Hyperspectral Pushbroom Research Articles

HYPERSPECTRAL IMAGING IN PRESERVATION OF CROATIA’S HISTORIC TREES: A CASE STUDY OF DEDEK OAK IN MAKSIMIR PARK

Abstract. This study focuses on the application of hyperspectral imaging techniques for the genetic preservation of historic trees, specifically investigating the 600-year-old "Dedek" Oak in Maksimir Park, Croatia. The research aims to contribute to the conservation of Croatia's natural heritage and underscore the potential of hyperspectral imaging in tree preservation. The Croatian Forest Research Institute has been actively involved in preserving the pedunculate oak trees, recognizing their cultural and ecological significance. Efforts have been made to cultivate seedlings from the original genetic material, thereby preventing the loss of valuable genes and ensuring their long-term preservation. Spectral signatures of "Dedek" Oak and its clones were captured using high-performance hyperspectral push broom cameras, including the HySpex VNIR-1800 and SWIR-384. Spectral camera scanning was conducted in the field and laboratory. The comparative analysis of the spectral samples revealed unique characteristics and demonstrated the effectiveness of hyperspectral imaging in studying historic trees for preservation purposes. The spectral signatures of vegetation display dynamic characteristics in terms of spectral resolution. Collecting and documenting these signatures is considerably more challenging, and their integration into spectral libraries should be approached with careful consideration. There are several spectral libraries that are organized by chapters and consist of samples that have a sufficient number of analysis and documentation to determine the quality of the spectrum. In this study, spectral signatures of pedunculate oak (Quercus robur L.) called Dedek and its clones were singled out. The objective of this research was to establish a foundation for a spectral library that would facilitate future research on the application of hyperspectral scanners for detecting protected tree species and their clones, all for the purpose of preservation of the genetic diversity of protected trees in Croatia. The findings of this research contribute to the conservation of Croatia's natural heritage by providing valuable insights into the genetic preservation of the 600-year-old "Dedek" Oak. Also, determining the spectral signatures of clones can help us with the identification of specific tree species (especially during aerial imaging, which is planned to be conducted in the future). In conclusion, this research showcases the importance of preserving historic trees and the potential of hyperspectral imaging as a valuable tool for genetic preservation efforts. The study's outcomes contribute to the long-term conservation of Croatia's natural heritage and provide a foundation for future research in the field of tree preservation and spectral library inception.

Geometric Correction of BaySpec OCI-F Pushbroom Hyperspectral Images via the Sequential Geometric Mappings among Simultaneous Video Frames Estimated by Least Square Matching

ضرورت دسترسی به کاربردهای وسیع تصاویر ابرطیفی سبب توسعة سیستم‌های تصویربرداری نوآورانه و اقتصادی در ثبت این تصاویر شده است. به‌منظور استفاده از این تصاویر، لازم است ارتباط هندسی دقیقی میان آنها و فضای زمین برقرار شود و این فرایند نیازمند نقاط کنترلی بسیاری است. این نکته ضرورت توسعة راهکارهای اصلاح هندسی منطبق با ساختار هریک از این دوربین‌ها را بارز می‌کند. سنجندة (nm 400-1000) BaySpec OCI-F یکی از سیستم‌های نوآورانه‌ای است که تصاویر ابرطیفی را با هندسة تصویربرداری پوش‌بروم دریافت می‌کند. این سنجنده، علاوه‌بر یک سنسور پوش‌بروم، از یک سنسور فریم نیز بهره می‌برد که هم‌زمان با سنسور پوش‌بروم و با رزولوشن مکانی زمانی مشابه، تصویر را دریافت می‌کند. در این مقاله، روشی برای اصلاح هندسی تصاویر پوش‌برومِ این سنجنده بیان شده است. در بخش اول این روش، با توجه به ساختار تصویربرداری دوربین، ارتباط هندسی میان آرایة خطی و سنسور فریم در قالب پارامترهای کالیبراسیونی مشخص می‌شود. در ادامه، به‌کمک برآورد ارتباط هندسی میان تصاویر فریم متوالی، پیکسل‌های تصویر پوش‌بروم در کنار یکدیگر چیده و تصویر اصلاح‌شده تولید می‌شود. در این روش، ارتباط هندسی میان هر جفت فریم متوالی به‌طور مستقیم، ازطریق تناظریابی کمترین مربعات، محاسبه می‌شود. نتایج به‌دست‌آمده نشان می‌دهد که این روش، به‌طور متوسط، 2/62% از اعوجاجات هندسی تصویر خام را کاهش داده است. این کاهش سبب شده است متوسط دقت مدل‌های درون‌یاب عمومی سادة دوبعدی و سه‌بعدی بین فضای تصویر و زمین، به‌ترتیب، 9/39% و 1/34% افزایش یابد.

Cross-Track Illumination Correction for Hyperspectral Pushbroom Sensor Images Using Low-Rank and Sparse Representations

A hyperspectral pushbroom sensor scans objects line-by-line using a detector array, and a cross-track illumination error (CTIE) exists in the imagery acquired in this way. When the illumination of the individual cells of the detector is not aligned well, or if some of the cells are degraded or old, the acquired images will exhibit nonuniform illumination in the cross-track direction. As additive Gaussian noise is found widely in hyperspectral images (HSIs), we develop a unified mathematical model that describes the image formation process corrupted by the CTIE and additive Gaussian noise. The CTIE produced by line-by-line scanning is replicated and modeled as an offset term with the equivalent values in the direction of flight. The main contribution of this study is the development of a hyperspectral image cross-track illumination correction (HyCIC) method, which corrects the cross-track illumination using column (along-track) mean compensation with total variation and sparsity regularizations, and attenuates the Gaussian noise by using a form of low-rank constraint. The effectiveness of the proposed method is illustrated using semireal data and real HSIs. The performance of the proposed HyCIC is found to be better than other existing methods.

An Integrated Solution of UAV Push-Broom Hyperspectral System Based on Geometric Correction with MSI and Radiation Correction Considering Outdoor Illumination Variation

The unmanned aerial vehicle (UAV)-borne hyperspectral imaging system has the advantages of high spatial resolution, flexible operation, under-cloud flying, and easy cooperation with ground synchronous tests. Because this platform often flies under clouds, variations in solar illumination lead to irradiance inconsistency between different rows of hyperspectral images (HSIs). This inconsistency causes errors in radiation correction. In addition, due to the accuracy limitations of the GPS/inertial measurement unit (IMU) and irregular changes in flight platform speed and attitude, HSIs have deformation and drift, which is harmful to the geometric correction and stitching accuracy between flight strips. Consequently, radiation and geometric error limit further applications of large-scale hyperspectral data. To address the above problems, we proposed an integrated solution to acquire and correct UAV-borne hyperspectral images that consist of illumination data acquisition, radiance and geometric correction, HSI, multispectral image (MSI) registration, and multi-strip stitching. We presented an improved three-parameter empirical model based on the illumination correction factor, and it showed that the accuracy of radiation correction considering illumination variation improved, especially in some low signal-to-noise ratio (SNR) bands. In addition, the error of large-scale HSI stitching was controlled within one pixel.

Fast and Effective Classification of Plastic Waste by Pushbroom Hyperspectral Sensor Coupled with Hierarchical Modelling and Variable Selection

Fast and Effective Classification of Plastic Waste by Pushbroom Hyperspectral Sensor Coupled with Hierarchical Modelling and Variable Selection

Comparison of different spectral cameras for image-guided organ transplantation.

.Significance: Hyperspectral and multispectral imaging (HMSI) in medical applications provides information about the physiology, morphology, and composition of tissues and organs. The use of these technologies enables the evaluation of biological objects and can potentially be applied as an objective assessment tool for medical professionals.Aim: Our study investigates HMSI systems for their usability in medical applications.Approach: Four HMSI systems (one hyperspectral pushbroom camera and three multispectral snapshot cameras) were examined and a spectrometer was used as a reference system, which was initially validated with a standardized color chart. The spectral accuracy of the cameras reproducing chemical properties of different biological objects (porcine blood, physiological porcine tissue, and pathological porcine tissue) was analyzed using the Pearson correlation coefficient.Results: All the HMSI cameras examined were able to provide the characteristic spectral properties of blood and tissues. A pushbroom camera and two snapshot systems achieve Pearson coefficients of at least 0.97 compared to the ground truth, indicating a very high positive correlation. Only one snapshot camera performs moderately to high positive correlation (0.59 to 0.85).Conclusion: The knowledge of the suitability of HMSI cameras for accurate measurement of chemical properties of biological objects offers a good opportunity for the selection of the optimal imaging tool for specific medical applications, such as organ transplantation.

Detection of anomalies in the UV–vis reflectances from the Ozone Monitoring Instrument

Abstract. Various instrumental or geophysical artifacts, such as saturation, stray light or obstruction of light (either coming from the instrument or related to solar eclipses), negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic-ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sunglint over open water or thick clouds. When saturation occurs, additional photoelectric charge generated at the saturated pixel may overflow to pixels adjacent to a saturated area and be reflected as a distorted image in the final sensor output. When these effects cannot be corrected to an acceptable level for science-quality retrievals, flagging of the affected pixels is indicated. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectra over a 10 nm spectral range; our decorrelation index (DI for brevity) is simply defined as a DI of 1−r. DI increases with anomalous additive effects or excessive noise in either radiances, the most likely cause in data from the Ozone Monitoring Instrument (OMI), or irradiances. DI is relatively straightforward to use and interpret and can be computed for different wavelength intervals. We developed a set of DIs for two spectral channels of the OMI, a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350–498 nm) and six DIs in its ultraviolet 2 (UV2) channel (310–370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary saturation possibility warning (SPW) flags currently provided for each individual spectral or spatial pixel in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.

Estimation of boreal forest floor reflectance from airborne hyperspectral data of coniferous forests

In open forest canopies, such as in boreal forests, forest floor can contribute significantly to the observed top of canopy reflectance. In order to retrieve the biophysical properties of the tree layer, correcting for forest floor is essential. Traditionally, the algorithms for retrieval of forest floor reflectance depend on tree layer information such as leaf area index, canopy cover, and site fertility. To overcome these circular dependencies, we propose an algorithm that can be applied only using airborne remote sensing data. We acquired airborne hyperspectral imagery over the Hyytiälä forest research station (61°50′N, 24°17′E) in central Finland on July 3rd in 2015 using a hyperspectral pushbroom line scanner. The image data had a spectral resolution of 4.6 nm, and the spatial resolution was 0.6 m. We developed a linear spectral unmixing algorithm, which is based on the definition of the reflectance factor, taking into account the variation of incident irradiance inside the canopy. The weights of the mixture can be computed from tree canopy gap fractions, a tree species insensitive leaf albedo, and average tree stand reflectance. Canopy gap fractions were retrieved with empirical methods available in scientific literature. The forest floor reflectance in the near-infrared increased with site fertility in agreement with the forest floor field measurements. Moreover, we found that in near infrared, the reflectance of moderately rich and moist upland forests was significantly different from all other fertility classes. Finally, we tested the reflectance decomposition on the photochemical reflectance index (PRI) known to be heavily affected by understory reflectance and canopy structure, and the forest PRI to be decoupled from the PRI of the over- and understory.

Disentangling the Relationship in Health of Zea Mays Crop using Photochemical Reflectance Index and Nitrogen Reflectance Index

Determining the spatial variation of different plant factors throughout growing season will help to resolve stress factors within a field in a timely basis. Whereas the spectral characterizes help to estimate the proper photosynthesis process. This research shows that the nitrogen reflectance index (NRI) help to predict the nitrogen level of healthy and diseased plants and photochemical reflectance index (PRI) affects the leaf spectral absorption. These indices are calibrated under the hyperspectral pushbroom camera Resonon PIKA-L (400-1000nm) which is non-destructive and less time consuming, it is available in RUSA lab in Dr. Babasaheb Ambedkar Marathawada University, Aurangabad, Maharashtra. The spectral bands considered for the calculation of NRI are 700nm, 670nm, 570nm and for PRI spectral bands considered were 531nm, 570nm. Statistical values for PRI were calculated like R-Square (0.727), RMSE (0.267), P-value (2.787), standard error (2.979) and the statistical values for NRI were R-Square (4.223), RMSE (0.512), P-value (0.968), standard error(2.648).Linear regression was calculated for finding the relation between the data.

Spatial Distortion Assessments of a Low-Cost Laboratory and Field Hyperspectral Imaging System.

This article describes the adaptation of an existing aerial hyperspectral imaging system in a low-cost setup for collecting hyperspectral data in laboratory and field environment and spatial distortion assessments. The imaging spectrometer system consists of an ImSpector V9 hyperspectral pushbroom scanner, PixelFly high performance digital CCD camera, and a subsystem for navigation, position determination and orientation of the system in space, a sensor bracket and control system. The main objective of the paper is to present the system, with all its limitations, and a spatial calibration method. The results of spatial calibration and calculation of modulation transfer function (MTF) are reported along with examples of images collected and potential uses in agronomy. The distortion value rises drastically at the edges of the image in the near-infrared segment, while the results of MTF calculation showed that the image sharpness was equal for the bands from the visible part of the spectrum, and approached Nyquist’s theory of digitalization. In the near-infrared part of the spectrum, the MTF values showed a less sharp decrease in comparison with the visible part. Preliminary image acquisition indicates that this hyperspectral system has potential in agronomic applications.

Wide Swath and High Resolution Airborne HyperSpectral Imaging System and Flight Validation.

Wide Swath and High Resolution Airborne Pushbroom Hyperspectral Imager (WiSHiRaPHI) is the new-generation airborne hyperspectral imager instrument of China, aimed at acquiring accurate spectral curve of target on the ground with both high spatial resolution and high spectral resolution. The spectral sampling interval of WiSHiRaPHI is 2.4 nm and the spectral resolution is 3.5 nm (FWHM), integrating 256 channels coving from 400 nm to 1000 nm. The instrument has a 40-degree field of view (FOV), 0.125 mrad instantaneous field of view (IFOV) and can work in high spectral resolution mode, high spatial resolution mode and high sensitivity mode for different applications, which can adapt to the Velocity to Height Ratio (VHR) lower than 0.04. The integration has been finished, and several airborne flight validation experiments have been conducted. The results showed the system’s excellent performance and high efficiency.

Effect of colony age on near infrared hyperspectral images of foodborne bacteria

Near infrared hyperspectral imaging (NIR-HSI) and multivariate image analysis were used to distinguish between foodborne pathogenic bacteria, Bacillus cereus, Escherichia coli, Salmonella Enteritidis, Staphylococcus aureus and a non- pathogenic bacterium, Staphylococcus epidermidis. Hyperspectral images of bacteria, streaked out on Luria—Bertani agar, were acquired after 20 h, 40 h and 60 h growth at 37 °C using a SisuCHEMA hyperspectral pushbroom imaging system with a spectral range of 920–2514 nm. Three different pre-processing methods: standard normal variate (SNV), Savitzky—Golay (1stderivative, 2nd order polynomial, 15-point smoothing) and Savitzky—Golay (2nd derivative, 3rd order polynomial, 15-point smoothing) were evaluated. SNV provided the most distinct clustering in the principal component score plots and was thus used as the sole pre-processing method. Partial least squares discriminant analysis (PLS-DA) models were developed for each growth period and was tested on a second set of plates, to determine the effect the age of the colony has on classification accuracies. The highest overall prediction accuracies where test plates required the least amount of growth time, was found with models built after 60 h growth and tested on plates after 20 h growth. Predictions for bacteria differentiation within these models ranged from 83.1 % to 98.8 % correctly predicted pixels.

Implementation of a UAV–Hyperspectral Pushbroom Imager for Ecological Monitoring

Hyperspectral remote sensing provides a wealth of data essential for vegetation studies encompassing a wide range of applications (e.g., species diversity, ecosystem monitoring, etc.). The development and implementation of UAV-based hyperspectral systems have gained popularity over the last few years with novel efforts to demonstrate their operability. Here we describe the design, implementation, testing, and early results of the UAV-μCASI system, which showcases a relatively new hyperspectral sensor suitable for ecological studies. The μCASI (288 spectral bands) was integrated with a custom IMU-GNSS data recorder built in-house and mounted on a commercially available hexacopter platform with a gimbal to maximize system stability and minimize image distortion. We deployed the UAV-μCASI at three sites with different ecological characteristics across Canada: The Mer Bleue peatland, an abandoned agricultural field on Ile Grosbois, and the Cowichan Garry Oak Preserve meadow. We examined the attitude data from the flight controller to better understand airframe motion and the effectiveness of the integrated Differential Real Time Kinematic (RTK) GNSS. We describe important aspects of mission planning and show the effectiveness of a bundling adjustment to reduce boresight errors as well as the integration of a digital surface model for image geocorrection to account for parallax effects at the Mer Bleue test site. Finally, we assessed the quality of the radiometrically and atmospherically corrected imagery from the UAV-μCASI and found a close agreement (<2%) between the image derived reflectance and in-situ measurements. Overall, we found that a flight speed of 2.7 m/s, careful mission planning, and the integration of the bundling adjustment were important system characteristics for optimizing the image quality at an ultra-high spatial resolution (3–5 cm). Furthermore, environmental considerations such as wind speed (<5 m/s) and solar illumination also play a critical role in determining image quality. With the growing popularity of “turnkey” UAV-hyperspectral systems on the market, we demonstrate the basic requirements and technical challenges for these systems to be fully operational.

Hyperspectral Push-Broom Microscope Development and Characterization

Currently, the use of hyperspectral imaging (HSI) for the inspection of microscopic samples is an emerging trend in different fields. The use of push-broom hyperspectral (HS) cameras against other HSI technologies is motivated by their high spectral resolution and their capabilities to exploit spectral ranges beyond 1000 nm. Nevertheless, using push-broom cameras in miscroscopes imposes to perform an accurate spatial scanning of the sample to collect the HS data. In this manuscript, we present a methodology to correctly set-up a push-broom HS microscope to acquire high-quality HS images. Firstly, we describe a custom 3D printed mechanical system developed to perform the spatial scanning by producing a precise linear movement of the microscope stage. Then, we discuss how the dynamic range maximisation, the focusing, the alignment and the adequate speed determination affect the overall quality of the images. Finally, we present some examples of HS data showing the most common defects that usually appear when capturing HS images using a push-broom camera, and also a set of images acquired from real microscopic samples.

High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

The efficient use of nitrogen fertilizer is a crucial problem in modern agriculture. Fertilization has to be minimized to reduce environmental impacts but done so optimally without negatively affecting yield. In June 2017, a controlled experiment with eight different nitrogen treatments was applied to winter wheat plants and investigated with the UAV-based hyperspectral pushbroom camera Resonon Pika-L (400–1000 nm). The system, in combination with an accurate inertial measurement unit (IMU) and precise gimbal, was very stable and capable of acquiring hyperspectral imagery of high spectral and spatial quality. Additionally, in situ measurements of 48 samples (leaf area index (LAI), chlorophyll (CHL), and reflectance spectra) were taken in the field, which were equally distributed across the different nitrogen treatments. These measurements were used to predict grain yield, since the parameter itself had no direct effect on the spectral reflection of plants. Therefore, we present an indirect approach based on LAI and chlorophyll estimations from the acquired hyperspectral image data using partial least-squares regression (PLSR). The resulting models showed a reliable predictability for these parameters (R2LAI = 0.79, RMSELAI [m2m−2] = 0.18, R2CHL = 0.77, RMSECHL [µg cm−2] = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R2yield = 0.88, RMSEyield [dt ha−1] = 4.18). With this model, a pixel-wise prediction of the hyperspectral image was performed. The resulting yield estimates were validated and opposed to the different nitrogen treatments, which revealed that, above a certain amount of applied nitrogen, further fertilization does not necessarily lead to larger yield.

ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery.

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively.

Non-destructive quality assessment of herbal tea blends using hyperspectral imaging

The consumption of herbal teas is increasing as consumers become more appreciative of the health benefits. Herbal tea blends comprise of two or more plant species blended to improve taste and multiply the health benefits. Quality control (QC) of herbal teas like other nutraceuticals, is important to ensure safety and efficacy. Current QC methods are chromatography-based and require destructive sample preparation using solvents. In this study, hyperspectral imaging is applied as a fast and non-destructive method for the quality control of herbal tea blends. The technique combines conventional spectroscopy and digital imaging to gather chemical information and visualise spatial distribution of chemical constituents within a matrix. Certified raw materials (Sceletium tortuosum and Cyclopia genistoides) and herbal tea blends were acquired from Parceval Pty (Ltd). Hyperspectral images of the raw material and tea blends were captured on a SisuChema® SWIR (short wave infrared) hyperspectral pushbroom imaging system using ChemaDAQ® software. The images were analysed using Evince® multivariate analysis software 2.4.0. Principal component analysis (PCA) revealed 54.2% chemical variation between S. tortuosum and C. genistoides raw materials. A partial least squares-discriminant analysis (PLS-DA) model with predictive ability of 95.8% was developed. Based on pixel classification, it was possible to visualise the tea blend constituents as S. tortuosum and C. genistoides and quantitatively predict C. genistoides as the major constituent (>97%) while S. tortuosum was present in lower amounts (<3%). The predictions confirm that HSI is a potentially favourable visual tool for the quality assessment of herbal tea blends. However, due to low instrument sensitivity quantitative determinations showed some deviation from the company formulation.

Geometrical and spectral data stitching for combining hyperspectral imaging systems

Abstract One way to increase the amount of information acquired via hyperspectral imaging and therefore to increase the possibility of data analysis is combining the spatial and spectral information of hyperspectral data sets. The aforementioned data sets are obtained by cameras covering different spectral ranges. The purpose of this article is to develop an algorithm which is able to combine two data sets acquired by two hyperspectral pushbroom imagers, covering the visible (VIS) and the near infrared (NIR) wavelength range. Initially, the effect of optical aberrations, as well as errors via the image registration were examined. Subsequently a correction algorithm for both the optical aberration and the image registration is elaborated.

Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends

In South Africa, indigenous herbal teas are enjoyed due to their distinct taste and aroma. The acclaimed health benefits of herbal teas include the management of chronic diseases such as hypertension and diabetes. Quality control of herbal teas has become important due to the availability of different brands of varying quality and the production of tea blends. The potential of hyperspectral imaging as a rapid quality control method for herbal tea blends from rooibos (Aspalathus linearis), honeybush (Cyclopia intermedia), buchu (Agathosma Betulina) and cancerbush (Sutherlandia frutescens) was investigated. Hyperspectral images of raw materials and intact tea bags were acquired using a sisuChema shortwave infrared (SWIR) hyperspectral pushbroom imaging system (920–2514 nm). Principal component analysis (PCA) plots showed clear discrimination between raw materials. Partial least squares discriminant analysis (PLS-DA) models correctly predicted the raw material constituents of each blend and accurately determined the relative proportions. The results were corroborated independently using ultra-high performance liquid chromatography coupled to mass spectrometry (UHPLC-MS). This study demonstrated the application of hyperspectral imaging coupled with chemometric modelling as a reliable, rapid and non-destructive quality control method for authenticating herbal tea blends and to determine relative proportions in a tea bag.

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

Reflectance anisotropy is a signal that contains information on the optical and structural properties of a surface and can be studied by performing multi-angular reflectance measurements that are often done using cumbersome goniometric measurements. In this paper we describe an innovative and fast method where we use a hyperspectral pushbroom spectrometer mounted on a multirotor unmanned aerial vehicle (UAV) to perform such multi-angular measurements. By hovering the UAV above a surface while rotating it around its vertical axis, we were able to sample the reflectance anisotropy within the field of view of the spectrometer, covering all view azimuth directions up to a 30° view zenith angle. We used this method to study the reflectance anisotropy of barley, potato, and winter wheat at different growth stages. The reflectance anisotropy patterns of the crops were interpreted by analysis of the parameters obtained by fitting of the Rahman-Pinty-Verstraete (RPV) model at a 5-nm interval in the 450–915 nm range. To demonstrate the results of our method, we firstly present measurements of barley and winter wheat at two different growth stages. On the first measuring day, barley and winter wheat had structurally comparable canopies and displayed similar anisotropic reflectance patterns. On the second measuring day the anisotropy of crops differed significantly due to the crop-specific development of grain heads in the top layer of their canopies. Secondly, we show how the anisotropy is reduced for a potato canopy when it grows from an open row structure to a closed canopy. In this case, especially the backward scattering intensity was strongly diminished due to the decrease in shadowing effects that were caused by the potato rows that were still present on the first measuring day. The results of this study indicate that the presented method is capable of retrieving anisotropic reflectance characteristics of vegetation canopies and that it is a feasible alternative for field goniometer measurements.