Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica

The thermal infrared portion of the electromagnetic spectrum has considerable potential for mineral and lithological mapping of the most abundant rock-forming silicates that do not display diagnostic features at visible and shortwave infrared wavelengths. Lithological mapping using visible and shortwave infrared hyperspectral data is well developed and established processing chains are available, however there is a paucity of such methodologies for hyperspectral thermal infrared data. Here we present a new fully automated processing chain for deriving lithological maps from hyperspectral thermal infrared data and test its applicability using the first ever airborne hyperspectral thermal data collected in the Antarctic. A combined airborne hyperspectral survey, targeted geological field mapping campaign and detailed mineralogical and geochemical datasets are applied to small test site in West Antarctica where the geological relationships are representative of continental margin arcs. The challenging environmental conditions and cold temperatures in the Antarctic meant that the data have a significantly lower signal to noise ratio than is usually attained from airborne hyperspectral sensors. We applied preprocessing techniques to improve the signal to noise ratio and convert the radiance images to ground leaving emissivity. Following preprocessing we developed and applied a fully automated processing chain to the hyperspectral imagery, which consists of the following six steps: (1) superpixel segmentation, (2) determine the number of endmembers, (3) extract endmembers from superpixels, (4) apply fully constrained linear unmixing, (5) generate a predictive classification map, and (6) automatically label the predictive classes to generate a lithological map. The results show that the image processing chain was successful, despite the low signal to noise ratio of the imagery; reconstruction of the hyperspectral image from the endmembers and their fractional abundances yielded a root mean square error of 0.58%. The results are encouraging with the thermal imagery allowing clear distinction between granitoid types. However, the distinction of fine grained, intermediate composition dykes is not possible due to the close geochemical similarity with the country rock

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

Atmospheric correction algorithm for POLDER data. Case study: DAISEX 1999 campaign

RESUMEN Este artículo presenta un algoritmo para corregir los efectos de la atmósfera de la reflectividad multiangulare hiperespectral de POLDER, prestando especial atención al efecto de los aerosoles. Los datos fueron adquiridos durante la campaña DAISEX-99 de la Agencia Espacial Europea. El algoritmo está basado en la inversión de la reflectividad medida en dos pasos. Primero, se invierte la reflectividad de POLDER para determinar los tres parámetros de la función de distribución de la reflectividad bidireccional de la superficie (BRDF). Estos valores son los datos de entrada de la superficie para el segundo paso. En este segundo paso, invertimos de nuevo la reflectividad para obtener tres parámetros de la superficie y cuatro parámetros de los aerosoles para localidades rurales y cinco en el resto. Los parámetros de los aerosoles son la densidad de partículas de los componentes básicos de los aerosoles: insoluble en agua, soluble en agua, hollín, sales marina es modo de acumulación y sales marinas en modo grueso. Por tanto, la salida del algoritmo es el contenido de varios componentes básicos y los parámetros del modelo de BRDF. Aplicando la teoría de dispersión de Mie hemos obtenido el espesor óptico de los aerosoles (AOD) y comparado el resultado con los valores determinados a partir de medidas de extinción de la radiación solar a nivel del suelo. Se ha obtenido como condición de contorno para la inversión la información disponible sobre los aerosoles obtenida a partir de las retrotrayectorias de las masas de aire. Utilizando esta información mostramos que los valores del AOD están más próximos a la medida y que por tanto el funcionamiento del algoritmo es mejor. ABSTRACT This paper presents an algorithm to correct the effects of the atmosphere of POLDER hyperspectral and multiangular reflectance, paying particular emphasis to the aerosol effect. The data were acquired during the European Space Agency campaign DAISEX-99. The algorithm is based on the inversion of measured reflectance in two steps. First, we invert the POLDER reflectances to determine the three parameters of a bidirectional reflectance distribution function (BRDF) of the surface. These values are the first guess of the surface parameters for the second step. In the second step, we invert again the reflectance to obtain three surface parameters and four aerosol variables, in rural sites, and five variables in the rest. The aerosol variables are the particle density of the basic aerosol components: water-insoluble, water soluble and soot particles, sea-salt in accumulation mode and sea-salt in coarse mode. Thus, the algorithm output is the content of some aerosol basic components and the BRDF parameters of the surface. Applying the Mie scattering theory we have obtained the aerosol optical depth (AOD) of the retrieved aerosols and compared it with the values obtained from ground-based solar irradiance extinction measurements. The available information about the aerosols coming from airmass backtrajectories and isobaric maps provides a boundary condition for the inversion. Using this information we show that the AOD values are closer to the measured values and thus the performance of the algorithm is better. icon

On the Feasibility of Imaging Carbonatite-Hosted Rare Earth Element Deposits Using Remote Sensing

Rare earth elements (REEs) generate characteristic absorption features in visible to shortwave infrared (VNIR-SWIR) reflectance spectra. Neodymium (Nd) has among the most prominent absorption features of the REEs and thus represents a key pathfinder element for the REEs as a whole. Given that the world’s largest REE deposits are associated with carbonatites, we present spectral, petrographic, and geochemical data from a predominantly carbonatitic suite of rocks that we use to assess the feasibility of imaging REE deposits using remote sensing. Samples were selected to cover a wide range of extents and styles of REE mineralization, and encompass calcio-, ferro- and magnesio-carbonatites. REE ores from the Bayan Obo (China) and Mountain Pass (United States) mines, as well as REE-rich alkaline rocks from the Motzfeldt and Ilímaussaq intrusions in Greenland, were also included in the sample suite. The depth and area of Nd absorption features in spectra collected under laboratory conditions correlate positively with the Nd content of whole-rock samples. The wavelength of Nd absorption features is predominantly independent of sample lithology and mineralogy. Correlations are most reliable for the two absorption features centered at ~744 and ~802 nm that can be observed in samples containing as little as ~1,000 ppm Nd. By convolving laboratory spectra to the spectral response functions of a variety of remote sensing instruments we demonstrate that hyperspectral instruments with capabilities equivalent to the operational Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and planned Environmental Mapping and Analysis Program (EnMAP) systems have the spectral resolutions necessary to detect Nd absorption features, especially in high-grade samples with economically relevant REE accumulations (Nd > 30,000 ppm). Adding synthetic noise to convolved spectra indicates that correlations between Nd absorption area and whole-rock Nd content only remain robust when spectra have signal-to-noise ratios in excess of ~250:1. Although atmospheric interferences are modest across the wavelength intervals relevant for Nd detection, most REE-rich outcrops are too small to be detectable using satellite-based platforms with >30-m spatial resolutions. However, our results indicate that Nd absorption features should be identifiable in high-quality, airborne, hyperspectral datasets collected at meter-scale spatial resolutions. Future deployment of hyperspectral instruments on unmanned aerial vehicles could enable REE grade to be mapped at the centimeter scale across whole deposits

Landsat 8 satellite data-based estimation of soil moisture in McMurdo Dry Valleys, Antarctica

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesSoil moisture is the total amount of water present in the upper 10 cm of soil and it represents the water in land surface which resides in the pores of the soil which is not in river, lakes or groundwater and which depends of the weather conditions, soil type and associated vegetation, among others. Soil moisture assessments are important to understand the hydrological cycles and biophysical processes caused by global climate changes (Finn et al., 2011). Usually, soil moisture has been mapped with airborne microwave radiometers (Klemas et al., 2014) to measure the water retained in the spaces between soil particles. Its importance is due to the microorganism metabolic activity, regulation of the soil temperature and carriage of nutrients, among others. Soil moisture typically takes the form of small ice crystals, vapour, or small parts of liquid water in cold desert soils (Campbell & Claridge, 1982). Antarctic soils are composed by basically no organic and very low moisture content (Campbell and Claridge, 1987). Antarctica is a sensitive area to balance the global climate and its changes and its soil ecosystems are strongly regulated by variables of the abiotic environment and due to this, a research measures the incidence and spatial occurrence of the layer freezing to know how regional climate change could affect the energy exchange of this layer and its invertebrate communities (Wlostowski et al., 2017). Also, knowing how the dynamic of the surface varies in polar regions is transcendent to predict the impact of climate change in global sea-level rise in the future (Quincey & Luckman, 2009)