Mid-infrared (MIR) spectroscopy has received widespread interest as a method to complement traditional soil analysis. Recently available portable MIR spectrometers additionally offer potential for on-site applications, given sufficient spectral data quality. We therefore tested the performance of the Agilent 4300 Handheld FTIR (DRIFT spectra) in comparison to a Bruker Tensor 27 bench-top instrument in terms of (i) spectral quality and measurement noise quantified by wavelet analysis; (ii) accuracy of partial least squares (PLS) calibrations for soil organic carbon (SOC), total nitrogen (N), pH, clay and sand content with a repeated cross-validation analysis; and (iii) key spectral regions for these soil properties identified with a Monte Carlo spectral variable selection approach. Measurements and multivariate calibrations with the handheld device were as good as or slightly better than Bruker equipped with a DRIFT accessory, but not as accurate as with directional hemispherical reflectance (DHR) data collected with an integrating sphere. Variations in noise did not markedly affect the accuracy of multivariate PLS calibrations. Identified key spectral regions for PLS calibrations provided a good match between Agilent and Bruker DHR data, especially for SOC and N. Our findings suggest that portable FTIR instruments are a viable alternative for MIR measurements in the laboratory and offer great potential for on-site applications.

Remote sensing systems are largely used in geology for regional mapping of mineralogy and lithology mainly from airborne or spaceborne platforms. Earth observers such as Landsat, ASTER or SPOT are equipped with multispectral sensors, but suffer from relatively poor spectral resolution. By comparison, the existing airborne and spaceborne hyperspectral systems are capable of acquiring imagery from relatively narrow spectral bands, beneficial for detailed analysis of geological remote sensing data. However, for vertical exposures, those platforms are inadequate options since their poor spatial resolutions (metres to tens of metres) and NADIR viewing perspective are unsuitable for detailed field studies. Here, we have demonstrated that field-based approaches that incorporate thermal infrared hyperspectral technology with about a 40-nm bandwidth spectral resolution and tens of centimetres of spatial resolution allow for efficient mapping of the mineralogy and lithology of vertical cliff sections. We used the Telops lightweight and compact passive thermal infrared hyperspectral research instrument for field measurements in the Jura Cement carbonate quarry, Switzerland. The obtained hyperspectral data were analysed using temperature emissivity separation algorithms to isolate the different contributions of self-emission and reflection associated with different carbonate minerals. The mineralogical maps derived from measurements were found to be consistent with the expected carbonate results of the quarry mineralogy. Our proposed approach highlights the benefits of this type of field-based lightweight hyperspectral instruments for routine field applications such as in mining, engineering, forestry or archaeology.

This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of significant societal relevance: food security and human health. The key variables comprise land and sea surface radiation temperature and emissivity, surface moisture, thermal inertia, evapotranspiration, soil minerals and grain size components, soil organic carbon, plant physiological variables, and heat fluxes. The retrieval of this information requires a TIR imaging system with adequate spatial and spectral resolutions and with day-night following observation capability. Another challenge is the monitoring of temporally high dynamic features like energy fluxes, which require adequate revisit time. The suggested solution is a sensor pointing concept to allow high revisit times for selected target regions (1–5 days at off-nadir). At the same time, global observations in the nadir direction are guaranteed with a lower temporal repeat cycle (>1 month). To account for the demand of a high spatial resolution for complex targets, it is suggested to combine in one optic (1) a hyperspectral TIR system with ~75 bands at 7.2–12.5 µm (instrument NEDT 0.05 K–0.1 K) and a ground sampling distance (GSD) of 60 m, and (2) a panchromatic high-resolution TIR-imager with two channels (8.0–10.25 µm and 10.25–12.5 µm) and a GSD of 20 m. The identified science case requires a good correlation of the instrument orbit with Sentinel-2 (maximum delay of 1–3 days) to combine data from the visible and near infrared (VNIR), the shortwave infrared (SWIR) and TIR spectral regions and to refine parameter retrieval.

The “High resolution temperature and spectral emissivity mapping” (HiTeSEM) initiative aims at developing a conceptual instrument design for a hyperspectral thermal satellite to find answers for the most pressing research and data requirements within the scope of Food Security and Human Health. The satellite is proposed to consist of two long-wave infrared (LWIR) sensors, (1) a hyperspectral system with ~ 75 bands at 7.2 - 12.5 μm (NEΔT of <; 0.05 K) and a ground sampling distance (GSD) of 60 m and (2) a panchromatic (PAN) LWIR high resolution imager with two bands (8.0 - 10.25 μm and 10.25 - 12.5 μm, NEΔT of ~0.06 K) but a three times higher GSD of 20 m to extend the system to regional applications where higher spatial accuracy is required. For an accurate water vapor content (CWV) estimation, which is needed for accurate atmospheric correction and temperature-emissivity separation (TES), three wavelengths within the range 7.2-7.3 μm are used. Based on the science case, key regions of interest were identified in India, Asia, Andes mountains, Mediterranean ecosystems and densely-populated as well as growing regions.

HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping) is a preparatory study, funded by the German Aerospace Center (DLR) that aims preparing the floor for a future spaceborn hyperspectral thermal mission. Thermal remote sensing is poised to become a major source of information on land surface processes. HiTeSEM aims at closing the research gap still hampering utilization of the thermal infrared data at reasonable spectral and spatial resolution and focusses on surface-solid Earth interactions to assess natural and human-induced changes. Land surface temperature (LST) and spectral emissivity (LSE) of the Earth are the basis for the extraction of sensitive variables in geology, pedology, and vegetation monitoring. Towards this end, HiTeSEM will enable the research community to evaluate the potential of emissive spectroscopy methodologies in Earth observation to answer a series of key science questions related to global change, human health, and food security. Relevant target variables include soil mineral composition, soil organic matter (SOM), surface moisture availability, evapotranspiration and stomatal/surface conductance. These are key indicators for soil productivity and plant stress in sensitive regions and can be used to govern and adapt land use practices under challenging ecological and climatic conditions. In urban remote sensing HiTeSEM is expected to furnish important information to define thermal models, which implies knowledge of the surface material composition by means of spectral emissivity retrieval. The methodological challenge of HiTeSEM lies in the development of a robust high performance temperature emissivity separation (TES) technique to allow optimum pre-processing of the measured thermal radiance signal at the sensor level. From these scientific goals a series of mission and instrument requirements has been derived that can be summarized as follows:

Monitoring soil surface dynamics in semi-arid agricultural landscapes becomes increasingly important due to the vulnerability of these ecosystems to desertification processes. Observations using remote sensing via the traditionally used visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelength regions can be limited due to the special characteristics of such soils (e.g. rich in quartz, poor in clay minerals, coarse textured, and grain coatings). In this laboratory-based work we demonstrate the capabilities of the thermal infrared between 8 and 14 μm (longwave infrared) to detect and quantify small ranges of the soil properties sand-, clay, and soil organic carbon (SOC) content, as they appear in the semi-arid agricultural landscapes of the Mullewa region in Western Australia. All of the three soil properties could be predicted using the longwave infrared (LWIR) spectra with higher accuracy and precision than from the VNIR-SWIR wavelength region. The study revealed the complex relationships between the soil properties and the VNIR-SWIR soil spectra, which were caused by the spectral influence of the soils' grain coatings (based on iron and clay minerals). These difficulties could be handled more appropriately by the prediction models based on the LWIR soil spectra. Our results indicate that in order to quantitatively monitor farming areas for such erosion-related soil properties; remote sensing using the LWIR wavelength region would produce better estimates than using the wavelength ranges in the VNIR-SWIR.

1. Helmholtz Center Potsdam, German Research Center for Geosciences, Telegrafenberg, Potsdam 14473, Germany; E-Mails: christian.mielke@gfz-potsdam.de (C.M.); daniel.scheffler@gfz-potsdam.de (D.S); nina.boesche@gfz-potsdam.de (N.B.); christin.lubitz@gfz-potsdam.de (C.L.); maximilian.brell@gfz-potsdam.de (M.B.); daniel.spengler@gfz-potsdam.de (D.S.); andreas.eisele@gfz-potsdam.de (A.E.); karl.segl@gfz-potsdam.de (K.S.); luis.guanter@gfz-potsdam.de (L.G.) 2. Helmholtz Center for Environmental Research-UFZ, Permoserstr 15, Leipzig 04318, Germany; E-Mail: angela.lausch@ufz.de

Digital soil mapping with the means of passive remote sensing basically relies on the soils' spectral characteristics and an appropriate atmospheric window, where electromagnetic radiation transmits without significant attenuation. Traditionally the atmospheric window in the solar-reflective wavelength region (visible, VIS: 0.4 – 0.7 µm; near infrared, NIR: 0.7 – 1.1 µm; shortwave infrared, SWIR: 1.1 – 2.5 µm) has been used to quantify soil surface properties. However, spectral characteristics of semi-arid soils, typically have a coarse quartz rich texture and iron coatings that can limit the prediction of soil surface properties.

Hyperspectral images are of increasing importance in remote sensing applications. Imaging spectrometers provide semi-continuous spectra that can be used for physics based surface cover material identification and quantification. Preceding radiometric calibrations serve as a basis for the transformation of measured signals into physics based units such as radiance. Pushbroom sensors collect incident radiation by at least one detector array utilizing the photoelectric effect. Temporal variations of the detector characteristics that differ with foregoing radiometric calibration cause visually perceptible along-track stripes in the at-sensor radiance data that aggravate succeeding image-based analyses. Especially, variations of the thermally induced dark current dominate and have to be reduced. In this work, a new approach is presented that efficiently reduces dark current related stripe noise. It integrates an across-effect gradient minimization principle. The performance has been evaluated using artificially degraded whiskbroom (reference) and real pushbroom acquisitions from EO-1 Hyperion and AISA DUAL that are significantly covered by stripe noise. A set of quality indicators has been used for the accuracy assessment. They clearly show that the new approach outperforms a limited set of tested state-of-the-art approaches and achieves a very high accuracy related to ground-truth for selected tests. It may substitute recent algorithms in the Reduction of Miscalibration Effects (ROME) framework that is broadly used to reduce radiometric miscalibrations of pushbroom data takes.

Normalized Difference Vegetation Indices (NDVIs) are typically determined using satellite or airborne remote sensing, or field portable spectrometers, which give an averaged signal on centimetre to metre scale plots. Biological soil crust (BSC) patches may have smaller sizes, and ecophysiological, hydrological as well as pedological processes may be heterogeneously distributed within this level of resolution. A ground-based NDVI imaging procedure using low-cost equipment (Olympus Camedia 5000z digital camera equipped with a Hoya R72 infrared filter) was developed in this study to fill this gap at the level of field research, where carrying costly and bulky equipment to remote locations is often the limiting factor for data collection. Method principle and field data are presented, and the field experiment was deepened comparing NDVI measurements and CO2 turnover of soil crust samples in the laboratory, backing the reliability of the approach.A commercially available colour rendition chart with known red (600-700 nm) and NIR (800-900 nm) reflectances was placed into each scene and used for calibration purposes on a per-image basis. Generation of NDVI images involved (i) determination of red and NIR reflectances from the pixel values of the red and NIR channels, respectively, and (ii) calculation and imaging of the NDVI, where NDVI values of 1 to +1 were mapped to grey values of 0 to 255. The correlation between NDVI values retrieved from these images and NDVI values determined using field spectrometry was close (r(2)=0.91), the 95% confidence interval amounted to 0.10 NDVI units. The pixel resolution was 0.8 mm in the field and 0.2 mm in the laboratory, but can still be improved significantly with closer distance to the crust or with higher camera resolution. NDVI values obtained using the new method were related to the net CO2 uptake of BSCs, where both slope and correlation coefficient of the respective regression function conformed with literature data. Geostatistical analysis revealed that both spatial variability of net CO2 uptake as well as size of individual hot spots of this parameter increased with crust development. The latter never exceeded 4 mm in the investigated crusts, which points to the necessity of high resolution imaging for linking remote sensing with ecophysiology. Perspectively, the new method could be used for field monitoring of both biological soil crusts and vascular vegetation. (c) 2012 Elsevier GmbH. All rights reserved.

In this study we tested the feasibility of the thermal infrared (TIR) wavelength region (within the atmospheric window between 8 and 11.5 μm) together with the traditional solar reflective wavelengths for quantifying soil properties for coarse-textured soils from the Australian wheat belt region. These soils have very narrow ranges of texture and organic carbon contents. Soil surface spectral signatures were acquired in the laboratory, using a directional emissivity spectrometer (μFTIR) in the TIR, as well as a bidirectional reflectance spectrometer (ASD FieldSpec) for the solar reflective wavelengths (0.4–2.5 μm). Soil properties were predicted using multivariate analysis techniques (partial least square regression). The spectra were resampled to operational imaging spectroscopy sensor characteristics (HyMAP and TASI-600). To assess the relevance of specific wavelength regions in the prediction, the drivers of the PLS models were interpreted with respect to the spectral characteristics of the soils’ chemical and physical composition. The study revealed the potential of the TIR (for clay: R2 = 0.93, RMSEP = 0.66% and for sand: R2 = 0.93, RMSEP = 0.82%) and its combination with the solar reflective region (for organic carbon: R2 = 0.95, RMSEP = 0.04%) for retrieving soil properties in typical soils of semi-arid regions. The models’ drivers confirmed the opto-physical base of most of the soils’ constituents (clay minerals, silicates, iron oxides), and emphasizes the TIR’s advantage for soils with compositions dominated by quartz and kaolinite.

With the focus on new available hyperspectral imaging sensors sensitive within the thermal infrared (TIR) wavelength region, this study is testing the ability of the TIR in deriving soil erosion relevant parameters (e.g. texture, organic carbon content) from soil spectral measurements with the respect to commonly used VNIR-SWIR spectrometers. Therefore a study site was chosen located within an agricultural area in Western Australia, which is suffering from soil loss through wind erosion processes. VNIR-SWIR and TIR soil spectra derived from laboratory measurements using common field instruments were therefore resampled to imaging sensor spectral specifications (HyMAP and TASI-600). Prediction models have been established via multivariate regression analysis techniques to quantitatively estimate the soils’ physical-chemical parameters using signatures from different spectral regions.

Two FP6 initiatives i) HYRESSA, hyperspectral remote sensing in Europe specific support action, and ii) EUFAR, European Facility for Airborne Research in Environmental and Geo-sciences, have joined forces in FP7. The FP7 Integrating Activity EUFAR (including HYRESSA) is now a network of 33 European airborne data providers and experts in airborne measurements. With the support of the European Commission, EUFAR provides European scientists with trans-national access to 6 airborne instruments (including hyperspectral imaging sensors) and 20 instrumented aircraft and early-stage researchers and university lecturers with training courses on airborne measurements. This paper reports on EUFAR activities and opportunities for European researchers with special attention to activities and opportunities related to airborne hyperspectral imaging.