Pushbroom Hyperspectral Imager Research Articles

High-resolution mapping of the NO<sub>2</sub> spatial distribution over Belgian urban areas based on airborne APEX remote sensing

Abstract. We present retrieval results of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs), mapped at high spatial resolution over three Belgian cities, based on the DOAS analysis of Airborne Prism EXperiment (APEX) observations. APEX, developed by a Swiss-Belgian consortium on behalf of ESA (European Space Agency), is a pushbroom hyperspectral imager characterised by a high spatial resolution and high spectral performance. APEX data have been acquired under clear-sky conditions over the two largest and most heavily polluted Belgian cities, i.e. Antwerp and Brussels on 15 April and 30 June 2015. Additionally, a number of background sites have been covered for the reference spectra. The APEX instrument was mounted in a Dornier DO-228 aeroplane, operated by Deutsches Zentrum für Luft- und Raumfahrt (DLR). NO2 VCDs were retrieved from spatially aggregated radiance spectra allowing urban plumes to be resolved at the resolution of 60 × 80 m2. The main sources in the Antwerp area appear to be related to the (petro)chemical industry while traffic-related emissions dominate in Brussels. The NO2 levels observed in Antwerp range between 3 and 35 × 1015 molec cm−2, with a mean VCD of 17.4 ± 3.7 × 1015 molec cm−2. In the Brussels area, smaller levels are found, ranging between 1 and 20 × 1015 molec cm−2 and a mean VCD of 7.7 ± 2.1 × 1015 molec cm−2. The overall errors on the retrieved NO2 VCDs are on average 21 and 28 % for the Antwerp and Brussels data sets. Low VCD retrievals are mainly limited by noise (1σ slant error), while high retrievals are mainly limited by systematic errors. Compared to coincident car mobile-DOAS measurements taken in Antwerp and Brussels, both data sets are in good agreement with correlation coefficients around 0.85 and slopes close to unity. APEX retrievals tend to be, on average, 12 and 6 % higher for Antwerp and Brussels, respectively. Results demonstrate that the NO2 distribution in an urban environment, and its fine-scale variability, can be mapped accurately with high spatial resolution and in a relatively short time frame, and the contributing emission sources can be resolved. High-resolution quantitative information about the atmospheric NO2 horizontal variability is currently rare, but can be very valuable for (air quality) studies at the urban scale.

Hyperspectral imaging for detection of codling moth infestation in GoldRush apples

Effective detection of insect infestation is important for preserving quality of fresh fruits like apples. The objective of this research was to study the effectiveness of visible (VIS)/near-infrared (NIR) hyperspectral imaging (HSI) (400–900nm) in the diffuse reflectance mode for detecting and classifying codling moth (CM) infestation in apples. A pushbroom HSI was implemented to acquire hyperspectral images for GoldRush apples of fresh-picked and stored at 4, 10, 17, and 27°C, s for 4 months. Mean reflectance spectra (MRS) were calculated for the images and several classification techniques were applied including linear discriminant analysis (LDA), decision trees, K-nearest neighbor (Knn), partial least squares discriminant analysis (PLSDA), feed forward artificial neural networks (FFNN) in addition to majority voting. The most influential wavelengths were determined using the sequential forwards selection (SFS) approach. The highest classification performance was obtained using decision trees at 5 selected wavelengths (434.0nm, 437.5nm, 538.3nm, 582.8nm and 914.5nm) with overall classification rate of 82% for the test set, and 81% and 86% for healthy and CM-infested apples. This study shows the potential of using VIS/NIR hyperspectral imaging as a non-destructive method for detecting CM infestation in apples.

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.

Improved support vector machines model based on multi-spectral parameters

To solve the classification accuracy instability problem of support vector machines (SVM) model caused by using original hyper-spectral data or a single spectral feature as modeling feature vector, an improved SVM model with multi-spectral parameters and joint kernel function is proposed in this paper. The feature vector of SVM model is built by multi-spectral parameters using feature selection method based on information quantity and between-class separability and feature extraction method based on minimum noise fraction. The kernel function of improved SVM model is optimized with linear combination of polynomial kernel function and radial basis kernel function to increase the learn ability and generalization ability. And the multi-class classification strategies based on an improved directed acyclic graph is presented in the proposed method. The airborne hyperspectral remote sensing images collected by pushbroom hyperspectral imager (PHI) and airborne visible infrared imaging spectrometer (AVIRIS) are applied to analyze and evaluate the performance of the proposed method in this paper. The experiment results show that the classification accuracy is better than 90% and the plant fine-classification ability for small sample and similar spectral features is realized.

Early Detection of Aphid (Myzus persicae) Infestation on Chinese Cabbage by Hyperspectral Imaging and Feature Extraction

Abstract. Hyperspectral imaging (HSI) in the visible/near-infrared region coupled with textural feature extraction were used to detect aphid infestation on Chinese cabbage plants. Hyperspectral images of Chinese cabbage plants, including 31 healthy plants and 32 aphid-infested plants, were captured in the region of 380 to 1030 nm with a pushbroom HSI system. A total of 160 spectra were selected from the hyperspectral images of plants. Six important wavelengths (IWs) at 550, 671, 694, 742, 911, and 968 nm were identified by x-loadings of PC-1 and PC-2 in principle component analysis (PCA). Textural features (TFs) including contrast, homogeneity, energy, and correlation based on gray-level co-occurrence matrix (GLCM) were extracted from the six selected IWs. Finally, least squares support vector machine (LS-SVM) models were developed to differentiate between healthy and aphid-infested plants based on the IWs and TFs. The results showed that the reflectance and textural features at 694 and 742 nm offered good performance in distinguishing aphid-infested samples from healthy samples. The overall results revealed the feasibility of HSI for early detection of aphid infestation on Chinese cabbage plants and its promising potential in practice. Keywords: Aphid infestation, Chinese cabbage, Hyperspectral imaging, Least squares support vector machine, Principal component analysis, Textural feature extraction.

Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements

Timely and accurate assessment of canopy nitrogen content (CNC) provides valuable insight into rapid and real-time nitrogen status monitoring in crops. A semi-empirical approach based on spectral index was extensively used for nitrogen content estimation. However, in many cases, due to specific vegetation types or local conditions, the applicability and robustness of established spectral indices for nitrogen retrieval were limited. The objective of this study was to investigate the optimal spectral index for winter wheat (Triticum aestivum L.) CNC estimation using Pushbroom Hyperspectral Imager (PHI) airborne hyperspectral data. Data collected from two different field experiments that were conducted during the major growth stages of winter wheat in 2002 and 2003 were used. Our results showed that a significant linear relationship existed between nitrogen and chlorophyll content at the canopy level, and it was not affected by cultivars, growing conditions and nutritional status of winter wheat. Nevertheless, it varied with growth stages. Periods around heading stage mainly worsened the relationship and CNC estimation, and CNC assessment for growth stages before and after heading could improve CNC retrieval accuracy to some extent. CNC assessment with PHI airborne hyperspectra suggested that spectral indices based on red-edge band including narrowband and broadband CIred-edge, NDVI-like and ND705 showed convincing results in CNC retrieval. NDVI-like and ND705 were sensitive to detect CNC changes less than 5g/m2, narrowband and broadband CIred-edge were sensitive to a wide range of CNC variations. Further evaluation of CNC retrieval using field measured hyperspectra indicated that NDVI-like was robust and exhibited the highest accuracy in CNC assessment, and spectral indices (CIred-edge and CIgreen) that established on narrow or broad bands showed no obvious difference in CNC assessment. Overall, our study suggested that NDVI-like was the optimal indicator for winter wheat CNC retrieval.

METHODOLOGY FOR DETERMINING OPTIMAL EXPOSURE PARAMETERS OF A HYPERSPECTRAL SCANNING SENSOR

Abstract. The purpose of the presented research was to establish a methodology that would allow the registration of hyperspectral images with a defined spatial resolution on a horizontal plane. The results obtained within this research could then be used to establish the optimum sensor and flight parameters for collecting aerial imagery data using an UAV or other aerial system. The methodology is based on an user-selected optimal camera exposure parameters (i.e. time, gain value) and flight parameters (i.e. altitude, velocity). A push-broom hyperspectral imager- the Headwall MicroHyperspec A-series VNIR was used to conduct this research. The measurement station consisted of the following equipment: a hyperspectral camera MicroHyperspec A-series VNIR, a personal computer with HyperSpec III software, a slider system which guaranteed the stable motion of the sensor system, a white reference panel and a Siemens star, which was used to evaluate the spatial resolution. Hyperspectral images were recorded at different distances between the sensor and the target- from 5m to 100m. During the registration process of each acquired image, many exposure parameters were changed, such as: the aperture value, exposure time and speed of the camera’s movement on the slider. Based on all of the registered hyperspectral images, some dependencies between chosen parameters had been developed: - the Ground Sampling Distance – GSD and the distance between the sensor and the target, - the speed of the camera and the distance between the sensor and the target, - the exposure time and the gain value, - the Density Number and the gain value. The developed methodology allowed us to determine the speed and the altitude of an unmanned aerial vehicle on which the sensor would be mounted, ensuring that the registered hyperspectral images have the required spatial resolution.

METHODOLOGY FOR DETERMINING OPTIMAL EXPOSURE PARAMETERS OF A HYPERSPECTRAL SCANNING SENSOR

The purpose of the presented research was to establish a methodology that would allow the registration of hyperspectral images with a defined spatial resolution on a horizontal plane. The results obtained within this research could then be used to establish the optimum sensor and flight parameters for collecting aerial imagery data using an UAV or other aerial system. The methodology is based on an user-selected optimal camera exposure parameters (i.e. time, gain value) and flight parameters (i.e. altitude, velocity). A push-broom hyperspectral imager- the Headwall MicroHyperspec A-series VNIR was used to conduct this research. The measurement station consisted of the following equipment: a hyperspectral camera MicroHyperspec A-series VNIR, a personal computer with HyperSpec III software, a slider system which guaranteed the stable motion of the sensor system, a white reference panel and a Siemens star, which was used to evaluate the spatial resolution. <br><br> Hyperspectral images were recorded at different distances between the sensor and the target- from 5m to 100m. During the registration process of each acquired image, many exposure parameters were changed, such as: the aperture value, exposure time and speed of the camera’s movement on the slider. Based on all of the registered hyperspectral images, some dependencies between chosen parameters had been developed: <br><br> - the Ground Sampling Distance – GSD and the distance between the sensor and the target,<br><br> - the speed of the camera and the distance between the sensor and the target,<br><br> - the exposure time and the gain value,<br><br> - the Density Number and the gain value.<br><br> The developed methodology allowed us to determine the speed and the altitude of an unmanned aerial vehicle on which the sensor would be mounted, ensuring that the registered hyperspectral images have the required spatial resolution.

Georeferencing of mobile ground-based hyperspectral digital single-lens reflex imagery

The georeferencing accuracy of a ground-based mobile mapping system designated for agricultural applications is tested. The system integrates a hyperspectral sensor, digital camera, global navigation satellite system receivers, and an inertial navigation system. Acquired imagery was synchronized with GPS time using custom hardware and software solutions developed in-house. The imaging platform was mounted on a forklift and used to conduct three imaging missions along a paved road segment and agricultural beds. Sixteen ground control points were established in each site and used to calibrate the system and test the positional accuracy. The control point coordinates were determined using GNSS and total station observations independent from the imaging data. The navigation data were postprocessed to extract sensor positions and attitude along the imaging trajectories. The pushbroom hyperspectral images were georeferenced using ReSe Parge software, while the digital camera images were analyzed using Agisoft PhotoScan software. Control point coordinates extracted from the georeferenced imagery were compared to corresponding ground-surveyed coordinates. The maximum root mean square errors obtained for the hyperspectral images in all experiments were 2.4 and 3.1 cm in the easting and northing directions, respectively. These results were achieved using only two control points at both ends of the scan line to estimate the boresight offsets. The RMSE values of the orthorectified image constructed using the digital camera images and two control points at each end of the agricultural site were 1.6 and 2.6 cm in the easting and northing directions.

Spatial-scanning hyperspectral imaging probe for bio-imaging applications.

The three common methods to perform hyperspectral imaging are the spatial-scanning, spectral-scanning, and snapshot methods. However, only the spectral-scanning and snapshot methods have been configured to a hyperspectral imaging probe as of today. This paper presents a spatial-scanning (pushbroom) hyperspectral imaging probe, which is realized by integrating a pushbroom hyperspectral imager with an imaging probe. The proposed hyperspectral imaging probe can also function as an endoscopic probe by integrating a custom fabricated image fiber bundle unit. The imaging probe is configured by incorporating a gradient-index lens at the end face of an image fiber bundle that consists of about 50,000 individual fiberlets. The necessary simulations, methodology, and detailed instrumentation aspects that are carried out are explained followed by assessing the developed probe's performance. Resolution test targets such as United States Air Force chart as well as bio-samples such as chicken breast tissue with blood clot are used as test samples for resolution analysis and for performance validation. This system is built on a pushbroom hyperspectral imaging system with a video camera and has the advantage of acquiring information from a large number of spectral bands with selectable region of interest. The advantages of this spatial-scanning hyperspectral imaging probe can be extended to test samples or tissues residing in regions that are difficult to access with potential diagnostic bio-imaging applications.

Estimation and Correction of Geometric Distortion in Pushbroom Hyperspectral System for Imaging Art Paintings

Spectral imaging has been proved as a promising technology to perform scientific documentation and analysis of cultural heritage objects. Imaging systems used for this purpose varies in different forms and complexity. Most of the spectral devices are designed not specifically for cultural heritage imaging, but for other application domains like remote sensing, satellite imaging etc. While these imaging systems used for cultural heritage scanning, the same processing workflow may not be adequate to meet the required image quality. In this paper, we investigate one of the several quality parameters, geometrical distortions in a spectral image caused by the translation stage of the camera. For this study, we used a hyperspectral image dataset derived from a pushbroom hyperspectral imaging system attached in a rotational translator. Using geometrical model, we have estimated the distortion, which is function of the scanning angle and distance. Image correction has been proposed and tested over a number of images acquired at the laboratory and in a museum environment.

Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds

A near-infrared (NIR) hyperspectral imaging (HSI) system was used to predict viability and vigor (in term of germination periods) in muskmelon seeds. Hyperspectral images of muskmelon seeds were acquired using a NIR push-broom HSI system covering the spectral range of 948–2494nm. After NIR spectra collection, all seeds underwent a germination test to confirm their viability and vigor. The spectra from seeds with 3 and 5 germination days and nongerminated seeds were further used for development of a classification model of partial least-squares discriminant analysis (PLSDA). Most effective wavelengths were selected using three model-based variable selection methods, i.e., variable important in projection (VIP), selectivity ratio (SR), and significance multivariate correlation (sMC), which selected 23, 18, and 19 optimal variables, respectively, from full set of 208 variables. The selected variables from different waveband selection methods were found genuine and significant for interpreting the prediction results of seed viability and vigor. Subsequently, the PLS-DA model was constructed using individual VIP-, SR-, or sMC-selected variables. The results demonstrated that the PLSDA model developed with the selected optimal variables from the different methods provided comparable results for the calibration set; however, the PLSDA-SR method afforded the highest classification accuracy (94.6%) for a validation set used to determine the viability and vigor of muskmelon seeds. The wavelengths selected by the different methods represents chemical components of the seed and the attribute of germination ability was chosen most often.

Toward High Altitude Airship Ground-Based Boresight Calibration of Hyperspectral Pushbroom Imaging Sensors

The complexity of the single linear hyperspectral pushbroom imaging based on a high altitude airship (HAA) without a three-axis stabilized platform is much more than that based on the spaceborne and airborne. Due to the effects of air pressure, temperature and airflow, the large pitch and roll angles tend to appear frequently that create pushbroom images highly characterized with severe geometric distortions. Thus, the in-flight calibration procedure is not appropriate to apply to the single linear pushbroom sensors on HAA having no three-axis stabilized platform. In order to address this problem, a new ground-based boresight calibration method is proposed. Firstly, a coordinate’s transformation model is developed for direct georeferencing (DG) of the linear imaging sensor, and then the linear error equation is derived from it by using the Taylor expansion formula. Secondly, the boresight misalignments are worked out by using iterative least squares method with few ground control points (GCPs) and ground-based side-scanning experiments. The proposed method is demonstrated by three sets of experiments: (i) the stability and reliability of the method is verified through simulation-based experiments; (ii) the boresight calibration is performed using ground-based experiments; and (iii) the validation is done by applying on the orthorectification of the real hyperspectral pushbroom images from a HAA Earth observation payload system developed by our research team—“LanTianHao”. The test results show that the proposed boresight calibration approach significantly improves the quality of georeferencing by reducing the geometric distortions caused by boresight misalignments to the minimum level.

Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions

Leaf area index (LAI) is an important indicator for monitoring crop growth conditions and forecasting grain yield. Many algorithms have been developed for remote estimation of the leaf area index of vegetation, such as using spectral vegetation indices, inversion of radiative transfer models, and supervised learning techniques. Spectral vegetation indices, mathematical combination of reflectance bands, are widely used for LAI estimation due to their computational simplicity and their applications ranged from the leaf scale to the entire globe. However, in many cases, their applicability is limited to specific vegetation types or local conditions due to species specific nature of the relationship used to transfer the vegetation indices to LAI. The overall objective of this study is to investigate the most suitable vegetation index for estimating winter wheat LAI under eight different types of fertilizer and irrigation conditions. Regression models were used to estimate LAI using hyperspectral reflectance data from the Pushbroom Hyperspectral Imager (PHI) and in-situ measurements. Our results showed that, among six vegetation indices investigated, the modified soil-adjusted vegetation index (MSAVI) and the normalized difference vegetation index (NDVI) exhibited strong and significant relationships with LAI, and thus were sensitive across different nitrogen and water treatments. The modified triangular vegetation index (MTVI2) confirmed its potential on crop LAI estimation, although second to MSAVI and NDVI in our study. The enhanced vegetation index (EVI) showed moderate performance. However, the ratio vegetation index (RVI) and the modified simple ratio index (MSR) predicted the least accurate estimations of LAI, exposing the simple band ratio index’s weakness under different treatment conditions. The results support the use of vegetation indices for a quick and effective LAI mapping procedure that is suitable for winter wheat under different management practices.

Hyperspectral imaging using RGB color for foodborne pathogen detection

This paper reports the development of a spectral reconstruction technique for predicting hyperspectral images from RGB color images and classifying food-borne pathogens in agar plates using reconstructed hyperspectral images. The six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown on Rainbow agar plates were used for the study. A line-scan pushbroom hyperspectral imaging spectrometer was used to scan full reflectance spectra of pure non-O157 STEC cultures in the visible and near-infrared spectral range from 400 to 1000 nm. RGB color images were generated by simulation from hyperspectral images. Polynomial multivariate least-squares regression analysis was used to reconstruct hyperspectral images from RGB color images. The mean R-squared value for hyperspectral image reconstruction was ∼0.98 in the spectral range between 400 and 700 nm for linear, quadratic, and cubic polynomial regression models. The accuracy of the hyperspectral image classification algorithm based on k-nearest neighbors algorithm of principal component scores was validated to be 92% with the test set (99% with the original hyperspectral images). The results of the study suggested that color-based hyperspectral imaging would be feasible without much loss of prediction accuracy compared to true hyperspectral imaging.

Pushbroom hyperspectral imaging system with selectable region of interest for medical imaging.

A spatial-scanning pushbroom hyperspectral imaging (HSI) system incorporating a video camera (VC) which is not only used for direct video imaging but also for the selection of the region of interest within the VC’s full field-of-view is presented. Using a VC for these two applications brings many benefits to a pushbroom HSI system, such as a minimized data acquisition time and smaller data storage requirement. A detailed description of the system followed by the methods and formulas used for calibration and electronic hardware interfacing were discussed and analyzed using United States Air Force resolution chart, chicken breast tissue, and fluorescent targets as test samples. The proposed concepts and developed system can find potential biomedical imaging applications and can be extended to endoscopic imaging applications as well.

Direct georeferencing of airborne pushbroom images

Direct georeferencing (DG) for orthoimage production based on collinearity equations has emerging as a standard photogrammetric operation. In this paper, the basic idea of DG is realized and refined with 19 self-calibration parameters for rectifying airborne pushbroom hyperspectral images collected using the intelligent spectral imaging system (ISIS) scanner. Two experimental ISIS images along with in-flight global positioning systems (GPS)/inertia measurement unit (IMU) data and 40 ground control points (GCPs) were used to solve these parameters by the iterative least squares method. By applying the proposed self-calibrated DG approach, the positional error of the GCPs is dramatically reduced to the pixel level from the 10-pixel level of in-flight DG-based rectification without self-calibration. Similar results were also found for the 30 check points, whose coordinates were either measured by GPS Real-Time Kinematic, or interpolated from 5-m to 20-m digital elevation model data, even when the six lens distortion parameters were omitted. It is demonstrated that the proposed self-calibrated DG approach promisingly improves the quality of georeferencing results by reducing the geometric distortions caused by instability of the platform, including misalignments in GPS/IMU, stabilizer effects on aircraft vibration and rotation, interior parameters of the sensor’s optical system, and variations in topography.

Hyperspectral imaging and chemometric modeling of echinacea - a novel approach in the quality control of herbal medicines.

Echinacea species are popularly included in various formulations to treat upper respiratory tract infections. These products are of commercial importance, with a collective sales figure of $132 million in 2009. Due to their close taxonomic alliance it is difficult to distinguish between the three Echinacea species and incidences of incorrectly labeled commercial products have been reported. The potential of hyperspectral imaging as a rapid quality control method for raw material and products containing Echinacea species was investigated. Hyperspectral images of root and leaf material of authentic Echinacea species (E. angustifolia, E. pallida and E. purpurea) were acquired using a sisuChema shortwave infrared (SWIR) hyperspectral pushbroom imaging system with a spectral range of 920–2514 nm. Principal component analysis (PCA) plots showed a clear distinction between the root and leaf samples of the three Echinacea species and further differentiated the roots of different species. A classification model with a high coefficient of determination was constructed to predict the identity of the species included in commercial products. The majority of products (12 out of 20) were convincingly predicted as containing E. purpurea, E. angustifolia or both. The use of ultra performance liquid chromatography-mass spectrometry (UPLC-MS) in the differentiation of the species presented a challenge due to chemical similarities between the solvent extracts. The results show that hyperspectral imaging is an objective and non-destructive quality control method for authenticating raw material.

Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat

Continuous monitoring leaf area index (LAI) of field crops in a growing season has a great challenge. The development of remote sensing technology provides a good tool for timely mapping LAI regionally. In this study, hyperspectral reflectance data (405-835 nm) obtained from an airborne hyperspectral imager (Pushbroom Hyperspectral Imager) were used to model LAI of winter wheat canopy in the 2002 crop growing season. LAI was modeled based on its semi-empirical relationships with six vegetation indices (VIs), including ratio vegetation index (RVI), modified simple ratio index (MSR), normalized difference vegetation index (NDVI), a newly proposed index NDVI-like (which resembles NDVI), modified triangular vegetation index (MTVI2), and modified soil adjusted vegetation index (MSAVI). To assess the performance of these VIs, root mean square errors (RMSEs) and determination coefficient (R-2) between estimated LAI and measured LAI were reported. Our result showed that NDVI-like was the most accurate predictor of LAI. The inclusion of a green band in MTVI2 trended to give a rise to a much quicker saturation with increase of LAI (e. g., over 3.5). MSAVI and MTVI2 showed comparable but lower potential than NDVI-like in estimating LAI. RVI and MSR demonstrated their lowest prediction accuracy, implying that they are more likely to be affected by environmental conditions such as atmosphere and cloud, thus cannot properly reflect the properties of winter wheat canopy. Our results support the use of VIs for a quick assessment of seasonal variations in winter wheat LAI. Among the indices we tested in this study, the newly developed NDVI-like model created the most accurate and reliable results.

Use of radiofrequency power to enable glow discharge optical emission spectroscopy ultrafast elemental mapping of combinatorial libraries with nonconductive components: nitrogen-based materials.

Combinatorial chemistry and high-throughput techniques are an efficient way of exploring optimal values of elemental composition. Optimal composition can result in high performance in a sequence of material synthesis and characterization. Materials combinatorial libraries are typically encountered in the form of a thin film composition gradient which is produced by simultaneous material deposition on a substrate from two or more sources that are spatially separated and chemically different. Fast spatially resolved techniques are needed to characterize structure, composition, and relevant properties of these combinatorial screening samples. In this work, the capability of a glow discharge optical emission spectroscopy (GD-OES) elemental mapping system is extended to nitrogen-based combinatorial libraries with nonconductive components through the use of pulsed radiofrequency power. The effects of operating parameters of the glow discharge and detection system on the achievable spatial resolution were investigated as it is the first time that an rf source is coupled to a setup featuring a push-broom hyperspectral imaging system and a restrictive anode tube GD source. Spatial-resolution optimized conditions were then used to characterize an aluminum nitride/chromium nitride thin-film composition spread. Qualitative elemental maps could be obtained within 16.8 s, orders of magnitude faster than typical techniques. The use of certified reference materials allowed quantitative elemental analysis maps to be extracted from the emission intensity images. Moreover, the quantitative procedure allowed correcting for the inherent emission intensity inhomogeneity in GD-OES. The results are compared to quantitative depth profiles obtained with a commercial GD-OES instrument.