Analytical Spectral Devices Research Articles

Mineralogical mapping using EO-1 Hyperion data for iron mine identification

The Tamera open pit mine is located in the Nefza mining district (north-west of Tunisia) where the reddish soil reveals its strong potential for heavy Fe mineralization, along with other metals (Pb, Zn, and Mn). This study explored Iron oxide mineralization in a particular nappe zone using multiscale field visible and near infrared/short waves infrared and EO-1 Hyperion hyperspectral data. Thus, spectral features fittings were conducted on Hyperion data for mineral mapping using scene-extracted endmembers. A first minimum noise fraction algorithm was used to reduce the hyperspectral data’s dimensionality, followed by the Pixel Purity Index and n-dimensional visualization to extract the pure pixels (endmembers) from the cluster of pixels in the data cube. Furthermore, 56 soil samples were collected from the test site and spectrally measured using Analytical Spectral Devices FieldSpecHiRes spectrometer, which covers the spectral range between 350 and 2500 nm. All samples were analyzed in the laboratory with x-ray diffraction. Endmember extracted spectra were compared with field measurements before EO-1 Hyperion processing. Mapping results revealed the occurrence of a mineralogy composition consisting particularly of iron oxide minerals (hematite and goethite) and bearing minerals, such as clays (kaolinite), within all samples.

Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer

Leaf chlorophyll content is used as a major indicator of plant stress and growth, and hyperspectral remote sensing is frequently used to monitor the chlorophyll content. Hyperspectral reflectance has been used to evaluate vegetation properties such as pigment content, plant structure and physiological features using portable spectroradiometers. However, the prices of these devices have not yet decreased to consumer-affordable levels, which prevents widespread use. In this study, a system based on a cost-effective fingertip-sized spectrometer (Colorcompass-LF, a total price for the proposed solution was approximately 1600 USD) was evaluated for its ability to estimate the chlorophyll contents of radish and wasabi leaves and was compared with the Analytical Spectral Devices FieldSpec4. The chlorophyll contents per leaf area (cm2) of radish were generally higher than those of wasabi and ranged from 42.20 to 94.39 μg/cm2 and 11.39 to 40.40 μg/cm2 for radish and wasabi, respectively. The chlorophyll content was estimated using regression models based on a one-dimensional convolutional neural network (1D-CNN) that was generated after the original reflectance from the spectrometer measurements was de-noised. The results from an independent validation dataset confirmed the good performance of the Colorcompass-LF after spectral correction using a second-degree polynomial, and very similar estimation accuracies were obtained for the measurements from the FieldSpec4. The coefficients of determination of the regression models based on 1D-CNN were almost same (with R2 = 0.94) and the ratios of performance to deviation based on reflectance after spectral correction using a second-degree polynomial for the Colorcompass-LF and the FieldSpec4 were 4.31 and 4.33, respectively.

Design and testing of a crop growth sensor aboard a fixed-wing unmanned aerial vehicle

• A set of crop growth sensors mounted on a fixed-wing UAV were developed. The system can obtain crop canopies and growth indices, such as LNC and LAI, on-line and in a convenient, high-throughput manner while ensuring that theUAV's airflow does not disturb the crop canopies below. • Flight dynamics computer simulation analysis of the fixed-wing UAV was conducted by the ADAMS software to obtain the deflection angle of the UAV during flight. A ball rolling-type sensor support was designed to ensure that the crop growth sensor is always aimed vertically downwards in-flight. • Due to the adjustment of the sensor support, the system has good dynamic stability and measurement accuracy within the working height range of the sensor. The aim of this study is to overcome disturbance of downwash flow field caused by the low-altitude operation of a multirotor unmanned aerial vehicle (UAV) on crop canopies and interference in spectral reflection information of canopies. For this purpose, a crop growth sensor aboard a fixed-wing UAV was developed through flight dynamics simulation analysis of a fixed-wing UAV. This sensor can collect index data on-line and in real-time including: the ratio vegetation index (RVI) of crop leaves, leaf area index (LAI), leaf dry weight (LDW), and leaf nitrogen content (LNC). Flight dynamics simulation analysis of the fixed-wing UAV was conducted by the automatics dynamic analysis of mechanical system (ADAMS) software to obtain the deflection angle of the UAV during flight. According to the flight characteristics and load on the UAV, a ball rolling-type sensor support was designed to ensure that the crop growth sensor is always aimed vertically downwards in-flight. The field test results show that the crop growth sensor aboard the fixed-wing UAV has good dynamic stability and high measurement accuracy. The RVIs measured by the onboard crop growth sensor in the plots and field were fitted with the results measured by a FieldSpec HandHeld 2 spectroradiometer (ASD, Analytical Spectral Device Co., USA). By analysing the fitted results, the coefficients of determination ( R 2 ) are 0.763 and 0.833 and the root mean square errors (RMSEs) are 0.16 and 0.17, respectively. By linearly fitting RVIs measured by the UAV with rice growth indices including LAI, LDW, and LNC, the coefficients of determination ( R 2 ) are 0.633, 0.581, and 0.528 and RMSEs are 0.18, 0.18, and 0.21, respectively.

Rapid Estimation of Water Stress in Choy Sum (Brassica chinensis var. parachinensis) Using Integrative Approach.

To optimize crop water consumption and adopt water-saving measures such as precision irrigation, early identification of plant water status is critical. This study explores the effectiveness of estimating water stress in choy sum (Brassica chinensis var. parachinensis) grown in pots in greenhouse conditions using Crop Water Stress Index (CWSI) and crop vegetation indicators to improve irrigation water management. Data on CWSI and Spectral reflectance were collected from choy sum plants growing in sandy loam soil with four different soil field capacities (FC): 90–100% FC as no water stress (NWS); 80–90% FC for light water stress (LWS); 70–80% FC for moderate water stress (MWS); and 60–70% FC for severe water stress (SWS). With four treatments and three replications, the experiment was set up as a completely randomized design (CRD). Throughout the growing season, plant water stress tracers such as leaf area index (LAI), canopy temperature (Tc), leaf relative water content (LRWC), leaf chlorophyll content, and yield were measured. Furthermore, CWSI estimated from the Workswell Wiris Agro R Infrared Camera (CWSIW) and spectral data acquisition from the Analytical Spectral Device on choy sum plants were studied at each growth stage. NDVI, Photochemical Reflectance Index positioned at 570 nm (PRI570), normalized PRI (PRInorm), Water Index (WI), and NDWI were the Vegetation indices (VIs) used in this study. At each growth stage, the connections between these CWSIW, VIs, and water stress indicators were statistically analyzed with R2 greater than 0.5. The results revealed that all VIs were valuable guides for diagnosing water stress in choy sum. CWSIW obtained from this study showed that Workswell Wiris Agro R Infrared Camera mounted on proximal remote sensing platform for assessing water stress in choy sum plant was rapid, non-destructive, and user friendly. Therefore, integrating CWSIW and VIs approach gives a more rapid and accurate approach for detecting water stress in choy sum grown under greenhouse conditions to optimize yield by reducing water loss and enhancing food security and sustainability.

Hyperspectral Imagery Detects Water Deficit and Salinity Effects on Photosynthesis and Antioxidant Enzyme Activity of Three Greek Olive Varieties

The olive tree (Olea europaea L.) is one of the main crops of the Mediterranean region which suffers from drought and soil salinization. We assessed the photosynthetic rate, leaf water content and antioxidative enzyme activity (APX, GPX, SOD and CAT) of three Greek olive cultivars (‘Amfisis’, ‘Mastoidis’ and ‘Lefkolia Serron’) subjected to drought and salinity stresses. Hyperspectral reflectance data were acquired using an analytical spectral device (ASD) FieldSpec® 3 spectroradiometer, while principal component regression, partial least squares regression and linear discriminant analysis were used to estimate the relationship between spectral and physiological measurements. The photosynthetic rate and water content of stressed plants decreased, while enzyme activity had an increasing tendency. ‘Amfisis’ was more resistant to drought and salinity stress than ‘Mastoidis’ and ‘Lefkolia Serron’. The NDVI appeared to have the highest correlation with the photosynthetic rate, followed by the PRI. APX enzyme activity was the most highly correlated with the 1150–1370 nm range, with an additional peak at 1840 nm. CAT enzyme activity resulted in the highest correlation with the visible part of the spectrum with two peaks at 1480 nm and 1950 nm, while GPX enzyme activity appeared to have a strong correlation within all the available spectral ranges except for 670–1180 nm. Finally, SOD activity showed high correlation values within 1190–1850 nm. This is the first time the correlation of hyperspectral imagery with photosynthetic rate and antioxidant enzyme activities was determined, providing the background for high-throughput plant phenotyping through a drone with a hyperspectral camera. This progress would provide the possibility of early stress detection in large olive groves and assist farmers in decision making and optimizing crop management, health and productivity.

Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels

As an essential element, the effect of Phosphorus (P) on plant growth is very significant. In the early growth stage of maize, it has a high sensitivity to the deficiency of phosphorus. The main purpose of this paper is to monitor the maize status under two phosphorus levels in soil by a nondestructive testing method and identify different phosphorus treatments by spectral data. Here, the Analytical Spectral Devices (ASD) spectrometer was used to obtain canopy spectral data of 30 maize inbred lines in two P-level fields, whose reflectance differences were compared and the sensitive bands of P were discovered. Leaf Area Index (LAI) and yield under two P levels were quantitatively analyzed, and the responses of different varieties to P content in soil were observed. In addition, the correlations between 13 vegetation indexes and eight phenotypic parameters were compared under two P levels so as to find out the best vegetation index for maize characteristics estimation. A Back Propagation (BP) neural network was used to evaluate leaf area index and yield, and the corresponding prediction model was established. In order to classify different P levels of soil, the method of support vector machine (SVM) was applied. The results showed that the sensitive bands of P for maize canopy included 763 nm, 815 nm, and 900–1000 nm. P-stress had a significant effect on LAI and yield of most varieties, whose reduction rate reached 41% as a whole. In addition, it was found that the correlations between vegetation indexes and phenotypic parameters were weakened under low-P level. The regression coefficients of 0.75 and 0.5 for the prediction models of LAI and yield were found by combining the spectral data under two P levels. For the P-level identification in soil, the classification accuracy could reach above 86%. These abilities potentially allow for phenotypic parameters prediction of maize plants by spectral data and different phosphorus contents identification with unknown phosphorus fertilizer status.

不同生活型植物非光合部分反射光谱和盖度的关系

非光合植被（Non-photosynthetic Vegetation，NPV）既是陆地生态系统重要组成部分，也是全球C、N平衡分析中关键节点，其盖度是判断我国北方沙尘源区风沙活动强弱的重要指征。基于ASD光谱仪（Analytical Spectral Devices）所测地面高光谱数据对鄂尔多斯沙地草场不同生活型植物（草本、半灌木和灌木） NPV光谱特征进行了分析，构建了干枯燃料指数（Dead Fuel Index，DFI）、纤维素吸收指数（Cellulose Absorption Index，CAI）和NPV盖度（f_NPV）应用模型，并使用MCD43A4数据在不同草原区进行了验证。结果表明：（1）草本、半灌木、灌木和总体混合NPV光谱反射率具有相似变化趋势，但反射值存在显著差异。（2） DFI和CAI、CAI和f_NPV、DFI和f_NPV均呈显著正相关关系（P<0.001），DFI可以代替CAI进行f_NPV的估算。（3）不同草原区f_NPV存在异质性，草甸草原、典型草原、沙地草场和荒漠草原分别为75.95%、50.61%、45.88%和32.77%。DFI在典型草原区估算f_NPV效果最好（f_NPV1和f_NPV2平均差值为3.11%，最小差值为0.03%）。在草原区进行f_NPV估算时，不可将NPV作为均质体处理，还要剔除裸地干扰。;Non-photosynthetic vegetation (NPV) is a vital component of terrestrial ecosystem, as well as a critical hotspot in global carbon and nitrogen studies. In addition, its coverage in the source area of wind-blown sand in northern China indicates the intensity of wind-blown sand activities. As revealed from recently conducted studies, cellulose absorption index (CAI) was highlighted to be capable of more effectively reflecting the fractional cover of non-photosynthetic vegetation (f_NPV). However, there was no corresponding band (2000-2050 nm, 2080-2130 nm, and 2100-2240 nm) for CAI calculation in the 36 bands of MODIS data. Subsequently, under the introduction of dead fuel index (DFI), it can be employed directly to estimate f_NPV, whereas a question is whether DFI accurately reflect the content of NPV cellulose? Is it necessary to establish the relationship between CAI and DFI to achieve more accurate estimation of f_NPV? It has always been neglected by researchers. Therefore, this paper intended to study the distribution of f_NPV in various grassland, and compared the difference between direct estimation of f_NPV by DFI and indirect estimation of f_NPV by CAI, the influence of bare land on the estimation results were analyzed in order to explain the spatial heterogeneity characteristics of f_NPV in different grasslands. We analyzed the NPV spectral characteristics of different habitats NPV (herb, semi-shrub and shrub) in sandy grassland of Ordos and constructed the application model of DFI, CAI and f_NPV based on the ground hyper-spectral data measured by analytical spectral devices, and then validated in different grassland areas. The results showed that the NPV reflectance had similar trends of herb, semi-shrub, shrub and mixed NPV. There was a significantly positive correlation between DFI and CAI, CAI and f_NPV, DFI and f_NPV at the level of P<0.001, and DFI could better reflect the change of f_NPV. But the f_NPV value directly calculated by DFI was larger than the f_NPV that calculated indirectly by CAI, the average overestimation was 3.11% when eliminating bare ground. The difference between two values in typical grassland areas was 0.03%. In addition, the NPV should not be treated as a homogeneous material, and bare ground interference should be eliminated when performing f_NPV estimation.

マイクロ分光器ハイパースペクトルデータを用いた茶葉のクロロフィル含量非破壊評価

Chlorophyll content has been used as an indicator for assessing photosynthetic ability, health and defense against a variety of degenerative diseases. Hyperspectral remote sensing offers some non-destructive methods and has played an important role in evaluating vegetation characteristics. However, the prices of traditional field portable spectroradiometers, such as Ocean Optics Hyperspectral Vis-NIR spectroradiometers and Analytical Spectral Devices FieldSpec series, have not yet decreased to consumer levels, which prevents much practical use. Recently, fingertip-sized spectrometers have been developed and then they could be powerful tools for evaluating vegetation characteristics. In the present study, a compact spectrometer (C12880MA-10, Hamamatsu Photonics) was used to evaluate chlorophyll content in tea leaves. Incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy and then de-trending was the best pre-processing technique in this study, achieving an RPD of 2.03 and an RMSE of 3.07 μg cm－2. The proposed method is cost effective, practical for consumers to apply and will enable effective crop management.

Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity

High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (Vcmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R2 ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for Vcmax) performed better than the trait-based method (R2 ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for Vcmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R2 ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.

Low-Cost NDVI Platform for Land Operation: Passive and Active

In this letter, a novel low-cost active normalized difference vegetation index (NDVI) platform is developed. In the first stage, the system was tested and calibrated outdoor in a passive mode. Then, vegetation illumination was done using two laser sources with a beam combiner. The active mode was benchmarked using a field spectrometer from analytical spectral devices. Successful operation of active NDVI system was shown indoor.

Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils.

Drought and water scarcity due to global warming, climate change, and social development have been the most death-defying threat to global agriculture production for the optimization of water and food security. Reflectance indices obtained by an Analytical Spectral Device (ASD) Spec 4 hyperspectral spectrometer from tomato growth in two soil texture types exposed to four water stress levels (70–100% FC, 60–70% FC, 50–60% FC, and 40–50% FC) was deployed to schedule irrigation and management of crops’ water stress. The treatments were replicated four times in a randomized complete block design (RCBD) in a 2 × 4 factorial experiment. Water stress treatments were monitored with Time Domain Reflectometer (TDR) every 12 h before and after irrigation to maintain soil water content at the desired (FC%). Soil electrical conductivity (Ec) was measured daily throughout the growth cycle of tomatoes in both soil types. Ec was revealing a strong correlation with water stress at R2 above 0.95 p < 0.001. Yield was measured at the end of the end of the growing season. The results revealed that yield had a high correlation with water stress at R2 = 0.9758 and 0.9816 p < 0.01 for sandy loam and silty loam soils, respectively. Leaf temperature (LT °C), relative leaf water content (RLWC), leaf chlorophyll content (LCC), Leaf area index (LAI), were measured at each growth stage at the same time spectral reflectance data were measured throughout the growth period. Spectral reflectance indices used were grouped into three: (1) greenness vegetative indices; (2) water overtone vegetation indices; (3) Photochemical Reflectance Index centered at 570 nm (PRI570), and normalized PRI (PRInorm). These reflectance indices were strongly correlated with all four water stress indicators and yield. The results revealed that NDVI, RDVI, WI, NDWI, NDWI1640, PRI570, and PRInorm were the most sensitive indices for estimating crop water stress at each growth stage in both sandy loam and silty loam soils at R2 above 0.35. This study recounts the depth of 858 to 1640 nm band absorption to water stress estimation, comparing it to other band depths to give an insight into the usefulness of ground-based hyperspectral reflectance indices for assessing crop water stress at different growth stages in different soil types.

Spectroradiometer spectral calibration, ISRF shapes, and related uncertainties.

A key characteristic of a spectroradiometer is the instrumental spectral response function (ISRF) that is determined during spectral characterization and calibration. The response shape of the ISRF is commonly assumed to be Gaussian, though this is known to not always be the best description. We show that in the context of laboratory calibration, the largest source of uncertainty lies in the ISRF assumption. We perform the spectral calibration of laboratory measurements obtained with four analytical spectral device field spectroradiometers using several different ISRF "modes" to investigate their respective fitting performance, and examine the impact of choosing an ISRF that differs from a Gaussian when calibrating a MODTRAN6 spectrum. Finally, we conduct the uncertainty analysis of our calibration by propagating uncertainty via a Monte Carlo method.

Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods

BackgroundTo accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture.MethodsThe UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models.ResultsThe results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model.ConclusionsThe Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

One case study shows an important phenomenon: Active fault can cause subtle spectral features change of soil

Buried fault identification in plains is one of the important and difficult problems in current active fault research. Seismic activity forces crustal fluid to migrate up, especially along faults, thereby altering the geochemical characteristics of faults. Hyperspectral remote sensing has an extremely high spectral resolution; data derived from hyperspectral remote sensing have the capacity to detect physicochemical characteristics for mining or the subtle recognition of different underground objects. It provides a new method to detect changes in the geochemical characteristics in the fault. This study is trying to locate the fault by spectrum feature. The Xiadian fault was chosen as the study area. Several samples across the fault were collected, and these samples were measured by an ASD Field Spec 3 spectrometer(Analytical Spectral Devices, Boulder, USA, produced in 2008). Continuum removal, the spectral classification method and the wavelet transform technique have been used for spectral analysis. It has been shown that the spectrum of soil in the fault zone has some special characteristics that are different from those of the spectrum away from fault. Four special spectral regions, 550–580 ​nm, 600–700 ​nm, 700–800 ​nm and 800–900 ​nm, have been extracted as the singular ranges. These individual ranges could be used for fault location in the plain area. There is weak absorption near 880 ​nm of the sample in the fault, indicating that Fe3+ content plays an important role in fault identification and that the iron valences can indicate the fault environment. This study also found that the spectral anomalies in space could reflect the intensity of active tectonics to a certain extent. Therefore, spectral features have the potential to identify a buried faults. This study is the first to use spectroscopic techniques to analyze active tectonics, and there is a new discovery. This new discovery provides the basis for the application of remote sensing in the study of active tectonics.

Use of spectral reflectance from a compact spectrometer to assess chlorophyll content in Zizania latifolia

Hyperspectral remote sensing is frequently used to monitor chlorophyll content, an important characteristic for assessing photosynthetic ability, health and defence against a variety of degenerative diseases. To obtain hyperspectral data, field portable spectroradiometers, such as Ocean Optics Hyperspectral Vis-NIR spectroradiometers and Analytical Spectral Devices FieldSpec series, have been widely used. The development of an affordable hyperspectral remote sensing system would be advantageous. Highly sensitive, affordable and cost-effective finger-tip size spectrometers have recently been released. In this study we investigate the potential of hyperspectral data obtained from such a compact spectrometer (C12880MA-10, Hamamatsu Photonics) for estimating chlorophyll content in Zizania latifolia. We also tested the efficacy of five pre-processing techniques (first derivative reflectance, continuum-removal transformation, de-trending, multiplicative scatter correction and standard normal variate) in conjunction with five machine learning algorithms.

Hyperspectral-reflectance dataset of dry, wet and submerged marine litter

Abstract. This paper describes a dataset consisting of 47 hyperspectral-reflectance measurements of plastic litter samples. The plastic litter samples include virgin and real samples from the Port of Antwerp. They were measured in dry conditions in the Flemish Institute for Technological Research (VITO) calibration facility, and a selection of the samples were also measured in wet conditions and submerged in a water tank at Flanders Hydraulics. The construction on top of the tank allowed us to submerge the plastics and keep sediments in suspension. The spectral measurements were performed using an Analytical Spectral Devices (ASD) FieldSpec 4 and a Spectral Evolution (SEV) spectrometer. The datasets are available on the 4TU.ResearchData open-access repository (ASD dataset: https://doi.org/10.4121/12896312.v2, Knaeps et al., 2020; SEV dataset: https://doi.org/10.4121/uuid:9ee3be54-9132-415a-aaf2-c7fbf32d2199; Garaba et al., 2020).

Evaluating satellite hyperspectral (Orbita) and multispectral (Landsat 8 and Sentinel-2) imagery for identifying cotton acreage

ABSTRACT Hyperspectral remote sensing demonstrates a great potential for identifying crops. Orbita hyperspectral image satellite (OHS) is a new hyperspectral satellite in orbit with the highest spectral and spatial resolutions and relatively short spectral range. In this study, the OHS, along with Land Remote-Sensing Satellite (System, Landsat) eight operational land imager (OLI) images (assigned as L8) and Sentinel-2B (S2B) images, was used to identify cotton and estimate its acreage in Beiquan town, Shihezi city, Xinjiang Uygur Autonomous Region of China based on spectral reflectance, sample separability, and accuracy. And Two Class Support vector machine was adapted to map cotton acreage. For the spectral reflectance of cotton, OHS is slightly higher than the analytical spectral device (ASD) in the visible range and is similar in the near-infrared range. Both the S2B and L8 spectrum curves of cotton fit well with the ASD spectrum’s. For the sample separability, the pair values between cotton and non-cotton (e.g. impervious surface, water, maize, grape, etc.) of OHS and S2B are better than that of L8 in general, indicating that OHS and S2B are more reliable than L8 for cotton identification using OHS, L8, and S2B. The results show that the overall accuracy of these three sensors is comparable: an overall accuracy of 0.96 from OHS, 0.95 from S2B, and 0.94 from L8. Moreover, the cotton identification accuracy is not only related to the spatial resolution but also to the spectral resolution and band position, and more importantly to each sensor specifications, even though each satellite hyperspectral and multispectral sensor has its own unique advantages in being complimentary for identifying cotton and other crops.

MAPPING LITHOLOGICAL AND MINERALOGICAL UNITS USING HYPERSPECTRAL IMAGERY

&nbsp;Hyperspectral images such as the Earth Observer-1 (EO-1) provides an efficient method of mapping surface mineralogy because it can measures the energy in narrower bands compared with multispectral sensors. The Kirkuk anticline northern Iraq is one of the most petroleum-rich provinces. It is characterized, that is an asymmetrical, cylindrical anticline, with a fold axis trend towards North West- East, South East. The study’s primary goal is to apply satellite processing and techniques on the Eo-1 imagery for identifying lithological and mineral units at a part of Kirkuk anticline northern Iraq. The EO-1 image was corrected at the beginning of atmospheric impacts using the FLAASH module in ENVI software. Processing of (Minimum Noise Fraction- MNF) processing was applied and then it&nbsp;reduced&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;data, as well as, the processing of (PPI) pixel purity index was applied to spatial reduction. This study tested the potential of (spectral angle mapper supervised classification-SAM) classification for mapping the lithological and mineral units using the Hyperion imagery. three different sources of endmembers or spectra are used for SAM classifications. The one:&nbsp; is done by Analytical Spectral Devices (ASD) Spectrometer. The second: reference spectra have been taken from the spectral library of USGS. Third: extracting endmembers from the purest pixels of the hyperion image, which was done by applying (MNF and PPI). The endmembers were provided, generated as the training area for SAM classification. &nbsp;The present results demonstrated the great potential of data used to map the distribution of alteration of minerals and lithological units in a part of Kirkuk anticline. The classified Hyperion image shows that Jarosite and illite are the most dominant altered minerals, as well as, the main lithological units of the upper member of Fatha formation are revealed in the core of the Kirkuk anticline with scattered and small outcrops towards the flanks.

Estimation of Soil Salt and Ion Contents Based on Hyperspectral Remote Sensing Data: A Case Study of Baidunzi Basin, China

Soil salinity due to irrigation diversion affects regional agriculture, and the development of soil composition estimation models for the dynamic monitoring of regional salinity is important for salinity control. In this study, we evaluated the performance of hyperspectral data measured using an analytical spectral device (ASD) field spec standard-res hand-held spectrometer and satellite sensor visible shortwave infrared advanced hyperspectral imager (AHSI) in estimating the soil salt content (SSC). First derivative analysis (FDA) and principal component analysis (PCA) were applied to the data using the raw spectra (RS) to select the best model input data. We tested the ability of these three groups of data as input data for partial least squares regression (PLSR), principal component regression (PCR), and multiple linear regression (MLR). Finally, an estimation model of the SSC, Na+, Cl−, and SO42− contents was established using the best input data and modeling method, and a spatial distribution map of the soil composition content was drawn. The results show that the soil spectra obtained from the satellite hyperspectral data (AHSI) and laboratory spectral data (ASD) were consistent when the SSC was low, and as the SSC increased, the spectral curves of the ASD data showed little change in the curve characteristics, while the AHSI data showed more pronounced features, and this change was manifested in the AHSI images as darker pixels with a lower SSC and brighter pixels with a higher SSC. The AHSI data demonstrated a strong response to the change in SSC; therefore, the AHSI data had a greater advantage compared with the ASD data in estimating the soil salt content. In the modeling process, RS performed the best in estimating the SSC and Na+ content, with the R2 reaching 0.79 and 0.58, respectively, and obtaining low root mean squared error (RMSE) values. FDA and PCA performed the best in estimating Cl− and SO42−, while MLR outperformed PLSR and PCR in estimating the content of the soil components in the region. In addition, the hyperspectral camera data used in this study were very cost-effective and can potentially be used for the evaluation of soil salinization with a wide range and high accuracy, thus reducing the errors associated with the collection of individual samples using hand-held hyperspectral instruments.

Onsite age discrimination of an endangered medicinal and aromatic plant species Valeriana jatamansi using field hyperspectral remote sensing and machine learning techniques

ABSTRACT Valeriana jatamansi Jones is an aromatic herb well known for its essential oil contents, and its high medicinal and commercial values. The amount of essential oils present in it increases with maturity (age) of the plant. In this study, Hyperspectral remote sensing data recorded in the field using Analytical Spectral Devices (ASD) handheld spectroradiometer was used to discriminate the age (6, 12, 24 and 36 months) of V. jatamansi. Principal Component Analysis (PCA) was used for feature selection and 06 machine learning classifiers were used to classify the plant based on their ages, i.e., Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Boosting Decision Tree (BDT), Decision Tree (DT) and k-Nearest Neighbourhood (kNN). For comparison, these classifiers were applied on full range of spectral reflectance data without feature selection and on feature-selected data using PCA. It was found that the accuracies of ANN, RF, BDT, SVM, DT and kNN were 91, 85, 57, 78, 35 and 42%, respectively for non-feature selected datasets. The accuracies of ANN and DT classifiers were, respectively, increased by 100% and 75% after applying PCA. The ANN classifiers resulted in 100% overall accuracy with a Kappa coefficient (K) of 1. The wavelength regions 860, 870 to 874, 876 to 885 nm in near-infrared (NIR), and 747 to 756 nm (red-edge) were identified as regions suitable for discriminate the age groups of V. jatamansi. The final trained model thus prepared was again validated on 60 plants (with different age group) grown in its natural habitat and the obtained accuracy was 88% (K = 0.84). Thus, the present study have provided a rapid technology for onsite identification of age of V. jatamansi in the field itself. The developed technology thus provides a scientific way for harvesting of this plant at its optimum age avoiding its wastage. The results of this study can also be applied to other endangered and valuable plants by way of finding its optimum growth stages for its harvesting.