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.     

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.     

Mapping percentage tree cover from Envisat MERIS data using linear and nonlinear techniques

The aim of this study was to predict percentage tree cover from Envisat Medium Resolution Imaging Spectrometer (MERIS) imagery with a spatial resolution of 300 m by comparing four common models: a multiple linear regression (MLR) model, a linear mixture model (LMM), an artificial neural network (ANN) model and a regression tree (RT) model. The training data set was derived from a fine spatial resolution land cover classification of IKONOS imagery. Specifically, this classification was aggregated to predict percentage tree cover at the MERIS spatial resolution. The predictor variables included the MERIS wavebands plus biophysical variables (the normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of photosynthetically active radiation (fPAR), fraction of green vegetation covering a unit area of horizontal soil (fCover) and MERIS terrestrial chlorophyll index (MTCI)) estimated from the MERIS data. An RT algorithm was the most accurate model to predict percentage tree cover based on the Envisat MERIS bands and vegetation biophysical variables. This study showed that Envisat MERIS data can be used to predict percentage tree cover with considerable spatial detail. Inclusion of the biophysical variables led to greater accuracy in predicting percentage tree cover. This finer-scale depiction should be useful for environmental monitoring purposes at the regional scale.     

Land cover classification using multi‐temporal MERIS vegetation indices

The spectral, spatial, and temporal resolutions of Envisat's Medium Resolution Imaging Spectrometer (MERIS) data are attractive for regional‐ to global‐scale land cover mapping. Moreover, two novel and operational vegetation indices derived from MERIS data have considerable potential as discriminating variables in land cover classification. Here, the potential of these two vegetation indices (the MERIS global vegetation index (MGVI), MERIS terrestrial chlorophyll index (MTCI)) was evaluated for mapping eleven broad land cover classes in Wisconsin. Data acquired in the high and low chlorophyll seasons were used to increase inter‐class separability. The two vegetation indices provided a higher degree of inter‐class separability than data acquired in many of the individual MERIS spectral wavebands. The most accurate landcover map (73.2%) was derived from a classification of vegetation index‐derived data with a support vector machine (SVM), and was more accurate than the corresponding map derived from a classification using the data acquired in the original spectral wavebands.     

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.     

The MERIS terrestrial chlorophyll index

The long wavelength edge of the major chlorophyll absorption feature in the spectrum of a vegetation canopy moves to longer wavelengths with an increase in chlorophyll content. The position of this red-edge has been used successfully to estimate, by remote sensing, the chlorophyll content of vegetation canopies. Techniques used to estimate this red-edge position (REP) have been designed for use on small volumes of continuous spectral data rather than the large volumes of discontinuous spectral data recorded by contemporary satellite spectrometers. Also, each technique produces a different value of REP from the same spectral data and REP values are relatively insensitive to chlorophyll content at high values of chlorophyll content. This paper reports on the design and indirect evaluation of a surrogate REP index for use with spectral data recorded at the standard band settings of the Medium Resolution Imaging Spectrometer (MERIS). This index, termed the MERIS terrestrial chlorophyll index (MTCI), was evaluated using model spectra, field spectra and MERIS data. It was easy to calculate (and so can be automated), was correlated strongly with REP but unlike REP was sensitive to high values of chlorophyll content. As a result this index became an official MERIS level-2 product of the European Space Agency in March 2004. Further direct evaluation of the MTCI is proposed, using both greenhouse and field data.     

Atmospheric correction of ENVISAT/MERIS data over case II waters: the use of black pixel assumption in oxygen and water vapour absorption bands

The Medium Resolution Imaging Spectrometer (MERIS) sensor, with its good physical design, can provide excellent data for water colour monitoring. However, owing to the shortage of shortwave-infrared (SWIR) bands, the traditional near-infrared (NIR)–SWIR algorithm for atmospheric correction in inland turbid case II waters cannot be extended to the MERIS data directly, which limits its applications. In this study, we developed a modified NIR black pixel method for atmospheric correction of MERIS data in inland turbid case II waters. In the new method, two special NIR bands provided by MERIS data, an oxygen absorption band (O₂ A-band, 761 nm) and a water vapour absorption band (vapour A-band, 900 nm), were introduced to keep the assumption of zero water-leaving reflectance valid according to the fact that both atmospheric transmittance and water-leaving reflectance are very small at these two bands. After addressing the aerosol wavelength dependence for the cases of single- and multiple-scattering conditions, we further validated the new method in two case lakes (Lake Dianchi in China and Lake Kasumigaura in Japan) by comparing the results with in situ measurements and other atmospheric correction algorithms, including Self-Contained Atmospheric Parameters Estimation for MERIS data (SCAPE-M) and the Basic ERS (European Remote Sensing Satellite) & ENVISAT (Environmental Satellite) (A)ATSR ((Advanced) Along-Track Scanning Radiometer) and MERIS (BEAM) processor. We found that the proposed method had acceptable accuracy in the bands within 560–754 nm (MERIS bands 5–10) (average absolute deviation (AAD) = 0.0081, average deviation (AD) = 0.0074), which are commonly used in the estimation models of chlorophyll-a (chl-a) concentrations. In addition, the performance of the new method was superior to that of the BEAM processor and only slightly worse than that of SCAPE-M in these bands. Considering its acceptable accuracy and simplicity both in principle and at implementation compared with the SCAPE-M method, the new method provides an option for atmospheric correction of MERIS data in inland turbid case II waters with applications aiming for chl-a estimation.     

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.     

The MERIS Global Vegetation Index (MGVI): Description and preliminary application

This paper describes the physical and mathematical approach followed to design a vegetation index optimized for the Medium Resolution Imaging Spectrometer (MERIS) sensor, i.e. the MERIS Global Vegetation Index (MGVI). It complements an earlier feasibility study presented elsewhere in this issue by Govaerts and collaborators. Specifically, the crucial issue of the dependency of the vegetation index on changes in illumination and observing geometries is addressed, together with the atmospheric contamination problem. The derivation of the optimal MGVI index formulae allows a comparison of its performance with that of the widely used Normalized Difference Vegetation Index (NDVI), both from a theoretical and an experimental point of view. Data collected by the MOS/IRS-P3 instrument since March 1996 in spectral bands analogous to those that will be available from MERIS can be used to evaluate the MVGI.     

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.     

The potential for estimating chlorophyll content from a vegetation canopy using the Medium Resolution Imaging Spectrometer (MERIS)

In 2000, the European Space Agency aims to launch the Envisat-1 satellite platform which will carry the Medium Resolution Imaging Spectrometer (MERIS), an advanced optical sensor designed to acquire Earth observation data at regional to global scales. Of particular relevance to terrestrial ecosystems functioning, the MERIS offers the potential to estimate the spectral location of the maximum slope, termed the red edge position (REP), which marks the boundary between chlorophyll absorption in the red wavelengths and the high infrared reflectance due to leaf internal scattering. However, although a first derivative transformation of the reflectance spectra will highlight the maximum slope position, the accurate location of the REP is limited by the spectral sampling resolution of the sensor. A theoretical analysis, using a combined leaf-canopy radiative transfer model, demonstrates that the MERIS, having five coarsely spaced wavebands in the region of the REP, can be utilized for monitoring spectral shifts of the REP, resulting from variation in leaf chlorophyll content or leaf area index.     

Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China

Evaluating vegetation phenology is crucial for a better understanding of the effects of climate change on the terrestrial ecosystem. The scientific community has used various vegetation index data sets from different sensors to quantify vegetation phenology from regional to global scales. The normalized difference vegetation index (NDVI) related to photosynthetic activities is the most widely used index. Recently, a number of published articles have used the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) to measure vegetation phenology. MTCI can closely represent the red-edge position (REP). Unlike NDVI, MTCI is more sensitive to high values of chlorophyll content. However, the consistency of vegetation phenological metrics derived from MTCI and NDVI needs to be further explored. This study compared two phenological metrics, i.e. onset of greenness (OG) and end of senescence (ES), extracted from MERIS MTCI data and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) first generation NDVI (NDVIg) data, which has the longest time records, at nine regions in China from 2003 to 2006. The results showed that the differences of OG and ES vary between different vegetation types, regions, and years, although both NDVI and MTCI time series capture the growth patterns well for most vegetation types. Compared to ES, the OG estimates are more consistent. NDVI yields in general later ES estimates than MTCI.     

Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer

Intense plankton blooms (colloquially called ‘red tides’) are becoming an increasingly important phenomenon in coastal waters. This Letter shows how the European satellite sensor MERIS (Medium Resolution Imaging Spectrometer) can be used to detect a peak in the optical spectrum of water‐leaving radiance near 705?nm which provides a more specific response to some types of these blooms. Images and spectra are presented, derived from a MERIS scene where small areas have this response. One such area is at the site of a fish farm where bloom conditions were confirmed by surface observations and measurements. The observed spectra are compared to model results to demonstrate how these responses arise. Inspection of data from other parts of the world shows similar features in US and European waters.     

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.     

High resolution spatio‐temporal water vapour mapping using GPS and MERIS observations

Improved knowledge of atmospheric water vapour and its temporal and spatial variability is of great scientific interest for climate research and weather prediction. Moreover, the availability of fine resolution water vapour maps is expected to reduce significant errors in applications using the Global Positioning System, GPS, or radar interferometry. Several methods exist to estimate water vapour using satellite systems. Combining radiances as measured in two spectral bands of the Medium Resolution Imaging Spectrometer (MERIS) results in an Integrated Water Vapor (IWV) product with high spatial resolution, up to 300 m, but a limited temporal resolution of about three days, in case of cloud free conditions. On the other hand, IWV estimates can be derived from the zenith total delays as observed by continuous GPS networks. The GPS IWV estimates have a higher temporal resolution of typically 1 hour, but, even in Western Europe, inter‐station distances are at least tenths of kilometres. Here we describe how to obtain IWV products with high spatio‐temporal resolution by combining GPS and MERIS IWV estimates. For this purpose an analysis is made of MERIS and GPS based IWV data, retrieved at the same day over Western Europe. A variance–covariance analysis is performed and is subsequently applied to produce time series of combined high‐resolution water vapour maps using Kriging. The research presented here is a first step towards near real‐time fine resolution water vapour products.     

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.     

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.     

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.     

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.     

A comparison of atmospheric phase delay estimated by ASAR and MERIS over the Campania area

Propagation delay through the atmosphere is a key problem in coherent processing of synthetic aperture radar (SAR) data. Modern multitemporal interferometric techniques compensate the atmospheric phase delay contribution by analysing a stack of data. However, assessment of the achieved accuracy of the retrieved atmospheric component is still an open issue. In this work we report the results of an experiment carried out over a wide area aimed at comparing the zenith delay (ZD) estimated by radar and multispectral sensors. In particular, we refer to the instruments onboard the Envisat satellite and specifically to the Advanced Synthetic Aperture Radar (ASAR) and Multispectral Medium Resolution Imaging Spectrometer (MERIS) sensors that simultaneously acquire data along the same orbit. The study is preliminary to the possible exploitation of the MERIS water vapour product for compensating the atmospheric phase delay signals in a long series of acquisitions used in the multipass differential interferometric synthetic aperture radar (DInSAR) techniques to achieve higher accuracy and/or to extend the applicability of the technique to emergency situations, as well as to the possible use of SAR interferometry in meteorological applications.