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Hyperspectral remote sensing in the thermal infrared region has been acknowledged as an innovative tool for earth environmental studies that complements the optical spectral region. The current study focuses on mapping surface mineral content using day and night airborne data in the longwave infrared (LWIR) spectral region over a well-known mineralogical site in Israel. Data were acquired with the AisaOWL hyperspectral sensor over Makhtesh Ramon in the Negev desert in southern Israel. Major minerals could be identified by locating similarities in day and night atsensor radiance spectra. The analysis resulted in the classification of quartz, carbonates, gypsum, kaolinite and other silicates according to their observed spectral features in both day and night data.

In a Mediterranean semiarid area in Central Spain, with dominant rainfed agriculture, hyperspectral airborne data, supported by field spectroscopy have been obtained, allowing a spectral identification of bare soil with the corresponding erosion stages. The definition of soil erosion stages is based on a spectral characterization supported by morphological, physical and chemical features of contrasted soil surfaces as a result of soil loss. Different soil erosion stages were defined within two bare-soil sites representing the soil variability of the area, and where such stages are spatially represented. The validation of selected image derived endmebers was a key step to carry out a partial unmixing for determining soil erosion stages. A preliminary spatial distribution of advanced and intermediate erosion stages was obtained for the most representative soil types.

This paper presents the correlation between soil-moisture content and the standard deviation of the angular variation of the backscattering coefficient of millimeter-wave radiation. This sets the basis for a method, which enables assessment of the soil-moisture content in the upper part of the root zone. Further development of the method should provide a micro-profile measurement of soil moisture up to the depth of the root zone. The method is based on the emerging technology of millimeter waves, providing improved resolution of the subsurface concurrent with surface mapping. Development and use of the method described herein will make it easier to analyze and understand processes governing the soil-water interface, such as soil crusting, penetration, runoff and erosion.

This paper presents a validation of physical based unmixing model in the radiative domain using airborne thermal hyperspectral data and ground truth measurements. The validation results show that if the optical property of a given surface element largely differs from the other mixed elements, it becomes more difficult to retrieve its proper characteristics, particularly when its size is small. The obtained results also show that in the case of flat and homogenous surfaces, there is a linear mixing of the emissivity but not of the temperature. In general, the accuracy of the physical unmixing model is degraded as the mixing proportion is increased (more than two materials) and the ratio of the considered elementary surface by the equivalent surface is reduced.

A full-chain process approach to extracting reflectance information from hyperspectral (HRS) data which is valid for all sensor qualities is proposed. This method is based on a mission-by-mission approach, followed by a unique vicarious calibration stage. As the HRS sensor's performance may vary in time and space, a vicarious calibration method to retrieve accurate at-sensor radiance values is necessary. In fact, vicarious calibration solutions usually rely on natural, well-known, bright and dark targets that are large in size and radiometrically homogeneous. Since such targets are not commonly found in the field for every mission and their spectral features can sometimes resemble artifacts in the corrected radiance, a new vicarious calibration approach is needed. This paper describes a new method that uses artificial agricultural black polyethylene nets of various densities as vicarious calibration targets that are set up along the airplane's trajectory (preferably near the airfield). The different densities of the nets combined with any bright background afford full coverage of the sensor's dynamic range. We show that these artificial targets can be used to assess data quality and correct at-sensor radiance within a short time. Several case studies are presented using Aisa-DUAL sensor data taken at different times from different locations. We found that even “lost data” (in terms of radiance drift) could be recovered by the suggested method. We term the suggested vicarious calibration approach supervised vicarious calibration (SVC) and demonstrate its performance in terms of spectral accuracy. The limitations of the method are also discussed but the overall conclusion is that the suggested procedure is functional, valuable and practical for sensors with questionable or uncertain laboratory-determined radiometric parameters.

A novel approach to estimating at-sensor hyperspectral (HRS) data quality Q/A of Q/I is proposed. As the HRS sensor's performance may vary in time and space, a method to assess at-sensor radiance values is necessary. In fact, vicarious calibration solutions usually rely on natural, well-known, bright and dark targets that are large in size and spectrally/radiometrically homogeneous. Since such targets are not commonly found in the field for every mission and their spectral features can sometimes resemble artifacts in the corrected radiance, a new vicarious calibration approach is needed. This paper is based on our new method Supervised Vicarious Calibration (SVC) that uses artificial agricultural black polyethylene nets of various densities as vicarious calibration targets that are set up along the airplane's trajectory (preferably near the airfield). The different densities of the nets combined with any bright background afford full coverage of the sensor's dynamic range. We show that these artificial targets can be used to assess at-sensor radiance data quality within a short time by two suggested indicators named Rad/Ref (at-sensor Radiance divided by ground truth Reflectance) and RRDF (Radiance to Reflectance difference factor). It's enables gaining immediate Q/A of Q/I information on the acquired data, prior to completion of the campaign, which could save on flight hours, effort and resources in the case of a radiometrically miscalibrated sensor. Several case studies are presented using AISA-Dual sensor data taken at different times and locations. We demonstrate the performance of the suggested indicators in both spectral and spatial domains and discuss their limitation.

A smart unmixing approach for quantitative detection of small amounts of dust that settle on the vegetation canopy using hyperspectral (HRS) airborne imagery data is proposed. A dust analog composed of Alumina (Aluminum Oxide Al 2 O 3 ) powder was artificially spread over vegetation that covered 4 × 4 pixels of the AISA-Dual sensor. The alumina spectral signal could not be extracted using ordinary methods such as supervised classification (e.g. SAM or MTMF), unsupervised classification (Maximum Likelihood or Minimum Distance), and linear unmixing (e.g. MESMA or VCA). Considering the limitations of the above methods for extracting endmembers in a nonlinear domain, we developed a new approach that is capable of detecting the alumina powder from HRS imagery covering the VIS-NIR-SWIR (400–2400 nm) spectral regions. This step wised approach is based on a sequence merge between a decision tree algorithm, several spectral indices and a flexible constrained nonlinear unmixing method. The endmember vectors and abundances are obtained through a gradient-based optimization approach. Ground-truth examination of the results showed that the method is reliable and that it may represent a new frontier for assessing sediment dust contamination on a dark background via airborne sensors.

A detailed spatial/spectral protocol to test the influence of compressed-decompressed on the hyperspectral image data is presented. The proposed scheme is evaluated by applying an hybrid algorithm (LCT and SPHIT) on AISA-Dual hyperspectral images. For the purpose of reliable thematic results, the preservation and recovering of reflectance spectral features should be on the main concern. Therefore, this protocol contains three stages of data processing: radiance, radiometric/spectral preprocessed radiance, and atmospheric corrected reflectance (generated from radiometric/spectral preprocessed radiance). As for impact on exploitation, we consider IsoData classification, anomaly-detection, and spectral indices as benchmark applications. Additionally, the proposed scheme is compared with common validation approaches, such as spectral response assessment and anomaly-detection techniques.

Spectroscopy has existed for more than three centuries now. Nonetheless, significant scientific advances have been achieved. We discuss the history of spectroscopy in relation to emerging technologies and applications. Advanced focal plane arrays, optical design, and intelligent on-board logic are prime prospective technologies. Scalable approaches in pre-processing of imaging spectrometer data will receive additional focus. Finally, we focus on new applications monitoring transitional ecological zones, where human impact and disturbance have highest impact as well as in monitoring changes in our natural resources and environment. We conclude that imaging spectroscopy enables mapping of biophysical and biochemical variables of the Earth's surface and atmospheric composition with unprecedented accuracy.

This study reports the results of a study that investigated the spectral relationship between structural crust and the infiltration rate of a grumusol soil. Structural crust is a thin compact layer generated on the soil surface after rainstorm events due to raindrop energy. The crust significantly affects several soil physical properties, such as infiltration, runoff and soil erosion. In this study, a controlled spectral investigation of the properties of structural crust, using infiltration rate as an indicator, was carried out. A set of soil samples was subjected to increased levels of cumulative energy from a simulated rainstorm while measuring the infiltration rate. After drying the soil samples, their spectral features across the SWIR region (1.2 /spl mu/m-2.4 /spl mu/m) were studied in the laboratory. Empirical relationships were found between the infiltration rate and reflectance values (either in raw or in first derivative domains). Although further study is required to fully account for other soils, rainstorm energies and water qualities, this technique suggests great potential as a tool for rapid, nondestructive, in situ assessment of crusted soil properties in soils.

The data acquired over an inner semiarid city in Israel, using the DAIS-7915 hyperspectral sensor were processed to yield thermal-based maps. This was done in order to assess the distribution effect of the urban heat island (UHI) in a non traditionally way (thermal fluxes and related properties instead of surface and air temperatures information). In this study, a spectral-based approach was used to process data from the DAIS sensor to yield diverse thermal parameters over Afula city, Israel. The DAIS sensor consists of 79 bands across the VIS-NIR-SWIR-TIR spectral region. Seven bands across the TIR region permit the extraction of parameters such as emissivity and energy fluxes on a pixel-by-pixel basis using the information from the VIS-NIR-SWIR region. Following R. Richter's (1996) steps we used the DAIS data in conjunction with ground data taken at the time of the overpass to obtain spatial thermal recognition of the selected city. Comparing the results to the traditional UHI map (air and surface temperature) shows that the spectral-based maps provide additional information about the UHI that cannot be extracted in the traditional way.