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We report infrared reflectance and ultraviolet fluorescence spectra of the surfaces and cleaved side of Beacon Sandstone from Antarctica that harbours a cryptoendolithic microbial community - a photosynthesis-based consortium of algae, lichen and bacteria present a few millimetres below the surface. Chlorophyll absorptions were present in the reflectance spectra of the exposed interior but not on the top or bottom surfaces and their band depths changed < 4% between measurements taken 19 years apart, indicating the stability of the microorganisms when the sample is kept dry. The presence of subsurface organic layers was detected in reflectance at 3.41 μm on the sample's surface. Fluorescence spectra of the cleaved side showed the blue fluorescence peaks expected from chlorophyll but no 0.65–0.80 μm peaks seen in fluorescence measurements of green vegetation. A weak fluorescence signal was detectable at the surface of the sample, presumably due to some light leaking into the subsurface through pores or cracks in the goethite coating the sample's surface. Theoretically, this weak fluorescence signal could possibly be observed in rock surfaces broken by erosion or meteor impacts on Mars. Sandstone outcrops have been reported on Mars and detection of organic layers in sandstones there would be of interest.

Around the world, imaging spectroscopy (hyperspectral imaging) has been successfully applied to mapping surface minerals over small areas. In this paper, we examine one challenge to reliably mapping surface mineral composition over large areas: the impacts of atmospheric artefacts in reflectance spectra on mineral identification. To examine these impacts, we use a set of Airborne Visible and Infrared Imaging Spectrometer-Classic (AVIRISc) data collected over ~30,000 sq. km of southern California (SoCal) in 2018. The AVIRISc2018 SoCal reflectance data showed residual atmospheric contamination in wavelength regions at the edges of strong water vapor absorption features: 1.44-1.5 µm, 1.75-1.8 µm, and 2.35-2.5 µm. These three regions of atmospheric residuals affect identification and discrimination of mineral absorption features, especially when mineral abundances are low and spectral features are weak. Of these three regions, improving atmospheric correction of imaging spectrometer data in the 2.35-2.5 µm region will result in the most enhancement in mineral identification, discrimination, and mapping. An additional region near 2.2 µm affects phyllosilicate mineral identification. The cause of the distorted reflectance in this region is still being investigated.

Imaging spectroscopy (hyperspectral imaging) has been used to successfully map minerals at the outcrop, deposit, district, and regional scale. This contribution presents spectral-based mineral maps of the Battle Mountain mining district, Nevada, USA, generated using multi-scale airborne imaging and ground-based point spectrometers. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and AisaFENIX 1K imaging spectrometer data were processed using Atmospheric and Topographic Correction (ATCOR-4) software with an empirical correction multiplier derived from field data. Data were used to generate spectral-based mineral maps with spatial resolutions of 13.5 and 1.8 m. A comparison of the various radiative transfer models used to convert radiance data to reflectance indicated that the ATCOR4 rugged model performed best for these datasets. These mineral maps were then used to spectrally characterize two potential porphyry mineral targets in the district.

Natural and anthropogenic hazards have the potential to impact all aspects of society including its economy and the environment. Diagnostic data to inform decision-making are critical for hazard management whether for emergency response, routine monitoring or assessments of potential risks. Imaging spectroscopy (IS) has unique contributions to make via the ability to provide some key quantitative diagnostic information. In this paper, we examine a selection of key case histories representing the state of the art to gain an insight into the achievements and perspectives in the use of visible to shortwave infrared IS for the detection, assessment and monitoring of a selection of significant natural and anthropogenic hazards. The selected key case studies examined provide compelling evidence for the use of the IS technology and its ability to contribute diagnostic information currently unattainable from operational spaceborne Earth observation systems. User requirements for the applications were also evaluated. The evaluation showed that the projected launch of spaceborne IS sensors in the near-, mid and long term future, together with the increasing availability, quality and moderate cost of off the shelf sensors, the possibilities to couple unmanned autonomous systems with miniaturized sensors, should be able to meet these requirements. The challenges and opportunities for the scientific community in the future when such data become available will then be ensuring consistency between data from different sensors, developing techniques to efficiently handle, process, integrate and deliver the large volumes of data, and most importantly translating the data to information that meets specific needs of the user community in a form that can be digested/understood by them. The latter is especially important to transforming the technology from a scientific to an operational tool. Additionally, the information must be independently validated using current trusted practices and uncertainties quantified before IS derived measurement can be integrated into operational monitoring services.

Imaging spectroscopy has been identified by ESA, NASA and other international space agencies as key to addressing a number of most important scientific and environmental management objectives. To implement the critical EU- and related policies for the management of natural resources, assets and benefits, and to achieve the objectives outlined by NASA’s Decadal Survey in ecosystem science, hydrology and geology, high fidelity imaging spectroscopy data with global coverage and high spatial resolution are required. As such, ESA’s CHIME (Copernicus Hyperspectral Imaging Mission for the Environment) and NASA’s SBG (Surface Biology and Geology) satellite missions aim to provide imaging spectroscopy data at global coverage at regular intervals of time with high spatial resolution.However, the scientific and applied objectives motivate more spatial coverage and more rapid revisit than any one agency’s observing system can provide. With the development of SBG and CHIME, the mid-to-late 2020s will see more global coverage spectroscopic observing systems, whereby these challenging needs can be more fully met by a multi-mission and multi-Agency synergetic approach, rather than by any single observing system.Therefore, an ESA-NASA cooperation on imaging spectroscopy space missions was seen as a priority for collaboration, specifically given the complementarity of mission objectives and measurement targets of the SBG and CHIME. Such cooperation is now being formalized as part of the ESA-NASA Joint Program Planning Group activities.Among the others, calibration and validation activities (Cal/Val) are fundamental for imaging spectroscopy while the satellites are in-orbit and operating. They determine the quality and integrity of the data provided by the spectrometers and become even more crucial when data from different satellites, carrying different imaging sensors, are used by users worldwide in a complementary and synergetic manner, like it will be the case for CHIME and SBG data. Indeed, Cal/Val activities not only have enormous downstream impacts on the accuracy and reliability of the products, but also facilitate cross-calibration and interoperability among several imaging spectrometers, supporting their synergistic use. Accordingly, within the context of this cooperation, a Working Group (WG) on Calibration/Validation has been set up, aiming to establish a roadmap for future SBG-CHIME coordination activities and collaborative studies.This contribution aims to outline the key areas of cooperation between SBG and CHIME in terms of Calibration and Validation, and present the establishment of a roadmap between the two missions, focusing on the following topics:Establishing an end-to-end cal/val strategy for seamless data products across missions, including transfer standards; Measurement Networks and commonly recognised Cal/Val reference sites; Status of atmospheric radiative transfer and atmospheric–correction procedures; Standardisation and Quality Control of reference data sets; Definition and implementation of joint airborne spectroscopy campaigns, such as the executed 2018 and planned 2021 campaigns, to simulate both missions and exercise the capabilities needed for eventual interoperability (incl. data collection, calibration, data product production); Continuous validation throughout the lifetime of products; Identifying other opportunities for efficiency and success through cooperation on calibration and validation, downlink capabilities and shared algorithms (e.g. compression and on-board data reduction).

This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R2 = 0.44), with significantly increased interference from green vegetation.

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions.

A calibration/validation (cal/val) working group (CVWG) was established as part of a US government study of the Surface Biology and Geology (SBG) observing system priority identified in the 2017 National Academies of Sciences, Engineering, and Medicine decadal survey of Earth sciences and applications [1]. The 2017 decadal survey recommended the SBG mission as a designated observable to improve measurements of Earth’s surface characteristics important for habitation, food, water, and many other natural resources. The candidate approach presented in the 2017 decadal survey was an imaging spectrometer spanning the visible and shortwave infrared (VSWIR) wavelength region and multi- or hyperspectral imagery in the thermal infrared (TIR). The SBG study is evaluating the candidate approach and other potential architectures in meeting science and application objectives. The SBG-CVWG, started in late 2018, has begun to identify elements of cal/val related to potential mission architectures. With the input of CVWG members and by engagement with the broader scientific community, other components of a cal/val framework are being defined including, pre-launch characterization and calibration, on-orbit calibration and monitoring, vicarious terrestrial/celestial calibration, data product validation, and intercomparison and potentially inter-calibration when multiple platforms are used.

Remote sensing has been hailed as a promising technology to provide spatially explicit information on tree species distribution. Such information is of high value for ecologists and forest managers, particularly in tropical environments in which it is acquired by costly field inventories performed at the plot level (∼1 ha). Over the last decade, hyperspectral sensors, usually on board airborne platforms, have been successfully employed for tropical tree species classification. Most of the studies focused on the improvement of the classification accuracy, without examining the sources of variability in canopy reflectance that contribute to discriminate among species. Trees of different species usually feature distinct crown structural and chemical characteristics defined by size, shape, leaf density, branching, pigment and nonpigment biochemical constituents, among others. Understanding the role of these characteristics on tree species discrimination is important to improve and optimize the use of hyperspectral data. Here, we show how spectral characteristics of tree species, from highly diverse tropical forests, are related to their chemical and structural properties. These spectral characteristics were retrieved by spectral mixture and feature analysis, using narrow-band data acquired in the optical domain (450–2400 nm). Finally, we assessed how the spectral resolution affects the classification accuracy and spectral separability.

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Atmospheric mineral dust can influence climate and biogeochemical cycles. An important component of mineral dust is ferric oxide minerals (hematite and goethite) which have been shown to influence strongly the optical properties of dust plumes and thus affect the radiative forcing of global dust. Here we report on the iron mineralogy of dust-source samples from the Bodele Depression (Chad, north-central Africa), which is estimated to be Earth’s most prolific dust producer and may be a key contributor to the global radiative budget of the atmosphere as well as to long-range nutrient transport to the Amazon Basin. By using a combination of magnetic property measurements, Mossbauer spectroscopy, reflectance spectroscopy, chemical analysis, and scanning electron microscopy, we document the abundance and relative amounts of goethite, hematite, and magnetite in dust-source samples from the Bodele Depression. The partition between hematite and goethite is important to know to improve models for the radiative effects of ferric oxide minerals in mineral dust aerosols. The combination of methods shows (1) the dominance of goethite over hematite in the source sediments, (2) the abundance and occurrences of their nanosize components, and (3) the ubiquity of magnetite, albeit in small amounts. Dominant goethite and subordinate hematite together compose about 2% of yellow-reddish dust-source sediments from the Bodele Depression and contribute strongly to diminution of reflectance in bulk samples. These observations imply that dust plumes from the Bodele Depression that are derived from goethite-dominated sediments strongly absorb solar radiation. The presence of ubiquitous magnetite (0.002–0.57 wt%) is also noteworthy for its potentially higher solubility relative to ferric oxide and for its small sizes, including PM

The April 2010 Deepwater Horizon (DWH) oil spill was the largest coastal spill in U.S. history. Monitoring subsequent change in marsh plant community distributions is critical to assess ecosystem impacts and to establish future coastal management priorities. Strategically deployed airborne imaging spectrometers, like the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), offer the spectral and spatial resolution needed to differentiate plant species. However, obtaining satisfactory and consistent classification accuracies over time is a major challenge, particularly in dynamic intertidal landscapes. Here, we develop and evaluate an image classification system for a time series of AVIRIS data for mapping dominant species in a heavily oiled salt marsh ecosystem. Using field-referenced image endmembers and canonical discriminant analysis (CDA), we classified 21 AVIRIS images acquired during the fall of 2010, 2011 and 2012. Classification results were evaluated using ground surveys that were conducted contemporaneously to AVIRIS collection dates. We analyzed changes in dominant species cover from 2010 to 2012 for oiled and non-oiled shorelines. CDA discriminated dominant species with a high level of accuracy (overall accuracy = 82%, kappa = 0.78) and consistency over three imaging dates (overall 2010 = 82%, overall 2011 = 82%, overall 2012 = 88%). Marshes dominated by Spartina alterniflora were the most spatially abundant in shoreline zones (≤ 28 m from shore) for all three dates (2010 = 79%, 2011 = 61%, 2012 = 63%), followed by Juncus roemerianus (2010 = 11%, 2011 = 19%, 2012 = 17%) and Distichlis spicata (2010 = 4%, 2011 = 10%, 2012 = 7%). Marshes that were heavily contaminated with oil exhibited variable responses from 2010 to 2012. Marsh vegetation classes converted to a subtidal, open water class along oiled and non-oiled shorelines that were similarly situated in the landscape. However, marsh loss along oil-contaminated shorelines doubled that of non-oiled shorelines. Only S. alterniflora dominated marshes were extensively degraded, losing 15% (354,604 m 2 ) cover in oiled shoreline zones, suggesting that S. alterniflora marshes may be more vulnerable to shoreline erosion following hydrocarbon stress, due to their landscape position.

Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science. To make these measurements, investigators calibrate instrument data to absolute units, invert physical models to estimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study quantifies the uncertainty in this process. Global missions demand predictive uncertainty models that can estimate future errors for varied environments and observing conditions. Here we validate uncertainty predictions with remote surface composition retrievals and in situ measurements in a field analogue of Earth and planetary exploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA's Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input uncertainties can benefit mineral detection methods such as constrained spectrum fitting. This suggests that operational uncertainty estimates could improve future NASA missions like the Earth Mineral dust source InvesTigation (EMIT) and the Lunar Trailblazer mission, as well as NASA's Decadal Surface Biology and Geology (SBG) Investigation.

We mapped oil presence in the marshes of Barataria Bay, Louisiana following the Deepwater Horizon oil spill using Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) data. Oil and non-photosynthetic vegetation (NPV) have very similar spectra, differing only in two narrow hydrocarbon absorption regions around 1700 and 2300 nm. Confusion between NPV and oil is expressed as an increase in oil fraction error with increasing NPV, as shown by Multiple Endmember Spectral Mixture Analysis (MESMA) applied to synthetic spectra generated with known endmember fractions. Significantly, the magnitude of error varied depending upon the type of NPV in the mixture. To reduce error, we used stable zone unmixing to identify a nine band subset that emphasized the hydrocarbon absorption regions, allowing for more accurate detection of oil presence using MESMA. When this band subset was applied to post-spill AVIRIS data acquired over Barataria Bay on several dates following the 2010 oil spill, accuracies ranged from 87.5% to 93.3%. Oil presence extended 10.5 m into the marsh for oiled shorelines, showing a reduced oil fraction with increasing distance from the shoreline.

Dust layers deposited to snow cover of the Wasatch Range (northern Utah) in 2009 and 2010 provide rare samples to determine the relations between their compositions and radiative properties. These studies are required to comprehend and model how such dust-on-snow (DOS) layers affect rates of snow melt through changes in the albedo of snow surfaces. We evaluated several constituents as potential contributors to the absorption of solar radiation indicated by values of absolute reflectance determined from bi-conical reflectance spectroscopy. Ferric oxide minerals and carbonaceous matter appear to be the primary influences on lowering snow-cover albedo. Techniques of reflectance and Mossbauer spectroscopy as well as rock magnetism provide information about the types, amounts, and grain sizes of ferric oxide minerals. Relatively high amounts of ferric oxide, indicated by hard isothermal remanent magnetization (HIRM), are associated with relatively low average reflectance (

Iron oxide minerals typically compose only a few weight percent of bulk atmospheric dust but are important for potential roles in forcing climate, affecting cloud properties, influencing rates of snow and ice melt, and fertilizing marine phytoplankton. Dust samples collected from locations across eastern Australia (Lake Cowal, Orange, Hornsby, and Sydney) following the spectacular “Red Dawn” dust storm on 23 September 2009 enabled study of the dust iron oxide assemblage using a combination of magnetic measurements, Mossbauer spectroscopy, reflectance spectroscopy, and scanning electron microscopy. Red Dawn was the worst dust storm to have hit the city of Sydney in more than 60 years, and it also deposited dust into the Tasman Sea and onto snow cover in New Zealand. Magnetization measurements from 20 to 400 K reveal that hematite, goethite, and trace amounts of magnetite are present in all samples. Magnetite concentrations (as much as 0.29 wt%) were much higher in eastern, urban sites than in western, agricultural sites in central New South Wales (0.01 wt%), strongly suggesting addition of magnetite from local urban sources. Variable temperature Mossbauer spectroscopy (300 and 4.2 K) indicates that goethite and hematite compose approximately 25–45% of the Fe-bearing phases in samples from the inland sites of Orange and Lake Cowal. Hematite was observed at both temperatures but goethite only at 4.2 K, thereby revealing the presence of nanogoethite (less than about 20 nm). Similarly, hematite particulate matter is very small (some of it d

This paper explores the use of spectral feature analysis to detect plant stress in visible/near infrared wavelengths. A time series of close range leaf and canopy reflectance data of two plant species grown in hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS airborne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over the field experiment. Parameters describing the chlorophyll 680 nm absorption feature (depth, width, and area) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detection Index (PSDI), was calculated using continuum-removed values near the chlorophyll feature centre (680 nm) and on the green-edge (560 and 575 nm). Chlorophyll feature’s depth, width and area, the PSDI and a narrow-band normalised difference vegetation index were evaluated for their ability to detect stressed plants. The objective was to analyse how the parameters/indices were affected by increasing degrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level (e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage (67–70%) of stressed plants. The PSDI also proved to be the best constraint for detecting the stress in hydrocarbon-impacted plants with field canopy spectra and airborne imaging spectroscopy data. This was particularly true using thresholds based on the ASD canopy data and considering the combination of higher percentage of stressed plants detected (across the thresholds) and fewer false-positives.

The composition and content of asphaltenes in spilled and original wellhead oils from the Deepwater Horizon (DWH) incident provide information on the amount of original oil lost and the processes most responsible for the losses within the first 80 days of the active spill. Spilled oils were collected from open waters, coastal waters and coastal sediments during the incident. Asphaltenes are the most refractory component of crude oils but their alteration in the spilled oils during weathering prevents them from being used directly as a conservative component to calculate original oil losses. The alteration is reflected by their increase in oxygen content and depletion in 12C. Reconnaissance experiments involving evaporation, photo-oxidation, microbial degradation, dissolution, dispersion and burning indicate that the combined effects of photo-oxidation and evaporation are responsible for these compositional changes. Based on measured losses and altered asphaltenes from these experiments, a mean of 61 ± 3 vol% of the original oil was lost from the surface spilled oils during the incident. This mean percentage of original oil loss is considerably larger than previous estimates of evaporative losses based on only gas chromatography (GC) amenable hydrocarbons (32–50 vol%), and highlights the importance of using asphaltenes, as well as GC amenable parameters in evaluating original oil losses and the processes responsible for the losses.

This study evaluated Earth Observing 1 (EO-1) Hyperion reflectance time series at established calibration sites to assess the instrument stability and suitability for monitoring vegetation functional parameters. Our analysis using three pseudo-invariant calibration sites in North America indicated that the reflectance time series are devoid of apparent spectral trends and their stability consistently is within 2.5-5 percent throughout most of the spectral range spanning the 12+ year data record. Using three vegetated sites instrumented with eddy covariance towers, the Hyperion reflectance time series were evaluated for their ability to determine important variables of ecosystem function. A number of narrowband and derivative vegetation indices (VI) closely described the seasonal profiles in vegetation function and ecosystem carbon exchange (e.g., net and gross ecosystem productivity) in three very different ecosystems, including a hardwood forest and tallgrass prairie in North America, and a Miombo woodland in Africa. Our results demonstrate the potential for scaling the carbon flux tower measurements to local and regional landscape levels. The VIs with stronger relationships to the CO2 parameters were derived using continuous reflectance spectra and included wavelengths associated with chlorophyll content and/or chlorophyll fluorescence. Since these indices cannot be calculated from broadband multispectral instrument data, the opportunity to exploit these spectrometer-based VIs in the future will depend on the launch of satellites such as EnMAP and HyspIRI. This study highlights the practical utility of space-borne spectrometers for characterization of the spectral stability and uniformity of the calibration sites in support of sensor cross-comparisons, and demonstrates the potential of narrowband VIs to track and spatially extend ecosystem functional status as well as carbon processes measured at flux towers.

This paper presents a methodology for assessing mineral abundances of mixtures having more than two constituents using absorption features in the 2.1–2.4 μm wavelength region. In the first step, the absorption behaviour of mineral mixtures is parameterised by exponential Gaussian optimisation. Next, mineral abundances are predicted by regression tree analysis using these parameters as inputs. The approach is demonstrated on a range of prepared samples with known abundances of kaolinite, dioctahedral mica, smectite, calcite and quartz and on a set of field samples from Morocco. The latter contained varying quantities of other minerals, some of which did not have diagnostic absorption features in the 2.1–2.4 μm region. Cross validation showed that the prepared samples of kaolinite, dioctahedral mica, smectite and calcite were predicted with a root mean square error (RMSE) less than 9 wt.%. For the field samples, the RMSE was less than 8 wt.% for calcite, dioctahedral mica and kaolinite abundances. Smectite could not be well predicted, which was attributed to spectral variation of the cations within the dioctahedral layered smectites. Substitution of part of the quartz by chlorite at the prediction phase hardly affected the accuracy of the predicted mineral content; this suggests that the method is robust in handling the omission of minerals during the training phase. The degree of expression of absorption components was different between the field sample and the laboratory mixtures. This demonstrates that the method should be calibrated and trained on local samples. Our method allows the simultaneous quantification of more than two minerals within a complex mixture and thereby enhances the perspectives of spectral analysis for mineral abundances.

Using imaging spectrometers at multiple scales, the USGS, in collaboration with the University of Alaska, is examining the application of hyperspectral data for identifying large-tonnage, base metal-rich deposits in Alaska. Recent studies have shown this technology can be applied to regional mineral mapping [1] and can be valuable for more local mineral exploration [2]. Passive optical remote sensing of high latitude regions faces many challenges, which include a short acquisition season and poor illumination due to low solar elevation [3]. Additional complications are encountered in the identification of surface minerals useful for mineral resource characterization because minerals of interest commonly are exposed on steep terrain, further challenging reflectance retrieval and detection of mineral signatures. Laboratory-based imaging spectrometer measurements of hand samples and field-based imaging spectrometer scans of outcrop are being analyzed to support and improve interpretations of remote sensing data collected by airborne imaging spectrometers and satellite multispectral sensors.