Medium Resolution Imaging Spectrometer Instrument Research Articles

Satellite-based water quality monitoring in Lake Vänern, Sweden

ABSTRACTLake Vänern, Sweden, is one of Europe’s largest lakes and has a historical, cultural, ecological as well as economic importance. Lake water quality monitoring is required by national and international legislations and directives, but present programmes are insufficient to meet the requirements. To complement in situ based monitoring, the possibility to obtain reliable information about spatial and temporal water quality trends in Lake Vänern from the ENVISAT mission’s MERIS instrument was evaluated. The complete archive (2002–2012) of MERIS (Medium Resolution Imaging Spectrometer) full resolution data was processed using the water processor developed by Free University Berlin (FUB) to derive aerosol optical thickness (AOT), remote-sensing reflectance (Rrs) and water quality parameters: chlorophyll-a (chl-a) concentration, coloured dissolved organic matter absorption at 443 nm (CDOM), and total suspended matter (TSM) concentration. The objective was to investigate if, either, FUB reflectance products in combination with potential lake-specific band ratio algorithms for water quality estimation, or directly, FUB water quality products, could complement the existing monitoring programme.Application of lake-specific band ratio algorithms requires high-quality reflectance products based on correctly estimated AOT. The FUB reflectance and AOT products were evaluated using Aerosol Robotic Network – Ocean Color (AERONET-OC) match-up data measured at station Pålgrunden in Lake Vänern. The mean absolute percentage differences (MAPDs) of the final reflectance retrievals at 413, 443, 490, 555, and 665 nm were 510%, 48%, 33%, 34%, and 33%, respectively, corresponding to a large positive bias in 413 nm, positive bias in 443–555 nm, and a negative bias in 665 nm. AOT was strongly overestimated in all bands.The FUB water quality products were evaluated using match-up in situ data of chl-a, filtered absorbance (AbsF(420)) and turbidity as AbsF(420) is related to CDOM and turbidity is strongly related to TSM. The in situ data was collected within the Swedish national and regional monitoring programmes. In order to widen the range of water constituents and add more data to the analysis, data from four large Swedish lakes (Vänern, Vättern, Mälaren, and Hjälmaren) was included in the analysis. High correlation (r ≥ 0.85) between in situ data and MERIS FUB derived water quality estimates were obtained, but the absolute levels were over- (chl-a) or under- (CDOM) estimated. TSM was retrieved without bias.Calibration algorithms were established for chl-a and CDOM based on the match-up data from all four lakes. After calibration of the MERIS FUB data, realistic time series could be derived that were well in line with in situ measurements. The MAPDs of the final retrievals of chl-a, AbsF(420) and Turbidity in Lake Vänern were 37%, 15%, and 35%, respectively, corresponding to mean absolute differences (MADs) of 0.9 µg l−1, 0.17 m−1, and 0.32 mg l−1 in absolute values.The partly inaccurate reflectance estimations in combination with both positive and negative bias imply that successful application of band ratio algorithms is unlikely. The high correlation between MERIS FUB water quality products and in situ data, on the other hand, shows a potential to complement present water quality monitoring programmes and improve the understanding and representability of the temporally and spatially sparse in situ observations. The monitoring potential shown in this study is applicable to the Sentinel-3 mission’s OLCI (Ocean Land Colour Instrument), which was launched by the European Space Agency (ESA) in February 2016 as a part of the EC Copernicus programme.

Merging Satellite Ocean Color Data With Bayesian Maximum Entropy Method

Merging multiple satellite ocean color data is one of the ways to create a unified ocean color product and improve the spatial coverage. In this paper, the Bayesian maximum entropy (BME), a probabilistic method, is used to integrate chlorophyll-a (chl-a) concentration data obtained by the seaviewing wide field-of-view sensor (SeaWiFS) on Orbview-2, the medium-resolution imaging spectrometer instrument (MERIS) on ENVISAT and the moderate-resolution imaging spectroradiometer (MODIS) on Aqua. MODIS chl-a concentration on current day is considered as the accurate hard data. A probabilistic model is developed to link hard data and chl-a concentration of other sensors on previous days. The latter are processed as soft data by this probabilistic model to take into account the differences between mission-specific products. The semivariogram of chl-a concentration, which presents the spatial variability and provides a priori knowledge, is developed to improve the spatial coverage. The average daily coverage of the merged chl-a field is 74% for the 1-day temporal integration which is about six times higher than any single mission, and 95% for the 3-day temporal integration which achieves basically a complete global coverage. Root-mean-square error (RMSE) and correlation between in situ chl-a measurements and the BME-merged chl-a from 1-day data are 0.42 and 0.72, and from 3-day data are 0.44 and 0.70, respectively. Compared with the existing GSM method and the weighted averaging (AVW) method, the BME method can greatly improve the spatial coverage and preserve the high accuracy, which demonstrates the potential advantages of the BME method to merge ocean color products from multiple sensors.

On the Estimation of Thickness of Marine Oil Slicks From Sun-Glittered, Near-Infrared MERIS and MODIS Imagery: The Lebanon Oil Spill Case Study

The detection of marine oil slicks using satellite sun-glittered optical imagery has been recently assessed. As the nature of the imaging mechanism involves the altered features of the wind-roughened oil-covered sea surface, it is expected that the radiation reflected from the oil-water system carries information about the physical properties of the floating oil layer. In this paper, we report an investigation on the capability to retrieve the average thickness of thin marine oil slicks by using the sun-glittered component of the solar radiation in the near-infrared (NIR) bands of MEdium Resolution Imaging Spectrometer Instrument (MERIS) and MODerate Resolution Imaging Spectroradiometer (MODIS) images. The developed procedure exploits the Cox and Munk model to compute sun glint reflectance at the sea surface level for both clean and oil polluted sea surface as well. It is assumed that the Fresnel reflection coefficient of the oil-water system carries the relevant optical dependence on oil layer thickness and oil type. The expected oil-water system reflectance is computed by taking into account the non-uniform spatial distribution of the oil volume. This is achieved by considering a pdf of oil thicknesses that matches the observations on controlled oil slicks already reported in the scientific literature. MERIS and MODIS images gathered during the Lebanon oil spill occurred on July and August 2006 were selected as case study. When available, co-located SAR imagery was also considered to corroborate NIR-detected oil slicks.

Detection of linear trends in multisensor time series in the presence of autocorrelated noise: Application to the chlorophyll‐a SeaWiFS and MERIS data sets and extrapolation to the incoming Sentinel 3‐OLCI mission

The detection of long‐term trends in geophysical time series is a key issue in climate change studies. This detection is affected by many factors: the size of the trend to be detected, the length of the available data sets, and the noise properties. Although the noise autocorrelation observed in geophysical time series does not bias the trend estimate, it affects the estimation of its uncertainty and consequently the ability to detect, or not, a significant trend. Ignoring the noise autocorrelation level typically leads to an overdetection of significant trends. Due to satellite lifetime, usually between 5 and 10 years, sea surface time series do not cover the same period and are acquired by different sensors with different characteristics. These differences lead to unknown level shifts (biases) between the data sets, which affect the trend detection. In this work, we develop a generic framework to detect and evaluate linear trends and level shifts in multisensor time series of satellite chlorophyll‐a concentrations, as provided by the Medium Resolution Imaging Spectrometer instrument (MERIS) and sea‐viewing wide field‐of‐view sensor (SeaWiFS) ocean‐color missions. We also discuss the optimization of the observation networks, in terms of needed time overlap between successive time series to reduce the uncertainty on the detection of long‐term trends. For the incoming Sentinel 3‐Ocean and Land Color Instrument (3‐OLCI) mission that should be launched at the end of 2014, we propose a global map of the number of months of observations to enhance the trend detection performed with the joint SeaWiFS‐MERIS analysis.

Harmonization of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from Sea-ViewingWide Field-of-View Sensor (SeaWiFS) and Medium Resolution Imaging Spectrometer Instrument (MERIS)

This paper describes the combination of terrestrial vegetation observations from two sensors, providing a historical dataset used for an in-depth analysis of the corresponding spatio-temporal patterns. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is an important variable suitable for regional to large-scale monitoring of climate impacts on vegetation. In this work, we create an extensive dataset of FAPAR using a 10-day product at ∼1 km resolution from September, 1997, to April, 2012, combining information from two sensors: the NASA/Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the European Space Agency (ESA)/Medium Resolution Imaging Spectrometer Instrument (MERIS). The proposed methodology reduces the noise, fills the gaps and corrects for the spurious trends in the data, providing a time-consistent coverage of FAPAR. We develop a fast merging method and evaluate its performance over Europe and the Horn of Africa.

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

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

Estimating total suspended matter concentration in tropical coastal waters of the Berau estuary, Indonesia

This study presents the application of a semi-empirical approach, based on the Kubelka–Munk (K-M) model, to retrieve the total suspended matter (TSM) concentration of water bodies from ocean colour remote sensing. This approach is validated with in situ data sets compiled from the tropical waters of Berau estuary, Indonesia. Compared to a purely empirical approach, the K-M model provides better results in the retrieval of TSM concentration on both data sets (in situ and Medium Resolution Imaging Spectrometer (MERIS)). In this study, the K-M model was calibrated with in situ measurements of remote-sensing reflectance (R rs) and TSM concentration. Next, the inverse K-M model was successfully applied to images taken by the MERIS instrument by generating regional maps of TSM concentration. MERIS top-of-atmosphere radiances were atmospherically corrected using the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN) radiative transfer model. The best correlation between R rs measured in situ and R rs MERIS was found to be at a wavelength of 620 nm. The TSM concentrations retrieved using the K-M model showed a lower root mean square error (RMSE), a higher coefficient of determination and a smaller relative error than those retrieved by the purely empirical approach.

A Relaxed Matrix Inversion Method for Retrieving Water Constituent Concentrations in Case II Waters: The Case of Lake Kasumigaura, Japan

The matrix inversion method (MIM) is an effective algorithm for estimating water constituent concentrations in case II waters. To apply this method, appropriate and accurate specific inherent optical properties (SIOPs) for each constituent in water are essential. However, many routine observations of lake water quality do not in fact provide SIOPs, thus limiting the application of the MIM. In this paper, an alternative MIM method based on linear matrix inversion theory was proposed to relax the requirement of SIOPs measurement. For this, so-called ESIOPs (Estimated SIOPs) were first derived by an unusual application of MIM based on adequate calibration samples; then the water constituent concentrations for the whole study area were retrieved by the standard application of MIM based on the derived ESIOPs. For each calibration sample, measurement of the reflectance spectrum and corresponding water constituent concentrations, which can be obtained from periodical satellite data and routine field surveys, is required. The performance of the proposed method was evaluated using the simulation data from Hydrolight and three MEdium Resolution Imaging Spectrometer Instrument (MERIS) images. The results showed that this method yielded satisfactory estimations of the water constituent concentrations for the noise-contaminated simulation data sets. For MERIS data in our study area (Lake Kasumigaura, Japan), the average bias (mean normalized bias or MNB) and relative random uncertainty (normalized root mean square error, or NRMS) were in the range of -11.2% to 3.4% and 4.8% to 29.7% for each water constituent concentration. These findings imply that the algorithm proposed in this study is theoretically reasonable and practically applicable.

Regional Objective Analysis for Merging High-Resolution MERIS, MODIS/Aqua, and SeaWiFS Chlorophyll- a Data From 1998 to 2008 on the European Atlantic Shelf

In this paper, we define the method that is used to merge high-resolution multisensor chlorophyll-a (chl-a) data on the Ireland-Biscay-Iberia Regional Ocean Observing System area from 1998 to the present at a resolution of 1.1 km. The method is based on geostatistics and is known as kriging. The merged variable is the daily anomaly of chl-a, with the anomaly being defined as the difference between the daily image and the mean historical field for the considered day. For each day, the continuous anomaly image is generated using the kriging method, and the mean historical field is then added to obtain the cloudless field of chl- a. The initial satellite chl-a data set used in the merging procedure is derived from the daily level-2 water leaving radiances of three ocean color sensors: the Sea-Viewing Wide Field of View Sensor on the Orbview platform, the Moderate Resolution Imaging Spectroradiometer on the Aqua platform, and the Medium Resolution Imaging Spectrometer Instrument on the ENVISAT platform. The chl-a concentration is obtained using a specific algorithm developed by Ifremer, known as OC5 product. Before merging, each satellite-derived chl-a product has been compared to in situ data and has been validated using a matchup data set. After this validation against in situ data, intercomparisons between the satellite data sets have been performed. As the chl-a anomaly variability depends, in this region, on the season and the distance from the shore, local space-time semivariograms have been calculated to estimate the spatiotemporal dependence or covariance of the chlorophyll anomalies. The semivariograms, used in the estimation of the kriged anomaly, are defined by their nuggets (noise), their spatial and temporal range (maximum distance for a nonnull covariance between the anomalies), and their sill (maximum variance). The spatial range of the semivariogram has been approximated locally on a regular grid. The nuggets and the sills have been deduced from the square of the mean of the chl-a concentration (the climatological reference) as we have observed a classical proportionality effect between the square of the chl-a mean, the variance of the distribution, and the parameters of the semivariograms. Compared to each original product, the analysis shows a complete coverage and differences with the in situ data that are statistically equivalent to those observed with the initial satellite data set. The merged product offers a number of applications for environmental monitoring such as the monitoring of the eutrophication risk required by the Water Framework Directive of the European Union.

Tropospheric phase delay in interferometric synthetic aperture radar estimated from meteorological model and multispectral imagery

ENVISAT Medium Resolution Imaging Spectrometer Instrument (MERIS) multispectral data and the mesoscale meteorological model MM5 are used to estimate the tropospheric phase delay in synthetic aperture radar (SAR) interferograms. MERIS images acquired simultaneously with ENVISAT Advanced Synthetic Aperture Radar data provide an estimate of the total water vapor content W limited to cloud‐free areas based on spectral bands ratio (accuracy 0.17 g cm−2 and ground resolution 300 m). Maps of atmospheric delay, 2 km in ground resolution, are simulated from MM5. A priori pertinent cumulus parameterization and planetary boundary layer options of MM5 yield near‐equal phase correction efficiency. Atmospheric delay derived from MM5 is merged with available MERIS W product. Estimates of W measured from MERIS and modeled from MM5 are shown to be consistent and unbiased and differ by ∼0.2 g cm−2 (RMS). We test the approach on data over the Lebanese ranges where active tectonics might contribute to a measurable SAR signal that is obscured by atmospheric effects. Local low‐amplitude (1 rad) atmospheric oscillations with a 2.25 km wavelength on the interferograms are recovered from MERIS with an accuracy of 0.44 rad or 0.03 g cm−2. MERIS water product overestimates W in the clouds shadow due to mismodeling of multiple scattering and underestimates W on pixels with undetected semitransparent clouds. The proposed atmospheric filter models dynamic atmospheric signal which cannot be recovered by previous filtering techniques which are based on a static atmospheric correction. Analysis of filter efficiency with spatial wavelength shows that ∼43% of the atmospheric signal is removed at all wavelengths.

Spectral Normalization and Fusion of Optical Sensors for the Retrieval of BRDF and Albedo: Application to VEGETATION, MODIS, and MERIS Data Sets

This paper aims at demonstrating the possibility of merging data from various medium-resolution spaceborne sensors to produce a consistent time series of surface bidirectional reflectance distribution function (BRDF) and albedo products. The spectral, directional, temporal, and spatial aspects of the multisensor fusion are presented. Emphasis is then given on the spectral normalization for the fusion of Medium Resolution Imaging Spectrometer Instrument (MERIS) data with Moderate Resolution Imaging Spectroradiometer (MODIS) and VEGETATION (VGT) data. Two methods are evaluated: a simple statistical method, which relies on a linear regression using all the available spectral bands, and a more innovative method called the spectral mode method, which is based on the restitution of the surface spectral signature by a combination of universal spectral functions. Analysis with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data and satellite products reveals that the spectral mode method is more efficient. This approach is used to merge top-of-canopy bidirectional reflectances from MERIS and VGT for the restitution of BRDF and albedo over a subset of West Africa. Compared to the products obtained with MERIS alone, the fusion with VGT demonstrates an improvement of the spatial coverage and a reduction of product uncertainty by about a third

Snow cover monitoring in Alpine regions using ENVISAT optical data

The ENVISAT mission with a suite of high performance sensors offers some opportunities for mapping snow cover at regional and catchment scales. The spatial resolution of the Medium Resolution Imaging Spectrometer Instrument (MERIS) data and the spectral characteristics of the Advanced Along Track Scanning Radiometer (AATSR) data are suitable for these purposes. A new approach has been developed for the generation of snow cover products in Alpine regions, based on the combined use of ENVISAT optical data and topographic information. The Alpine region is selected as a test area to demonstrate the potential and the limitations of the novel approach. In particular, attention is focused on three regions of northern Italy (Valle d'Aosta, Piemonte, Lombardia). The first results obtained by the application of this new method to Earth Observation data will be presented and analysed.

A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters

A chlorophyll (Chl) a retrieval algorithm, originally developed for spectral subsurface irradiance reflectance determined from above-water shipboard measurements, was adapted for use with satellite imagery to be acquired by the MERIS (Medium Resolution Imaging Spectrometer) instrument. This MERIS algorithm was calibrated for Chl a concentrations in the range 3-185 mg m(-3) using spectral reflectance calculated from shipboard measurements on the IJssel Lagoon (The Netherlands). Next, the algorithm was validated for various inland and coastal waters covering this concentration range. Despite the lower spectral resolution of MERIS as compared to the shipboard spectroradiometer, the standard error of estimate is expected to be similar, i.e. similar to9 mg m(-3) of Chl a in mesotrophic and eutrophic lakes, rivers, estuaries and coastal waters.

Designing optimal spectral indices: A feasibility and proof of concept study

Vegetation indices constitute a simple and convenient approach to extract useful information from satellite remote sensing data, provided they are designed to address the needs of specific applications and take advantage of the characteristics of particular instruments. Two factors motivate the development of better spectral indices at this time. The first one is the upcoming arrival of a new generation of advanced Earth observation sensors such as the Medium Resolution Imaging Spectrometer (MERIS) on Envisat, the VEGETATION instrument on the SPOT-4 platform, and GLI on ADEOS II, among others. The second is the recent publication of methodological papers on the design and evaluation of optimal spectral indices. The present contribution describes preliminary results obtained in the definition of a spectral index optimized to monitor the state of terrestrial vegetation, where the fraction of absorbed photosynthetically active radiation in plant canopies is considered the key observable physical process. The specifications of the MERIS instrument are used as an example, but the approach can be extended to other sensors. These results are encouraging and show the feasibility of defining optimal indices that exploit advanced characteristics of new instruments to meet the needs of specific applications.