terrestrial chlorophyll vegetation indices (NDVI and MTCI, respectively) and water‐soluble carbohydrate concentrations in stolons also varied among genotypes and were affected by winter cold. The high degree of phenotypic variability allows for the use of this population as a genetic source for cold stress resistance in white clover.

Evaluation of Feature Ranking and Regression Methods for Oceanic Chlorophyll-a Estimation

This paper evaluates two alternative regression techniques for oceanic chlorophyll-a (Chl-a) content estimation. One of the investigated methodologies is the recently introduced Gaussian process regression (GPR) model. We explore two feature ranking methods derived for the GPR model, namely sensitivity analysis (SA) and automatic relevance determination (ARD). We also investigate a second regression method, the partial least squares regression (PLSR) for oceanic Chl-a content estimation. Feature relevance in the PLSR model can be accessed through the variable importance in projection (VIP) feature ranking algorithm. This paper thus analyzes three feature ranking models, SA, ARD, and VIP, which are all derived from different fundamental principles, and uses the ranked features as inputs to the GPR and PLSR to assess regression strengths. We compare the regression performances using some common performance measures, and show how the feature ranking methods can be used to find the lowest number of features to estimate oceanic Chl-a content by using the GPR and PLSR models, while still producing comparable performance to the state-of-the-art algorithms. We evaluate the models on a global MEdium Resolution Imaging Spectrometer matchup dataset. Our results show that the GPR model has the best regression performance for most of the input feature sets we used, and our conclusion is this model can favorably be used for Chl-a content retrieval, already with two features, ranked by either the SA or ARD methods.

MITIGATION ATMOSPHERIC EFFECTS IN INTERFEROGRAM WITH USING INTEGRATED MERIS/MODIS DATA AND A CASE STUDY OVER SOUTHERN CALIFORNIA

Abstract. Interferometric synthetic aperture radar(InSAR), as a space geodetictechnology, had been testified a high potential means of earth observation providing a method fordigital elevation model (DEM) and surface deformation monitoring of high precision. However, the accuracy of the interferometric synthetic aperture radar is mainly limited by the effects of atmospheric water vapor. In order to effectively measure topography or surface deformations by synthetic aperture radar interferometry (InSAR), it is necessary to mitigate the effects of atmospheric water vapor on the interferometric signals. This paper analyzed the atmospheric effects on the interferogram quantitatively, and described a result of estimating Precipitable Water Vapor (PWV) from the the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) and the ground-based GPS, compared the MERIS/MODIS PWV with the GPS PWV. Finally, a case study for mitigating atmospheric effects in interferogramusing with using the integration of MERIS and MODIS PWV overSouthern California is given. The result showed that such integration approach benefits removing or reducing the atmospheric phase contribution from the corresponding interferogram, the integrated Zenith Path Delay Difference Maps (ZPDDM) of MERIS and MODIS helps reduce the water vapor effects efficiently, the standard deviation (STD) of interferogram is improved by 23 % after the water vapor correction than the original interferogram.

DUSTFALL EFFECT ON HYPERSPECTRAL INVERSION OF CHLOROPHYLL CONTENT – A LABORATORY EXPERIMENT

Abstract. Dust pollution is serious in many areas of China. It is of great significance to estimate chlorophyll content of vegetation accurately by hyperspectral remote sensing for assessing the vegetation growth status and monitoring the ecological environment in dusty areas. By using selected vegetation indices including Medium Resolution Imaging Spectrometer Terrestrial Chlorophyll Index (MTCI) ，Double Difference Index (DD) and Red Edge Position Index (REP), chlorophyll inversion models were built to study the accuracy of hyperspectral inversion of chlorophyll content based on a laboratory experiment. The results show that: (1) REP exponential model has the most stable accuracy for inversion of chlorophyll content in dusty environment. When dustfall amount is less than 80 g/m2, the inversion accuracy based on REP is stable with the variation of dustfall amount. When dustfall amount is greater than 80 g/m2, the inversion accuracy is slightly fluctuation. (2) Inversion accuracy of DD is worst among three models. (3) MTCI logarithm model has high inversion accuracy when dustfall amount is less than 80 g/m2; When dustfall amount is greater than 80 g/m2, inversion accuracy decreases regularly and inversion accuracy of modified MTCI (mMTCI) increases significantly. The results provide experimental basis and theoretical reference for hyperspectral remote sensing inversion of chlorophyll content.

Satellite observations of sub-mesoscale vortex trains in the western boundary of the South China Sea

Abstract This study uses high resolution MEdium Resolution Imaging Spectrometer (MERIS) satellite data to analyze sub-mesoscale ocean vortex trains (OVTs) in the western boundary of the South China Sea (SCS). Thirteen cases of vortex trains on the lee side of the Phu Quý Island were observed in the boreal summer from May to August of 2005−2010. The average diameter of the vortices is 28.1 ± 13.8 km. The average spacing distance estimated between two consecutive vortices is 66.3 ± 27.8 km. The dynamic parameters of a case observed on July 26, 2009 were extracted using a theoretical model. The aspect ratio is 2.8. The ratio of the propagation velocity of the vortex to the undisturbed flow velocity is 0.73. We use a nested high-resolution numerical model with the finest grid of 500 m, to analyze the dynamics of OVT induced by the local topography in the western SCS (WSCS). The results give a propagation velocity of vortex 0.49 of m s−1 and a mean current velocity of undisturbed flow 0.58 m s−1. Therefore, the ratio is 0.84. The shedding frequency is determined to be 1.14 × 10−5 s−1, corresponding to 1.02 day. The results also suggest that the sub-mesoscale cyclonic vortices along the OVT in the WSCS are characterized by low temperature and high Rossby number. The strong vertical vorticity and associated horizontal velocity shear of an individual vortex brings uplifted cold water on its leading side and warm water on its trailing side, which could drive an enhanced vertical exchange between the surface and oceanic interior.

Seasonal Impact of Adjacency Effects on Ocean Color Radiometry at the AAOT Validation Site

The seasonal impact of adjacency effects (AE) on satellite ocean color data at visible and near-infrared (NIR) wavelengths by the Sea-Viewing Wide Field-of-View Sensor, the Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODISA), the Medium Resolution Imaging Spectrometer, the Ocean and Land Color Instrument, the Operational Land Imager (OLI), and the MultiSpectral Imagery (MSI) was theoretically evaluated at a validation site in the northern Adriatic Sea. The analysis made use of comprehensive simulations accounting for multiple scattering, sea surface roughness, sensor viewing geometry, actual coastline, typical and extreme atmospheric conditions, and the seasonal variability of solar illumination and, land and water optical properties. Results, obtained by relying on the normalization of the radiometric sensitivity of each sensor to the same input radiance, show that the spectral and seasonal impacts of AE considerably vary among sensors. AE significantly exceed the radiometric sensitivity of MSI at its sole blue band in winter, whereas they significantly outdo the noise threshold of OLI and MODISA high-resolution data exclusively in the NIR in summer. Conversely, for all other sensors and for MODISA low-resolution data, AE are particularly significant at NIR bands between March and October and at the blue-green bands in winter.

Comparison of Satellite-Derived Phytoplankton Size Classes Using In-Situ Measurements in the South China Sea

Ocean colour remote sensing is used as a tool to detect phytoplankton size classes (PSCs). In this study, the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) phytoplankton size classes (PSCs) products were compared with in-situ High Performance Liquid Chromatography (HPLC) data for the South China Sea (SCS), collected from August 2006 to September 2011. Four algorithms were evaluated to determine their ability to detect three phytoplankton size classes. Chlorophyll-a (Chl-a) and absorption spectra of phytoplankton (aph(λ)) were also measured to help understand PSC’s algorithm performance. Results show that the three abundance-based approaches performed better than the inherent optical property (IOP)-based approach in the SCS. The size detection of microplankton and picoplankton was generally better than that of nanoplankton. A three-component model was recommended to produce maps of surface PSCs in the SCS. For the IOP-based approach, satellite retrievals of inherent optical properties and the PSCs algorithm both have impacts on inversion accuracy. However, for abundance-based approaches, the selection of the PSCs algorithm seems to be more critical, owing to low uncertainty in satellite Chl-a input data

On the continuity of quantifying floating algae of the Central West Atlantic between MODIS and VIIRS

ABSTRACTStudying abundance and distributions of floating macroalgae such as pelagic Sargassum calls for long-term continuous and consistent observations from multiple satellite sensors. Previous studies mainly relied on observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Medium Resolution Imaging Spectrometer (MERIS). As a follow-on sensor, the Visible Infrared Imager Radiometer Suite (VIIRS) also has the appropriate spectral bands to detect and quantify floating macroalgae. Based on previous works on MODIS, this study presents an improved procedure to extract floating algae pixels from VIIRS Alternative Floating Algae Index (AFAI) imagery, with image filtering used to suppress noise and adjusted thresholds used to mask sun glint, clouds, and cloud shadows. The overall extraction accuracy is about 85%. Simultaneous daily observations from MODIS and VIIRS over the Central West Atlantic (CWA) show consistent spatial patterns, but VIIRS estimations of the algae coverage (in km2) are consistently lower than MODIS (around – 19% mean relative difference or MRD), possibly due to lower sensitivity of the VIIRS near-infrared (NIR) bands than the corresponding MODIS bands. Similarly, at monthly scale VIIRS also shows lower coverage than MODIS, and their difference (around – 29% MRD) is larger than the difference between MODIS-Aqua and MODIS-Terra estimates (around – 14% MRD). Despite these differences, the spatial and temporal patterns between VIIRS and MODIS observed algae distributions match very well at all spatial and temporal scales. These results suggest that VIIRS can provide continuous and consistent observations of floating algae distributions and abundance from MODIS as long as their differences are accounted for, thus assuring continuity in the future. Furthermore, once Sargassum biomass per unit Sargassum area is determined from field measurements, conversion of these area estimates to Sargassum biomass is straightforward.

Inherent Optical Properties of the Baltic Sea in Comparison to Other Seas and Oceans

In order to retrieve geophysical satellite products in coastal waters with high coloured dissolved organic matter (CDOM), models and processors require parameterization with regional specific inherent optical properties (sIOPs). The sIOPs of the Baltic Sea were evaluated and compared to a global NOMAD/COLORS Reference Data Set (RDS), covering a wide range of optical provinces. Ternary plots of relative absorption at 442 nm showed CDOM dominance over phytoplankton and non-algal particle absorption (NAP). At 670 nm, the distribution of Baltic measurements was not different from case 1 waters and the retrieval of Chl a was shown to be improved by red-ratio algorithms. For correct retrieval of CDOM from Medium Resolution Imaging Spectrometer (MERIS) data, a different CDOM slope over the Baltic region is required. The CDOM absorption slope, SCDOM, was significantly higher in the northwestern Baltic Sea: 0.018 (±0.002) compared to 0.016 (±0.005) for the RDS. Chl a-specific absorption and ad [SPM]*(442) and its spectral slope did not differ significantly. The comparison to the MERIS Reference Model Document (RMD) showed that the SNAP slope was generally much higher (0.011 ± 0.003) than in the RMD (0.0072 ± 0.00108), and that the SPM scattering slope was also higher (0.547 ± 0.188) vs. 0.4. The SPM-specific scattering was much higher (1.016 ± 0.326 m2 g−1) vs. 0.578 m2 g−1 in RMD. SPM retrieval could be improved by applying the local specific scattering. A novel method was implemented to derive the phase function (PF) from AC9 and VSF-3 data. b ˜ was calculated fitting a Fournier–Forand PF to the normalized VSF data. b ˜ was similar to Petzold, but the PF differed in the backwards direction. Some of the sIOPs showed a bimodal distribution, indicating different water types—e.g., coastal vs. open sea. This seems to be partially caused by the distribution of inorganic particles that fall out relatively close to the coast. In order to improve remote sensing retrieval from Baltic Sea data, one should apply different parameterization to these distinct water types, i.e., inner coastal waters that are more influenced by scattering of inorganic particles vs. open sea waters that are optically dominated by CDOM absorption.

A Four-Band Semi-Analytical Model for Estimating Phycocyanin in Inland Waters From Simulated MERIS and OLCI Data

Existing remote-sensing algorithms to estimate the phycocyanin (PC) concentration in turbid inland waters have high associated uncertainties, especially at low PC concentrations in diverse phytoplankton communities. This paper provides the theoretical framework for a four-band semi-analytical algorithm (FBA_PC) which isolates PC absorption from second-order variability caused by yellow matter and other phytoplankton pigment absorption. The algorithm suits the band configuration of both the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3 Ocean and Land Color Instrument (OLCI). Calibration of the algorithm was based on absorption data from 12 inland water bodies in the USA, The Netherlands, and China, combined with measurements from laboratory-grown cultures, which demonstrated that the assumptions underlying FBA-PC are an improvement over existing three-band approaches. Validation of FBA_PC in seven inland water bodies in the USA, The Netherlands, and China showed good agreement of FBA_PC adjusted to the MERIS/OLCI band configuration with measured PC, with root-mean-square error =27.691 $\text {mg} \cdot \text {m}^{-3}$ , mean absolute percentage error = 172.863%, and coefficient of determination ( $R^{2}) = 0.730$ . FBA_PC outperformed previously proposed PC algorithms that can be applied to MERIS or OLCI data, and is expected to be more robust when applied to a wider range of water bodies.

XBAER-derived aerosol optical thickness from OLCI/Sentinel-3 observation

Abstract. A cloud identification algorithm used for cloud masking, which is based on the spatial variability of reflectances at the top of the atmosphere in visible wavelengths, has been developed for the retrieval of aerosol properties by MODIS. It is shown that the spatial pattern of cloud reflectance, as observed from space, is very different from that of aerosols. Clouds show a high spatial variability in the scale of a hundred metres to a few kilometres, whereas aerosols in general are homogeneous. The concept of spatial variability of reflectances at the top of the atmosphere is mainly applicable over the ocean, where the surface background is sufficiently homogeneous for the separation between aerosols and clouds. Aerosol retrievals require a sufficiently accurate cloud identification to be able to mask these ground scenes. However, a conservative mask will exclude strong aerosol episodes and a less conservative mask could introduce cloud contamination that biases the retrieved aerosol optical properties (e.g. aerosol optical depth and effective radii). A detailed study on the effect of cloud contamination on aerosol retrievals has been performed and parameters are established determining the threshold value for the MODIS aerosol cloud mask (3×3-STD) over the ocean. The 3×3-STD algorithm discussed in this paper is the operational cloud mask used for MODIS aerosol retrievals over the ocean. A prolonged pollution haze event occurred in the northeast part of China during the period 16–21 December 2016. To assess the impact of such events, the amounts and distribution of aerosol particles, formed in such events, need to be quantified. The newly launched Ocean Land Colour Instrument (OLCI) onboard Sentinel-3 is the successor of the MEdium Resolution Imaging Spectrometer (MERIS). It provides measurements of the radiance and reflectance at the top of the atmosphere, which can be used to retrieve the aerosol optical thickness (AOT) from synoptic to global scales. In this study, the recently developed AOT retrieval algorithm eXtensible Bremen AErosol Retrieval (XBAER) has been applied to data from the OLCI instrument for the first time to illustrate the feasibility of applying XBAER to the data from this new instrument. The first global retrieval results show similar patterns of aerosol optical thickness, AOT, to those from MODIS and MISR aerosol products. The AOT retrieved from OLCI is validated by comparison with AERONET observations and a correlation coefficient of 0.819 and bias (root mean square) of 0.115 is obtained. The haze episode is well captured by the OLCI-derived AOT product. XBAER is shown to retrieve AOT well from the observations of MERIS and OLCI.

Using High-Resolution Airborne Data to Evaluate MERIS Atmospheric Correction and Intra-Pixel Variability in Nearshore Turbid Waters

The implementation of accurate atmospheric correction is a prerequisite for satellite observation and water quality monitoring in coastal areas. The potential of the fast-line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) was investigated here for the medium resolution imaging spectrometer (MERIS). As the comparison between discrete field sampling points and macro-scale satellite pixels is subject to spatial biases associated with small-scale spatial patchiness in the turbid and highly dynamic nearshore zone, an alternative approach was proposed here using high spatial resolution (1 m) airborne hyperspectral images as radiometric truthing references. While FLAASH was not optimal for moderately turbid offshore waters (suspended particulate matter (SPM) concentration < 50 g∙m−3), it yields satisfactory results in the 50–1500 g∙m−3 range, where MERIS standard atmospheric correction was subject to significant biases and failures. Due to the significant intra-pixel variability of SPM distribution in highly turbid areas, the acquisition of high resolution airborne images should be considered as a consistent strategy for the validation of medium resolution satellite remote sensing in the spatially heterogeneous and optically diverse nearshore waters.