EO-1 Hyperion Data Research Articles

HYPERSPECTRAL TRANSFORMATION FROM EO-1 ALI IMAGERY USING PSEUDO-HYPERSPECTRAL IMAGE SYNTHESIS ALGORITHM

Hyperspectral remote sensing is more effective than multispectral remote sensing in many application fields because of having hundreds of observation bands with high spectral resolution. However, hyperspectral remote sensing resources are limited both in temporal and spatial coverage. Therefore, simulation of hyperspectral imagery from multispectral imagery with a small number of bands must be one of innovative topics. Based on this background, we have recently developed a method, Pseudo-Hyperspectral Image Synthesis Algorithm (PHISA), to transform Landsat imagery into hyperspectral imagery using the correlation of reflectance at the corresponding bands between Landsat and EO-1 Hyperion data. This study extends PHISA to simulate pseudo-hyperspectral imagery from EO-1 ALI imagery. The pseudo-hyperspectral imagery has the same number of bands as that of high-quality Hyperion bands and the same swath width as ALI scene. The hyperspectral reflectance data simulated from the ALI data show stronger correlation with the original Hyperion data than the one simulated from Landsat data. This high correlation originates from the concurrent observation by the ALI and Hyperion sensors that are on-board the same satellite. The accuracy of simulation results are verified by a statistical analysis and a surface mineral mapping. With a combination of the advantages of both ALI and Hyperion image types, the pseudo-hyperspectral imagery is proved to be useful for detailed identification of minerals for the areas outside the Hyperion coverage.

Discriminating Spectral Signatures Among and Within Two Closely Related Grapevine Species

Several North American Vitis species are used to breed scions and rootstocks, including V. riparia and V. rupestris. However, the degree to which Vitis species can be distinguished using remote sensing is not well known. Here we explore whether two North American Vitis species and genotypes growing in a common garden can be discriminated with leaf and canopy hyperspectral reflectance factor data (350-2500 nm) using independent t-test and derivative analysis. Foliar properties and spectral indices of the grapevines were evaluated with analysis of the variance (ANOVA) and pair-wise Bonferroni adjusted t-tests. The results showed that V. riparia and V. rupestris can be distinguished at the leaf level spectra of visible, near- and infrared spectral regions. At the canopy level, genotypes were spectrally discriminated with limited success. The Photochemical Reflectance Index (PRI) demonstrated the highest potential not only to differentiate two species, but also two genotype pair groups within V. rupestris. This finding was also true for the PRI calculated with simulated EO-1 Hyperion data. These capacities to distinguish Vitis species, and to a lesser extent genotypes, using spectral signatures have important applications in remote monitoring of vineyards for plants health and also for locating wild Vitis populations for future crop improvement efforts.

Hyperspectral Remote Sensing in Characterizing Soil Salinity Severity using SVM Technique - A Case Study of Alluvial Plains

Hyperspectral remote sensing is widely used for analyzing and estimating the severity of soil salinity in arid and semi-arid regions, throughout the world. The present study is an attempt to map the various soil salinity severity classes using different hyperspectral indices generated using EO-1 Hyperion data and Support Vector Machine (SVM) method, in the Mathura region of Indo-Gangetic plain of India. Various hyperspectral indices such as Soil Adjusted Vegetation Index (SAVI), Desertification Soil Index (DSI), Salinity Index (SI) and Normalized Difference Water Index (NDWI) were chosen, generated and effectively used for characterizing and mapping soil salinity severity. Salt infestation in the study area was categorized into four classes of normal, slight, moderate, high soil salinity. Hyperspectral indices helped in identification of various features like vegetation, waterlogged area and soil areas under various classes of soil salinity. The salinity index and desertification soil indices were found to respond well to varying degrees of soil salinity. The SVM technique generated soil salinity map with overall classification accuracy of 78.13 percent, with a kappa statistic of 0.71. The results indicated highest accuracy in high soil salinity class in comparison to other classes, attaining producers and users accuracies of 85.71% and 90.0% respectively. Slight saline class showed poor producers and users accuracy. The result showed high accuracy for mapping soil salinity severity with machine learning classifier like SVM using various indices generated from hyperspectral remote sensing data. These generated images can be effectively used in planning of various management practices and effective reclamation measures of salt affected soils.

Evaluation of EO-1 Hyperion Data for Crop Studies in Part of Indo-Gangatic Plains: A Case Study of Meerut District

Due to the high number of bands in the hyperspectral image, the selection of optimum bands for crop classification is a prerequisite. The Hyperion sensor has 242 spectral bands out of which 143 useable bands were selected. The bands reflected wavelength from 400 to 1000 nm to the VNIR spectrometer and transmitted the band from 900 to 2500 nm to the SWIR spectrometer. Spectral Angle Mapping Classification (SAMC) approach and a multi-scale object oriented method are applied for crop studies. The result obtained from the accuracy assessment by comparing Ground Control Points (GCP) with the help of image spectra shows 78% of overall accuracy. This shows that these data are highly useful in studying the crop diversification.

Urban land cover classification using hyperspectral data

Abstract. Urban land cover classification using remote sensing data is quite challenging due to spectrally and spatially complex urban features. The present study describes the potential use of hyperspectral data for urban land cover classification and its comparison with multispectral data. EO-1 Hyperion data of October 05, 2012 covering parts of Bengaluru city was analyzed for land cover classification. The hyperspectral data was initially corrected for atmospheric effects using MODTRAN based FLAASH module and Minimum Noise Fraction (MNF) transformation was applied to reduce data dimensionality. The threshold Eigen value of 1.76 in VNIR region and 1.68 in the SWIR region was used for selection of 145 stable bands. Advanced per pixel classifiers viz., Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were used for general urban land cover classification. Accuracy assessment of the classified data revealed that SVM was quite superior (82.4 per cent) for urban land cover classification as compared to SAM (67.1 per cent). Selecting training samples using end members significantly improved the classification accuracy by 20.1 per cent in SVM. The land cover classification using multispectral LISS-III data using SVM showed lower accuracy mainly due to limitation of spectral resolution. The study indicated the requirement of additional narrow bands for achieving reasonable classification accuracy of urban land cover. Future research is focused on generating hyperspectral library for different urban features.

Mapping forest biomass from space – Fusion of hyperspectral EO1-hyperion data and Tandem-X and WorldView-2 canopy height models

Spaceborne sensors allow for wide-scale assessments of forest ecosystems. Combining the products of multiple sensors is hypothesized to improve the estimation of forest biomass. We applied interferometric (Tandem-X) and photogrammetric (WorldView-2) based predictors, e.g. canopy height models, in combination with hyperspectral predictors (EO1-Hyperion) by using 4 different machine learning algorithms for biomass estimation in temperate forest stands near Karlsruhe, Germany. An iterative model selection procedure was used to identify the optimal combination of predictors. The most accurate model (Random Forest) reached a r2 of 0.73 with a RMSE of 14.9% (29.4t/ha). Further results revealed that the predictive accuracy depended highly on the statistical model and the area size of the field samples. We conclude that a fusion of canopy height and spectral information allows for accurate estimations of forest biomass from space.

Narrowband Bio-Indicator Monitoring of Temperate Forest Carbon Fluxes in Northeastern China

Developments in hyperspectral remote sensing techniques during the last decade have enabled the use of narrowband indices to evaluate the role of forest ecosystem variables in estimating carbon (C) fluxes. In this study, narrowband bio-indicators derived from EO-1 Hyperion data were investigated to determine whether they could capture the temporal variation and estimate the spatial variability of forest C fluxes derived from eddy covariance tower data. Nineteen indices were divided into four categories of optical indices: broadband, chlorophyll, red edge, and light use efficiency. Correlation tests were performed between the selected vegetation indices, gross primary production (GPP), and ecosystem respiration (Re). Among the 19 indices, five narrowband indices (Chlorophyll Index RedEdge 710, scaled photochemical reflectance index (SPRI)*enhanced vegetation index (EVI), SPRI*normalized difference vegetation index (NDVI), MCARI/OSAVI[705, 750] and the Vogelmann Index), and one broad band index (EVI) had R-squared values with a good fit for GPP and Re. The SPRI*NDVI has the highest significant coefficients of determination with GPP and Re (R2 = 0.86 and 0.89, p < 0.0001, respectively). SPRI*NDVI was used in atmospheric inverse modeling at regional scales for the estimation of C fluxes. We compared the GPP spatial patterns inversed from our model with corresponding results from the Vegetation Photosynthesis Model (VPM), the Boreal Ecosystems Productivity Simulator model, and MODIS MOD17A2 products. The inversed GPP spatial patterns from our model of SPRI*NDVI had good agreement with the output from the VPM model. The normalized difference nitrogen index was well correlated with measured C net ecosystem exchange. Our findings indicated that narrowband bio-indicators based on EO-1 Hyperion images could be used to predict regional C flux variations for Northeastern China’s temperate broad-leaved Korean pine forest ecosystems.

Spaceborne Mine Waste Mineralogy Monitoring in South Africa, Applications for Modern Push-Broom Missions: Hyperion/OLI and EnMAP/Sentinel-2

Remote sensing analysis is a crucial tool for monitoring the extent of mine waste surfaces and their mineralogy in countries with a long mining history, such as South Africa, where gold and platinum have been produced for over 90 years. These mine waste sites have the potential to contain problematic trace element species (e.g., U, Pb, Cr). In our research, we aim to combine the mapping and monitoring capacities of multispectral and hyperspectral spaceborne sensors. This is done to assess the potential of existing multispectral and hyperspectral spaceborne sensors (OLI and Hyperion) and future missions, such as Sentinel-2 and EnMAP (Environmental Mapping and Analysis Program), for mapping the spatial extent of these mine waste surfaces. For this task we propose a new index, termed the iron feature depth (IFD), derived from Landsat-8 OLI data to map the 900-nm absorption feature as a potential proxy for monitoring the spatial extent of mine waste. OLI was chosen, because it represents the most suitable sensor to map the IFD over large areas in a multi-temporal manner due to its spectral band layout; its (183 km × 170 km) scene size and its revisiting time of 16 days. The IFD is in good agreement with primary and secondary iron-bearing minerals mapped by the Material Identification and Characterization Algorithm (MICA) from EO-1 Hyperion data and illustrates that a combination of hyperspectral data (EnMAP) for mineral identification with multispectral data (Sentinel-2) for repetitive area-wide mapping and monitoring of the IFD as mine waste proxy is a promising application for future spaceborne sensors. A maximum, absolute model error is used to assess the ability of existing and future multispectral sensors to characterize mine waste via its 900-nm iron absorption feature. The following sensor-signal similarity ranking can be established for spectra from gold mining material: EnMAP 100% similarity to the reference, ALI 97.5%, Sentinel-2 97%, OLI and ASTER 95% and ETM+ 91% similarity.

Data-Driven Compressive Sampling and Learning Sparse Coding for Hyperspectral Image Classification

Exploring the sparsity in classifying hyperspectral vectors proves to lead to state-of-the-art performance. To learn a compact and discriminative dictionary for accurate and fast classification of hyperspectral images, a data-driven Compressive Sampling (CS) and learning sparse coding scheme are use to reduce the dimensionality and size of the dictionary respectively. First, a sparse radial basis function (RBF) kernel learning network (S-RBFKLN) is constructed to learn a compact dictionary for sparsely representing hyperspectral vectors. Then a data-driven compressive sampling scheme is designed to reduce the dimensionality of the dictionary, and labels of new samples are derived from coding coefficients. Some experiments are taken on NASA EO-1 Hyperion data and AVIRIS Indian Pines data to investigate the performance of the proposed method, and the results show its superiority to its counterparts.

Forest tree species discrimination in western Himalaya using EO-1 Hyperion

The information acquired in the narrow bands of hyperspectral remote sensing data has potential to capture plant species spectral variability, thereby improving forest tree species mapping. This study assessed the utility of spaceborne EO-1 Hyperion data in discrimination and classification of broadleaved evergreen and conifer forest tree species in western Himalaya. The pre-processing of 242 bands of Hyperion data resulted into 160 noise-free and vertical stripe corrected reflectance bands. Of these, 29 bands were selected through step-wise exclusion of bands (Wilk's Lambda). Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) algorithms were applied to the selected bands to assess their effectiveness in classification. SVM was also applied to broadband data (Landsat TM) to compare the variation in classification accuracy. All commonly occurring six gregarious tree species, viz., white oak, brown oak, chir pine, blue pine, cedar and fir in western Himalaya could be effectively discriminated. SVM produced a better species classification (overall accuracy 82.27%, kappa statistic 0.79) than SAM (overall accuracy 74.68%, kappa statistic 0.70). It was noticed that classification accuracy achieved with Hyperion bands was significantly higher than Landsat TM bands (overall accuracy 69.62%, kappa statistic 0.65). Study demonstrated the potential utility of narrow spectral bands of Hyperion data in discriminating tree species in a hilly terrain.

Experimental simulation on mixed spectra of leaves and calcite for inversion of carbonate minerals from EO-1 Hyperion data

For geological passive optical remote sensing of minerals for vegetation covered areas, it is necessary to unmix the spectral mixture of leaf and mineral, and to estimate the cover proportions of mineral. In this study, a spectral mixture experiment of calcite and green single-layer leaf was performed to present a novel spectral mixture model (SMM), where the leaf transmission was taken into account compared with the traditional linear spectral model. The measurement of the leaf cover proportions presents a generally good agreement with the inversed results. And the mean relative error is less than 10% between the inversion and measurement of the cover proportions. Furthermore, the absorption depths of calcite at wavelength 2.33 μm will be lower with the increase of the leaf cover proportions, but the absorption positions have no shift. The continuum-removed band depths (CRBD) after continuum removal for measured mixture spectra were fitted well to the cover proportion of calcite as a third-degree fitting equation. Then the spectral feature fitting method was applied to the Hyperion image to verify the existence of calcite in the study area and was used to retrieve the cover proportions of carbonate mineral for the Luanping area, located in Hebei Province, China. Therefore, it was robust to unmix the spectra of leaf and carbonate mineral and to inverse their cover proportions using the proposed method.

Sub-pixel mineral mapping of a porphyry copper belt using EO-1 Hyperion data

The main aim of the present study was to examine the feasibility of the EO-1 Hyperion data in discriminating and mapping diagnostic alteration minerals around porphyry copper deposits (PCDs), verified by field surveys and laboratory analyses. A partial sub-pixel method, mixture tuned matched filtering (MTMF), was implemented on a pre-processed and calibrated Hyperion dataset. The tested area is situated at the Central Iranian Volcano-Sedimentary Complex, where abundant porphyry copper deposits like Sarcheshmeh, Darrehzar, and Sereidun are located. The characteristic alteration minerals identified by Hyperion data included biotite, muscovite, illite, kaolinite, goethite, hematite, jarosite, pyrophyllite, and chlorite. Discrimination of these minerals especially biotite and iron oxide (hematite and goethite) can provide valuable evidences for PCD exploration projects. Results revealed that Hyperion data prove to be powerful in discriminating and mapping various types of alteration zones while the data were subjected to adequate pre-processing.

Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data

Understanding the dynamics of mangroves at the species level is the key for securing sustainable conservation of mangrove forests around the globe. This study demonstrates the capability of the hyper-dimensional remote sensing data for discriminating diversely-populated tropical mangrove species. It was found that five different tropical mangrove species of Southern Thailand, including Avicennia alba, Avicennia marina, Bruguiera parviflora, Rhizophora apiculata, and Rhizophora mucronata, were correctly classified. The selected data treatment (a well-established spectral band selector) helped improve the overall accuracy from 86% to 92%, despite the remaining confusion between the two members of the Rhizophoraceae family and the pioneer species. It is therefore anticipated that the methodology presented in this study can be used as a practical guideline for detailed mangrove species mapping in other study areas. The next stage of this work will be to exploit the differences between the leaf textures of the two Rhizophoraceae mangroves in order to refine the classification outcome.

Using EO-1 Hyperion Data as HyspIRI Preparatory Data Sets for Volcanology Applied to Mt Etna, Italy

One of the main goals of the Hyperspectral and Infrared Imager (HyspIRI) mission is to provide global observations of surface attributes at local and landscape spatial scales (tens of meters to hundreds of kilometers) to map volcanic gases and surface temperatures, which are identified as indicators of impending volcanic hazards, as well as plume ejecta which pose risks to aircraft and people and property downwind. Our project has created precursor HyspIRI data sets for volcanological analyses, using existing data over Mt. Etna, Italy. We have identified 28 EO-1 Hyperion data acquisitions, and 12 near-coincident ASTER data acquisitions, covering six eruptive periods between 2001 and 2010. These data sets provide us with 30 m hyperspectral VSWIR data and 90 m multispectral TIR data (satellite). They allowed us to examine temporal sequences of several Etnaean eruptions. We addressed the following critical questions, directly related to understanding eruption hazards: 1) What do changes in SO2 emissions tell us about a volcano's activity? How well do these measurements compare with ground-based COSPEC measurements? 2) How do we use measurements of lava flow temperature and volume to predict advances of the flow front? 3) What do changes in lava lake temperatures and energy emissions tell us about possible eruptive behavior?

Spectral pathways for effective delineation of high-grade bauxites: a case study from the Savitri River Basin, Maharashtra, India, using EO-1 Hyperion data

Bauxite, the only source of aluminium, is an aggregate of minerals, most of which are oxides and hydroxides of aluminium and iron such as gibbsite, bohemite, goethite and haematite. Bauxite is used in the chemical and refractory industries and its quality is controlled by the presence of impurities such as iron and silica. Bauxite commonly occurs together with iron-rich laterites as alteration products of parental igneous and metamorphic rocks. Aluminium-rich bauxites grade towards highly ferruginous laterites with a transitional Al-rich laterites or ferruginous bauxite, herein described as Al-laterites. In the Savitri River Basin, bauxite contains 58–75% gibbsite, 6–11% goethite and 19–26% haematite, whereas the mineralogy of Al-laterites and Fe-laterites are dominated by haematite (29–68%) and goethite (6–25%) with subordinate amounts of gibbsite. Conventional techniques to demarcate the high-grade pockets of bauxites rich in gibbsite are tedious, time consuming and involve detailed field sampling and geochemical analyses. Our work illustrates how spectral properties of these three litho-units can be effectively utilized in mapping of high-grade bauxites occurring over wide areas using hyperspectral remote sensing (HRS). The methodology adopted herein involves generation of noise-free field spectral database of target materials, linear unmixing of field spectra for constituent minerals, classification of preprocessed Hyperion images using field spectra and finally accuracy assessment for ore grade estimation. It is observed that bauxite mapping using Hyperion data and noise-free field spectra yielded results that correlate well with the chemistry and mineralogy of representative samples. By adopting the above procedure, we achieved classification accuracies of 100%, 71% and 89% for bauxite, Al-laterite and Fe-laterite classes, respectively.

三种回归分析方法在Hyperion影像LAI反演中的比较

This paper focuses on Leaf Area index(LAI) inversion,using EO-1 Hyperion data for Huangfengqiao forest farm,YouXian County,Hunan Province.First,LAI was acquired using a LAI-2000 canopy analyzer at 130 sample plots(60 m × 60 m),with a Global Positioning System(Trimble GPS Geo XT).Second,atmospheric correction was applied to Hyperion data using the ground-synchronous canopy observation data.Third,effective vegetation indexes were selected to estimate LAI,according to research on correlation between LAI,bands and vegetation indexes derived from Hyperion imagery.Finally,an optimal estimation model of LAI was built by curve estimation,stepwise regression,and a partial least-squares regression algorithm.Results show that sensitivity of ratio vegetation index(RVI) was highest among all model factors,followed by SARVI0.1,NDVI705,NDVI,SARVI0.1,and SARVI0.25.Among all fit models,the effect of the partial least-squares regression was best,with R2 coefficient 0.84,whereas the curve estimation effect was worst,with R2 coefficient 0.64.Model precision analysis shows that it is reliable to build the model using 5 to 6 independent variables,and prediction accuracy of the partial least-square regression was the greatest.

Influence of Atmospheric Water Vapour on IRS NIR Measurements for Detecting Vegetation Signal. Part II: Validation of Simulation Results using Hyperion Data

This is the second paper of the series on the influence of the atmospheric water vapour (WV) on IRS NIR measurements. In the first paper (Pandya et al. 2011) a simulation study was presented where through the radiative transfer calculations it was shown that the variation of 0 to 6 g/cm2 in the WV hampered the IRS NIR reflectance up to 14%. In that study splitting of IRS NIR (0.770–0.860 μm) into two bands, such as NIR1 (0.775–0.805 μm) and NIR2 (0.845–0.875 μm) was also proposed, which facilitated a considerable improvement in NIR reflectance as well as in NDVI. Objective of the present paper is to validate the findings of simulation study with the use of EO1-Hyperion data. An improvement of the order of 7% in the top-of-atmosphere reflectance over vegetation target was obtained from the satellite data analysis, which is in good agreement to that of simulation results (3.7 to 7.9%) for the continental WV conditions of 1 to 3 g/cm2. This is also true for NDVI values, which illustrated a good agreement between the satellite observations (2.5%) and simulation results (2 to 4.6%) for the magnitude of improvement. Findings of the present study are preliminary in the nature but it provides a basis for enhanced NIR observations for future IRS sensors.

Influence of Atmospheric Water Vapour on IRS NIR Measurements for Detecting Vegetation Signal. Part I: A Simulation Study Using MODTRAN

Quantitative remote sensing involving accurate estimation of vegetation properties relies greatly on the measurements of the near infrared (NIR) channel because of unique interaction property between light and leaf. It is generally assumed that the NIR measurements are made in the absence of atmospheric absorption. However, relatively weak water vapour absorption features still persist in the NIR channel, which has bearing on the quantitative estimates of the vegetation properties and long-term data series. This paper reports the results of a study that was carried out to infer the possible influence of the atmospheric water vapour (WV) on the NIR measurements (0.77–0.86 μm) of Indian Remote Sensing (IRS) satellite sensors through radiative transfer simulations using MODTRAN model. The study also suggests and evaluates the alternate band-positions for the NIR channel to improve the IRS NIR measurements. It was observed that the water absorption features present around 0.810 μm reduces the WV transmission of IRS NIR channel from 1 to 0.91 when atmospheric WV content increased from 0 to 6 g/cm2 and thus hampered the NIR reflectance by 14% as compared to reference signal. A significant improvement of the order of 6.5 to 12% in the NIR reflectance and 4.2 to 7% in NDVI was observed, when IRS NIR channel was split into NIR1 (0.775–0.805 μm) and NIR2 (0.845–0.875 μm) channels by avoiding the WV absorption features. The companion paper in this issue (Pandya et al. 2011) will support results of this simulation study through the EO1-Hyperion data analysis.

Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TM and EO-1Hyperion data

Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative mixture analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral mixture analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-spectral (R2no age correction=0.55 vs R2age correction=0.73 for Landsat) and hyperspectral (R2no age correction=0.12 vs R2age correction=0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for analysis of multi-spectral datasets such as MODIS and SPOT-VEGETATION.

Discrimination of sedimentary lithologies using Hyperion and Landsat Thematic Mapper data: a case study at Melville Island, Canadian High Arctic

The use of remote-sensing techniques in the discrimination of rock and soil classes in northern regions can support a diverse range of activities, such as environmental characterization, mineral exploration and the study of Quaternary paleoenvironments. Although images with low spectral resolution can commonly be used in the mapping of classes possessing distinct spectral properties, hyperspectral images offer greater potential for discrimination of materials characterized by more subtle reflectance properties. In an effort to better constrain the utility of broadband and hyperspectral datasets in high-latitude research, this study investigated the effectiveness of Landsat Thematic Mapper (TM) and EO-1 Hyperion data for discrimination of lithological classes at eastern Melville Island, Nunavut, Canada. TM data were classified using a standard neural-network algorithm, and both TM and Hyperion data were linearly unmixed using ground-truth spectra. TM classification results successfully discriminate between classes over much of the study area, although with incomplete separation between clastic and carbonate materials. TM unmixing results are poor, with useful class separation restricted to vegetation and red-weathered sandstone classes. Hyperion results effectively depict the fractional cover of end members, although the abundance images of several classes contain background abundance values that overestimate surface exposure in some areas. For the study area and surface classes involved, noisy hyperspectral data were found to be of greater utility than higher-fidelity broadband multispectral data in the generation of fractional abundance images for an inclusive set of surface-cover classes.