Geostatistical interpolation of missing data and downscaling of spatial resolution for remotely sensed atmospheric methane column concentrations

Since the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument on the Environmental Satellite (ENVISAT) was launched in 2002, CH₄ measurements from the satellite at regional or global scales became available. However, many gaps of missing data exist on the maps of the retrieved atmospheric CH₄ column concentrations from SCIAMACHY/ENVISAT. Moreover, the gridded CH₄ map with 50?×?50 km is a bit coarse for local interpretation. In this study, two geostatistical methods of ordinary kriging (OK) and ordinary cokriging (OCK) associated with 5 km normalized difference vegetation index (NDVI) images were examined to fill in missing data and to downscale the spatial resolution of CH₄ images. The 50 km CH₄ images interpolated by the two methods presented similar spatial patterns to the original 50 km CH₄ image and provided good results for the missing data. Taking into account the statistical results, the OCK method achieved better performance than OK in filling gaps of missing data. In further downscaling the CH₄ image from 50 to 5 km, the OCK method achieved a significant amount of spatial detail, and the statistical results also showed that OCK performed better than OK.     

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.     

Cover. Wind energy mapping of coastal zones by synthetic aperture radar (SAR) for siting potential windmill locations

MERIS (Medium Resolution Imaging Spectrometer) is a fine spectral and medium spatial resolution satellite sensor and is part of the core instrument payload of Envisat, the European Space Agency's (ESA) environmental research satellite, launched in March 2002. Designed primarily for ocean (‘MER’) and coastal zone remote sensing, this imaging spectrometer (‘IS’) now has a much broader environmental remit covering also land and atmospheric applications. This paper reviews (i) MERIS's development history, focusing on its changing mission objectives; (ii) MERIS's technical specification, including its radiometric, spectral and geometric characteristics, programmability and onboard calibration; (iii) decisions that led to modifications of MERIS's spectral, geometric and radiometric performance for land applications; (iv) MERIS's data products; and (v) some of the ways in which MERIS data might be used to provide information on terrestrial vegetation.     

Global patterns in daytime cloud properties derived from GOME backscatter UV-VIS measurements

In this paper, we present an overview of the cloud property data set derived from 8 years of reflected solar ultraviolet-visible (UV-VIS) measurements taken by the global ozone monitoring experiment (GOME) instrument from April 1996 to June 2003. We consider four such properties: cloud amount, cloud-top pressure, cloud optical thickness and cloud type. Cloud amounts are generated from GOME broadband polarization data using data fusion techniques, while cloud-top height (pressure) and cloud-top albedo are retrieved from GOME backscatter measurements in the oxygen (O₂) A-band via neural network inversion of simulated reflectances. Cloud optical thickness is derived as an additional parameter from the cloud-top albedo and radiative transfer model simulations, and cloud type is determined from the cloud-top pressure and optical thickness. We analyse global and seasonal patterns for these properties, looking at monthly means, standard deviations and the 8-year average values. We compare GOME results with the longer-period multisatellite international satellite cloud climatology project (ISCCP) D-series cloud climatology. The overall good agreement demonstrates that GOME provides accurate and complementary cloud information. Differences in cloud amount, cloud-top height and optical thickness values are due primarily to contrasting measurement strategies (GOME measures daytime-only UV-VIS backscatter, ISCCP is based on several day and night infrared satellite observations). We look forward to the extension of this UV-VIS cloud parameter series with the advent of more recent backscatter atmospheric composition instruments such as the scanning imaging absorption spectrometer for atmospheric cartography (SCIAMACHY) on-board the environmental satellite (ENVISAT) and the GOME-2 series on the MetOp platforms.     

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models

A major source of error for repeat‐pass Interferometric Synthetic Aperture Radar (InSAR) is the phase delay in radio signal propagation through the atmosphere (especially the part due to tropospheric water vapour). Based on experience with the Global Positioning System (GPS)/Moderate Resolution Imaging Spectroradiometer (MODIS) integrated model and the Medium Resolution Imaging Spectrometer (MERIS) correction model, two new advanced InSAR water vapour correction models are demonstrated using both MERIS and MODIS data: (1) the MERIS/MODIS combination correction model (MMCC); and (2) the MERIS/MODIS stacked correction model (MMSC). The applications of both the MMCC and MMSC models to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the Southern California Integrated GPS Network (SCIGN) region showed a significant reduction in water vapour effects on ASAR interferograms, with the root mean square (RMS) differences between GPS‐ and InSAR‐derived range changes in the line‐of‐sight (LOS) direction decreasing from ～10 mm before correction to ～5 mm after correction, which is similar to the GPS/MODIS integrated and MERIS correction models. It is expected that these two advanced water vapour correction models can expand the application of MERIS and MODIS data for InSAR atmospheric correction. A simple but effective approach has been developed to destripe Terra MODIS images contaminated by radiometric calibration errors. Another two limiting factors on the MMCC and MMSC models have also been investigated in this paper: (1) the impact of the time difference between MODIS and SAR data; and (2) the frequency of cloud‐free conditions at the global scale.     

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.     

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.     

Atmospheric correction algorithm for MERIS above case‐2 waters

The development and validation of an atmospheric correction algorithm designed for the Medium Resolution Imaging Spectrometer (MERIS) with special emphasis on case‐2 waters is described. The algorithm is based on inverse modelling of radiative transfer (RT) calculations using artificial neural network (ANN) techniques. The presented correction scheme is implemented as a direct inversion of spectral top‐of‐atmosphere (TOA) radiances into spectral remote sensing reflectances at the bottom‐of‐atmosphere (BOA), with additional output of the aerosol optical thickness (AOT) at four wavelengths for validation purposes. The inversion algorithm was applied to 13 MERIS Level1b data tracks of 2002–2003, covering the optically complex waters of the North and Baltic Sea region. A validation of the retrieved AOTs was performed with coincident in situ automatic sun–sky scanning radiometer measurements of the Aerosol Robotic Network (AERONET) from Helgoland Island located in the German Bight. The accuracy of the derived reflectances was validated with concurrent ship‐borne reflectance measurements of the SIMBADA hand‐held field radiometer. Compared to the MERIS Level2 standard reflectance product generated by the processor versions 3.55, 4.06 and 6.3, the results of the proposed algorithm show a significant improvement in accuracy, especially in the blue part of the spectrum, where the MERIS Level2 reflectances result in errors up to 122% compared to only 19% with the proposed algorithm. The overall mean errors within the spectral range of 412.5–708.75 nm are calculated to be 46.2% and 18.9% for the MERIS Level2 product and the presented algorithm, respectively.     

Satellite‐derived cloud top pressure product validation using aircraft‐based cloud physics lidar data from the ATReC field campaign

The primary objective of this study was to assess the accuracy of satellite‐derived estimates of cloud‐top height (CTH). These estimates were derived using hourly data from the Geostationary Operational Environmental Satellite (GOES‐12) Imager and Sounder instruments. In addition, CTHs were derived using data from the MODerate resolution Imaging Spectrometer (MODIS), located on the polar‐orbiting Aqua platform. Cloud physics lidar (CPL) data taken during the Atlantic‐THORPEX Regional Campaign (ATReC) were used as the reference data set. Two cases were examined, one containing clouds at many different levels (5 December 2003) and one consisting entirely of mid‐level clouds (between 4 and 10 km, 28 November 2003). For the first case, 19.4% of the Sounder pixels and 28.0% of the Imager pixels were within ±0.5 km of the CPL measurement, while 51.5% of the Sounder pixels and 64.3% of the Imager pixels were within ±1.5 km. For the second case, 29.7% of the Sounder pixels and 39.9% of the Imager pixels were within ±0.5 km of the CPL measurement, while 85.2% of the Sounder pixels and 85.1% of the Imager pixels were within ±1.5 km. The results indicate that MODIS CTH retrievals may provide an improvement over heights derived using geostationary instruments, especially for cases where cloud heights are not highly variable.     

The MERIS ground segment

The Medium Resolution Imaging Spectrometer (MERIS) component of the Envisat ground segment is introduced, outlining the characteristics of the MERIS products in terms of their availability in time, spatial scale and coverage. The definition of processing levels and the overall philosophy of the design of the data products is presented. It will be possible to order MERIS data products consisting of different geophysical parameters depending on the surface type encountered. In addition, mixed level products consisting of surface leaving radiances, reflectances and geophysical parameters will be available from the same product. These geophysical parameters are briefly described.     

Total ozone observations at Athens,Greece by satellite-borne and ground-based instrumentation

A comparison is made of total ozone (TOZ) content observations conducted by the Dobson spectrophotometer No. 118, the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY), the Total Ozone Mapping Spectrometer (TOMS) and the Ozone Monitoring Instrument (OMI) over Athens, Greece, during 1991–2008. Spearman's and Wilcoxon's tests were used to determine the measure of the agreement between the ground-based and satellite column ozone data. The correlation coefficient between Dobson and Nimbus-7, ADEOS, Earth Probe, OMI and SCIAMACHY observations was found to be 0.95, 0.96, 0.94, 0.93 and 0.87, respectively, while the correlation coefficient between total ozone observations of SCIAMACHY and Earth Probe-TOMS and OMI is 0.85 and 0.93, respectively. SCIAMACHY overestimates the column ozone with respect to Dobson, Earth Probe-TOMS and OMI by 10, 15 and 3 DU, respectively, while Dobson underestimates the column ozone with respect to Nimbus-7, ADEOS and OMI by 5, 10 and 8 DU. The results obtained confirm that the Athens Dobson station may continue to be considered as a ground-truth total ozone station for the validation of the satellite column ozone observations. In addition, linear regression analysis of the deseasonalized monthly mean column ozone, as derived from Dobson measurements, gives an increase of +0.33 ± 0.07% per year during 1991–2000 and a decrease of –0.33 ± 0.07% per year for the period 2001–2008.     

Retrieval of atmospheric and oceanic properties from MERIS measurements: A new Case‐2 water processor for BEAM

A freely available data processor for the B asic E RS & ENVISAT ( A )ATSR and M ERIS Toolbox (BEAM) was developed to retrieve atmospheric and oceanic properties above and of Case‐2 waters from Medium Resolution Imaging Spectrometer (MERIS) Level1b data. The processor was especially designed for European coastal waters and uses MERIS Level1b Top‐Of‐Atmosphere (TOA) radiances to retrieve atmospherically corrected remote sensing reflectances at the Bottom‐Of‐Atmosphere (BOA), spectral aerosol optical thicknesses (AOT) and the concentration of three water constituents, namely chlorophyll‐a (CHL), total suspended matter (TSM) and the absorption of yellow substance at 443 nm (YEL). The retrieval is based on four separate artificial neural networks which were trained on the basis of the results of extensive radiative transfer (RT) simulations by taking various atmospheric and oceanic conditions into account. The accuracy of the retrievals was acquired by comparisons with concurrent in situ ground measurements and was published in full detail elsewhere. For the remote sensing reflectance product a mean absolute percentage error (MAPE) of 18% was derived within the spectral range 412.5–708.75 nm while the accuracy of the AOT at 550 nm in terms of MAPE was calculated to be 40%. The accuracies for CHL, TSM and YEL were derived from match‐up analysis with MAPEs of 50%, 60% and 71%, respectively.     

Validation of atmospheric scattering functions used in atmospheric correction over the ocean

A new approach has been developed to validate atmospheric correction (AC) over the ocean. The latter has been applied to the ground-segment data from the Medium Resolution Imaging Spectrometer (MERIS) on board the Environmental Satellite (Envisat) platform. An atmospheric validation database has been built up with a ground-based instrument, i.e. the Cimel radiometer from the Aerosol Robotic Network (AERONET). The aim of this work is to assess the atmospheric scattering functions needed to perform AC of remotely sensed data. The inputs to this new methodology were provided by AERONET, after inversion of radiometric measurements (i.e. solar direct extinctions and sky diffuse radiances) to get the inherent optical properties (IOPs) of the aerosols. The successive orders (SOs) of scattering code have been used as the radiative transfer tool in this study. This new concept for the validation of AC has been illustrated with the MERIS level-2 data extracted from the Meris Matchup In-situ Database (MERMAID) over the Acqua Alta Oceanographic Tower (AAOT, Venice – Italy). Results indicate first, an overestimate of the MERIS aerosol optical thickness (AOT) at 865 nm, and second, a marine reflectance affected by a negative bias of about 13% at 412.5 nm. This yields to an overestimate of the MERIS algal-1 pigment index, which may exceed 50%, over AAOT. The same trend is also observed in the determination of the algal-2 pigment index.     

The inter-comparison of AATSR dual-view aerosol optical thickness retrievals with results from various algorithms and instruments

The Advanced Along-Track Scanning Radiometer (AATSR) dual-view (ATSR-DV) aerosol retrieval algorithm is evaluated for a single scene over Germany (49–53? N, 7–12? E) on 13 October 2005 by comparison of the aerosol optical thickness (AOT) at 550 nm with products from Multiangle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS), in addition to the Atmospheric Aerosol Retrieval using Dual-View Angle Reflectance Channels (AARDVARC) algorithm developed at Swansea University. The AOT was retrieved from the AATSR using the ATSR-DV algorithm, for the pixel size of 1 km × 1 km (at nadir). Then these values were meshed to be consistent with the sampling of the products from the other satellite instruments. The ATSR-DV results compare favourably with the products from orbiting optical instruments dedicated to aerosol retrieval, such as MODIS and MISR, which leads to the conclusion that AATSR is well suited for aerosol retrieval over land when the dual view is used with the ATSR-DV algorithm.     

Towards the development of a regional version of MOD17 for the determination of gross and net primary productivity of oil palm trees

Conducting quantitative studies on the carbon balance or productivity of oil palm is important for understanding the role of this ecosystem in global climate change. The MOD17 algorithm is used for processing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to generate the values of gross primary productivity (GPP) and net primary productivity for input to global carbon cycle modelling. In view of the increasing importance of data on carbon sequestration at regional and national levels, we have studied one important factor affecting the accuracy of the implementation of MOD17 at the sub-global level, namely the database of MODIS land cover (MOD12Q1) used by MOD17. By using a study area of approximately 7 km × 7 km (49 MODIS pixels) in semi-rural Johor in Peninsular Malaysia and using Google Earth 0.75 m resolution images as ground data, we found that the land-cover type for only 16 of these 49 MODIS pixels was correctly identified by MOD12Q1 using its 1 km resolution land-cover database. This leads to errors of 24% to 50% in the maximum light use efficiency, leading to corresponding errors of 24% to 50% in the GPP. We show that by using the Finer Resolution Observation and Monitoring – Global Land Cover (FROM-GLC) land-cover database developed by Gong et al., this particular error can be essentially eliminated, but at the cost of using extra computing resources.     

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.     

Mesoscale cloud pattern classification over ocean with a neural network using a new index of cloud variability

The purpose of this study is to determine the feasibility of a mesoscale (<300 km) cloud classification using infrared radiance data of satellite‐borne instruments. A new method is presented involving an index called the diversity index (DI), derived from a parameter commonly used to describe ecosystem variability. In this respect, we consider several classes of value ranges of standard deviation of the brightness temperature at 11 µm (σ_BT). In order to calculate DI for 128×128 km² grids, subframes of 8 km×8 km are superimposed to the satellite image, and then σ_BT is calculated for all 256 subframes and assigned to one of the classes. Each observed cloud pattern is associated with an index characterized by the frequency of σ_BT classes within the scene, representative of a cloud type. Classification of different clouds is obtained from Advanced Very High Resolution Radiometer (AVHRR)‐NOAA 16 data at 1 km resolution. Stratus, stratocumulus and cumulus are specifically recognized by this window analysis using a DI threshold. Then, a six‐class scheme is presented, with the standard deviation of the infrared brightness temperature of the entire cloud scene (σ_c) and DI as inputs of a neural network algorithm. This neural network classifier achieves an overall accuracy of 77.5% for a six‐class scheme, and 79.4% for a three‐class scheme, as verified against the analyses of nephanalists as verified against a cloud classification from Météo France. As an application of the proposed methodology, regional cloud variability over Pacific is examined using cloud patterns derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) carried aboard Earth Observing System (EOS) Terra polar orbiter platform, for February 2003 and 2004. The comparison shows regional change in monthly mean cloud types, associated with 2003 El Niño and 2004 neutral events. A significant increase in the occurrence of convective clouds (+15%) and a decrease in stratiform clouds (?10%) are observed between the two months.     

Determination of ice cloud models using MODIS and MISR data

Representation of ice clouds in radiative transfer simulations is subject to uncertainties associated with the shapes and sizes of ice crystals within cirrus clouds. In this study, we examined several ice cloud models consisting of smooth, roughened, homogeneous and inhomogeneous hexagonal ice crystals with various aspect ratios. The sensitivity of the bulk scattering properties and solar reflectances of cirrus clouds to specific ice cloud models is investigated using the improved geometric optics method (IGOM) and the discrete ordinates radiative transfer (DISORT) model. The ice crystal habit fractions in the ice cloud model may significantly affect the simulations of cloud reflectances. A new algorithm was developed to help determine an appropriate ice cloud model for application to the satellite-based retrieval of ice cloud properties. The ice cloud particle size retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data, collocated with Multi-angle Imaging Spectroradiometer (MISR) observations, is used to infer the optical thicknesses of ice clouds for nine MISR viewing angles. The relative differences between view-dependent cloud optical thickness and the averaged value over the nine MISR viewing angles can vary from??0.5 to 0.5 and are used to evaluate the ice cloud models. In the case for 2 July 2009, the ice cloud model with mixed ice crystal habits is the best fit to the observations (the root mean square (RMS) error of cloud optical thickness reaches 0.365). This ice cloud model also produces consistent cloud property retrievals for the nine MISR viewing configurations within the measurement uncertainties.     

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

In this paper we evaluate the potential of ENVISAT–Medium Resolution Imaging Spectrometer (MERIS) fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes. A series of MERIS fused images (15 spectral bands; 25 m pixel size) is created using the linear mixing model and a Landsat Thematic Mapper (TM) image acquired over the Netherlands. First, the fused images are classified to produce a map of the eight main land-cover types of the Netherlands. Subsequently, the maps are validated using the Dutch land-cover/land-use database as a reference. Then, the fused image with the highest overall classification accuracy is selected as the best fused image. Finally, the best fused image is used to compute three vegetation indices: the normalized difference vegetation index (NDVI) and two indices specifically designed to monitor vegetation status using MERIS data: the MERIS terrestrial chlorophyll index (MTCI) and the MERIS global vegetation index (MGVI).

Results indicate that the selected data fusion approach is able to downscale MERIS data to a Landsat-like spatial resolution. The spectral information in the fused images originates fully from MERIS and is not influenced by the TM data. Classification results for the TM and for the best fused image are similar and, when comparing spectrally similar images (i.e. TM with no short-wave infrared bands), the results of the fused image outperform those of TM. With respect to the vegetation indices, a good correlation was found between the NDVI computed from TM and from the best fused image (in spite of the spectral differences between these two sensors). In addition, results show the potential of using MERIS vegetation indices computed from fused images to monitor individual fields. This is not possible using the original MERIS full resolution image. Therefore, we conclude that MERIS–TM fused images are very useful to map heterogeneous landscapes.     

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.