G.F. Epema

Derivation of the red edge index using the MERIS standard band setting

Within ESAs Earth Observation programme, the Medium Resolution Imaging Spectrometer (MERIS) is one of the payload components of the European polar platform ENVISAT-1. MERIS will be operated with a standard band setting of 15 bands. The objective of this paper was to study whether the vegetation red edge index can be derived from the MERIS standard band setting. This red edge provides useful information on the physiological status of the vegetation. Two different data sets are explored for simulating the red edge using MERIS spectral bands. Results show that the maximum first derivative and a three-point Lagrangian technique are not appropriate measures for the red edge index. A linear method, estimating the inflexion point as the reflectance midpoint between the NIR plateau and the red minimum, is a more robust method. Results also show that the MERIS bands at 665, 705, 753.75 and 775 nm can be used for applying the linear method for red edge index estimation. However, since the band at 753.75 nm is located very close to the oxygen absorption feature of the atmosphere, an atmospheric correction must be applied previous to calculating the position of the red edge using the MERIS bands.

Determining iron content in Mediterranean soils in partly vegetated areas, using spectral reflectance and imaging spectroscopy

The possibility of quantifying iron content in the topsoil of the slopes of the El Hacho Mountain complex in Southern Spain using imaging spectroscopy is investigated. Laboratory, field and airborne spectrometer (ROSIS) data are acquired, in combination with soil samples, which are analysed for dithionite extractable iron (Fed) content. Analysis of the properties of two iron related absorption features present in laboratory spectra demonstrates good relations, especially between the standard deviation (S.D.) of the values in an absorption feature and the Fed content (R2 = 0.67) as well as the ratio based Redness Index (R2 = 0.51). Such derived relations are less strong for the ROSIS data (R2 for S.D. = 0.26 and R2 for Redness Index = 0.22). The spatial distribution of iron in vegetated areas shows a strong sensitivity of these relations with the presence of vegetation. A combination of both methods shows that the overestimation of the Fed content with the one method is (partly) compensated by the underestimation with the other method.

MERIS and the red-edge position

Abstract The Medium Resolution Imaging Spectrometer (MERIS) is a payload component of Envisat-1. MERIS will be operated over land with a standard 15 band setting acquiring images with a 300 m spatial resolution. The red-edge position (REP) is a promising variable for deriving foliar chlorophyll concentration, which plays an important role in ecosystem processes. The objectives of this paper are: (1) to study which factors effect the REP of vegetation, (2) to study whether this REP can be derived from the MERIS standard band setting and (3) to show what REP represents at the scale of MERIS data. Two different data sets were explored for simulating the REP using MERIS bands: (1) simulated data using reflectance models and (2) airborne reflectance spectra of an agricultural area obtained by the airborne visible-infrared imaging spectrometer (AVIRIS). A “linear method”, assuming a straight slope of the reflectance spectrum around the midpoint of the slope, was a robust method for determining the REP and the MERIS bands at 665, 708.75, 753.75 and 778.75 nm could be used for applying the “linear method” for REP estimation. Results of the translation to the scale of MERIS data were very promising for applying MERIS at, for instance, the ecosystem level.

Scaling dimensions in spectroscopy of soil and vegetation

Abstract The paper revises and clarifies definitions of the term scale and scaling conversions for imaging spectroscopy of soil and vegetation. We demonstrate a new four-dimensional scale concept that includes not only spatial but also the spectral, directional and temporal components. Three scaling remote sensing techniques are reviewed: (1) radiative transfer, (2) spectral (un)mixing, and (3) data fusion. Relevant case studies are given in the context of their up- and/or down-scaling abilities over the soil/vegetation surfaces and a multi-source approach is proposed for their integration. Radiative transfer (RT) models are described to show their capacity for spatial, spectral up-scaling, and directional down-scaling within a heterogeneous environment. Spectral information and spectral derivatives, like vegetation indices (e.g. TCARI/OSAVI), can be scaled and even tested by their means. Radiative transfer of an experimental Norway spruce (Picea abies (L.) Karst.) research plot in the Czech Republic was simulated by the Discrete Anisotropic Radiative Transfer (DART) model to prove relevance of the correct object optical properties scaled up to image data at two different spatial resolutions. Interconnection of the successive modelling levels in vegetation is shown. A future development in measurement and simulation of the leaf directional spectral properties is discussed. We describe linear and/or non-linear spectral mixing techniques and unmixing methods that demonstrate spatial down-scaling. Relevance of proper selection or acquisition of the spectral endmembers using spectral libraries, field measurements, and pure pixels of the hyperspectral image is highlighted. An extensive list of advanced unmixing techniques, a particular example of unmixing a reflective optics system imaging spectrometer (ROSIS) image from Spain, and examples of other mixture applications give insight into the present status of scaling capabilities. Simultaneous spatial and temporal down-scaling by means of a data fusion technique is described. A demonstrative example is given for the moderate resolution imaging spectroradiometer (MODIS) and LANDSAT Thematic Mapper (TM) data from Brazil. Corresponding spectral bands of both sensors were fused via a pyramidal wavelet transform in Fourier space. New spectral and temporal information of the resultant image can be used for thematic classification or qualitative mapping. All three described scaling techniques can be integrated as the relevant methodological steps within a complex multi-source approach. We present this concept of combining numerous optical remote sensing data and methods to generate inputs for ecosystem process models.

Remotely sensed habitat indicators for predicting distribution of impala Aepyceros melampus in the Okavango Delta, Botswana

We studied the spatial and temporal habitat use of impala in Botswanas Okavango Delta at landscape level with the aid of satellite imagery, with minimal fieldwork. We related remotely sensed vegetation to impala habitat preferences, by first distinguishing three vegetation types through a multi-temporal classification, and dividing these into subclasses on the basis of their Normalized Difference Vegetation Index (NDVI). This indicator for abundance and greenness of biomass was assessed for wet and dry season separately. Similarly, habitat use was assessed for both seasons by allocating vegetation classes to bimonthly impala observations. Impala distribution patterns coincided with NDVI-based subclasses of the landscape, nested within broad vegetation types, to which impala did not show a marked seasonal response. We suggest that this methodology, using limited field data, offers a functional habitat classification for sedentary herbivores, which appears particularly valuable for application in extensive areas with high spatial variability, but with restricted access.

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 MERISs 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.

Simulation of MERIS data: potentials and limitations for mapping (soil) mineralogy

Abstract Within the framework of ESAs Earth Observation Program, the Medium Resolution Imaging Spectrometer (MERIS) is being developed as one of the payload components of the ENVISAT-1. Although MERIS is a fully programmable imaging spectrometer, a standard 15 channel band set will be transmitted for each 300 m pixel (over land) covering the visible and near-infrared wavelength range. Since MERIS is a multidisciplinary sensor providing data that can be input into ecosystem models at various scales, we studied MERIS performance for mineral mapping relative to the scale of observation using simulated data sets degraded to various resolutions in the range of 10 m to 300 m. Algorithms to simulate MERIS data using airborne imaging spectrometer data sets are presented using data from HyMAP acquired on 2 June 1999 over the Tabernas area of southern Spain (Almeria province). A spectral library of mineral spectra was examined to identify potential mappable mineral suites at the MERIS spectral resolution and band setting. A total of 74 (out of 160) minerals have absorption features in the MERIS wavelengths; most of them represent ore (or related) minerals not likely to be “seen” by the sensor given its FOV. The study thus focused on goethite and hematite mapping. The HyMAP data was used to simulate MERIS data at various resolutions. Mineral maps were produced using the cross correlogram spectral mapping (CCSM) approach. The results were evaluated against the mineral maps produced using the original HyMAP data using the (1) mis-classification, (2) RMS value of the CCSM and (3) the optimal sampling size derived from local variance estimates. (1) and (2) show that accuracy decreases rapidly with larger FOV, possibly due to increased spectral mixing. The optimal sampling sizes calculated for hematite and goethite reflect this. Values were to be 20–30 m for goethite and

Scaling to the MERIS Resolution: Mapping Accuracy and Spatial Variability

Abstract Within the framework of ESAs Earth Observation Program, the Medium Resolution Imaging Spectrometer (MERIS) is developed as one of the payload components of the ENVISAT‐1. MERIS is a fully programmable imaging spectrometer, however a standard 15 channel band set will be transmitted for each 300 m. pixel (over land) covering the visible and near‐infrared wavelength range. Since MERIS is a multidisciplinary sensor providing data that can be input into ecosystem models at various scales, we studied MERIS performance relative to the scale of observation using simulated data sets degraded to various resolutions in the range of 12m. to 300m. Algorithms to simulate MERIS data using airborne imaging spectrometer data sets are presented, including a case study from GERIS 63 channel data over a agricultural site in central Spain (the Almaden test site). Through a pixel purity analysis, end members are derived from the MERIS‐type data and subsequently used as input to a spectral unmixing analysis yielding fraction of end member (abundance) images. The original data as well as the abundance images are spatially analyzed using variogram surfaces and mapping accuracy is modeled at various spatial scales. We observe differences between the sampling resolutions (i.e., pixel size) found to be optimal for the different ground cover types. The optimal scale for observing different components of spectral mixtures varies depending on the type of mixture, however, the best possible resolutions in all cases of mixtures studied is below the envisaged 300 m. field of view for the MERIS sensor. The analysis of semivariogram surfaces demonstrates that the spatial distribution of the variance of the mixtures is invariant with scale, thus the observed mapping discrepancies are not related to the data processing but to the observations themselves.