Hyperion Imaging Spectrometer Research Articles

Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers

Abstract. New methods for optimizing data storage and transmission are required as orbital imaging spectrometers collect ever-larger data volumes due to increases in optical efficiency and resolution. In Earth surface investigations, storage and downlink volumes are the most important bottleneck in the mission's total data yield. Excising cloud-contaminated data on board, during acquisition, can increase the value of downlinked data and significantly improve the overall science performance of the mission. Threshold-based screening algorithms can operate at the acquisition rate of the instrument but require accurate and comprehensive predictions of cloud and surface brightness. To date, the community lacks a comprehensive analysis of global data to provide appropriate thresholds for screening clouds or to predict performance. Moreover, prior cloud-screening studies have used universal screening criteria that do not account for the unique surface and cloud properties at different locations. To address this gap, we analyzed the Hyperion imaging spectrometer's historical archive of global Earth reflectance data. We selected a diverse subset spanning space (with tropical, midlatitude, Arctic, and Antarctic latitudes), time (2005–2017), and wavelength (400–2500 nm) to assure that the distributions of cloud data are representative of all cases. We fit models of cloud reflectance properties gathered from the subset to predict locally and globally applicable thresholds. The distributions relate cloud reflectance properties to various surface types (land, water, and snow) and latitudinal zones. We find that taking location into account can significantly improve the efficiency of onboard cloud-screening methods. Models based on this dataset will be used to screen clouds on board orbital imaging spectrometers, effectively doubling the volume of usable science data per downlink. Models based on this dataset will be used to screen clouds on board NASA's forthcoming mission, the Earth Mineral Dust Source Investigation (EMIT).

Satellite-ground integrated destriping network: A new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets

From the EO-1 Hyperion imaging spectrometer to the newly launched Chinese satellite hyperspectral imagers, stripe noise is a ubiquitous phenomenon that seriously degrades the data quality and usability. Although previous efforts have achieved inspiring results, hyperspectral image (HSI) destriping remains a challenging task, as the stripe degradations are sometimes more complicated than the predefined assumptions, i.e., the preselected reference, filter, or handcrafted priors. With the rapid advances in deep learning technologies, convolutional neural networks (CNNs) provide a new potential to learn essential priors in an automatic manner. However, the training of CNNs is highly reliant on a large high-quality standard dataset, which is difficult to acquire for hyperspectral spaceborne sensors. In this paper, an innovative approach termed the satellite-ground integrated destriping network (SGIDN) is proposed for HSIs. Rather than using self-training, a satellite-ground integrated strategy is proposed, for the first time, to mitigate the data dependency, so that a large set of striped-clean pairs is generated from the ground-based HSIs. Considering the varied stripes among different bands, a unique CNN architecture design, including the combination of 3D convolution and 2D convolution, residual learning, and supplementary gradient channels, is integrated to capture the intrinsic spectral-spatial features in the HSIs and the unidirectional property of stripe noise. Compared with the traditional methods, SGIDN can be flexibly extended to specific HSI destriping tasks, e.g., coexisting horizontal and vertical stripes, and generalizes well to different hyperspectral satellite sensors. Given the same study area (Shanghai, China), three HSIs acquired by the EO-1 Hyperion imaging spectrometer, the Chinese HJ-1A HSI sensor, and the wide-range hyperspectral imager onboard the Chinese SPARK spectral micro-nano satellite are adopted to assess the proposed SGIDN model. Both simulated and real-data experiments confirm that SGIDN can consistently outperform the benchmark methods, with a higher degree of efficiency. Moreover, the land-cover mapping results further demonstrate the necessity of destriping and the suitability of the destriped results for use in further applications.

Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015

Abstract. The distribution of ice, liquid, and mixed phase clouds is important for Earth's planetary radiation budget, impacting cloud optical properties, evolution, and solar reflectivity. Most remote orbital thermodynamic phase measurements observe kilometer scales and are insensitive to mixed phases. This under-constrains important processes with outsize radiative forcing impact, such as spatial partitioning in mixed phase clouds. To date, the fine spatial structure of cloud phase has not been measured at global scales. Imaging spectroscopy of reflected solar energy from 1.4 to 1.8 µm can address this gap: it directly measures ice and water absorption, a robust indicator of cloud top thermodynamic phase, with spatial resolution of tens to hundreds of meters. We report the first such global high spatial resolution survey based on data from 2005 to 2015 acquired by the Hyperion imaging spectrometer onboard NASA's Earth Observer 1 (EO-1) spacecraft. Seasonal and latitudinal distributions corroborate observations by the Atmospheric Infrared Sounder (AIRS). For extratropical cloud systems, just 25 % of variance observed at GCM grid scales of 100 km was related to irreducible measurement error, while 75 % was explained by spatial correlations possible at finer resolutions.

Space‐based remote imaging spectroscopy of the Aliso Canyon CH4 superemitter

AbstractThe Aliso Canyon gas storage facility near Porter Ranch, California, produced a large accidental CH4 release from October 2015 to February 2016. The Hyperion imaging spectrometer on board the EO‐1 satellite successfully detected this event, achieving the first orbital attribution of CH4 to a single anthropogenic superemitter. Hyperion measured shortwave infrared signatures of CH4 near 2.3 μm at 0.01 μm spectral resolution and 30 m spatial resolution. It detected the plume on three overpasses, mapping its magnitude and morphology. These orbital observations were consistent with measurements by airborne instruments. We evaluate Hyperion instrument performance, draw implications for future orbital instruments, and extrapolate the potential for a global survey of CH4 superemitters.

Measuring Intensity of Tillage and Plant Residue Cover Using Remote Sensing

The objectives of this study were to evaluate several spectral indices for estimating soil tillage practices and determining crop residue cover using multispectral and Hyperspectral satellite images. For this purpose, Landsat satellite and EO-1 Hyperion imaging spectrometer data were acquired over agricultural fields in central Ohio, near Columbus, in April and May. According to the calculated mean normalized difference tillage index data (NDTI), in the period 2003 to 2014, area of conservation tillage fields increased at a rate of 8.41 %, while no-tillage fields decreased at a rate of 14.45 %. Comparing normalized difference index (NDI) data from 2003 and in 2014 it was determined that a reduction of 10% vegetated areas occurred. Normalized difference senescent vegetation index (NSDVI) values did not evidence significant change over a nearly 10 year period. The best determination of tillage practices was obtained using the cellulose absorption index (CAI) value calculated from Hyperspectral data. By combining information on crop classification with indexes, similar results were obtained for crop distribution over a ten year period.

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

We evaluated the accuracy and sensitivity of six previously published reflectance based algorithms to retrieve Phycocyanin (PC) concentration in inland waters. We used field radiometric and pigment data obtained from two study sites located in the United States and Brazil. All the algorithms targeted the PC absorption feature observed in the water reflectance spectra between 600 and 625 nm. We evaluated the influence of chlorophyll-a (chl-a) absorption on the performance of these algorithms in two contrasting environments with very low and very high cyanobacteria content. All algorithms performed well in low to moderate PC concentrations and showed signs of saturation or decreased sensitivity for high PC concentration with a nonlinear trend. MM09 was found to be the most accurate algorithm overall with a RMSE of 15.675%. We also evaluated the use of these algorithms with the simulated spectral bands of two hyperspectral space borne sensors including Hyperion and Compact High-Resolution Imaging Spectrometer (CHRIS) and a hyperspectral air borne sensor, Hyperspectral Infrared Imager (HyspIRI). Results showed that the sensitivity for chl-a of PC retrieval algorithms for Hyperion simulated data were less noticable than using the spectral bands of CHRIS; HyspIRI results show that SC00 could be used for this sensor with low chl-a influence. This review of reflectance based algorithms can be used to select the optimal approach in studies involving cyanobacteria monitoring through optical remote sensing techniques.

Vicarious Calibration of EO-1 Hyperion

The Hyperion imaging spectrometer on the Earth Observing-1 satellite is the first high-spatial resolution imaging spectrometer to routinely acquire science-grade data from orbit. Data gathered with this instrument needs to be quantitative and accurate in order to derive meaningful information about ecosystem properties and processes. Also, comprehensive and long-term ecological studies require these data to be comparable over time, between coexisting sensors and between generations of follow-on sensors. One method to assess the radiometric calibration is the reflectance-based approach, a common technique used for several other earth science sensors covering similar spectral regions. This work presents results of radiometric calibration of Hyperion based on the reflectance-based approach of vicarious calibration implemented by University of Arizona during 2001–2005. These results show repeatability to the 2% level and accuracy on the 3–5% level for spectral regions not affected by strong atmospheric absorption. Knowledge of the stability of the Hyperion calibration from moon observations allows for an average absolute calibration based on the reflectance-based results to be determined and applicable for the lifetime of Hyperion.

Constraints on determining the eruption style and composition of terrestrial lavas from space

The surface temperatures of active lavas relate to cooling rates, chemistry, and eruption style. We analyzed 61 hyperspectral satellite images acquired by the National Aeronautics and Space Administration s Earth Observing-1 (EO-1) Hyperion imaging spectrometer to document the surface temperature distributions of active lavas erupted at 13 volcanoes. Images were selected to encompass the range of common lava eruption styles, specifically, lava fountains, flows, lakes, and domes. Our results reveal temperature distributions for terrestrial lavas that correlate with composition (i.e., a statistically significant difference in the highest temperatures retrieved for mafic lavas and intermediate and felsic lavas) and eruption style. Maximum temperatures observed for mafi c lavas are approx.200 C higher than for intermediate and felsic lavas. All eruption styles exhibit a low-temperature mode at approx.300 C; lava fountains and 'a' a flows also exhibit a higher-temperature mode at approx.700 C. The observed differences between the temperatures are consistent with the contrasting rates at which the lava surfaces are thermally renewed. Eruption styles that allow persistent and pervasive thermal renewal of the lava surface (e.g., fractured crusts on channel-fed 'a' a flows) exhibit a bimodal temperature distribution; eruption styles that do not (e.g., the continuous skin of pahoehoe lavas) exhibit a single mode. We conclude that insights into composition and eruption style can only be gained remotely by analyzing a large spatio-temporal sample of data. This has implications for determining composition and eruption style at the Jovian moon Io, for which no in situ validation is available.

Examination of spaceborne imaging spectroscopy data utility for stratigraphic and lithologic mapping

Due to the increasing development of image spectroscopy techniques, airborne and spaceborne hyperspectral images have in recent years become readily available for use in geological applications. One of the prominent advantages of imaging spectroscopy is its high spectral resolution, producing detailed spectral information in each pixel. The current study aims at exploring the feasibility of the Earth-Observing-1 Hyperion imaging spectrometer to map the geology arena over the Dana Geology National Park, Jordan. After overcoming the common preprocessing difficulties (e.g., smile effect), a classification scheme of two levels was applied. The first level resulted in a stratigraphic classification product of eleven classes and the second level in a lithologic classification product of six classes. The overall accuracy of the stratigraphic product was 57%, while that of the lithologic product was 79%. Mismatches in classification were mostly related to terrestrial cover of the lower topography formation by rock and sand debris. In addition, low accuracy values can be attributed to Hyperion's high sensitivity, leading to recognition of different mineral compositions as different classes within a rock formation, while the conventional geology-stratigraphic map generalizes these different classes into one formation. The methods practiced in the current research can advance the Hyperion's classification capabilities and therefore can be applied in different geological settings and additional disciplines such as penology, agriculture, ecology, forestry, urban, and other environmental studies.

Cooling rate of some active lavas determined using an orbital imaging spectrometer

The surface temperature of an active lava flow is an important physical property to measure. Through its influence on lava crystallinity, cooling exerts a fundamental control on lava rheology. Remotely sensed thermal radiance data acquired by multispectral sensors such as Landsat Thematic Mapper and the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer are of insufficient spectral and radiometric fidelity to allow for realistic determination of lava surface temperatures from Earth orbit. This paper presents results obtained from the analysis of active lava flows using hyperspectral data acquired by NASA's Earth Observing‐1 Hyperion imaging spectrometer. The contiguous nature of the measured radiance spectrum in the 0.4–2.5 μm region means that, although sensor saturation most certainly occurs, unsaturated radiance data are always available from even the hottest, and most radiant, active lava flow surfaces. The increased number of wave bands available allows for the assumption of more complex flow surface temperature distributions in the radiance‐to‐temperature inversion processes. The technique is illustrated by using a hyperspectral image of the active lava lake at Erta Ale volcano, Ethiopia, a well‐characterized calibration target, a time series of three Hyperion images of an active lava flow acquired during a 4 day period at Mount Etna, Sicily, as well as a lava flow erupted at Nyamuragira, Democratic Republic of Congo. The results provide insights into the temperature–radiance mixture modeling problem that will aid in the analysis of data acquired by future hyperspectral remote sensing missions, such as NASA's proposed HyspIRI mission.

NASA Cold Land Processes Experiment (CLPX 2002/03): Spaceborne Remote Sensing

Abstract This paper describes satellite data collected as part of the 2002/03 Cold Land Processes Experiment (CLPX). These data include multispectral and hyperspectral optical imaging, and passive and active microwave observations of the test areas. The CLPX multispectral optical data include the Advanced Very High Resolution Radiometer (AVHRR), the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Multi-angle Imaging Spectroradiometer (MISR). The spaceborne hyperspectral optical data consist of measurements acquired with the NASA Earth Observing-1 (EO-1) Hyperion imaging spectrometer. The passive microwave data include observations from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS; AMSR-E). Observations from the Radarsat synthetic aperture radar and the SeaWinds scatterometer flown on QuikSCAT make up the active microwave data.

Toward scene-based retrieval of spectral response functions for hyperspectral imagers using Fraunhofer features

Initial steps are proposed and tested in the development of a method for retrieving and (or) refining instrument spectral characteristics for dispersive hyperspectral imagers such as the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS), Compact Airborne Spectrographic Imager (casi), HyMap, Hyperion, and compact high-resolution imaging spectrometer (CHRIS) based on data acquired by the instrument in operation using statistical spectrum matching with moderate-resolution transmittance code (MODTRAN) modelled instrument results in the vicinity of reference Fraunhofer feature windows. Until now, such scene-based retrieval has focused primarily on refining spectral band-centre shifts while assuming that spectral response function (SRF) parameters remain static. In particular, most methods assume that the SRF is of a Gaussian shape. As a consequence of recent investigations showing that scene-based discernment of SRF shape should be feasible given current typical instrument performance, this paper explores algorithmic components deemed necessary for the development of a look-up table (LUT) based retrieval method for obtaining SRF parameters on a band-by-band basis, even in the presence of minor band-centre or bandwidth deviations from nominal instrument specifications. The proposed method employing these components is appropriate for dispersive hyperspectral imagers but not for others, for example Fourier transform hyperspectral imagers. In experiments using nominal implementations of the proposed components, reference spectra match expected LUT spectra in nearly all cases, even when band-centre and bandwidth deviations are considered. This holds true for all three modelled instruments and nearly all of the six selected Fraunhofer windows. Expected signal-to-noise requirements are in many cases challenging, yet feasible using signal-enhancement techniques such as along-track averaging.

Spectral Characteristics of Forest Vegetation in Moderate Drought Condition Observed by Laboratory Measurements and Spaceborne Hyperspectral Data

Although there have been several studies on the spectral characteristics related to leaf water content, it remains unclear whether the spectral property of leaves can be extended to the canopy-level. In this study, we attempt to compare the spectral characteristics of forest vegetation in moderate drought condition observed by laboratory measurement and satellite hyperspectral image data. Spectral reflectance data were measured from detached pine needles and oak leaves in the laboratory with a spectroradiometer. Canopy reflectance spectra of the same species were collected from temperate forest stands with dense canopy conditions using EO-1 Hyperion imaging spectrometer data obtained during the moderate drought season in 2001, and then compared with those obtained in the normal precipitation season of 2002. The relationship between leaf-level spectral reflectance and leaf water content was the clearest at the shortwave infrared (SWIR) regions. However, the canopy-level spectral characteristics of forest stands did not quite correspond with the leaf-level reflectance spectra. Further, four water-related spectral indices (WI, NDWI, MSI, and NDII) developed mainly with leaf-level reflectance were not very effective to be used with the canopy-level reflectance in dense forest condition. Forest canopy spectra under moderate drought status may be more influenced by canopy foliage mass, rather than by canopy moisture level.

Remote sensing of crop residue cover and soil tillage intensity

Management of plant litter or crop residues in agricultural fields is an important consideration for reducing soil erosion and increasing soil organic C. Current methods of quantifying crop residue cover are inadequate for characterizing the spatial variability of residue cover within fields or across large regions. Our objectives were to evaluate several spectral indices for measuring crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. Landsat Thematic Mapper (TM) and EO-1 Hyperion imaging spectrometer data were acquired over agricultural fields in central Iowa in May and June 2004. Crop residue cover was measured in corn ( Zea mays L.) and soybean ( Glycine max Merr.) fields using line-point transects. Spectral residue indices using Landsat TM bands were weakly related to crop residue cover. With the Hyperion data, crop residue cover was linearly related to the cellulose absorption index (CAI), which measures the relative intensity of cellulose and lignin absorption features near 2100 nm. Coefficients of determination ( r 2) for crop residue cover as a function of CAI were 0.85 for the May and 0.77 for the June Hyperion data. Three tillage intensity classes, corresponding to intensive (<15% residue cover), reduced (15–30% cover) and conservation (>30% cover) tillage, were correctly identified in 66–68% of fields. Classification accuracy increased to 80–82% for two classes, corresponding to conventional (intensive + reduced) and conservation tillage. By combining information on previous season's (2003) crop classification with crop residue cover after planting in 2004, an inventory of soil tillage intensity by previous crop type was generated for the whole Hyperion scene. Regional surveys of soil management practices that affect soil conservation and soil C dynamics are possible using advanced multispectral or hyperspectral imaging systems.

Hyperion, a space-based imaging spectrometer

The Hyperion Imaging Spectrometer was the first imaging spectrometer to routinely acquire science-grade data from Earth orbit. Instrument performance was validated and carefully monitored through a combination of calibration approaches: solar, lunar, earth (vicarious) and atmospheric observations complemented by onboard calibration lamps and extensive prelaunch calibration. Innovative techniques for spectral calibration of space-based sensors were also tested and validated. Instrument performance met or exceeded predictions including continued operation well beyond the planned one-year program.

Overview of the earth observing one (eo-1) mission

The Earth Observing One (EO-1) satellite, a part of National Aeronautics and Space Administration's New Millennium Program, was developed to demonstrate new technologies and strategies for improved Earth observations. It was launched from Vandenburg Air Force Base on November 21, 2000. The EO-1 satellite contains three observing instruments supported by a variety of newly developed space technologies. The Advanced Land Imager (ALI) is a prototype for a new generation of Landsat-7 Thematic Mapper. The Hyperion Imaging Spectrometer is the first high spatial resolution imaging spectrometer to orbit the Earth. The Linear Etalon Imaging Spectral Array (LEISA) Atmospheric Corrector (LAC) is a high spectral resolution wedge imaging spectrometer designed to measure atmospheric water vapor content. Instrument performances are validated and carefully monitored through a combination of radiometric calibration approaches: solar, lunar, stellar, Earth (vicarious), and atmospheric observations complemented by onboard calibration lamps and extensive prelaunch calibration. Techniques for spectral calibration of space-based sensors have been tested and validated with Hyperion. ALI and Hyperion instrument performance continue to meet or exceed predictions well beyond the planned one-year program. This paper reviews the EO-1 satellite system and provides details of the instruments and their performance as measured during the first year of operation. Calibration techniques and tradeoffs between alternative approaches are discussed. An overview of the science applications for instrument performance assessment is presented.

Evaluation of the potential of hyperion for fire danger assessment by comparison to the airborne visible/infrared imaging spectrometer

Parameters derived from remote sensing that can be used to assess fire danger include surface reflectance, live and dead biomass, canopy water content, species composition, and fuel state. Spectral bands and wavelength locations of traditional multispectral data make assessment of fire danger in Mediterranean shrublands difficult, although fire danger parameters have been derived from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. We compare nearly simultaneous acquisition of Hyperion and AVIRIS to evaluate spaceborne monitoring potential of fire danger in Southern California chaparral. Field spectra were acquired to support reflectance retrieval and construct a spectral library for vegetation mapping. Reflectance spectra retrieved from Hyperion and AVIRIS had similar shape and albedo, but SNR was five times higher in AVIRIS. Fuel condition was assessed using the endmember fractions from spectral mixture analysis, with both Hyperion and AVIRIS imaging spectrometer data providing similar fractions and spatial distributions. Hyperion demonstrated good capability for separating spectral signals from bare soil and dry plant litter. Canopy water content was compared using the 980- and 1200-nm liquid water bands, the water index, and the normalized difference water index. Results showed that Hyperion is capable of retrieving canopy water at 1200 nm, but demonstrates poor performance at 980 nm. Sensor noise and instrumental artifacts account for poor performance in this spectral region. Overall, full-spectrum measures outperformed band ratios because of a lower sensitivity to sensor noise in individual bands. Species and community mapping showed similar patterns with better accuracy for AVIRIS relative to Hyperion, but with both instruments achieving only 79% and 50% overall accuracy, respectively.

Imaging spectroscopy for desertification studies: comparing aviris and eo-1 hyperion in argentina drylands

Arid and semiarid ("dryland") regions are complex mosaics of vegetation cover, structure, and phenology. Few multispectral remote sensing approaches have quantitatively resolved the spatial complexity of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare soil in drylands. In combination, these surface properties provide insight to land degradation and desertification known to be occurring in many drylands worldwide. Given sufficient spatial resolution and sensor performance, imaging spectroscopy provides this information using reflectance measurements over the 0.4-2.5-/spl mu/m region. We tested and compared the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the spaceborne Earth Observing 1 Hyperion imaging spectrometer for measuring PV, NPV, and bare soil fractional cover in the Monte Desert biome of Central Argentina. A probabilistic spectral mixture model was used to decompose image pixels into subpixel surface constituents using a spectral endmember bundling approach. AVIRIS (4.5-m pixels) and Hyperion (30-m pixels) data were collected over a 763-km/sup 2/ region containing the Nacunan Man-and-Biosphere Reserve and surrounding unprotected areas. The AVIRIS data, combined with the mixture modeling, provided highly accurate estimates of PV, NPV, and bare soil in comparison with field measurements (0.83<r/sup 2/<0.86). Hyperion provided accurate estimates of PV (r/sup 2/=0.68) due to good red-edge (0.69-0.71 /spl mu/m) performance, but NPV and bare soil estimates were accurate only when cover values exceeded /spl sim/30% (r/sup 2/=0.39-0.40). Lower NPV and soil cover accuracies from Hyperion were due to lower S/N performance in the shortwave-infrared (2.0-2.4 /spl mu/m) region. Convolving the AVIRIS reflectance data to 30-m pixel size showed that apparent accuracies decrease by 20% due to field/remote sensing colocation errors. AVIRIS 30-m results for PV were statistically similar to Hyperion 30-m results.

Comparative alteration mineral mapping using visible to shortwave infrared (0.4-2.4 μm) hyperion, ALI, and ASTER imagery

Advanced Land Imager (ALI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Hyperion imaging spectrometer data covering an area in the Central Andes between Volcan Socompa and Salar de Llullaillaco were used to map hydrothermally altered rocks associated with several young volcanic systems. Six ALI channels in the visible and near-infrared wavelength range (0.4-1.0 /spl mu/m) were useful for discriminating between ferric-iron alteration minerals based on the spectral shapes of electronic absorption features seen in continuum-removed spectra. Six ASTER channels in the short wavelength infrared (1.0-2.5 /spl mu/m) enabled distinctions between clay and sulfate mineral types based on the positions of band minima related to Al-OH vibrational absorption features. Hyperion imagery embedded in the broader image coverage of ALI and ASTER provided essential leverage for calibrating and improving the mapping accuracy of the multispectral data. This capability is especially valuable in remote areas of the earth where available geologic and other ground truth information is limited.