Spectral Reflectance Data Research Articles

Assessment of food safety risk using machine learning-assisted hyperspectral imaging: Classification of fungal contamination levels in rice grain

A rapid and nondestructive assessment of food safety risk using machine learning-assisted hyperspectral imaging was developed for classification of fungal contamination in brown rice grain. Brown rice was inoculated with Penicillium. The fungal infected rice was then mixed with healthy rice to obtain 0 %, 5 %, 25 %, 50 % and 100 % (w/w) contamination of infected rice. Volatile compounds including pentamethyl-heptane, decane, dodecane, 3-octanone, and 1-octen-3-ol were found in fungal infected rice, as analyzed using gas chromatography-mass spectrometry. The HSI system was used to collect spectral reflectance and spatial data of the samples covering the wavelength range of 400–1000 nm. The hypercubed data were analyzed using machine learning algorithms, including principal component analysis (PCA), discriminant factor analysis (DFA) and support vector machine (SVM). Using PCA for data reduction, 3 principal components were extracted with a cumulative variance of 90.53 %. DFA (linear and quadratic algorithms) and SVM (linear, quadratic, cubic, and Gaussian algorithms) were then used to classify the samples. HSI integrated with Gaussian SVM gave 93.4% accuracy which was best for classifying rice with different percentages of contamination. The image analysis gave a pseudo-color distribution map which facilitated the visualization of the contaminated rice by presenting data in an uncomplicated image. The machine learning-assisted HSI can be used as a rapid, nondestructive and chemical-free tool for an assessment of food safety risk for rice grain.

Estimating the chromium concentration of farmland soils in an arid zone from hyperspectral reflectance by using partial least squares regression methods

Heavy metal contamination of farmland soils is a severe concern worldwide.Chromium (Cr), a major environmental carcinogen, is a crucial indicator of farmland soil pollution. Compared with conventional chemical analysis, the hyperspectral analysis is superior in accurately and rapidly surveilling heavy metals in soils. However, various heavy metals, and types or geochemical traits of soils often affect the accuracy of hyperspectral estimation models, which makes guaranteeing the concentration estimation precision for a designated heavy metal in different regions considerably challenging. We here propose an optimal hyperspectral model for determining the Cr concentration of farmland soils first time in the Yanqi Basin of China's northwest arid zone. In total, 171 soil samples were gathered, and their Cr concentrations and relevant spectral reflectance data were determined. The best spectral transformation for measuring the Cr concentration was determined by using 12 transformation types of soil spectral curves, and characteristic wavebands were obtained. Then, by using random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR) models, we constructed hyperspectral estimation models for Cr concentration in farmland soils. The estimated results of these models were compared to identify the best estimation model. Results show that the spectral transformations allow an improvement in the association between the soil Cr concentration and spectra. The accuracy of PLSR was significantly higher than that of SVM and RF. Finally, the second-order differentiation (SD) combined with the PLSR model (R2 = 0.903, RMSE = 40.363, MAE = 30.631) was identified as the optimal model for estimating the Cr concentration of farmland soils in the investigated area. The study findings offer a technical reference for the hyperspectral assessment of the Cr concentration in arid zone farmland soils.

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

To enhance the accuracy of multispectral detection using unmanned aerial vehicles (UAVs), multispectral data of jujube fruit with different soluble solids content (SSC) and moisture content (MC) were obtained under different relative azimuth angles. Prediction models for SSC and MC of jujube fruit were established using partial least squares regression (PLSR) and support vector machines (SVM), respectively. The findings revealed that the MC of jujube fruit had the best prediction effect when the relative azimuth angle was 90°, while the SSC of the jujube fruit had the best prediction effect at an azimuth angle of 180°. Then, the spectral reflectance data corresponding to the eight relative azimuth angles were used as input variables to establish a jujube fruit quality detection model. The results showed that the prediction model for MC and SSC, established using the angle fusion method, had higher detection accuracy compared to the prediction model established at a single angle. This research provides a technical reference for improving the accuracy of outdoor jujube fruit quality detection using spectral technology.

Tourism and marine crises: The impact of Sargassum invasion on Caribbean small island developing sates

Since 2011 the Caribbean has been experiencing large amounts of Sargassum on its beaches causing disruptions to tourism. There are nonetheless no large-scale empirical studies across the regizon on the impact of Sargassum on tourism and local economies. In addressing this knowledge gap this study investigates the impact of Sargassum on sandy beaches on tourism and sandy beach economic activity across 30 Caribbean Small Island Developing States. Spectral reflectance data from the MODIS satellite mission and a Floating Algae Index were used to detect the presence of Sargassum on sandy beaches. Tourism activity was measured using monthly tourist arrivals data from the Caribbean Tourism Organization and near sandy beaches nightlights data from the United States Air Force Defense Meteorological Satellite Program-Operational Linescan System. A Fixed effects panel regression model which controls for island specific time invariant effects, monthly and yearly time effects, and island specific time trends and allow for cross-sectional dependence and serial correlation was adopted. The results demonstrate that the average incidence of Sargassum reduces tourist arrivals growth by 1.1 percentage points up to 8 months after an outbreak with the highest incidence event having a 9 percentage points reduction. The results from nightlight intensity show a fall in sandy beach economic activity up to 6 months after an outbreak. An average Sargassum event of 0.25 and 0.15 constitute a reduction in sandy beach nightlight intensity of about 1.6 and 0.9 percent respectively.

Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System.

Hyperspectral imaging (HSI) is a promising tool in chlorophyll quantification, providing a non-invasive method to collect important information for effective crop management. HSI contributes to food security solutions by optimising crop yields. In this study, we presented a custom HSI system specifically designed to provide a quantitative analysis of leaf chlorophyll content (LCC). To ensure precise estimation, significant wavelengths were identified using optimal-band analysis. Our research was centred on two sets of 120 leaf samples sourced from Thailand's unique Chaew Khing rice variant. The samples were subjected to (i) an analytical LCC assessment and (ii) HSI imaging for spectral reflectance data capture. A linear regression comparison of these datasets revealed that the green (575 ± 2 nm) and near-infrared (788 ± 2 nm) bands were the most outstanding performers. Notably, the green normalised difference vegetation index (GNDVI) was the most reliable during cross-validation (R2=0.78 and RMSE = 2.4 µg∙cm-2), outperforming other examined vegetable indices (VIs), such as the simple ratio (RED/GREEN) and the chlorophyll index. The potential development of a streamlined sensor dependent only on these two wavelengths is a significant outcome of identifying these two optimal bands. This innovation can be seamlessly integrated into farming landscapes or attached to UAVs, allowing real-time monitoring and rapid, targeted N management interventions.

The Iris CubeSat mission: Science payload description for a pathfinder geological space weathering investigation

The Iris mission is a student-led 3-U CubeSat comprised of a science payload of 20 geological samples placed in low-Earth orbit (LEO) to study the effects of space weathering (i.e., those aspects that can be investigated in LEO). This experiment is the first of its kind to take place in the vacuum of space. The science objective of the mission is to investigate how space weathering processes affect the surfaces of airless planetary bodies by monitoring the spectral reflectance properties of geological samples over time. Two three-color cameras acquire spectral reflectance data within the visible spectrum to measure any spectral and/or textural changes exhibited throughout the ∼1-year orbital mission. This mission will provide valuable data to help understand how the space environment may affect the surfaces of the Moon, Mercury, and main-belt and near-Earth asteroids. The acquired data will help constrain meteorite-asteroid links and provide insight into the spectroscopic analysis of samples returned from the Moon, and near-Earth asteroids Itokawa, Bennu, and Ryugu. The results of this mission will also complement the results of laboratory simulations of space weathering. All samples selected for flight on the CubeSat have undergone several tests to ensure suitability for launch and operations. This paper describes the payload subsystem, sample selection and preparation.

A single phase Li2Ba5W3O15:Dy3+/Eu3+ phosphor for color tunable devices and non-contact optical thermometry

A set of warm white phosphor samples Li2Ba5W3O15 (LBW):xDy3+, yEu3+ were prepared via a high-temperature solid-state process. Field emission scanning electron microscopy (FE-SEM) and Energy-dispersive X-ray analysis (EDX) with X-ray diffraction (XRD) are the structural analyses that show the incorporation of Dy3+ and Eu3+ ions into the LBW host matrix, without causing any damage to the lattice structure. Fourier Transform Infrared Spectroscopy (FT-IR) approves the presence of distinct vibrational characteristics of the proposed phosphors. The band gaps of the prepared phosphor samples were determined using diffused reflectance spectral (DRS) data. The energy transfer between Dy3+ and Eu3+ ions was observed, contributing to the adjustable luminescence of the LBW:Dy3+/Eu3+ phosphors. By varying the activator doping levels while keeping the excitation wavelength constant, the emission colors of LBW:Dy3+/Eu3+ phosphors shift from natural white with negligible color purity to a relatively pure orange-red region. The decay curves follow the bi-exponential behavior of decreasing lifetime with increasing concentration of the activator ions. A temperature measurement model using fluorescence intensity ratio (FIR) was developed using D3E0.75 phosphor demonstrating excellent optical temperature measurement properties, with a maximum value of Sr (=0.97 % K−1). Therefore, all the experimental results indicate that the prepared Dy3+/Eu3+ co-doped LBW phosphors can be a suitable choice for UV-excitable warm white lighting devices with color tunability and non-contact optical thermometry measurements.

A machine learning based approach for prediction and interpretation of soil properties from soil spectral data

Aim: An active agricultural sector depends on good soil quality, essential for sustained food cultivation. However, intensive farming and rising demand can lead to soil deterioration, affecting crop yields. Smart soil prediction driven by machine learning is crucial for precision farming and efficient nutrient distribution. Methodology: Visible-near infrared Spectroscopy (vis-NIRS) is used to capture the soil's spectral data.Then, the spectral data is preprocessed with Savitzky-Golay Smoothing.The data that has been preprocessed is then used to train the machine learning model.The preprocessed data enhances model performance compared to spectral reflectance data in its unprocessed state.The machine learning model acquires data-based knowledge, identifies patterns, and predicts soil quality parameters. The Random Forest and Gradient Boosted Regression Tree are two algorithms employed in this study. Results: The spectral reflectance data is used to train, validate, and evaluate the machine learning model.In determining soil properties, both algorithms demonstrated a high degree of prediction accuracy, as demonstrated by the results.Gradient Boosted Regression Tree out performs Random Forest, but is expensive and requires sequential data. Random forest algorithm works well with large datasets, but over-fitting issues arise in some instances. Interpretation: The findings of the study indicate that machine learning can automate the current soil testing procedure in laboratories, thereby making it more efficient, affordable, and environmentally friendly. Key words: Gradient Boosted Regression Tree, Machine learning, Random forest, Soil fertility, Soil moisture

Classification of wheat powdery mildew based on hyperspectral: From leaves to canopy

Powdery mildew is considered to be one of the important diseases that threaten wheat production. It can cause serious economic losses and damage food security. Therefore, early detection of powdery mildew infection is essential to limit its amplification in reproduction. The purpose of this study is to use the hyperspectral image (HSI) to detect wheat powdery mildew (WPM) in an objective and accurate way. A method is proposed to classify the infection degree of WPM by combining cascade interval selection sensitive band with support vector machine (SVM). Firstly, HSI of leaf and canopy scale are obtained, and the image was segmented based on HSV (hue, saturation, value) color space and OTSU algorithm (HSV + OTSU). The spectral reflectance data were extracted after removing the background region of interest (ROI), and then the multiple scattering correction (MSC) was performed. The spectral data after MSC was called RAW, and then the first derivative (D1) and second derivative (D2) operations were performed respectively. Subsequently, the method of backward interval partial least squares (BiPLS) was used to screen out the characteristic bands of the sensitive interval. Finally, the classification models of WPM disease degree at the leaf scale and the canopy scale (RAW-BiPLS-SVM, D1-BiPLS-SVM and D2-BiPLS-SVM) were established respectively. The results showed that the spectral data after derivative operation was enhanced, and the classification effect of D1-BiPLS-SVM model had advantages at both leaf and canopy scales, in which the overall accuracy (OA) at the leaf scale was 95.2 %, and the OA at canopy scale was 74.4 %. In order to increase the difference between diseases, the canopy-scale disease severity levels were integrated. The correct rates of health and disease were 96.9 % and 99.2 %, respectively, and the OA of disease grade recombination was 87.9 %. Therefore, the method proposed in this paper can not only accurately identified healthy and infected wheat, but also realized the classification of different degrees of WPM, and provide technical support for the prevention and control of WPM.

Exploring the optimal model for assessing SOC and TN in Zanthoxylum bungeanum forest on the Loess Plateau using VNIR spectroscopy

Zanthoxylum bungeanum forest is an important economic forest on the Loess Plateau. The rapid assessment of its soil organic carbon (SOC) and total nitrogen (TN) concentrations is critical for the evaluation of soil quality. Soil samples of different forest ages and soil depths were collected for laboratory analyses and spectral measurements, and the visible and near-infrared reflectance (VNIR) spectral data were processed by mathematical transformations, such as first-order derivative (FD) and second-order derivative, multiple scattering correction, and logarithm of the reciprocal. The importance bands of SOC and TN were screened on the basis of competitive adaptive reweighted sampling. In addition, partial least squares regression and support vector machine regression models were constructed, and the applicability of SOC and TN to the models were compared. Results showed that forest age and soil depth remarkably affect SOC and TN concentrations. Among the calibration and validation models, the combination of the SVMR model with Savitzky–Golay smoothing and FD estimated SOC and TN with the highest accuracy, and the validation accuracy of the surface SOC and TN was higher than that of the bottom depths. Meanwhile, the importance bands involved in the modeling were mainly distributed in 400–600 nm and 1000–2400 nm. The combination of the FD transformation and the SVMR model is suitable for determining SOC and TN concentrations in Z. bungeanum forests, and the model has great potential for the quantitative evaluation of soil quality in the Loess Plateau.

Specular gloss estimation by measuring the colorimetric data produced by a reflection measurement at 8° of incidence

This paper presents an experimental investigation into the estimation of specular gloss from the colorimetric data. The 28 samples were selected from the Natural Color System (NCS) gloss scale, which had different combinations of lightness and gloss levels. The samples’ spectral reflectance and colorimetric data Y and L* were measured by a d:8° geometry reflectance spectrophotometer in both SCE (Specular Component Excluded) and SCI (Specular Component Included) modes. Additionally, the specular gloss of the samples at three common angles 20◦, 60◦, and 85◦ was measured using a gloss meter. The correlation and relationship between DYSCI-SCE and DL*SCI-SCE and the specular gloss values measured for the samples at different angles were evaluated and analyzed with linear and second-polynomial regression functions. The results were validated with a different set of data acquired from 32 random solid-coated samples. The results showed the best fitting was achieved at 60◦ of gloss measurement with a second-polynomial function. As the specular gloss of DY decreased, like in a matt sample, the estimation error of specular gloss increased with a large error of over 10%.

Spatially constrained atmosphere and surface retrieval for imaging spectroscopy

Imaging spectrometers provide important Earth surface data for terrestrial and aquatic ecology, hydrology, geology through retrieved surface reflectance spectra. Surface reflectance is generally recovered from measured radiance through model inversions of the surface and atmospheric system. Most retrieval approaches treat all pixels independently and ignore spatial correlations in the atmosphere for neighboring pixels, resulting in surface-related biases in the retrieved surface and atmospheric parameters. In this work, we present a mathematical framework to more accurately retrieve surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. This framework retrieves the full spatial–spectral reflectance data cube in a computationally efficient manner and is to our knowledge the first approach capable of accurately finding this global solution for large imaging spectroscopy datasets. We implement this mathematical framework in an example proposed algorithm which we call spatially constrained optimal estimation (SCOE). We show that SCOE improves the overall surface reflectance retrieval error, reduces surface-related biases in the retrieved surface reflectance and atmospheric parameters, and is more robust to noise than a traditional pixel-by-pixel approach and an operational empirical line approach in simulated and experimental results. This reduction in surface reflectance error will impact science data products across application areas, making imaging spectroscopy algorithms more robust for global acquisitions.

Remote sensing of flowers

The language of color pervades the natural world, with flowers being prime exemplars of communicating with color. Our understanding of floral colors is predominantly qualitative. It hinders the comprehensive decoding of information relayed through vibrant displays. Here we present the results of quantitative studies of floral colors by integrating hyperspectral remote sensing and color science. Further, we present the Indian Floral Spectral database, comprising spectral reflectance data for 686 angiosperm species in Kerala, India. Analysis of the elements in the database reveals intriguing insights into the altitudinal variability in human-perceived floral colors across nine angiosperm species. Our research introduces a novel approach that utilizes floral spectral reflectance data to study subtle changes in the landscape. Through the examination of floral color variations, we gain valuable insights into intra and inter-specific plant communications. The quantified floral colors posit as an objective plant trait, a prospective Tier I indicator of biodiversity targets.

Rapid Determination of Soil Horizons and Suborders Based on VIS-NIR-SWIR Spectroscopy and Machine Learning Models

In an effort to improve the efficiency of soil classification, traditional methods are being combined with analytical and computational techniques. This integration has strengthened the connection between conventional classification and the application of machine-learning (ML) models to interpret soil spectral reflectance data. Due to the time and computational cost, several studies are limited to testing the classification performance of a few algorithms and do not always explore the best parameters for model optimization. The study aims to assess the efficiency of combining soil spectral reflectance with prevalent ML models for classifying pedogenetic horizons and soil suborders, enhancing traditional classification methods. We collected seven soil monoliths, previously classified according to the Brazilian Soil Classification System (SiBCS) and soil taxonomy. Using the ASD Fieldspec spectroradiometer, we obtained spectral reflectance samples along each monolith (n = 800 per monolith) to classify horizons and n = 5600 for suborder classification. Spectral fingerprints were obtained and explored by Principal Component Analysis (PCA). The spectral data were subdivided into training (70%) and test (30%) sets and submitted to the Logistic Regression (LR), Artificial Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) models for the classification of horizons and suborders, considering the model’s adjustment parameters. Accuracy and F-Score were used to verify the performance of the models. There was a significant influence of particle size and soil organic carbon on the spectral fingerprint of the soils. The PCA indicated that topsoil horizons clustered in most of the monoliths analyzed, while most of the subsoil horizons showed data overlap. The NN model showed the highest accuracy in the classification of horizons (97%), while the SVM showed the lowest performance (52% accuracy). The classification of soil suborders presented accuracies between 95% and 98%. Therefore, our study concludes that spectral data combined with ML models can enhance the discrimination and classification of soil horizons and suborders, improving upon traditional methods.

基于可见/近红外高光谱成像无损预测猕猴桃可溶性固形物含量

Soluble solid content (SSC) is a major quality index of kiwifruits. Visible near-infrared (Vis/NIR) hyperspectral imaging with the genetic algorithm (GA) was adopted in this study to realize the non-destructive prediction of kiwifruit SSC. A laboratory Vis/NIR hyperspectral imaging system was established to collect the hyperspectral imaging of 120 kiwifruit samples at a range of 400–1100 nm. The average reflectance spectral data of the region of interest of the kiwifruit hyperspectral imaging were obtained after different preprocessing method, namely, Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), and their combination method. The prediction models of partial least squares regression, multiple linear regression, and least squares support vector machine (LS-SVM) were built for determining kiwifruit SSC by using the average reflectance spectral data and effective feature wavelength variables selected by GA, respectively. The results show that SG+MSC is the best preprocessing method. The precisions of the prediction models built using the effective feature wavelength variables selected by GA are higher than that established using full average reflectance spectral data. The GA-LS-SVM prediction model has a best performance with correlation coefficient for prediction (R=0.932) and standard error of prediction (SEP=0.536° Bx) for predicting kiwifruit SSC. The prediction accuracy has been improved by 5.6% compared with that of the prediction models established by using the full-band reflectance spectral data. This study provides an effective method for non-destructive detection of kiwifruit SSC.

The Detection of Kiwifruit Sunscald Using Spectral Reflectance Data Combined with Machine Learning and CNNs

Sunscald in kiwifruit, an environmental stress caused by solar radiation during the summer, reduces fruit quality and yields and causes economic losses. The efficient and timely detection of sunscald and similar diseases is a challenging task but helps to implement measures to control stress. This study provides high-precision detection models and relevant spectral information on kiwifruit physiology for similar statuses, including early-stage sunscald, late-stage sunscald, anthracnose, and healthy. Primarily, in the laboratory, 429 groups of spectral reflectance data for leaves of four statuses were collected and analyzed using a hyperspectral reflection acquisition system. Then, multiple modeling approaches, including combined preprocessing methods, feature extraction algorithms, and classification algorithms, were designed to extract bands and evaluate the performance of the models to detect the statuses of kiwifruit. Finally, the detection of different stages of kiwifruit sunscald under anthracnose interference was accomplished. As influential bands, 694–713 nm, 758–777 nm, 780–799 nm, and 1303–1322 nm were extracted. The overall accuracy, precision, recall, and F1-score values of the models reached 100%, demonstrating an ability to detect all statuses with 100% accuracy. It was concluded that the combined processing of moving average and standard normal variable transformations (MS) could significantly improve the data; the near-infrared support vector machine and visible convolutional neural network with MS (NIR-MS-SVM and VIS-MS-CNN) were established as high-precision detection techniques for the classification of similar kiwifruit statuses, demonstrating 25.58% higher accuracy than the single support vector machine. The VIS-MS-CNN model reached convergence with a stable cross-entropy loss of 0.75 in training and 0.77 in validation. The techniques developed in this study will improve orchard management efficiency and yields and increase researchers’ understanding of kiwifruit physiology.

Identification of Eroded and Erosion Risk Areas Using Remote Sensing and GIS in the Quebrada Seca watershed

The aim of this research was to identify eroded areas and areas at risk of erosion (EAER) as indicators of soil degradation by water erosion in a semiarid watershed of the Venezuelan Andes in 2017. To this effect, remote sensing techniques and geographic information systems (GIS) were used, focusing on spectral reflectance data from a satellite image, given the absence of continuous pluviographic information and data on soil properties in developing countries. This methodology involved estimating the potential water erosion risk (PWER) and mapping eroded and erosion risk areas (EAER) based on calculating the spectral Euclidean distance to bare soils and a remote sensing technique, which was selected via linear regression. Receiver operating characteristics (ROC) curves were determined to define classification thresholds, which were validated by means of a supervised classification and associated to PWER values. The main results indicate that EAER1 identified more eroded areas with bare soils (229,77 ha) as opposed to EAER2 (195,57 ha). Similarly, it was evident that the first alternative was more successful that the second (sum of the first three principal components). The PWER analysis, in addition to the erosion mapping developed and other data and criteria, such as mini-mum area size of interest, could help to consider necessary soil conservation measures.

Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2

Lakes and ponds are extensive features throughout the circumpolar region, spanning a broad range of environmental conditions which controls their hydro-ecological processes and spatiotemporal distribution. The physical characteristics of these freshwater ecosystems, including their extent and depth, are particularly responsive to climatic conditions. Thus, having the ability to efficiently map and monitor these elements is crucial as the climate continues to warm, especially over large spatial extents. In this study, satellite derived bathymetry (SDB) methods were implemented to model regional inland Arctic water depths within the open-source and cloud-based Google Earth Engine (GEE) platform. High-resolution (10 m) spectral reflectance data from Sentinel-2 was used as covariates in non-parametric and non-linear machine learning (ML) models, namely random forest (RF), support vector regression (SVR), and classification and regression trees (CART). These three ML models were also compared to a more classical and parametric multiple linear regression (MLR) model. All models were calibrated using in situ bathymetric data collected near the Toolik Field Station, in the Alaskan Arctic tundra. With such a large sample repository available, the effects of training set size, noise, and multicollinearity on model performance was comprehensively investigated. Results clearly demonstrated the superior efficacy and robustness of the RF algorithm for predicting water depths, achieving a best coefficient of determination (R2) of 0.74, mean absolute error (MAE) of 2.12 m, and root mean square error (RMSE) of 3.04 m. Overall, this study highlights the potential of advanced and flexible ML algorithms within the GEE, and demonstrates the capabilities of the polar-orbiting Copernicus Sentinel-2 satellite for this Arctic application. In the future, this ML-driven methodology can be applied using GEE’s cloud infrastructure to produce updated, cost-effective, and accurate bathymetry maps over large expanses of the Arctic tundra.

Reflectance spectroscopy as a promising tool for ‘sensing’ metals in hyperaccumulator plants

Main conclusionThe VNIR reflectance spectra of nickel hyperaccumulator plant leaves have spectral variations due to high nickel concentrations and this property could potentially be used for discovery of these plants. Hyperaccumulator plants accumulate high concentrations of certain metals, including manganese, cobalt, or nickel. Of these metals, the divalent ions of nickel have three absorption bands in the visible to near-infrared region which may cause variations in the spectral reflectance of nickel hyperaccumulator plant leaves, but this has not been investigated previously. In this shortproof-of-concept study, the spectral reflectance of eight different nickel hyperaccumulator plant species leaves were subjected to visible and near-infrared and shortwave infrared (VNIR-SWIR) reflectance spectrum measurements in dehydrated state, and for one species, it was also assessed in hydrated state. Nickel concentrations in the plant leaves were determined with other methods and then correlated to the spectral reflectance data. Spectral variations centred at 1000 ± 150 nm were observed and had R-values varying from 0.46 to 0.96 with nickel concentrations. The extremely high nickel concentrations in nickel hyperaccumulator leaves reshape their spectral reflectance features, and the electronic transition of nickel-ions directly contributes to absorption at ~ 1000 nm. Given that spectral variations are correlated with nickel concentrations it make VNIR-SWIR reflectance spectrometry a potential promising technique for discovery of hyperaccumulator plants, not only in the laboratory or herbarium, but also in the field using drone-based platforms. This is a preliminary study which we hope will instigate further detailed research on this topic to validate the findings and to explore possible applications.

A dataset of normalized difference vegetation index in the Three-river Headwaters during 2000-2018

Vegetation is an important component of the terrestrial ecosystem. The changes of vegetation are assumed to well indicate the dynamic changes of the ecosystem. However, the changing global climate and the intensifying human activities have a great effect on vegetation growth, which particularly highlights the implications to monitor and assess vegetation changes. Vegetation changes are usually measured by vegetation indexes. The normalized difference vegetation index (NDVI) based on remote sensing is widely used in the studies on vegetation changes and climate impact. In this study, we used the spectral reflectance data product (MOD09Q1) of the Moderate Resolution Imaging Spectrometer (MODIS) from 2000 to 2018 to calculate the NDVI (with a spatial resolution of 250m and temporal step of 8 days). The S-G filtering method of the TIMESAT3.2 software is applied to remove the noise in the NDVI time series for the reconstruction of time series. In this way, we finally obtained this dataset, which is open to the public for sharing and downloading. It is expected to support further studies on the dynamic changes of vegetation in the Three-river Headwaters.