The ESA Medium Resolution Imaging Spectrometer MERIS a review of the instrument and its mission

Following the successful operations of the ERS-1 and 2 satellites which are mainly dedicated to physical oceanography and ice observations from space, the European Space Agency (ESA) developed a multidisciplinary Earth observation instrument within its polar Earth Observation Programme with a focus on biological ocean observations. The Medium Resolution Imaging Spectrometer (MERIS) will be launched onboard Envisat-1 in the 1999-2000 time frame, providing a European remote sensing capability for observing for example oceanic biology and marine water quality through observations of water colour. MERIS will have a medium spectral and high radiometric resolution and a dual spatial resolution, within a global mission, covering open ocean and coastal zone waters, important aspects of the atmosphere, and large ecosystems over land. The global mission of MERIS will have a major contribution to scientific projects aimed at greater understanding of the role of oceans and ocean productivity in the climate system and our ability to forecast change through models. Secondary objectives of the MERIS mission will be directed to the measurement of atmospheric parameters associated with clouds, water vapour and aerosols in addition to land surface parameters, important in particular for the understanding of vegetation processes. In advance of the launch of MERIS, algorithms are being developed for the interpretation of MERIS observations and dedicated studies are ongoing to establish the means of validating the data products. The aim of this paper is to provide a comprehensive overview of the MERIS concept, its mission and data products in context of the driving scientific requirements.     

Coupled retrieval of aerosol optical thickness, columnar water vapor and surface reflectance maps from ENVISAT/MERIS data over land

An algorithm for the derivation of atmospheric parameters and surface reflectance data from MEdium Resolution Imaging Specrometer Instrument (MERIS) on board ENVIronmental SATellite (ENVISAT) images has been developed. Geo-rectified aerosol optical thickness (AOT), columnar water vapor (CWV) and spectral surface reflectance maps are generated from MERIS Level-1b data over land. The algorithm has been implemented so that AOT, CWV and reflectance products are provided on an operational manner, making no use of ancillary parameters apart from those attached to MERIS products. For this reason, it has been named Self-Contained Atmospheric Parameters Estimation from MERIS data (SCAPE-M). The fundamental basis of the algorithm and applicable error figures are presented in the first part of this paper. In particular, errors of ± 0.03, ± 4% and ± 8% have been estimated for AOT, CWV and surface reflectance retrievals, respectively, by means of a sensitivity analysis based on a synthetic data set simulated under a usual MERIS scene configuration over land targets. The assumption of a fixed aerosol model, the coarse spatial resolution of the AOT product and the neglection of surface reflectance directional effects were also identified as limitations of SCAPE-M. Validation results are detailed in the second part of the paper. Comparison of SCAPE-M AOT retrievals with data from AErosol RObotic NETwork (AERONET) stations showed an average Root Mean Square Error (RMSE) of 0.05, and an average correlation coefficient R² of about 0.7-0.8. R² values grew up to more than 0.9 in the case of CWV after comparison with the same stations. A good correlation is also found with the MERIS Level-2 ESA CWV product. Retrieved surface reflectance maps have been successfully compared with reflectance data derived from the Compact High Resolution Imaging Spectrometer (CHRIS) on board the PRoject for On-Board Autonomy (PROBA) in the first place. Reflectance retrievals have also been compared with reflectance data derived from MERIS images by the Bremen AErosol Retrieval (BAER) method. A good correlation in the red and near-infrared bands was found, although a considerably higher proportion of pixels was successfully processed by SCAPE-M.     

Validation of MERIS LAI and FAPAR products for winter wheat-sown test fields in North-East Bulgaria

Progress in deriving land surface biophysical parameters in a spatially explicit manner using remotely sensed data has greatly enhanced our ability to model ecosystem processes and monitor crop development. A multitude of satellite sensors and algorithms have been used to generate ready-to-use maps of various biophysical parameters. Validation of these products for different vegetation types is needed to assess their reliability and consistency. While most of the current satellite biophysical products have spatial resolution of one kilometre, a recent effort utilizing data from the Medium Resolution Imaging Spectrometer (MERIS) provided leaf area index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and other canopy parameters in a resolution as fine as 300 m over the European continent. This resolution would be more appropriate for application at the regional scale, particularly for crop monitoring. This higher-resolution MERIS product has been evaluated in a limited number of studies to date. This article aims to validate LAI and FAPAR from the MERIS 10-day composite BioPar BP-10 product over winter wheat fields in northeast Bulgaria. The ground measurements of LAI and FAPAR were up-scaled and 30 m resolution reference raster layers were created using empirical relationships with Landsat TM (RMSE = 0.06 and RMSE = 0.22 for FAPAR and LAI, respectively). MERIS FAPAR and LAI were found to have significant correlation with FAPAR and LAI from the reference raster layers (R² = 0.84 and R² = 0.78, respectively). When MERIS Green LAI was calculated (incorporating the fraction of vegetation and brown vegetation cover from the BioPar BP-10 product), better correspondence with LAI values from the reference raster layer was achieved, with RMSE and bias reduced by 30–35%. The results from this study confirm the findings of previous validations showing that MERIS Green LAI tends to overestimate LAI values lower than 1. As a conclusion of the study, the BioPar BP-10 product was found to provide reliable estimates of FAPAR and acceptably accurate estimates of LAI for winter wheat crops in North-East Bulgaria.     

Assessment of SeaWiFS,MODIS, and MERIS ocean colour products in the South China Sea

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) remote-sensing radiometric and chlorophyll-a (chl-a) concentration products for the South China Sea (SCS) from October 2003 to May 2010 were assessed using in situ data. A strict spatiotemporal match-up method was used to minimize the temporal variability effects of atmosphere and seawater around the measurement site. A comparison of the remote-sensing reflectance (R_rs(λ)) of the three sensors with in situ values from the open waters of the SCS showed that the mean absolute percentage difference varied from 13% to 55% in the 412–560 nm spectral range. Generally, the MERIS radiometric products exhibited higher typical uncertainties and bias than the SeaWiFS and MODIS products. The R_rs(443) to R_rs(555/551/560) band ratios of the satellite data were in good agreement with in situ observations for these sensors. The SeaWiFS, MODIS, and MERIS chl-a products overestimated in situ values by 74%, 42%, and 120%, respectively. MODIS retrieval accuracy was better than those of the other sensors, with MERIS performing the worst. When the match-up criteria were relaxed, the assessment results degraded systematically. Therefore, strict spatiotemporal match-up is recommended to minimize the possible influences of small-scale variation in geophysical properties around the measurement site. Coastal and open-sea areas in the SCS should be assessed separately because their biooptical properties are different and the results suggest different atmospheric correction problems.     

Uncertainty estimates for the FAPAR operational products derived from MERIS — Impact of top-of-atmosphere radiance uncertainties and validation with field data

This paper discusses the accuracy of the operational Medium Resolution Imaging Spectrometer (MERIS) Level 2 land product which corresponds to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The FAPAR value is estimated from daily MERIS spectral measurements acquired at the top-of-atmosphere, using a physically based approach. The products are operationally available at the reduced spatial resolution, i.e. 1.2 km, and can be computed at the full spatial resolution, i.e. at 300 m, from the top-of-atmosphere MERIS data by using the same algorithm. The quality assessment of the MERIS FAPAR products capitalizes on the availability of five years of data acquired globally. The actual validation exercise is performed in two steps including, first, an analysis of the accuracy of the FAPAR algorithm itself with respect to the spectral measurements uncertainties and, second, with a direct comparison of the FAPAR time series against ground-based estimations as well as similar FAPAR products derived from other optical sensor data. The results indicate that the impact of top-of-atmosphere radiance uncertainties on the operational MERIS FAPAR products accuracy is expected to be at about 5-10% and the agreement with the ground-based estimates over different canopy types is achieved within ± 0.1.     

Evaluation of RapidEye data for mapping algal blooms in inland waters

The high spatial resolution multispectral imaging sensor onboard RapidEye (RE) has a red-edge band centred at 710 nm, which can be used to produce a product equivalent to the Maximum Chlorophyll Index (MCI) that was developed to detect algal blooms with Medium Resolution Imaging Spectrometer (MERIS) data. The RapidEye system, with five satellites, offers a greater repeat frequency than other high-resolution satellites. In this study, we compared RapidEye and MERIS derived MCI products for the Harris Chain of Lakes in central Florida, USA, to determine if RapidEye can produce an equivalent product similar to MERIS. Data from two RapidEye satellites (RapidEye-2 and RapidEye-5) were used. Band-by-band matchups used RapidEye Top of the Atmosphere (TOA) reflectance and MERIS ρ_s (reflectance corrected only for Raleigh scattering and molecular absorption). The RapidEye TOA reflectance data differed from MERIS, but when the bands were calibrated to the MERIS, the MCI products matched between the two RapidEye satellites and the MERIS MCI. Estimated chlorophyll-a concentrations using a relationship established for Lake Erie matched in situ chlorophyll-a concentrations with a median error of 1.09 mg m^?3. The results indicate that RapidEye is useful for this purpose, which also suggests that other high-resolution satellites with similar red-edge bands may also provide MCI-type products that would allow estimation of chlorophyll-a. RapidEye provides a context for applying future constellation of small satellites for monitoring water quality issues. Lake water quality managers and environmental agencies could effectively use such high-resolution products to assess and manage algal bloom events.     

Using MERIS full resolution data to monitor coastal waters — A case study from Himmerfjärden, a fjord-like bay in the northwestern Baltic Sea

In this paper we investigate if MERIS full resolution (FR) data (300 m) is sufficient to monitor changes in optical constituents in Himmerfjärden, a fjord-like, north–south facing bay of about 30 km length and 4 km width. The MERIS FR products were derived using a coastal processor (FUB Case-2 Plug-In). We also compared the performance between FUB and standard processor (MEGS 7.4), using reduced resolution (RR) data (1 km resolution) from the open Baltic Sea, and compared the products to sea-truthing data. The optical variables measured for sea-truthing were chlorophyll, suspended particulate matter (SPM), as well as coloured dissolved organic matter (CDOM, also termed yellow substances), and the spectral diffuse attenuation coefficient, K_d(490). The comparison of the RR data to the sea-truthing data showed that, in the open Baltic Sea, the MERIS standard processor overestimated chlorophyll by about 59%, and SPM by about 28%, and underestimated yellow substance by about 81%, whereas the FUB processor underestimated SPM by about 60%, CDOM by about 78%, and chlorophyll a by about 56%. The FUB processor showed a relatively high precision for all optical components (standard deviation: 6–18%), whereas the precision for the MEGS 7.4 was rather low (standard deviation: 43–73%), except for CDOM (standard deviation: 13%). The analysis of the FR data showed that all FR level 2 water products derived from MERIS followed a polynomial decline in concentration when moving off-shore. The distribution of chlorophyll and SPM was best described by a 2nd order polynomial, and the distribution of CDOM by a 3rd order polynomial, verifying the diffusional model described in Kratzer and Tett [Kratzer, S. and Tett, P. (in press). Using bio-optics to investigate the extent of coastal waters — a Swedish case study. Hydrobiologia.]. A new K_d(490) and Secchi depth algorithm based on MERIS channel 3 (490 nm) and channel 6 (620 nm) each was derived from radiometric sea-truthing data (TACCS, Satlantic). Applying the K_d(490) algorithm to the MERIS FR data over Himmerfjärden, and comparing to sea-truthing data the results showed a strong correlation (r = 0.94). When comparing the FR data to the sea-truthing data CDOM and K_d(490) showed a low accuracy, but a high precision with a rather constant off-set. In summary, one may state that the precision of MERIS data improves by applying the FUB Case-2 processor and the accuracy improves with improved spatial resolution for chlorophyll and SPM. Furthermore, the FUB processor can be used off-the-shelf for open Baltic Sea monitoring, provided one corrects for the respective off-set from sea-truthing data which is most likely caused by an inaccuracy in the atmospheric correction. Additionally, the FR data can be used to derive CDOM, K_d(490) and Secchi depth in Himmmerfjärden if one corrects for the respective off-set. We will need to perform more comparisons between sea-truthing and MERIS FR data before the new K_d(490) algorithm can be made operational, including also scenes from other times of year. In order to provide a level 2 product that can be used reliably by the Baltic Sea user community, our recommendation to ESA is to include the spectral attenuation coefficient as a MERIS standard product.     

Use of satellite images of chlorophyll fluorescence to monitor the spring bloom in coastal waters

This article demonstrates a successful application of fluorescence line height (FLH) images from the Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagers and provides a strong argument for making more widespread use of FLH in monitoring surface phytoplankton in coastal waters. In the present example, MERIS and MODIS FLH images show the start of the spring bloom in coastal waters of the Strait of Georgia in British Columbia, Canada. The images clearly show a recurring pattern in five of the eight years from 2003 to 2010 covered by MERIS, which suggests seeding of the early spring bloom from narrow coastal inlets. Such seeding has been suggested before, but never observed. FLH images show the blooms more clearly than images of surface chlorophyll based on the ratios of water-leaving radiances in the blue and green spectral range (440–560 nm). FLH images used here have been derived with no atmospheric correction. Alternative products based on the blue/green ratio require atmospheric correction, which is difficult in coastal areas. Such products also tend to be more significantly confused with other constituents of coastal waters.     

Spatial scale variations in vegetation indices and above-ground biomass estimates: Implications for MERIS

The Medium Resolution Imaging Spectrometer (MERIS) is one of the sensors carried by Envisat. MERIS is a fully programmable imaging spectrometer, however a standard 15-channel band set will be transmitted for each 300 m pixel (over land while over the ocean the pixels will be aggregated to 1200 m spatial resolution) covering visible and near-infrared wavelengths. Since MERIS is a multidisciplinary sensor providing data that can be input into ecosystem models at various scales, we studied MERIS's performance relative to the scale of observation using simulated datasets degraded to various spatial resolutions in the range of 6-300 m. Algorithms to simulate MERIS data using airborne imaging spectrometer datasets were presented, including a case study from DAIS (i.e. Digital Airborne Imaging Spectrometer) 79-channel imaging spectrometer data acquired on 8 July 1997 over the Le Peyne test site in southern France. For selected target endmembers garrigue, maquis, mixed oak forest, pine forest and bare agricultural field, regions-of-interest (ROI) were defined in the DAIS scene. For each of the endmembers, the vegetation index values in the corresponding ROI is calculated for the MERIS data at the spatial resolutions ranging from 6 to 300 m. We applied the NDVI, PVI, WDVI, SAVI, MSAVI, MSAVI2 and GEMI vegetation indices. Above-ground biomass (AGB) was estimated in the field and derived from the DAIS image and the MERIS datasets (6-300 m spatial resolution). The vegetation indices are shown to be constant with the spatial scale of observation. The strongest correlation between the MERIS and DAIS NDVI is obtained when using a linear model with an offset of 0.15 ( r =0.31). A Pearson correlation matrix between AGB measured in the field and each spectral band reveals a modest but significant ( p <0.05) correlation for most spectral bands. When mathematical functions are fitted through the NDVI and biomass data, an exponential fit shows the extinction and saturation at larger vegetation biomass values. The correlation between biomass and NDVI for DAIS as well as for the MERIS simulated dataset is modest. Further research is required to analyse the scale effects that limit the correlation between field and image AGB estimates.     

MERIS potential for land applications

The Medium Resolution Imaging Spectrometer (MERIS), to be flown on the Envisat platform, contributes to the effort made by space agencies to generate Earth observation data that are responsive to the needs of the users. Although optimized for oceanic applications, this instrument should also be useful for a range of terrestrial investigations. This paper reviews the relevance of a suite of features specific to MERIS (e.g. fine radiometric and spectral resolution, programmability, the availability of an onboard calibration system) for terrestrial applications. Scientific challenges related to scale issues, the definition of appropriate algorithms for the optimal exploitation of these data, the opportunity for synergistic studies and the comparison of MERIS data with other data collected by the Advanced Very High Resolution Radiometer (AVHRR) and other precursor or future instruments are reviewed. The urgent need for an intensive and sustained research and development programme to define and validate a panoply of highlevel products optimized for terrestrial applications is stressed.     

MERIS potential for coastal zone applications

The Medium Resolution Imaging Spectrometer (MERIS) will be flown on the Envisat mission in 1999 and will provide the user community with a unique instrument for monitoring important water quality parameters in coastal waters. The instrument will be of special interest for coastal zone research projects such as the International Geosphere Biosphere Program (IGBP), Land Ocean Interactions in the Coastal Zone (LOICZ) and for environmental impact monitoring, assessment and management programmes. MERIS characteristics include nine spectral channels (out of 15) covering the visible spectral range (410-705nm) which are optimized to the radiance level of water surfaces. Within this range there are bands dedicated to mapping concentrations of suspended particulate matter, phytoplankton, gelbstoff (or coloured dissolved organic matter) and bands for determining sunlight-stimulated fluorescence of phytoplankton chlorophyll. Its spatial resolution of 300m and revisit period of 3 days are well suited to the observation of most of the phenomena which are of interest for coastal water quality research and management. New algorithms will have to be developed for computing the different water constituents from the observations and for atmospheric correction of turbid waters. Examples of applications, for which the design of MERIS is optimized, are presented.     

Quality assessment of Landsat surface reflectance products using MODIS data

Surface reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level surface geophysical parameters. With the development of automated atmospheric correction algorithms, it is now feasible to produce large quantities of surface reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The surface reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat surface reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat-MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat surface reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary surface reflectance products derived using the Global Land Survey (GLS) Landsat images for the 2000 epoch. As surface reflectance likely will be a standard product for future Landsat missions, the approach developed in this study can be adapted as an operational quality assessment system for those missions.     

Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics

Monitoring vegetation dynamics is fundamental for improving Earth system models and for increasing our understanding of the terrestrial carbon cycle and the interactions between biosphere and climate. Medium spatial resolution sensors, like MERIS, exhibit a significant potential to study these dynamics over large areas because of their spatial, spectral and temporal resolution. However, the spatial resolution provided by MERIS (300 m in full resolution mode) is not appropriate to monitor heterogeneous landscapes, where typical length scales of these dynamics rarely reach 300 m. We, therefore, motivate the use of data fusion techniques to downscale medium spatial resolution data (MERIS full resolution, FR) to a Landsat-like spatial resolution (25 m). An unmixing-based data fusion approach was applied to a time series of MERIS FR images acquired over The Netherlands. The selected data fusion approach is based on the linear mixing model and uses a high spatial resolution land use database to produce images having the spectral and temporal resolution as provided by MERIS, but a Landsat-like spatial resolution. A quantitative assessment of the quality of the fused images was done in order to test the validity of the proposed method and to evaluate the radiometric characteristics of the MERIS fused images. The resulting series of fused images was subsequently used to compute two vegetation indices specifically designed for MERIS: the MERIS terrestrial chlorophyll index (MTCI) and the MERIS global vegetation index (MGVI). These indices represent continuous fields of canopy chlorophyll (MTCI) and of the fraction of photosynthetically active radiation absorbed by the canopy (MGVI). Results indicate that the selected data fusion approach can be successfully used to downscale MERIS data and, therefore, to monitor vegetation dynamics at Landsat-like spatial, and MERIS-like spectral and temporal resolution.     

利用MERIS产品数据反演太湖叶绿素a浓度研究

第三代水色传感器MERIS的荧光通道的合理设置为荧光遥感法的应用提供了广阔的发展前景。利用MERIS数据、同步地面光谱和水质监测数据,分别通过基线荧光高度（FLH）、归一化荧光高度（NFH）和最大叶绿素指数（MCI）建立了太湖叶绿素a浓度的荧光遥感估算模型。结果表明：MERIS荧光参数中最大叶绿素指数（MCI）较基线荧光高度（FLH）更适合太湖水体叶绿素a浓度的反演;归一化荧光高度（NFH）与实测叶绿素a浓度间的拟合效果最好。最后选取NFH进行MERIS荧光遥感模型的太湖叶绿素a浓度的反演,其结果客观地反映了太湖水体叶绿素a浓度的空间分布格局。     

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.     

Uncertainties in radiative transfer computations: consequences on the MERIS products over land

The main objectives of MERIS (MEdium Resolution Imaging Spectrometer) consist of atmospheric processes related to the water vapour column and aerosol optical properties designed for meteorological applications, and the land surface properties as well as the bio‐optical oceanography. In this context, operational MERIS level‐2 processing uses auxiliary data generated by two radiative transfer tools. These two codes simulate upwelling radiances within a coupled ‘atmosphere–land’ system, using different approaches based on the matrix‐operator method (FUB, Freie Universität Berlin), the discrete ordinate method and the successive orders technique (ULCO, Université du Littoral Côte d'Opale). Intervalidation of these two radiative transfer tools was performed in order to implement them in the MERIS level‐2 processing. For cases without gaseous absorption, the scattering processes both by the molecules and the aerosols were retrieved within a few tenths of a percentage point. Nevertheless, some substantial discrepancies occur if the polarization is not accounted for, mainly in the Rayleigh scattering computations. Errors on the aerosol optical thickness reach up to 25% in some geometries as observed in the MERIS images. The parametrization of gaseous absorption (H₂O and O₂) defined for each of these two codes leads to a good agreement for the MERIS bands with residual absorption. In the strong absorption bands (761.75 nm and 900 nm), the FUB computations well match the results derived from a line‐by‐line (LBL) code with a very high spectral resolution. Note that the oxygen absorption at 761.75 nm is very sensitive to the characteristics of the sensor spectral response and requires accurate calculations with the LBL code. Consequently, the ULCO code has been implemented in the MERIS level‐2 processing to include polarization in the scattering processes and to correct for slightly gaseous absorption, the FUB code to derive the water vapour abundance, and the LBL code to determine the barometric pressure. Impacts of the differences in the look‐up table generation on the level‐2 products (aerosol model, surface reflectance and barometric pressure) are also analysed and illustrated.     

Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues

The characteristics and benefits of ocean color merged data sets created using a semi-analytical model and the normalized water-leaving radiance observations from the SeaWiFS, MODIS-AQUA and MERIS ocean color missions are presented. Merged data products are coalesced from multiple mission observations into a single data product with better spatial and temporal coverage than the individual missions. Using the data from SeaWiFS, MODIS-AQUA and MERIS for the 2002-2009 time period, the average daily coverage of a merged product is ∼ 25% of the world ocean which is nearly twice that of any single mission's observations. The frequency at which a particular area is sampled from space is also greatly improved in merged data as some areas can be sampled as frequently as 64% of the time (in days). The merged data presented here are validated through matchup analyses and by comparing them to the data sets obtained from individual missions. Further, a complete error budget for the final merged data products was developed which accounts for uncertainty associated with input water-leaving radiances and provides uncertainty levels for the output products (i.e. the chlorophyll concentration, the combined dissolved and detrital absorption coefficient and the particulate backscattering coefficient). These merged products and their uncertainties at each pixel were developed within the NASA REASON/MEaSUREs and ESA GlobColour projects and are available to the scientific community. Our approach has many benefits for the creation of unified Climate Data Records from satellite ocean color observations.     

MERIS potential for ocean colour studies in the open ocean

The interest of space observations of ocean colour for determining variations in phytoplankton distribution and for deriving primary production (via models) has been largely demonstrated by the Coastal Zone Color Scanner (CZCS) which operated from 1978 to 1986. The capabilities of this pioneer sensor, however, were limited both in spectral resolution and radiometric accuracy. The next generation of ocean colour sensors will benefit from major improvements. The Medium Resolution Imaging Spectrometer (MERIS), planned by the European Space Agency (ESA) for the Envisat platform, has been designed to measure radiances in 15 visible and infrared channels. Three infrared channels will allow aerosol characterization, and therefore accurate atmospheric corrections, to be performed for each pixel. For the retrieval of marine parameters, nine channels between 410 and 705nm will be available (as opposed to only four with the CZCS). In coastal waters this should, in principle, allow a separate quantification of different substances (phytoplankton, mineral particles, yellow substance) to be performed. In open ocean waters optically dominated by phytoplankton and their associate detrital matter, the basic information (i.e. the concentration of phytoplanktonic pigments) will be retrieved with improved accuracy due to the increased radiometric performances of MERIS. The adoption of multi-wavelength algorithms could also lead to additional information concerning auxiliary pigments and taxonomic groups. Finally, MERIS will be one of the first sensors to allow measurements of Sun-induced chlorophyll a in vivo fluorescence, which could provide a complementary approach for the assessment of phytoplankton abundance. The development of these next-generation algorithms, however, requires a number of fundamental studies.     

Determination of the cloud fraction in the SCIAMACHY ground scene using MERIS spectral measurements

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) is a passive remote sensing spectrometer observing backscattered radiation from the atmosphere and the Earth's surface, in the wavelength range between 240 and 2380 nm. The instrument is onboard ENVironmental SATellite (ENVISAT) which was launched on 1 March 2002. The Medium Resolution Imaging Spectrometer (MERIS) is also one of the 10 instruments onboard the ENVISAT satellite. MERIS is a 68.5° field-of-view nadir-pointing imaging spectrometer which measures the solar radiation reflected by the Earth in 15 spectral bands (visible and near-infrared). It obtains a global coverage of the Earth in three days. Its main objective is to measure sea colour and quantify ocean chlorophyll content and sediment, thus providing information on the ocean carbon cycle and thermal regime. It is also used to derive the cloud top height, aerosol and cloud optical thickness, and water vapour column. The ground spatial resolution of the instrument is 260 m × 290 m. This paper is aimed at determining the cloud fraction in SCIAMACHY pixels (typically, 30 km × 60 km ground scenes) using MERIS observations and number of thresholds for MERIS top-of-atmosphere reflectances and their ratios. Thresholds utilize the fact that clouds are bright white objects having similar reflectances in the blue and red. The MERIS cloud fraction has been derived for a number of SCIAMACHY states with area of 916 km × 400 km. The results are compared with correspondent cloud fractions obtained using SCIAMACHY polarization measurement devices (PMDs). Large differences are found between cloud fractions derived using SCIAMACHY and MERIS measurements. It is recommended to use highly spatially resolved MERIS observations instead of SCIAMACHY PMD measurements to retrieve cloud fractions in SCIAMACHY pixels. The improvements advised will enhance SCIAMACHY trace gas and cloud retrievals in the presence of broken cloud fields.     

Atmospheric correction of ENVISAT/MERIS data over inland waters: Validation for European lakes

Traditional methods for aerosol retrieval and atmospheric correction of remote sensing data over water surfaces are based on the assumption of zero water reflectance in the near-infrared. Another type of approach which is becoming very popular in atmospheric correction over water is based on the simultaneous retrieval of atmospheric and water parameters through the inversion of coupled atmospheric and bio-optical water models. Both types of approaches may lead to substantial errors over optically-complex water bodies, such as case II waters, in which a wide range of temporal and spatial variations in the concentration of water constituents is expected. This causes the water reflectance in the near-infrared to be non-negligible, and that the water reflectance response under extreme values of the water constituents cannot be described by the assumed bio-optical models. As an alternative to these methods, the SCAPE-M atmospheric processor is proposed in this paper for the automatic atmospheric correction of ENVISAT/MERIS data over inland waters. A-priori assumptions on the water composition and its spectral response are avoided by SCAPE-M by calculating reflectance of close-to-land water pixels through spatial extension of atmospheric parameters derived over neighboring land pixels. This approach is supported by the results obtained from the validation of SCAPE-M over a number of European inland water validation sites which is presented in this work. MERIS-derived aerosol optical thickness, water reflectance and water pigments are compared to in-situ data acquired concurrently to MERIS images in 20 validation match-ups. SCAPE-M has also been compared to specific processors designed for the retrieval of lake water constituents from MERIS data. The performance of SCAPE-M to reproduce ground-based measurements under a range of water types and the ability of MERIS data to monitor chlorophyll-a and phycocyanin pigments using semiempirical algorithms after SCAPE-M processing are discussed. It has been found that SCAPE-M is able to provide high accurate water reflectance over turbid waters, outperforming models based on site-specific bio-optical models, although problems of SCAPE-M to cope with clear waters in some cases have also been identified.