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• Soil surface reflectance can be measured in the field with a laboratory quality. • Harmonization of soil field reflectance from different sources is possible. • Harmonization of field soil reflectance with laboratory has been achieved. • The field measurement protocol open up the path for generating field SSL. • The SoilPRO® may revolutionize the remote sensing ground truth measurement. This study introduces an innovative standard and protocol approach for accurate assessment and harmonization of undisturbed soil surface spectra under field conditions, achieving laboratory-grade precision, while minimizing systematic discrepancies. The SoilPRO® assembly was employed for its efficacy in precisely measuring seven different soil samples under similar field and laboratory conditions in six different laboratories. While some discrepancies were noted in and between field and laboratory measurements, aligning the laboratory measurements with a reference sample, Lucky Bay (LB) internal soil standard (ISS), significantly reduced variations across different laboratory settings. A similar approach was employed to align field spectra using a field ISS (FISS), which could be practically applied in the field and adjusted to the laboratory reference LB before utilization. Correcting to the FISS facilitated the alignment of field reflectance among the six laboratories and closely matched with laboratory ISS measurements adjusted to the LB standard. This alignment of field reflectance with ISS-corrected laboratory measurements represents a ground-breaking achievement in field soil spectroscopy, which suffers from instabilities. It not only ensures preservation of the soil surface condition in the field, but also enables objective comparisons with all soil spectral libraries (SSLs) measurements and robust harmonization of field spectral data from different sources. The suggested method masks out technical aspects of spectral measurements in the field and prepares the signals to be analyzed for the physicochemical characteristics of the soil surface. The current study marks the first use of a controlled method for soil-surface spectroscopy measurements, opening a path for the construction of in-situ field SSLs. This advancement represents a significant step toward obtaining more accurate and standardized soil analyses of diverse surface properties under field conditions. [ABSTRACT FROM AUTHOR]

In modeling chemical attributes using hyperspectral data, nonlinear relationships between the predictor and the response are frequent. The common nonlinear modeling techniques improve prediction accuracy but suffer from low interpretability of the models. In this paper, we demonstrate a new multivariate modeling method, denoted as spectral assignment-oriented partial least squares (SAO-PLS), which is designed to provide a nonlinear modeling solution with strong interpretability products. The need for this approach is apparent when a given sample population consists of different spectral features for different levels of the response. Accordingly, the suggested SAO-PLS algorithm segments the data in an optimal location on the response distribution by maximizing the difference in spectral assignments between two clusters. SAO-PLS is applied here to two test cases with different characteristics: 1) an established data set containing airborne hyperspectral data of asphaltic roads, merged with in situ measured dynamic friction values captured using a standardized method and 2) a soil spectral library, spectrally measured with an analytical spectral device spectrometer, to which organic carbon measurements were applied. Our results demonstrate the superiority of SAO-PLS over partial least-squares regression for both model accuracy and interpretability, providing a deeper understanding of the underlying processes. [ABSTRACT FROM AUTHOR]

Abstract One of the most important characteristics of any quantitative method is its detection limit, which is the lowest detectable amount of analyte in a sample in a given method. The detection limit helps to know to which extent a method is applicable and valid. The detection of petroleum hydrocarbon contamination in bare soils is important, because of its vast distribution and its negative effects on humans and the environment. Hyperspectral remote sensing is an acceptable, cost-effective and spatially comprehensive tool, which has detection possibilities of petroleum hydrocarbon. However, previous studies that engaged with this topic did not directly consider the detection limit of the method and no detection limit has been systematically explored. In this study, an outdoor experiment was executed in which three types of soil were contaminated with 14 levels of pure crude oil and measured in outdoor conditions with a hyperspectral camera (950–2500 nm) at three distances from the camera (4, 8 and 12 m). For each soil type, and for each distance, the detection limit was systematically calculated. The results show that the detection limit is a dynamic range affected by the spatial domain and the soil type. Moreover, a significant (p < 0.001) difference between the average detection limit (1.32, 2.09 and 2.90 wt%) of the three soil types was found, suggesting that the detection limit is not only related to the PHC wt% but also depends on the soil type. The results presented in this study can help to improve our understanding of the limitations of hyperspectral remote sensing in direct detection of petroleum hydrocarbon and can be projected to other hyperspectral domains and soils. Highlights • Detection limit of crude oil in 3 soils was calculated using hyperspectral images. • Detection limit was effected by spatial resolution and soil type. • Bright soil with large grain size show lowest detection limit. • Findings should be considered in airborne hyperspectral campaigns. [ABSTRACT FROM AUTHOR]

There are several methods to extract soil information by spectral sensing. For this reason, the database should be built including standards and protocols both in the lab and in field acquisitions. If we does not align the measurement one to each other, the models will have no merit in term of large-scale application and stay as an academic exercise only. The use of standard samples with known spectra is expected to allow parameterizing mathematically soil spectra collected by different equipment and with distinct geometries. This study was proposed for the reason that to date, the new methodology proposed has not been evaluated in Brazilian tropical soils (Oxisols) in visible, near and short infrared (VIS-NIR-SWIR – 350 to 2500 nm) spectral regions from three spectrometers (FieldSpec 3) in three distinct protocols (Long Light, Near Light and Contact Probe). The current study compared the spectral intensity of each combination between spectrometers and protocols by ANOVA module and the clay prediction capacity by PLSR with cross-validation, before and after the internal soil standard (ISS) method application. The results showed that visual spectral variation is minimized by the ISS method and its correction enables better proxy modeling of clay content especially if the data are mixed from different protocols. The model for clay prediction was improved showing a favorable case to use the ISS technique in any soil spectral measurement in a better way to merge spectral libraries from different sources. [ABSTRACT FROM AUTHOR]

A new assembly for measuring surface reflectance in the field was developed and built to fill the gap between laboratory and field spectral measurements of the soil surface. This device, named SoilPRO (Soil field PRObe), can be connected to any field spectrometer fiber tip and used to measure representative and undisturbed surfaces of different soil types, low grass and artificial materials. Here, the SoilPRO is presented and compared to reflectance-measurement methods that are commonly used in the laboratory (dark box configuration and the ASD® contact probe) and the field (bare fiber handheld by pistol grip and ASD contact probe). The SoilPRO's performance was evaluated by conducting continuous tests in the laboratory and outdoors with soil samples, and in the field with five different surface types as test targets. The SoilPRO's reflectance products in the field were very similar to those of commonly used methods under optimal conditions, while eliminating most of their associated errors. The device provides a new approach to spectral data acquisition in the field that enables measuring undisturbed soil surfaces while maintaining constant and stable environmental and operational conditions. The SoilPRO measures the reflectance of a representative target's footprint across the spectrometer's full range of sensitivity. The measurement is not dependent on the sun's radiation or atmospheric variations, or on operator stability or measurement geometry, and it does not disturb the surface being measured. Reflectance is an inherent property, and the assembly provides superb reflectance accuracy for the surface area in question. [ABSTRACT FROM AUTHOR]

The study tested a data mining engine (PARACUDA®) to predict various soil attributes (BC, CEC, BS, pH, Corg, Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The engine was designed to utilize large data in parallel and automatic processing to build and process hundreds of diverse models in a unified manner while avoiding bias and deviations caused by the operator(s). The system is able to systematically assess the effect of diverse preprocessing techniques; additionally, it analyses other parameters, such as different spectral resolutions and spectral coverages that affect soil properties. Accordingly, the system was used to extract models across both optical and thermal infrared spectral regions, which holds significant chromophores. In total, 2880 models were evaluated where each model was generated with a different preprocessing scheme of the input spectral data. The models were assessed using statistical parameters such as coefficient of determination (R2), square error of prediction (SEP), relative percentage difference (RPD) and by physical explanation (spectral assignments). It was found that the smoothing procedure is the most beneficial preprocessing stage, especially when combined with spectral derivation (1st or 2nd derivatives). Automatically and without the need of an operator, the data mining engine enabled the best prediction models to be found from all the combinations tested. Furthermore, the data mining approach used in this study and its processing scheme proved to be efficient tools for getting a better understanding of the geochemical properties of the samples studied (e.g., mineral associations). [ABSTRACT FROM AUTHOR]

A method for estimating the level of noise of a hyperspectral data cube is proposed. The method includes noise reduction using the Minimum Noise Fraction (MNF) transform and mapping the spectral dissimilarity between the pixels in the resulting noise-reduced radiance image, as well as in the original noisy radiance image, using the Spectral Angle Mapper (SAM) algorithm. Comparing the two maps, on a pixel by pixel basis, gives a value indicating the addition of noise to the spectrum of each pixel. An average value for the entire image is calculated, defined as the Image Noise Indicator (INI). In practice, this value indicates the quality of the data. Combining the INI value with the level of radiance enables estimating the Image Noise Level (INL). The method was applied and examined on a noisy synthetic image and then implemented for over 20 acquired images from different hyperspectral sensors and their noise level was estimated. Further examination showed that the INI value is independent of the heterogeneity of the hyperspectral data cube. The INI value, indicating the noise level, might increase as a geo-reference procedure is applied to hyperspectral data cube. The INI–INL can also be used as a quality indicator (QI) in the ongoing effort to objectively certify hyperspectral remote sensing images for practical usages. [ABSTRACT FROM AUTHOR]

The internal soil standard (ISS) concept in which a soil standard sample, exhibiting stable spectral performance, is used to normalize and align all other soil spectral measurements – was further examined herein. Different spectrometers (Spectral Evolution and ASD Spectral Pro) were used to measure a set of soil samples with the soil standards sample as a reference. Two sand dune samples served as the ISS to align measurements made under different conditions and the results were compared. It was shown that the ISS method was able to correct the spectral information from one spectrometer to another; however, the differences in the results obtained when using the two different soil standards are discussed. The main conclusion of this paper is that the soil spectral user community should adopt the ISS method for the benefit of all, and the sooner the better. This will allow much more effective exploitation of all data sets acquired on a daily basis by the growing soil spectral community that still lacks standardization procedures. [ABSTRACT FROM AUTHOR]

• Regression analyses of large group of soil attributes isolate 4 cementing agents. • Pedotransfer function using 4 cementing agents indicates aggregation stage. • Cementing agents in soils found to be spectrally active. • Soil reflectance can predict soil aggregate fractions. Soil aggregation is an important property, affecting issues such as soil structure and dust load. To investigate the potential to evaluate soil aggregation status via proximal sensing, we conducted a comprehensive study using the legacy soil spectral library (SSL) of Israel representing arid and semiarid environments. The SSL was segregated into six soil aggregate size fractions as follows: 2–1.4 mm, F1; 1.4–1.0 mm, F2; 1.0–0.5 mm, F3; 0.5–0.25 mm, F4; 0.25–0.1 mm, F5; <0.1 mm, F6; and the average aggregate size (AVG, in mm) was calculated. In addition, another 74 soil attributes were measured along with their sample reflectance spectra across the 0.4–2.5-μm spectral region. A comprehensive correlation matrix between all soil attributes enabled isolating four cementing agents (CAs) that bind the primary particles into aggregates: clay content, clay mineral (smectite), organic matter and free iron oxide content. Generating pedotransfer functions (PTFs) with these CAs revealed equations that fairly predicted the aggregate size fractions F1 (R2 = 0.68), F2 (R2 = 0.79), F3 (R2 = 0.67), F5 (R2 = 0.61) and AVG (R2 = 0.78) with high accuracy. The six aggregate size fractions and AVG were divided into three groups based on their relation to the CAs: group A (F1, F2, F3, AVG) presenting positive correlations with the CAs, group B (F4, F6) presenting poor relationships with the CAs, and group C (F5) presenting negative correlations with the CAs. As the CAs were found to be chromophoric substances, it was possible to predict each CA from spectral-based models. A separate spectral-based analysis was also performed to evaluate the aggregate size fractions directly with no a priori information or PTF adoption. This analysis revealed high statistical agreement with spectral assignments for the four selected CAs. Whereas groups A and C were successfully predicted in the validation stage spectral-based models (F1 [R2 = 0.68], F2 [R2 = 0.79], F3 [R2 = 0.7], F5 [R2 = 0.57] and AVG [R2 = 0.67]), the predictions of group B were poorer relationships against the selected CAs that present important spectral assignments. We concluded that soil aggregation stage can be assessed directly or indirectly (via PTF) using spectral analysis and a data-mining approach. Assuming that the reflectance information from hyperspectral remote-sensing means, such as EMIT (NASA initiative), will soon be available from orbit (2022), this approach may pave the way for monitoring soil aggregation status from afar, to determine the soil's potential as a dust source. [ABSTRACT FROM AUTHOR]

For the past 20 years, soil reflectance measurement in the laboratory has been a common and extensively used procedure. Based on soil spectroscopy, a proxy strategy using a chemometrics approach has been developed for soils, along with massive construction of soil spectral libraries worldwide. Surprisingly however, there are no agreed-upon standards or protocols for reliable reflectance measurements in the laboratory and field. Consequently, almost every user reconstructs his or her own protocol based on the literature, experience, convenience and infrastructure. This yields significant problems for comparing and sharing soil spectral data between users, as spectral variations can be encountered from one protocol to the next. This further prevents the generation of a robust model for a given soil property using the worldwide data archive. To solve this problem in the laboratory, a joint project between CSIRO — Perth and Tel Aviv University (TAU) was conducted to establish a standard protocol for soil measurement, along with the use of an internal standard procedure to generate correction factors to normalize all possible variations to a soil benchmark (SBM) setup. The method is based on a proof of concept reported by Pimestein et al. (2011) for the use of an internal soil standard (ISS), a concept adopted from the wet chemistry discipline. To formulate the ISS method proposed herein, we used samples from two sand dunes that were characterized for their mineralogy, stability and reflectance reproducibility to be suitable for the standardization mission. These samples were found to be stable in space and time and to hold a stable soil structure and spectral response common to soils. Five soil samples were used to examine the performance of the suggested ISS approach. The ISS and soil samples were carefully measured for reflectance at the CSIRO laboratory using a new calibrated ASD and different protocols and procedures harboring minor and major differences. In addition, the exact same ISS and soil samples were measured in the TAU laboratory with a different ASD, protocols and ambient conditions. A systemic quantitative study was conducted to judge the performance of the ISS method. The results demonstrated that each protocol provides significant spectral variations from the SBM protocol, but all can be corrected to the SBM measurements. The results are promising as they can be achieved with any protocol if the systematic variations are kept constant. The ISS samples can also be used to track spectrometer deterioration and measurement-setup stability. The current paper provides information on how to obtain and use the ISS, and recommends a simple protocol for measuring soil reflectance under laboratory conditions. [ABSTRACT FROM AUTHOR]

Introduced in 2011, the supervised vicarious calibration (SVC) approach is a promising approach to radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data. This paper presents a comprehensive study by which the SVC method has been systematically examined and a complete protocol for its practical execution has been established-along with possible limitations encountered during the campaign. The technique was applied to multi-sourced HRS data in order to: (1) verify the at-sensor radiometric calibration and (2) obtain radiometric and atmospheric correction coefficients. Spanning two select study sites along the southeast coast of France, data were collected simultaneously by three airborne sensors (AisaDUAL, AHS and CASI-1500i) aboard two aircrafts (CASA of National Institute for Aerospace Technology INTA ES and DORNIER 228 of NERC-ARSF Centre UK). The SVC ground calibration site was assembled along sand dunes near Montpellier and the thematic data were acquired from other areas in the south of France (Salon-de-Provence, Marseille, Avignon and Montpellier) on 28 October 2010 between 12:00 and 16:00 UTC. The results of this study confirm that the SVC method enables reliable inspection and, if necessary, in-situ fine radiometric recalibration of airborne hyperspectral data. Independent of sensor or platform quality, the SVC approach allows users to improve at-sensor data to obtain more accurate physical units and subsequently improved reflectance information. Flight direction was found to be important, whereas the flight altitude posed very low impact. The numerous rules and major outcomes of this experiment enable a new standard of atmospherically corrected data based on better radiometric output. Future research should examine the potential of SVC to be applied to super-and-hyperspectral data obtained from on-orbit sensors. [ABSTRACT FROM AUTHOR]

The hyperspectral (HSR) sensors Earth Surface Mineral Dust Source Investigation (EMIT) of the National Aeronautics and Space Administration (NASA) and Environmental Mapping and Analysis Program (EnMAP) of the German Aerospace Center (DLR) were recently launched. These state-of-the-art sensors have joined the already operational HSR sensors DESIS (DLR), PRISMA (Italian Space Agency), and HISUI (developed by the Japanese Ministry of Economy, Trade, and Industry METI and Japan Aerospace Exploration Agency JAXA). The launching of more HSR sensors is being planned for the near future (e.g., SBG of NASA, and CHIME of the European Space Agency), and the challenge of monitoring and maintaining their calibration accuracy is becoming more relevant. We proposed two test sites: Amiaz Plain (AP) and Makhtesh Ramon (MR) for spectral, radiometric, and geometric calibration/validation (CAL/VAL). The sites are situated in the arid environment of southern Israel and are in the same overpass coverage. Both test sites have already demonstrated favorable results in assessing an HSR sensor's performance and were chosen to participate in the EMIT and EnMAP validation stage. We first evaluated the feasibility of using AP and MR as CAL/VAL test sites with extensive datasets and sensors, such as the multispectral sensor Landsat (Landsat5 TM and Landsat8 OLI), the airborne HSR sensor AisaFENIX 1K, and the spaceborne HSR sensors DESIS and PRISMA. Field measurements were taken over time. The suggested methodology integrates reflectance and radiometric CAL/VAL test sites into one operational protocol. The method can highlight degradation in the spectral domain early on, help maintain quantitative applications, adjust the sensor's radiometric calibration during its mission lifetime, and minimize uncertainties of calibration parameters. A PRISMA sensor case study demonstrates the complete operational protocol, i.e., performance evaluation, quality assessment, and cross-calibration between HSR sensors. These CAL/VAL sites are ready to serve as operational sites for other HSR sensors. [ABSTRACT FROM AUTHOR]

• Laboratory soil spectral measurements do not represent field conditions. • Developed transfer function to predict field from laboratory spectra was validated. • The spectral assessment of water-infiltration rate was examined. • Water-infiltration rate is a soil surface-dependent property. • The transfer function improved the accuracy of water-infiltration rate assessments. Spectral-based models extracted from laboratory reflectance in the 400–2500 nm spectral range to predict soil attributes may not be applicable to soil spectra acquired in the field. This is because laboratory sampling procedures disturb the natural soil surface's status. We investigated this issue by using the soil surface-dependent property of water-infiltration rate (WIR). We created a dataset with 114 samples collected from six fields with varying textures located in three different Mediterranean countries (Israel, Greece, Italy). Using the field and laboratory spectral datasets, we demonstrated that WIR is better predicted by field vs. laboratory measurements (R2 = 0.92 and 0.56, respectively). We also developed a transfer function (TF) to predict the field spectral measurements from the laboratory spectra. Use of the TF-processed dataset considerably improved the WIR prediction using laboratory information (from R2 = 0.56 to 0.76). It was concluded that soil surface reflectance values can be estimated based on laboratory spectra using a TF. The generated TF enables exploiting soil spectral libraries for remote-sensing views and for assessing surface-related soil properties. [ABSTRACT FROM AUTHOR]

Investigation of urban built environment includes a wide range of applications that require 3-D information. New approaches are needed for near-real-time analysis of the urban environment with natural 3-D visualisation of extensive coverage. Hyperspectral remote sensing technology is a promising and powerful tool to assess quantitative classification of urban materials by exploring possible chemical/physical changes using spectral information across the VIS-NIR-SWIR spectral region. Light Detection And Ranging (LiDAR) technology offers precise information about the geometrical properties of the surfaces and can reflect the different shapes and formations in the complex urban environment. Generating a monitoring system that is based on integrative fusion of hyperspectral and LiDAR data may enlarge the application envelope of each individual technology and contribute valuable information on urban built environments and planning. A fusion process defined by a data-registration algorithm and including spectral/spatial and 3-D information is developed and presented. The proposed practical 3-D urban environment application for photogrammetric and urban planning purposes integrates the hyperspectral (spectrometer, ground camera and airborne sensor) and LiDAR data. This application may provide urban planners, civil engineers and decision-makers with tools to consider quantitative spectral information in the 3-D urban space. [ABSTRACT FROM AUTHOR]

Dual pushbroom hyperspectral sensors consist of two different instruments (covering different wavelengths) that are usually mounted on the same optical bench. This configuration leads to problems, such as co-registration of pixels and squint of the field of view, known as the boresight effect. Determination of image-orientation parameters is due to the combination of an inertial measurement system (IMU) and global position system (GPS). The different positions of the IMU, the GPS antenna and the imaging sensors cause the orientation and boresight effect. Any small change in the correction of the internal orientation affects the co-registration between images extracted from the two instruments. Correcting the boresight effect is a key and almost automatic task performed by all dual-system users to better analyze the full spectral information of a given pixel. Thus, the boresight effect is considered to be noise in the system and a problem that needs to be corrected prior to any (thematic) data analysis. We propose using the boresight effect, prior to its correction, as a tool to monitor and detect spectral phenomena that can provide additional information not present in the corrected images. The advantage of using this effect was investigated with the AISA-Dual sensor, composed of an EAGLE sensor for the VIS-NIR (VNIR) region (400-970 nm) and HAWK for the SWIR region (980-2,450 nm). During the course of more than six years of operating this sensor, we have found that the boresight effect provides a new capacity to analyze hyperspectral data, reported herein. Accordingly, we generated a protocol to use this effect for three applications: (1) enhancing the shadow effect; (2) generating a 3-D view; and (3) better detecting spectral/spatial anomalies based on sub-pixel edge detection. This paper provides examples of these applications and suggests possible uses from an airborne platform. [ABSTRACT FROM AUTHOR]

Image registration is widely used in remote-sensing applications. The existing automatic image registration techniques fall into two categories: Intensity-based and feature-based; the latter (which extracts structures from both images) being more suitable for multi-sensor fusion, detection of temporal changes and image mosaicking. Conventional image registration algorithms have proven to be inaccurate, time-consuming, and unfeasible due to image complexity which makes it cumbersome or even impossible to discern the appropriate control points. In this study, we propose a novel method for automatic image registration based on topology (AIRTop) for change detection and multi-sensor (airborne and spaceborne) fusion. In this algorithm, we first apply image-processing methods (SURF-Speeded-Up Robust Features) to extract the landmark structures (roads and buildings) and convert them to a features (vector) map. The following stages are applied in GIS (Geographic Information System), where topology rules, which define the permissible spatial relationships between features, are defined. The relationships between features are established by weight-based topological map-matching algorithm (tMM). The suggested algorithm presents a robust method for image registration. The main focus in this study is on scale and image rotation, when the quality of the scanning system is constant. These seem to offer a good compromise between feature complexity and robustness to commonly occurring deformations. The skew and the anisotropic scaling are assumed to be second-order effects that are covered to some degree by the overall robustness of the sensor. [ABSTRACT FROM AUTHOR]

Earth Observing-1 Hyperion data were found to be relatively noisy and to contain significant cross-track spectral curvature nonlinearity disturbances, known as the smile/frown effect. A method for the correction of spectral curvature effects (smile) in Hyperion images, termed trend line smile correction (TLSC), is presented. The method is based on the assumption that there is a partial correlation between data spectral nonuniformity, due to the smile and eigenvalues gradient that mostly appears in the first minimum noise fraction (MNF) image (MNF-1). However, MNF-1 consists of both spatial and spectral information. Therefore, it is hypothesized that adaptation applied to MNF-1, according to exclusively spectrally derived parameters (e.g., atmospheric absorption features) can account specifically for the smile effect in the data. A set of normalization factors, calculated from the spectral derivative at the right-hand side of the O2 absorption feature (760 nm), MNF-1 and the moderate-resolution atmospheric transmittance radiative transfer model, are used to scale the initial MNF-1. The image is corrected after the inverse conversion of the MNF to radiance space. The methodology was tested on four different Hyperion scenes and consistently outperformed other tested methods by up to nine times. As a result, thematic mapping, using the TLSC-corrected reflectance data cube, was shown to be consistent with the geology maps of the study area. [ABSTRACT FROM AUTHOR]

Quantitative mapping by means of hyperspectral remote sensing (HRS) can be hampered by reflectance anisotropy emerging in large field of view (FOV) optics, and may contain spectral radiometric distortions. This paper presents an algorithm for the rectification of reflectance anisotropy for rough terrain. A new method is offered for correction of radiometric bias caused by topography and sensing geometry. The correction of HRS data of lawn grass is demonstrated, and the method is tested on a large park area. To record elevation we used airborne laser scanning data to obtain a digital surface model (DSM). The Compact Airborne Spectral Imager (CASI) recorded reflectance of the same area. Anisotropy of reflectance was recorded by a laboratory spectro-goniometer. An analysis of the effect of correction on the normalized difference vegetation index (NDVI) shows that even moderate slopes, medium sensor FOV and high illumination conditions will result in reflectance anisotropy. Further analysis shows a clear inverse relationship between sensitivity of interpretation and spatial or spectral resolutions. We conclude with an outlook on the utilization of this method among other pre-processing tasks. [ABSTRACT FROM AUTHOR]

An EO-1 Hyperion scene was used to identify and map hydrothermally altered rocks and a Precambrian metamorphic sequence at and around the Alid volcanic dome, at the northern Danakil Depression, Eritrea. Mapping was coupled with laboratory analyses, including reflectance measurements, X-ray diffraction, and petrographic examination of selected rock samples. Thematic maps were compiled from the dataset, which was carefully pre-processed to evaluate and to correct interferences in the data. Despite the difficulties, lithological mapping using narrow spectral bands proved possible. A spectral signature attributed to ammonium was detected in the laboratory measurements of hydrothermally altered rocks from Alid. This was expressed as spectral absorption clues in the atmospherically corrected cube, at the known hydrothermally altered areas. The existence of ammonium in hydrothermally altered rocks within the Alid dome has been confirmed by previous studies. Spectral information of endmember's mineralogy found in the area (e.g. dolomite) enables a surface mineral map to be produced that stands in good agreement with the known geology along the overpass. These maps are the first hyperspectral overview of the surface mineralogy in this arid terrain and may be used as a base for future studies of remote areas such as the Danakil. [ABSTRACT FROM AUTHOR]

Abstract: Urban and environment development plans commonly lack spectrally based value-added information layers such as expected albedo, emissivity and temperature of the planned landscapes. These can be integrated into plans in order to assist in using specific materials or in the way new landscapes and urban spaces are designed. In contrast to existing space-borne remotely sensed imagery from which information layers as such can be extracted using atmospheric correction tools, development plans are set on paper, in a geographic information system (GIS) or as perspective “artistic images” at best. This paper describes a new software tool within the environment for visualization of images (ENVI 4.1) software, for automatic simulation of such multispectral reflectance images, given thematic maps of planned landscapes and associated spectral signatures. We discuss issues related to the image generation process, the method of spectral signature integration, and to quality assessment measures. An example is provided. We assess the simulated output quantitatively using a pixel-based “goodness-of-fit” measure and by calculating Pearson''s correlation coefficients. Results show that simulation of images based on local neighborhood spectral mixtures, have all, mean total-goodness-of-fit measures amounting 99%, and have a general positive linear correlation of around 0.86 with real data. A class-wise correlation of a simulated image with a real reference image shows that large image segments of homogenous land-cover classes, such as vegetation classes, inland waters and some soils, match about 80–90% of corresponding real data. On the other hand, simulated data will match only 20–40% of real values for highly textured land-cover classes with relatively small spatial extent over the image, such as for built-up areas. We conclude with two prospective applications related to the validation of classification algorithms, and to planning exercises. [Copyright &y& Elsevier]

Abstract: Mapping and dating of arid and semi-arid alluvial fans are of great importance in many Quaternary studies. Yet the most common mapping method of these features is based on visual, qualitative interpretation of air-photos. In this study we examine the feasibility of mapping arid alluvial surfaces by using airborne hyperspectral reflective remote sensing methodology. This technique was tested on Late Pleistocene to Holocene alluvial fan surfaces located in the hyperarid southern Arava valley, Israel. Results of spectral field measurements showed that the surface reflectance is controlled by two main surficial processes, which are used as relative age criteria: the degree of desert pavement development (gravel coverage %) controls the absorption feature depths, while the rock coating development influences significantly the overall reflectance of the surface, but its effect on the absorption feature depths is limited. We show that as the percent of the surface covered by gravels increases, the absorption feature depth of the common gravels, in this case carbonate at 2.33 μm, increases as well; whereas the absorption features depth of the fine particle in-between the gravels, decrease (hydroxyl and ferric absorption features at 2.21 μm, and 0.87 μm, respectively), as the fines are removed from the surface. Using these correlations we were able to map the surface gravel coverage (%) on the entire alluvial fan, by calculating the gravel coverage (%) in each pixel of the hyperspectral image. The prediction of gravel coverage (%) is with accuracy of ±15% (e.g. gravel coverage of 50% can be predicted to be 35% to 65%). Using extensive accuracy assessment data, we show that the spectral based mapping maintained high accuracy degree (R 2 =0.57 to 0.83). The quantitative methodology developed in this study for mapping alluvial surfaces can be adapted for other surfaces and piedmonts throughout the arid regions of the world. [Copyright &y& Elsevier]

Unsuccessful efforts to interpret and analyze several sets of images acquired over Israel by Landsat-7 during the first 2 years of its operation (August 1999–August 2001) provided the motivation to examine the hypothesis that image data produced over the desert regions along the climatic transition zone of Israel were subject to radiometric saturation. The objectives of the current study are to characterize the saturation phenomenon, by inspecting different images of Landsat-7, and to suggest a statistic method for solution in the spectral domain. Entropy analysis was also performed on these images in order to compare the information content of the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor with that of the Landsat-5 Thematic Mapper (TM) over the desert environment before and after saturation correlation. The study reveals that radiometric saturation affected several ETM+ bands (especially band 3). Consequently, in terms of entropy, less information can be extracted from the saturated bands relative to equivalent bands of Landsat-5 TM. A statistical solution, based on multivariate correlation analysis among all spectral bands, is proposed to overcome the saturation problem. Satisfactory results were achieved by applying the statistical methods on several samples of saturated scenes. An operational revision in the spectral radiance range and the gain setting, applied to Landsat-7 ETM+ that is affected from July 1, 2000, has improved the saturation phenomenon over the region. The current proposed method can be applied for other climatic regions, such as scenes partially covered by snow, and other multi and hyper spectral sensors. [Copyright &y& Elsevier]

Atmospheric correction (ATC) of radiance image data is a preliminary and necessary procedure to reach a coherent unsupervised classification. Though ATC results in removal of noise artefacts related to path radiance, loss of some data is inherent by the process. The unsupervised principal component analysis-based classification (PCABC) was harnessed in this paper using radiance data that bypass the ATC protocol. Being primarily based on the variability of the input hyperspectral remote sensing (HRS) image regardless of its physical attributes, it was assumed that PCABC can be applied to radiance HRS image just as already shown on reflectance domain. To test this assumption, PCABC was tested on a radiance HRS image of Specim's AisaFENIX taken over the Mediterranean forest of Mount Horshan, Israel. With no application of ATC or noise reduction, while tested unsupervised classification methods were insufficient, PCABC was able to classify four different plant species with an overall accuracy of 68%. [ABSTRACT FROM AUTHOR]

This study examined the feasibility of using a very sensitive video thermal radiometer for deriving information about subsoil objects from the air. We mounted the thermal sensor onboard a helicopter and acquired digital data from an altitude of 4000 feet over an archaeological site on the Golan Heights, Israel. The site, Tel Leviah, is a Bronze Age settlement that is covered by a thin layer of soil. The buried structures, made from basalt, could not be observed from the ground or in aerial photos. In the thermal images, however, buried basalt structures were significantly enhanced because they have different thermal characteristics than the ground's surface. Based on these images, it was possible to generate a map for future excavations. Verification on the ground revealed positive agreement between the thermal images and the buried structures in selected areas. The research highlights the fact that this technology can contribute additional and useful information to the field of archaeology. Based on these preliminary results, further study is planned in order to examine the capability of the sensor in different conditions and to further excavate Tel Leviah. [ABSTRACT FROM AUTHOR]

Spectroscopic measurements of soil samples are reliable because they are highly repeatable and reproducible. They characterise the samples' mineral–organic composition. Estimates of concentrations of soil constituents are inevitably less precise than estimates obtained conventionally by chemical analysis. But the cost of each spectroscopic estimate is at most one‐tenth of the cost of a chemical determination. Spectroscopy is cost‐effective when we need many data, despite the costs and errors of calibration. Soil spectroscopists understand the risks of over‐fitting models to highly dimensional multivariate spectra and have command of the mathematical and statistical methods to avoid them. Machine learning has fast become an algorithmic alternative to statistical analysis for estimating concentrations of soil constituents from reflectance spectra. As with any modelling, we need judicious implementation of machine learning as it also carries the risk of over‐fitting predictions to irrelevant elements of the spectra. To use the methods confidently, we need to validate the outcomes with appropriately sampled, independent data sets. Not all machine learning should be considered 'black boxes'. Their interpretability depends on the algorithm, and some are highly interpretable and explainable. Some are difficult to interpret because of complex transformations or their huge and complicated network of parameters. But there is rapidly advancing research on explainable machine learning, and these methods are finding applications in soil science and spectroscopy. In many parts of the world, soil and environmental scientists recognise the merits of soil spectroscopy. They are building spectral libraries on which they can draw to localise the modelling and derive soil information for new projects within their domains. We hope our article gives readers a more balanced and optimistic perspective of soil spectroscopy and its future. Highlights: Spectroscopy is reliable because it is a highly repeatable and reproducible analytical technique.Spectra are calibrated to estimate concentrations of soil properties with known error.Spectroscopy is cost‐effective for estimating soil properties.Machine learning is becoming ever more powerful for extracting accurate information from spectra, and methods for interpreting the models exist.Large libraries of soil spectra provide information that can be used locally to aid estimates from new samples. [ABSTRACT FROM AUTHOR]

The investigation of iron oxides in soil using spectral reflectance is very common. Their spectral signal is significant across the visible–near infrared (VIS–NIR) spectral range (400–1000 nm). However, this range overlaps with other soil chromophores, such as those for water and soil organic matter (SOM). This study aimed to investigate the effect of different SOM species on red soil from Israel, which is rich in hematite iron oxide, under air-dried conditions. We constructed datasets of artificially mixed soil and organic matter (OM) with different percentages of added compost from two sources (referred to as A2 and A5). Eighty subsamples of mixed soil–OM were prepared for each of the OM (compost) types. To investigate the effect of OM on the strong iron-oxide absorbance at 880 nm, we generated two indices: CRDC, the absorbance spectral depth change at 880 nm after continuous removal, and NRIR, the normalized red index ratio using 880 and 780 nm wavelengths. The different OM types influenced the soil reflectance differently. At low %SOM, up to 1.5%, the OM types behaved more similarly, but as the OM content increased, their effect on the iron-oxide signal was greater, enhancing the significant differences between the two OM sources. Moreover, as the SOM content increased, the iron-oxide signal decreased until it was completely masked out from the reflectance spectrum. The masking point was observed at different SOM contents: 4% for A5 and 8% for A2. A mechanism that explains the indirect chromophore activity of SOM in the visible region, which is related to the iron-oxide spectral features, was provided. We also compared the use of synthetic linear-mixing practices (soil–OM) to the authentic mixed samples. The synthetic mixture could not imitate the authentic soil reflectance status, especially across the overlapping spectral position of the iron oxides and OM, and hence may hinder real conditions. [ABSTRACT FROM AUTHOR]

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO2) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals' spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R2 = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R2 = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R2 = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R2 = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks. [ABSTRACT FROM AUTHOR]

Mineral nutrition is essential for optimal plant growth. Phosphorus (P) is a relatively small component of leaf dry weight, with a concentration in plant foliage of less than 1%. Despite its low concentration, P is an essential element in plants, mainly used for energy transfer. Mapping P concentration using traditional methods is expensive and usually limited to a small area; it is time-consuming and covers only a few plant individuals or species. In this study, we demonstrate the use of remote-sensing (RS) data acquired from the feld and airborne hyperspectral sensors to predict and map the P concentration in leaves of different woody Mediterranean plant species. Comprehensive field work included leaf sampling, laboratory analyses, and spectral measurements using a visible, near-infrared and shortwave-infrared (VIS–NIR–SWIR) field spectrometer. Using different spectral configurations, we built accurate models to predict P concentration in leaf samples. The models were built using a NIR data analysis technique with the data mining software PARACUDA II. This software allowed us to identify the correlative wavelengths for P-bearing molecules in selected woody Mediterranean plant species. The hyperspectral-based model for leaf P concentration was extracted from the reflectance data acquired using a manned aircraft carrying a hyperspectral sensor (Specim AisaFenix 1 K). The model gave a reliable correlation between points extracted from the hyperspectral image and samples measured in the field. We believe that the methodology used in this study will help forest ecologists better understand the concentration of P in the foliage of woody Mediterranean plant species. [ABSTRACT FROM AUTHOR]

Quartz is the most abundant mineral on the earth's surface. It is spectrally active in the longwave infrared (LWIR) region with no significant spectral features in the optical domain, i.e., visible–near-infrared–shortwave-infrared (Vis–NIR–SWIR) region. Several space agencies are planning to mount optical image spectrometers in space, with one of their missions being to map raw materials. However, these sensors are active across the optical region, making the spectral identification of quartz mineral problematic. This study demonstrates that indirect relationships between the optical and LWIR regions (where quartz is spectrally dominant) can be used to assess quartz content spectrally using solely the optical region. To achieve this, we made use of the legacy Israeli soil spectral library, which characterizes arid and semiarid soils through comprehensive chemical and mineral analyses along with spectral measurements across the Vis–NIR–SWIR region (reflectance) and LWIR region (emissivity). Recently, a Soil Quartz Clay Mineral Index (SQCMI) was developed using mineral-related emissivity features to determine the content of quartz, relative to clay minerals, in the soil. The SQCMI was highly and significantly correlated with the Vis–NIR–SWIR spectral region (R2 = 0.82, root mean square error (RMSE) = 0.01, ratio of performance to deviation (RPD) = 2.34), whereas direct estimation of the quartz content using a gradient-boosting algorithm against the Vis–NIR–SWIR region provided poor results (R2 = 0.45, RMSE = 15.63, RPD = 1.32). Moreover, estimation of the SQCMI value was even more accurate when only the 2000–2450 nm spectral range (atmospheric window) was used (R2 = 0.9, RMSE = 0.005, RPD = 1.95). These results suggest that reflectance data across the 2000–2450 nm spectral region can be used to estimate quartz content, relative to clay minerals in the soil satisfactorily using hyperspectral remote sensing means. Graphical Abstract [ABSTRACT FROM AUTHOR]

Manmade crude oil contamination, which has negative impacts on the environment and human health, can be found in various ecosystems all over the globe. Hyperspectral remote sensing (HRS) is an efficient tool to investigate this crude oil contamination where its electromagnetic spectrum is analyzed. This exploratory study used an innovative HRS imagery sensor to study the effect of petroleum hydrocarbon (PHC), found in crude oil, on the spectrum of soils across the longwave infrared (LWIR 8–12 μm) spectral region. This contrasts with previous studies that focused on shortwave and midwave infrared (SWIR 1–2.5 and MWIR 3–8 μm, respectively) regions. An outdoor HRS image of three different types of soils, contaminated with 11 PHC concentrations, was processed and analyzed. Since PHC is spectrally featureless in the LWIR region, the analysis focused on the spectral alteration of the dominant minerals in the soils. Good evaluation metrics of R2 > 0.83 and a root-mean-squared-error (RMSE) between 1.06 and 1.33 wt % showed that the PHC level can be predicted with relatively good accuracy, even without direct spectral features of crude oil PHC, using an airborne LWIR camera in field conditions. This study can be used as a proof of concept for future airborne remote sensing of PHC-contaminated soils. [ABSTRACT FROM AUTHOR]

Soil is a complex material that is extremely variable in its physical and chemical properties.Soil quantitative analysis using the reflected solar radiation region is well known and covered in the literature, proving to be a powerful tool, from laboratory to field and airborne domains. As the hyperspectral technology in the emitted radiation in the LWIR region is entering into the field of terrestrial mapping, it is important to study the potential of this technology for soil mapping. Analyzing soil mineralogy and chemical attributes from the emitted radiation in the LWIR region (8.0–13 μm) is thus the purpose of this study. In general, soil mineralogy is an important factor to understand soils' behavior, quality and growth potential. Most common minerals in soils—quartz, clay minerals and carbonates—present fundamental spectral features in the thermal infrared region (mainly in the LWIR). Whereas quartz is featureless in the optical region but very active in the LWIR region, the new technology can provide new information about one of the most abundant minerals in the Earth's crust. Applying a chemometric analysis to ground- and air- based LWIR data is an important issue that have not been considered yet. A set of ninety soil samples from the Israeli Legacy Soil Spectral Library were selected to examine the potential of the LWIR region for soil analysis under pseud field condition. The soils underwent a comprehensive chemical determination of more than seventy soil attributes based on common methods in soil science. In addition, a complete soil mineralogy determination using semi quantitative XRD was executed. Ground-based hyperspectral LWIR images were acquired with the hyperspectral sensor Telops' Hyper-Cam. The emissivity spectra of all soil samples were calculated and analyze. The soil minerals quartz, clay minerals and carbonates, and their abundance, from major to minor, in all soil sample were determined. The resulted mineral content was correlated with the chemical elements abundances and mineral composition, obtained from chemical analyses. We also applied a "deep learning" approach in which the soil emissivity spectra were correlated with the chemical information, using a "data mining" machine term "PARACUDA-II®, to extract "soil spectral models" for each chemical attribute. The analyses revealed significant models for many soil such as: SiO2, Fe2O3, CaO, TiO2, LOI, Specific surface area, Clay content and aggregate size distribution. [ABSTRACT FROM AUTHOR]

In this research, the geotechnical quality of rock masses is investigated by means of hyperspectral remote sensing techniques. Geological engineering projects such as tunneling, supporting unstable rock slopes, or natural hazards assessments, require evaluation of the quality of the rock mass using existing classification systems. As rock mass is composed of an assemblage of rock material that is separated by rock discontinuities, the existing rock mass classification systems relies on information about mechanical behavior of the intact rock material and the spatial distribution of rock discontinuities. In recent decades, remote sensing technologies have been used as data acquisition methods for geological mapping of regions without physical contact with the areas being explored. The use of technologies such as photogrammetry and LiDar scanning allow geologists to efficiently build large 3D models of rock structures that can then be utilized for assessing stability issues in rock masses. Significant progress in geology remote sensing has been achieved through hyperspectral remote sensing technology. These advances enable the identification and recognition of objects based on the spectral absorption features of a specific chemical attribute, and permit quantitative remote sensing of the Earth, from space, for several terrestrial applications. While information regarding spatial distribution of discontinuities can already be obtained by means of remote sensing techniques such as photogrammetry and LiDar, the mechanical characteristics of the material from which the rock is made cannot yet be assessed remotely. This study seeks to advance the ability to geotechnically characterize the rock by remote sensing, with emphasis on the mechanical properties of the intact rock material. Rock blocks of seven different carbonate rock formations belonging to Cenomanian and Turonian ages were collected from outcrops in southern Israel. The blocks were drilled and cylindrical rock samples 54 mm in diameter and 110 mm long were prepared. The cores were measured in the VNIR-SWIR region using the ASD FieldSpec Spectrometer. The reflectance was acquired from the round smooth cores and from the up and down surfaces. The uniaxial compressive strength of the rock samples was then determined and analyzed to depict a model to describe the strength of the samples solely from spectroscopy. Preliminary results from this pioneering study show that there is a relationship between rock strength and absorption levels at the wavelengths characteristic for carbonate minerals. Still, it is necessary to further deepen the database and the analytical method in order to establish a solid spectral based model to depict the rock strength using spectral measurements. [ABSTRACT FROM AUTHOR]

In this paper, the use of a novel evolutionary fuzzy rule-based system (FRBS) for the prediction of Soil Organic Carbon from visible, near-infrared, and short-wave infrared (VNIR/SWIR) spectra and the textural information as additional predictor is examined. Compared to other techniques, the proposed model generates a compact set of rules with a high interpretation degree, mapping local input to local output regions. This is achieved through an evolutionary learning procedure which is applied to establish linguistic rules and assist in the interpretation of the association between spectra and the target property. The rule base may be also decomposed into texture-specific sets of rules, allowing a more detailed analysis on a per textural class basis. These intrinsic properties enable the development of spectral prototype signatures and sparse feature utilization histograms at different levels of aggregation, i.e. per textural class and/or output region. The proposed model is applied to the LUCAS topsoil database comprised of roughly 18,000 mineral samples across 23 European Union member-states. We first demonstrate the enhanced interpretation capabilities of our fuzzy approach, which can assist in the extraction of fruitful knowledge governing the association between soil properties and VNIR/SWIR spectra. The model is then compared with other contemporary approaches, namely PLS, SVM, and Cubist. The results indicate that our approach produced compact and interpretable results with fair prediction accuracies (equivalent with the best approach). • A novel interpretable fuzzy rule-based system for predicting properties from VNIR/SWIR spectra is proposed • It can use additional predictors for enhancing the accuracy and the interpretation capacity • Attains comparable performance with the state-of-the-art in the LUCAS soil spectral library • The interpretation capabilities are shown through spectral prototypes and feature utilization histograms [ABSTRACT FROM AUTHOR]

Abstract The current study was driven by the need to derive improved soil information whereby the strengths of memory-based learning and soil spectroscopy are exploited towards addressing regional challenges and supporting sustainable development across the Balkan, North Africa, and Middle East regions. In this study we focused on a novel derivation of the Spectrum Based Learner (SBL) algorithm by i) taking into account both the geographical proximity and the spectral similarity in the computation of the distance between samples, and ii) using an optimal pair of spectral pre-treatments with the first used for the determination of neighbours while the latter for the estimation of the target property. The proposed approach was tested on a recently developed standardized Soil Spectral Library (SSL) across the VIS-NIR-SWIR spectral region range (350–2500 nm) which comprises 1760 soil samples from 9 different countries and is considered the largest and most diverse soil database infrastructure in the region compatible with other SSLs. Here, we found that our approach presents a great potential for predicting soil texture contents (Clay, Sand, and Silt), organic carbon (OC), Calcium Carbonate (CaCO3), pH, and Electrical Conductivity (EC). These results outperform the predictive performance of other state of the art global and local approaches that have been applied to similar large and complex soil datasets. The use of geographical coordinates in the computation of the sample similarities enhanced the predictions of soil properties, since it allowed the generation of local subsets that present similar soil compositions. In addition, we conclude that the various spectral sources as derived by a set of predefined spectral pre-processing techniques contain complementary information which should be combined instead of relying solely on the best spectral pre-processing technique. This approach could be effectively utilized to enhance the predictions of soil properties in large and complex SSLs, since it decreased the Root Mean Square Errors (RMSEs) by a relative mean of 6.47% (average value across the properties – decrease ranging from 2.90% to 9.09%) for the various soil properties, compared to other global and local algorithms. We conclude that national SSLs that were measured under a standardization process could further contribute to the global initiative to address challenges and support a data-centric approach for informed decision making with regards to environmental and agricultural issues. Highlights • Development of a local regression approach to model large and diverse SSL. • Internal standard methods are useful to enhance data merging capability. • Combining pre-processing techniques yields better predictive performance. • Utilize both geographical and spectral distance vectors for neighbour selection. • The proposed approach outperforms other state of the art regression algorithms. [ABSTRACT FROM AUTHOR]

Biological soil crusts (BSCs), composed of cyanobacteria, algae, mosses, lichens, and fungi, are important ecosystem engineers that stabilize the quartz-rich dunes in the Nitzana study area near the Israel--Egypt border. The longwave infrared (LWIR) region of the electromagnetic spectrum is very useful for quartz identification since quartz reflectance in the visible, near infrared, and shortwave infrared (VIS-NIR-SWIR, 0.4-2.5 µm) spectral regions lacks identifying features, whereas in the LWIR region, the quartz emissivity spectrum presents a strong doublet feature. This emissivity feature can be used as a diagnostic tool for BSCs development in desert environments, because BSCs attenuate the quartz feature as a function of their successional development. A pair of day and night airborne hyperspectral images were acquired using the Specim AisaOWL LWIR sensor (7.7-12 µm) and processed using an innovative algorithm to reduce the atmospheric interference in this spectral domain. The resulting day and night apparent emissivity products were used to produce a surface quartz content map of the study area. The significant reduction in atmospheric interference resulted in a high correlation (R² = 0.88) between quartz content in field samples determined by X-ray powder diffraction analysis and emissivity estimations from the airborne images. This, in turn, served as the ground truth to our quartz content map of the surface, and by proxy to the BSC. [ABSTRACT FROM AUTHOR]

Reflection spectroscopy, in the visible–near-infrared–shortwave infrared region (Vis-NIR-SWIR, 350–2500 nm), is a useful technology to extract chemical and physical properties of materials, but might be useless in identifying the spectral features of transparent or dark opaque liquids. Low reflectance values of a liquid reduce the ability to identify characteristic absorption features at specific wavelengths in the reflectance spectrum. In this study, we present a rapid and easy-to-use method to increase the measured reflectance spectrum and expose characteristic absorption features of a liquid. This was done by mixing the liquid with a white enhanced substance (WES). For this purpose, we used aluminum oxide (Al2O3) powder—a very bright (high albedo) substance and featureless across the entire Vis-NIR-SWIR region. The reflectance spectrum of the mixture—liquid and WES—was measured using a spectroradiometer. This procedure enabled to identify characteristic spectral features of the liquids that would have not been observed in the reflectance spectrum measured from the liquid alone. [ABSTRACT FROM AUTHOR]

Soil degradation includes a number of processes, ranging from soil erosion to soil contamination, which reduce the capability of soil to work as a base for vegetation roots. Methods to quantify soil degradation due to contamination on a large area with a proper domain are needed and must be studied and developed. Proximal and remote sensing techniques are essential tools, well-suited for surveying large areas, and monitoring soil contamination at a high temporal and spatial interval. Recently developed and forthcoming satellites also dedicated to land monitoring and provide inimitable data streams, which have potential of soil contamination detection. This study peruses the potential of spectroscopy methods in various domains to assess selected soil contaminants including potentially toxic elements and petroleum hydrocarbons from reflectance information, plus a preliminary review of the new-generation orbital Earth observation sensors. An aim is to review the means to do so from spaceborne sensors, which are considered to be state-of-the-art Earth orbit observation technologies. This review will help to answer the question: how can spectral information from proximal and remote sensing techniques in different domains be used for soil contamination modelling? This direction will pave the way for soil contamination monitoring using these techniques. [ABSTRACT FROM AUTHOR]

This paper presents measurements of the azimuth and zenith angle-dependence of the targets that are used for supervised vicarious calibration (SVC) methodology of hyperspectral sensors. Surface radiance measure at-sensor from airborne instruments, such as the AisaDUAL and HyMap sensors, show an apparent noise and shift that limit the information content and even impair the collected data (after removing the atmospheric attenuation). The drift from actual radiance is contributed by various sources; e.g., atmospheric gases and aerosols, and sensor stability. Yet one of the major contributors to it during nadir scanning is the directional effect of target reflectance at variable illumination geometry, also known as Bidirectional Reflectance Distribution Function (BRDF). The SVC method, which is based on ground dark nets of varying densities, was able to correct for the above factors and recalibrate the sensor's radiance during data acquisition. The main assumption of the SVC method is that the net targets are a Lambertian surface. In this paper, the BRDF measurements of the SVC net targets were carried out using a goniometer and controlled laboratory conditions. These measurements revealed anisotropic properties of the nets targets, which were further introduced to the radiometric calibration process are significant, especially with the high density nets (> 50% black net cover). The paper presents a look-up-table of the BRDF correction coefficient factors for each net target (identified and separated by its density and mixing level once placed over a bright background) at various zenith (from − 10° to − 30°) and azimuth (from 0° to 360° at intervals of 10°) angles. The coefficients were calculated for the near infrared spectral region (at 981 nm), and should be applied to the full-reflected spectral range across visible, near and shortwave-infrared wavelengths (400–2500 nm). The acquired results in this empirical study showed that the SVC calibration nets targets are characterized by incoherent BRDF effect. The darker and denser calibration targets absorb the down-welling radiance, and thus, reduce the amount of intermediate multiple scattering, normally accrued in all other (less dense) examined targets. The impact of target density on the BRDF correction coefficient and its dependence with illumination zenith and azimuth angles is studied and presented. The correction coefficient factors obtained in the laboratory were implemented in two real airborne hyperspectral campaigns of two different sensors: AisaDUAL and HyMAP over the same calibration site. The proposed BRDF SVC net corrections enables fine-tuning of the vicarious calibration procedure, while increasing the quality of the radiometric data. Thus, it is recommended to use the BRDF coefficients obtained in this paper for any airborne hyperspectral sensor that uses the SVC method in order to achieve better calibration accuracy. [ABSTRACT FROM AUTHOR]

Visible, near-infrared and shortwave-infrared (VNIR–SWIR) spectroscopy has proven to be an efficient, rapid and low-cost method for soil spectral analysis that can improve on the results obtained from today's traditional methods of conducting soil surveys. Nonetheless, this tool is used mostly in the laboratory and at surface level. The main objective of this paper is to develop a new optical method for characterizing soil profiles, towards improving the efficiency and accuracy of the traditional soil survey. We used airborne hyperspectral data from the AisaFENIX sensor for surface classification and ASD spectral measurements of soil samples for subsurface analysis. A total of 643 soil samples were extracted from 48 cores, each core representing a soil profile. All samples were air-dried, crushed and sieved, and then analyzed by ASD spectrometer under laboratory conditions. Clay content was also measured to provide additional information. The 3D spectral data were analyzed using SAM algorithm, spectral gradient ( m ), k-means clustering and gley horizon parameter ( G ) to classify soils and distinguish between soil horizons in each core. The results suggest that these parameters can provide satisfactory results both from laboratory measurements and hyperspectral remote sensing data (R 2 = 0.81 for clay content and R 2 = 0.78 for gleying conditions) in order to distinguish between the soil horizons using 3D spectral information. Moreover, the method is satisfactory for obtaining soil types from 3D spectral sensing as well as evaluating the catena development and other spatial soil distributions. [ABSTRACT FROM AUTHOR]

The at-sensor radiance of a pixel in a longwave infrared (LWIR) image represents the surface temperature, the surface emissivity, which is similar during the day and at night, and the atmospheric contribution, which is expressed differently during the day and at night. Based on this, an automated procedure, which locates pixels for which each absorption feature in their radiance during the day appears as an emission feature at night, indicative of atmospheric contribution, was developed. The average day or night spectrum of these indicative pixels was applied as a gain factor spectrum to the entire day or night image, respectively, reducing the atmospheric contribution and emphasizing the surface spectral features, represented by emissivity, of each pixel in the image. The procedure was examined on LWIR hyperspectral data cubes acquired over two different areas, and enabled effective reduction of the atmospheric features in both area data sets. [ABSTRACT FROM AUTHOR]

Soils are an essential factor contributing to the agricultural production of rainfed crops such as barley and triticale cereals. Changing environmental conditions and inadequate land management are endangering soil quality and productivity and, in turn, crop quality and productivity are affected. Advances in hyperspectral remote sensing are of great use for the spatial characterization and monitoring of the soil degradation status, as well as its impact on crop growth and agricultural productivity. In this study, hyperspectral airborne data covering the visible, near-infrared, short-wave infrared, and thermal infrared (VNIR–SWIR–TIR, 0.4–12 µm) were acquired in a Mediterranean agricultural area of central Spain and used to analyze the spatial differences in vegetation vitality and grain yield in relation to the soil degradation status. Specifically, leaf area index (LAI), crop water stress index (CWSI), and the biomass of the crop yield are derived from the remote sensing data and discussed regarding their spatial differences and relationship to a classification of erosion and accumulation stages (SEAS) based on previous remote sensing analyses during bare soil conditions. LAI and harvested crop biomass yield could be well estimated by PLS regression based on the hyperspectral and in situ reference data (R2 of 0.83, r of 0.91, and an RMSE of 0.2 m2 m−2 for LAI and an R2 of 0.85, r of 0.92, and an RMSE of 0.48 t ha−1 for grain yield). In addition, the soil erosion and accumulation stages (SEAS) were successfully predicted based on the canopy spectral signal of vegetated crop fields using a random forest machine learning approach. Overall accuracy was achieved above 71% by combining the VNIR–SWIR–TIR canopy reflectance and emissivity of the growing season with topographic information after reducing the redundancy in the spectral dataset. The results show that the estimated crop traits are spatially related to the soil's degradation status, with shallow and highly eroded soils, as well as sandy accumulation zones being associated with areas of low LAI, crop yield, and high crop water stress. Overall, the results of this study illustrate the enormous potential of imaging spectroscopy for a combined analysis of the plant-soil system in the frame of land and soil degradation monitoring. [ABSTRACT FROM AUTHOR]

Hyperspectral remote-sensing techniques offer an efficient procedure for mineral mapping, with a unique hyperspectral remote-sensing fingerprint in the longwave infrared spectral region enabling identification of the most abundant minerals in the continental crust--quartz and feldspars. This ability was examined by acquiring airborne data with the AisaOWL sensor over the Makhtesh Ramon area in Israel. The at-sensor radiance measured from each pixel in a longwave infrared image represents the emissivity, expressing chemical and physical properties such as surface mineralogy, and the atmospheric contribution which is expressed differently during the day and at night. Therefore, identifying similar features in day and night radiance enabled identifying the major minerals in the surface--quartz, silicates (feldspars and clay minerals), gypsum and carbonates--and mapping their spatial distribution. Mineral identification was improved by applying the radiance of an in situ surface that is featureless for minerals but distinctive for the atmospheric contribution as a gain spectrum to each pixel in the image, reducing the atmospheric contribution and emphasizing the mineralogical features. The results were in agreement with the mineralogy of selected rock samples collected from the study area as derived from laboratory X-ray diffraction analysis. The resulting mineral map of the major minerals in the surface was in agreement with the geological map of the area. [ABSTRACT FROM AUTHOR]

Soil spectroscopy with machine learning (ML) can estimate soil properties. Extensive soil spectral libraries (SSLs) have been developed for this purpose. However, general models built with those SSLs do not generalize well on new 'unseen' local data. The main reason is the different characteristics of the observations in the SSL and the local data, which cause their conditional and marginal distributions to differ. This makes the modelling of soil properties with spectra challenging. General models developed using large 'global' SSLs offer broad, systematic information on the soil-spectra relationships. However, to accurately generalize in a local situation, they must be adjusted to capture the site-specific characteristics of the local observations. Most current methods for 'localizing' spectroscopic modelling report inconsistent results. An understanding of spectroscopic 'localization' is lacking, and there is no framework to guide further developments. Here, we review current localization methods and propose their reformulation as a transfer learning (TL) undertaking. We then demonstrate the implementation of instance-based TL with rs - local 2.0 for modelling the soil organic carbon (SOC) content of 12 sites representing fields, farms and regions from 10 countries on the seven continents. The method uses a small number of instances or observations (measured soil property values and corresponding spectra) from the local site to transfer relevant information from a large and diverse global SSL (GSSL 2.0) with more than 50,000 records. We found that with ≤ 30 local observations, rs - local 2.0 produces more accurate and stable estimates of SOC than modelling with only the local data. Using the information in the GSSL 2.0 and reducing the number of samples for laboratory analysis, the method improves the cost-efficiency and practicality of soil spectroscopy. We interpreted the transfer by analysing the data, models, and soil and environmental relationships of the local and the 'transferred' data to gain insight into the approach. Transferring instances from the GSSL 2.0 to the local sites helped to align their conditional and marginal distributions, making the spectra-SOC relationships in the models more robust. Finally, we propose directions for future research. The guiding principle for developing practical and cost-effective spectroscopy should be to think globally but fit locally. By reformulating the localization problem within a TL framework, we hope to have acquainted the soil science community with a set of methodologies that can inspire the development of new, innovative algorithms for soil spectroscopic modelling. [ABSTRACT FROM AUTHOR]

Abstract: Change detection techniques aim to identify changes between two or more images taken at different times. In this paper, we explore the capabilities of identifying changes in an unsupervised manner between different soil types using two laboratory HySpex imaging spectroscopy sensors in the visible near infrared (VNIR) and short-wave infrared (SWIR) spectral ranges. The experiment was carried under controlled laboratory conditions with the same lighting and no atmospheric distortions. The 69 selected soil samples covered the arid and semiarid climate zones of Israel. The well-known change vector analysis technique was used to generate the difference image, and several thresholding methods were tested to generate the final binary change map. The performance capabilities of the VNIR, SWIR and combined VNIR–SWIR sensors were examined. Our study demonstrates that changes in different soil types can be identified using imaging spectroscopy sensors; the SWIR sensor generated better change detection capabilities than the VNIR sensor, and the combination of the two sensors did not outperform the SWIR sensor alone. Results showed that it is important to combine a spectral domain thresholding approach with a spatial domain thresholding approach. The benefit of combining these approaches is a low false-alarm rate with a relatively high probability of detection. Although the change experiment was conducted under almost perfect conditions without any atmospheric or lighting differences, the change detection techniques did not detect all soil type changes and changes between spectrally similar soils remain undetected. The results of this study can be further extended to other spatial scales and can provide a foundation for soil change detection using upcoming imaging spectroscopy satellite platforms that acquire spatial–spectral–temporal information. [Copyright &y& Elsevier]

Change detection and multitemporal analyses aim to detect changes occurring over a specific geographical area using two or more images acquired at two or more different times. In this article, we present a new thresholding approach for unsupervised change detection. This approach focuses on determining the threshold that discriminates between change and no-change pixels. The differences between pixels in the two images are associated with real changes or noise. We propose a thresholding scheme that separates the threshold into two parts: (1) a spectral domain threshold that accounts for errors related to sensor stability, atmospheric conditions, and data-processing variations, and (2) a spatial domain threshold associated with georectification errors. We demonstrate our method using both multispectral Landsat images and airborne imaging spectroscopy HyMap images. The results show that the spectral domain threshold gives high detection capabilities with moderate false-alarm rate. Adding the spatial domain threshold to the spectral domain threshold reduces the false-alarm rates while maintaining good detection capabilities. [ABSTRACT FROM AUTHOR]

Drug use, mainly of Cannabis, has dramatically increased over the past two decades, calling for efficient drug monitoring and prevention tools. This study evaluates the use of ground-based hyperspectral detection for surveying and mapping Cannabis cultivations. The ability to identify Cannabis plants at high spectral resolution using a ground-based hyperspectral detector (imaging spectroscopy sensor) outdoors was measured using the AISA Eagle hyperspectral detector at 400-1000 nm wavelengths, at a distance of 75 m. Analysis of the measured data by image-processing and statistical variation revealed that the spectral characteristics of Cannabis are unique only within a wavelength range of 500-750 nm. It is important to notice that variation was tested only with two species, and background was unique. Error in classification (false alarm) was found between Cannabis canopy and citrus canopy: 15% of Citrus was classified as Cannabis. [ABSTRACT FROM AUTHOR]

A wide range of electronic and mechanical noise factors can affect soil spectra when using different instruments or even when repeating a specific sample's measurements with the same spectrometer. In soil samples where very weak spectral features are monitored for chemometric purposes, alterations in wavelength location, peak absorption shape, or albedo intensity can limit the use of previously developed spectral models. To quantify this alteration and propose a standardization method, 12 soil samples and three different materials for internal standards (sand, glass and polyethylene) were analyzed. This population was concurrently measured with three identical spectrometers using a strict measurement protocol, and then by different operators with different protocols. Significant changes in the soil spectra were found when different operators performed the measurements, being reduced >50% when the strict protocol was applied. Sand was found to be the ideal internal standard for correcting the spectra to a reference spectrometer, even when different measuring protocols were used. This standardization also showed an improvement in the prediction of soil properties when applying chemometric spectral models even with different instruments, concluding that the use of an internal standard and a strict protocol must be applied for soil spectral measurements. As the measuring factors described in this research also affect any infrared diffuse reflectance spectroscopy measurements, the proposed method should be applicable to any instrumentation and configuration being used. This is crucial to enabling spectral comparisons between different spectrometers or, more importantly, to establishing robust chemometric models and to exchange soil spectral information. [ABSTRACT FROM AUTHOR]

Dune plants both modify the wind field around them and are impacted by various stress factors, among them sand erosion and sand deposition. As coastal dunes are being either stabilized or remobilized, in response to the changes in the rates of sand and dune movement, the vegetation cover and composition are expected to vary reflecting the differences in the sedimentary conditions. In this field study 315 quadrats of 100 m2, in which the perennial plant species were sampled, were analysed with respect to annual rates of sand erosion and deposition that were measured using erosion pins. A visual exploratory data analysis was introduced, based on selective filtering of samples according to their vegetation cover. This method, combined with established statistical tools, enabled the authors to uncover the inclination and indicative power of nine perennial dune plants to either a stabilized or a mobile environment, and to establish whether they are more prevalent in places undergoing sand erosion or sand burial. Two species were found to be clear indicators of a stabilized environment, Stipagrostis lanata, and Retama raetam. Of the species indicating a mobile environment, only one may be stated as a clear indicator of sand erosion: Silene succulenta, with Cyperus macrorrhizus coming close to being an indicator of a less mobile erosive environment. The best indicator species for sand burial was found to be, as expected, Ammophila arenaria, with Artemisia monosperma also indicating high rates of sand mobility especially when its relative cover is higher than 80%. Such information can be used to monitor natural processes of dune stabilization or reactivation, or to assess the success of a management plan that aims at stabilizing a dune, or at remobilizing it by removing vegetation. [ABSTRACT FROM AUTHOR]

The coastal dunes of Israel have been undergoing a process of stabilization since 1948. One of the major features of this process is a change in the surface properties of the dunes – the development of a biological soil crust (BSC), and a change in the properties of the sand grains themselves. In Ashdod, at the southern coastal plain of Israel, sand properties that include the BSC, their fines (silt and clay) content and free iron-oxide (indicating their degree of rubification) have been analysed in detail using field and lab spectroscopy methods. In addition, sand erosion and deposition were measured using erosion pins to determine their effect on the presence of the above-mentioned factors. It was found that the BSC over these dunes is comprised of green algae that differs in its reflectance spectra from cyanobacterial crust, especially in the blue band. The crust was found to be particularly developed in the stable areas (mainly the interdunes) and on the north-facing slopes rather than on the south-facing slopes. A positive correlation was found between the crust fines and chlorophyll content, with stable areas showing more developed BSC. The stable areas showed also a lower albedo and slightly more developed reddish colour, indicating a slightly higher rate of rubification. This study demonstrates that the intensity of sand erosion/deposition rates affects soil properties, with the BSC being the fastest to react to the stabilization process (months to several years), followed by the content of fine particles (several years to a decade), whereas the rubification process is a much weaker marker and may need much longer time periods to develop (decades to centuries). [ABSTRACT FROM AUTHOR]