Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants

Hg and As pollution in the soil-plant system evaluated by combining multispectral UAV-RS, geochemical survey and machine learning

The combination of a low-density geochemical survey, multispectral data obtained with Unmanned Aerial Vehicle-Remote Sensing (UAV-RS), and a machine learning technique was tested in the search for a statistically robust prediction of contaminant distribution in soil and vegetation, for zones with a highly variable pollutant load. To this end, a novel methodology was devised by means of a limited geochemical study of topsoil and vegetation combined with multispectral data obtained by UAV-RS. The methodology was verified in an area affected by Hg and As contamination that typifies abandoned mining-metallurgy sites in recent decades. A broad selection of spectral indices were calculated to evaluate soil-plant system response, and four machine learning techniques (Multiple Linear Regression, Random Forest, Generalized Boosted Models, and Multivariate Adaptive Regression Spline) were tested to obtain robust statistical models. Random Forest (RF) provided the best non-biased models for As and Hg concentration in soil and vegetation, with R2 and rRMSE (%) ranging from 0.501 to 0.630 and from 180.72 to 46.31, respectively, and with acceptable values for RPD and RPIQ statistics. The prediction and mapping of contaminant content and distribution in the study area were well enough adjusted to the geochemical data and revealed superior accuracy for As than Hg, and for vegetation than topsoil. The results were more precise than those obtained in comparable studies that applied satellite or spectrometry data. In conclusion, the methodology presented emerges as a powerful tool for studies addressing soil and vegetation pollution and an alternative approach to classical geochemical studies, which are time-consuming and expensive.

Estimation of heavy metal concentrations (Cd and Pb) in plant leaves using optimal spectral indicators and artificial neural networks

The necessity of continuously monitoring the agricultural products in terms of their health has enforced the development of rapid, low-cost, and non-destructive monitoring solutions. Heavy metal contamination of the plants is known as a source of health threats that are made by their proximities with pollutant soil, water, and air. In this paper, a method was proposed to measure lead (Pb) and cadmium (Cd) contamination of plant leaves through field spectrometry as a low-cost solution for continuous monitoring. The study area was Mahneshan county of Zanjan province in Iran with rich heavy metal mines that have more potential for plant contamination. At first, we collected different plant samples throughout the study area and measured the Pb and Cd concentrations using ICP-AES, in which we observed that the concentrations of Pb and Cd are in the range of 1.4 ~ 282.6 and 0.3 ~ 66.7 μgg-1, respectively, and then we tried to find the optimum estimator model through a multi-objective version of genetic algorithm (GA) optimization that finds simultaneously the structure of an artificial neural network and its input features. The features extracted from the raw spectrums have been collimated to be compatible with the Sentinel-2 multispectral bands for the possibility of further developments. The results demonstrate the efficiency of the optimum estimator model in estimation of the leaves' Pb and Cd contamination, irrespective of the plant type, which has reached the R2 of 0.99 and 0.85 for Pb and Cd, respectively. Additionally, the results suggested that the 783-, 842-, and 865-nm spectral bands, which are similar to the 7, 8, and 8a sentinel-2 spectral bands, are more efficient for this purpose.

Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping

Climate change and anthropopression significantly impact plant communities by leading to the spread of expansive and alien invasive plants, thus reducing their biodiversity. Due to significant elevation gradients, high-mountain plant communities in a small area allow for the monitoring of the most important environmental changes. Additionally, being a tourist attraction, they are exposed to direct human influence (e.g., trampling). Airborne hyperspectral remote sensing is one of the best data sources for vegetation mapping, but flight campaign costs limit the repeatability of surveys. A possible alternative approach is to use satellite data from the Copernicus Earth observation program. In our study, we compared multitemporal Sentinel-2 data with HySpex airborne hyperspectral images to map the plant communities on Tatra Mountains based on open-source R programing implementation of Random Forest and Support Vector Machine classifiers. As high-mountain ecosystems are adapted to topographic conditions, the input of Digital Elevation Model (DEM) derivatives on the classification accuracy was analyzed and the effect of the number of training pixels was tested to procure practical information for field campaign planning. For 13 classes (from rock scree communities and alpine grasslands to montane conifer and deciduous forests), we achieved results in the range of 76–90% F1-score depending on the data set. Topographic features: digital terrain model (DTM), normalized digital surface model (nDSM), and aspect and slope maps improved the accuracy of HySpex spectral images, transforming their minimum noise fraction (MNF) bands and Sentinel-2 data sets by 5–15% of the F1-score. Maps obtained on the basis of HySpex imagery (2 m; 430 bands) had a high similarity to maps obtained on the basis of multitemporal Sentinel-2 data (10 m; 132 bands; 11 acquisition dates), which was less than one percentage point for classifications based on 500–1000 pixels; for sets consisting of 50–100 pixels, Random Forest (RF) offered better accuracy.

Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve

Mountain forests are exposed to extreme conditions (e.g., strong winds and intense solar radiation) and various types of damage by insects such as bark beetles, which makes them very sensitive to climatic changes. Therefore, continuous monitoring is crucial, and remote-sensing techniques allow the monitoring of transboundary areas where a common policy is needed to protect and monitor the environment. In this study, we used Sentinel-2 and Landsat 8 open data to assess the forest stands classification of the UNESCO Krkonoše/Karkonosze Transboundary Biosphere Reserve, which is undergoing dynamic changes in recovering woodland vegetation due to an ecological disaster that led to damage and death of a large portion of the forests. Currently, in this protected area, dry big trunks and branches coexist with naturally occurring young forests. This heterogeneity generates mixes, which hinders the automation of classification. Thus, we used three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—to classify dominant tree species (birch, beech, larch and spruce). The best results were obtained for the SVM RBF classifier, which offered an average median F1-score that oscillated around 67.2–91.5% depending on the species. The obtained maps, which were based on multispectral satellite images, were also compared with classifications made for the same area on the basis of hyperspectral APEX imagery (288 spectral bands with three-meter resolution), indicating high convergence in the recognition of woody species.

Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

The study was focused on a plant native to Poland, the European dewberry Rubus caesius L., which is a species with the ability to become excessively abundant within its original range, potentially causing significant changes in ecosystems, including biodiversity loss. Monitoring plant distributions over large areas requires mapping that is fast, reliable, and repeatable. For Rubus, different types of data were successfully used for classification, but most of the studies used data with a very high spectral resolution. The aim of this study was to indicate, using hyperspectral and Light Detection and Ranging (LiDAR) data, the main functional trait crucial for R. caesius differentiation from non-Rubus. This analysis was carried out with consideration of the seasonal variability and different percentages of R. caesius in the vegetation patches. The analysis was based on hyperspectral HySpex images and Airborne Laser Scanning (ALS) products. Data were acquired during three campaigns: early summer, summer, and autumn. Differentiation based on Linear Discriminate Analysis (LDA) and Non-Parametric Multivariate Analysis of Variance (NPMANOVA) analysis was successful for each of the analysed campaigns using optical data, but the ALS data were less useful for identification. The analysis indicated that selected spectral ranges (VIS, red-edge, and parts of the NIR and possibly SWIR ranges) can be useful for differentiating R. caesius from non-Rubus. The most useful indices were ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR. The obtained results indicate that it is possible to classify R. caesius using images with lower spectral resolution than hyperspectral data.

The Photosynthetic Performance of Red Leaf Lettuce under UV-A Irradiation

The objective of this study was to evaluate how different UV-A wavelengths influence the morphology and photosynthetic behavior of red-leaf lettuce (Lactuca sativa L. cv. Maiko). In the experiments, the main photosynthetic photon flux consisted of red (R) and blue (B) light, supplemented with equal doses of different UV-A wavelengths (402, 387 and 367 nm). Treating the crops with low dosages of specific narrow-band UV-A radiation at key points in the life cycle initiated a cascade of responses in the above-ground biomass. According to the results, red-leaf lettuces acclimated to longer UV-A wavelengths by increasing biomass production, whereas different UV-A wavelengths had no significant effect on plant senescence reflectance, nor on the normalized difference vegetation index. A significant decrease in the maximum quantum yield of the PSII photochemistry of dark (Fv/Fm) and light (ΦPSII) adapted plants was observed. A lack of significant changes in non-photochemical fluorescence quenching indicates that photo-inhibition occurred under RBUV367, whereas the photosynthetic response under RB, RBUV402, and RBUV387 suggests that there was no damage to PSII. The correlation of the photosynthetic rate (Pr) with the stomatal conductance (gs) indicated that the increase in the Pr of lettuce under supplemental UV-A radiation was due to the increase of gs, instead of the ratio of the intracellular to ambient CO2 content (Ci/Ca) or stomatal limitations.

Multitemporal Hyperspectral Data Fusion with Topographic Indices—Improving Classification of Natura 2000 Grassland Habitats

Accurately identifying Natura 2000 habitat areas with the support of remote sensing techniques is becoming increasingly feasible. Various data types and methods are used for this purpose, and the fusion of data from various sensors and temporal periods (terms) within the phenological cycle allows natural habitats to be precisely identified. This research was aimed at selecting optimal datasets to classify three grassland Natura 2000 habitats (codes 6210, 6410 and 6510) in the Ostoja Nidziańska Natura 2000 site in Poland based on hyperspectral imagery and botanical on-ground reference data acquired in three terms during one vegetative period in 2017 (May, July and September), as well as a digital terrain model (DTM) obtained by airborne laser scanning (ALS). The classifications were carried out using a random forest (RF) algorithm on minimum noise fraction (MNF) transform output bands obtained for single terms, as well as data fusion combining the topographic indices (TOPO) calculated from the DTM, multitemporal hyperspectral data, or a combination of the two. The classification accuracy statistics were analysed in various combinations based on the datasets and their terms of acquisition. Topographic indices improved the classification accuracy of habitats 6210 and 6410, with the greatest impact noted in increased classification accuracy of xerothermic grasslands. The best terms for identifying specific habitats were autumn for 6510 and summer for 6210 and 6410, while the best results overall were obtained by combining data from all terms. The highest obtained values of the F1 coefficient were 84.5% for habitat 6210, 83.2% for habitat 6410, and 69.9% for habitat 6510. Comparing the data fusion results for habitats 6210 and 6410, greater accuracy was obtained by adding topographic indices to multitemporal hyperspectral data, while for habitat 6510, greater accuracy was obtained by fusing only multitemporal hyperspectral data.

Semiautomated Detection and Mapping of Vegetation Distribution in the Antarctic Environment Using Spatial-Spectral Characteristics of WorldView-2 Imagery

Effective monitoring of changes in the geographic distribution of cryospheric vegetation requires high-resolution and accurate baseline maps. The rationale of the present study is to compare multiple feature extraction approaches to remotely mapping vegetation in Antarctica, assessing which give the greatest accuracy and reproducibility relative to those currently available. This study provides precise, high-resolution, and refined baseline information on vegetation distribution as is required to enable future spatiotemporal change analyses of the vegetation in Antarctica. We designed and implemented a semiautomated customized normalized difference vegetation index (NDVI) approach for extracting cryospheric vegetation by incorporating very high resolution (VHR) 8-band WorldView-2 (WV-2) satellite data. The viability of state-of-the-art target detection, spectral processing/matching, and pixel-wise supervised classification feature extraction techniques are compared with the customized NDVI approach devised in this study. An extensive quantitative and comparative assessment was made by evaluating four semiautomatic feature extraction approaches consisting of 16 feature extraction standalone methods (four customized NDVI plus 12 existing methods) for mapping vegetation on Fisher Island and Stornes Peninsula in the Larsemann Hills, situated on continental east Antarctica. The results indicated that the customized NDVI approach achieved superior performance (average bias error ranged from ~6.44 ± 1.34% to ~11.55 ± 1.34%) and highest statistical stability in terms of performance when compared with existing feature extraction approaches. Overall, the accuracy analysis of the vegetation mapping relative to manually digitized reference data (supplemented by validation with ground truthing) indicated that the 16 semi-automatic mapping methods representing four general feature extraction approaches extracted vegetated area from Fisher Island and Stornes Peninsula totalling between 2.38 and 3.72 km2 (2.85 ± 0.10 km2 on average) with bias values ranging from 3.49 to 31.39% (average 12.81 ± 1.88%) and average root mean square error (RMSE) of 0.41 km2 (14.73 ± 1.88%). Further, the robustness of the analyses and results were endorsed by a cross-validation experiment conducted to map vegetation from the Schirmacher Oasis, East Antarctica. Based on the robust comparative analysis of these 16 methods, vegetation maps of the Larsemann Hills and Schirmacher Oasis were derived by ensemble merging of the five top-performing methods (Mixture Tuned Matched Filtering, Matched Filtering, Matched Filtering/Spectral Angle Mapper Ratio, NDVI-2, and NDVI-4). This study is the first of its kind to detect and map sparse and isolated vegetated patches (with smallest area of 0.25 m2) in East Antarctica using VHR data and to use ensemble merging of feature extraction methods, and provides access to an important indicator for environmental change.

Lead-Induced Changes in Fluorescence and Spectral Characteristics of Pea Leaves

Chlorophyll fluorescence parameters can provide useful indications of photosynthetic performance in vivo. Coupling appropriate fluorescence measurements with other noninvasive techniques, such as absorption spectroscopy or gas exchange, can provide insights into the limitations to photosynthesis under given conditions. Chlorophyll content is one of the dominant factors influencing the conditions of a vegetation growing season, and can be tested using both fluorescence and remote sensing methods. Hyperspectral remote sensing and recording the narrow range of the spectrum can be used to accurately analyze the parameters and properties of plants. The aim of this study was to analyze the influence of lead ions (Pb, 5 mM Pb(NO3)2) on the growth of pea plants using spectral properties. Hyperspectral remote sensing and chlorophyll fluorescence measurements were used to assess the physiological state of plants seedlings treated by lead ions during the experiment. The plants were growing in hydroponic cultures supplemented with Pb ions under various conditions (control, complete Knop + phosphorus (+P); complete Knop + phosphorus (+P) + Pb; Knop (-P) + Pb, distilled water + Pb) affecting lead uptake via the root system. Spectrometric measurements allowed us to calculate the remote sensing indices of vegetation, which were compared with chlorophyll and carotenoids content and fluorescence parameters. The lead contents in the leaves, roots, and stems were also analyzed. Spectral characteristics and vegetation properties were analyzed using statistical tests. We conclude that: (1) pea seedlings grown in complete Knop (with P) and in the presence of Pb ions were spectrally similar to the control plants because lead was not transported to the shoots of plants; (2) lead most influenced plants that were grown in water, according to the highest lead content in the leaves; and (3) the effects of lead on plant growth were confirmed by remote sensing indices, whereas fluorescence parameters identified physiological changes induced by Pb ions in the plants.

Remote Sensing of Explosives-Induced Stress in Plants: Hyperspectral Imaging Analysis for Remote Detection of Unexploded Threats

Explosives contaminate millions of hectares from various sources (partial detonations, improper storage, and release from production and transport) that can be life-threatening, e.g., landmines and unexploded ordnance. Exposure to and uptake of explosives can also negatively impact plant health, and these factors can be can be remotely sensed. Stress induction was remotely sensed via a whole-plant hyperspectral imaging system as two genotypes of Zea mays, a drought-susceptible hybrid and a drought-tolerant hybrid, and a forage Sorghum bicolor were grown in a greenhouse with one control group, one group maintained at 60% soil field capacity, and a third exposed to 250 mg kg−1 Royal Demolition Explosive (RDX). Green-Red Vegetation Index (GRVI), Photochemical Reflectance Index (PRI), Modified Red Edge Simple Ratio (MRESR), and Vogelmann Red Edge Index 1 (VREI1) were reduced due to presence of explosives. Principal component analyses of reflectance indices separated plants exposed to RDX from control and drought plants. Reflectance of Z. mays hybrids was increased from RDX in green and red wavelengths, while reduced in near-infrared wavelengths. Drought Z. mays reflectance was lower in green, red, and NIR regions. S. bicolor grown with RDX reflected more in green, red, and NIR wavelengths. The spectra and their derivatives will be beneficial for developing explosive-specific indices to accurately identify plants in contaminated soil. This study is the first to demonstrate potential to delineate subsurface explosives over large areas using remote sensing of vegetation with aerial-based hyperspectral systems.

In Situ Hyperspectral Remote Sensing for Monitoring of Alpine Trampled and Recultivated Species

Vegetation, through its condition, reflects the properties of the environment. Heterogeneous alpine ecosystems play a critical role in global monitoring systems, but due to low accessibility, cloudy conditions, and short vegetation periods, standard monitoring methods cannot be applied comprehensively. Hyperspectral tools offer a variety of methods based on narrow-band data, but before extrapolation to an airborne or satellite scale, they must be verified using plant biometrical variables. This study aims to assess the condition of alpine sward dominant species (Agrostis rupestris, Festuca picta, and Luzula alpino-pilosa) of the UNESCO Man&Biosphere Tatra National Park (TPN) where the high mountain grasslands are strongly influenced by tourists. Data were analyzed for trampled, reference, and recultivated polygons. The field-obtained hyperspectral properties were verified using ground measured photosynthetically active radiation, chlorophyll content, fluorescence, and evapotranspiration. Statistically significant changes in terms of cellular structures, chlorophyll, and water content in the canopy were detected. Lower values for the remote sensing indices were observed for trampled plants (about 10–15%). Species in recultivated areas were characterized by a similar, or sometimes improved, spectral properties than the reference polygons; confirmed by fluorescence measurements (Fv/Fm). Overall, the fluorescence analysis and remote sensing tools confirmed the suitability of such methods for monitoring species in remote mountain areas, and the general condition of these grasslands was determined as good.

Yield Estimates by a Two-Step Approach Using Hyperspectral Methods in Grasslands at High Latitudes

Ruminant fodder production in agricultural lands in latitudes above the Arctic Circle is constrained by short and hectic growing seasons with a 24-hour photoperiod and low growth temperatures. The use of remote sensing to measure crop production at high latitudes is hindered by intrinsic challenges, such as a low sun elevation angle and a coastal climate with high humidity, which influences the spectral signatures of the sampled vegetation. We used a portable spectrometer (ASD FieldSpec 3) to assess spectra of grass crops and found that when applying multivariate models to the hyperspectral datasets, results show significant predictability of yields (R2 > 0.55, root mean squared error (RMSE) < 180), even when captured under sub-optimal conditions. These results are consistent both in the full spectral range of the spectrometer (350–2500 nm) and in the 350–900 nm spectral range, which is a region more robust against air moisture. Sentinel-2A simulations resulted in moderately robust models that could be used in qualitative assessments of field productivity. In addition, simulation of the upcoming hyperspectral EnMap satellite bands showed its potential applicability to measure yields in northern latitudes both in the full spectral range of the satellite (420–2450 nm) with similar performance as the Sentinel-2A satellite and in the 420–900 nm range with a comparable reliability to the portable spectrometer. The combination of EnMap and Sentinel-2A to detect fields with low productivity and portable spectrometers to identify the fields or specific regions of fields with the lowest production can help optimize the management of fodder production in high latitudes.

Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data

Expansive species classification with remote sensing techniques offers great support for botanical field works aimed at detection of their distribution within areas of conservation value and assessment of the threat caused to natural habitats. Large number of spectral bands and high spatial resolution allows for identification of particular species. LiDAR (Light Detection and Ranging) data provide information about areas such as vegetation structure. Because the species differ in terms of features during the growing season, it is important to know when their spectral responses are unique in the background of the surrounding vegetation. The aim of the study was to identify two expansive grass species: Molinia caerulea and Calamagrostis epigejos in the Natura 2000 area in Poland depending on the period and dataset used. Field work was carried out during late spring, summer and early autumn, in parallel with remote sensing data acquisition. Airborne 1-m resolution HySpex images and LiDAR data were used. HySpex images were corrected geometrically and atmospherically before Minimum Noise Fraction (MNF) transformation and vegetation indices calculation. Based on a LiDAR point cloud generated Canopy Height Model, vegetation structure from discrete and full-waveform data and topographic indexes were generated. Classifications were performed using a Random Forest algorithm. The results show post-classification maps and their accuracies: Kappa value and F1 score being the harmonic mean of producer (PA) and user (UA) accuracy, calculated iteratively. Based on these accuracies and botanical knowledge, it was possible to assess the best identification date and dataset used for analysing both species. For M. caerulea the highest median Kappa was 0.85 (F1 = 0.89) in August and for C. epigejos 0.65 (F1 = 0.73) in September. For both species, adding discrete or full-waveform LiDAR data improved the results. We conclude that hyperspectral (HS) and LiDAR airborne data could be useful to identify grassland species encroaching into Natura 2000 habitats and for supporting their monitoring.

Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective

To meet a growing demand for accurate high-fidelity vegetation cover mapping in urban areas toward biodiversity conservation and assessing the impact of climate change, this paper proposes a complete approach to species and vitality classification at single tree level by synergistic use of multimodality 3D remote sensing data. So far, airborne laser scanning system(ALS or airborne LiDAR) has shown promising results in tree cover mapping for urban areas. This paper analyzes the potential of mobile laser scanning system/mobile mapping system (MLS/MMS)-based methods for recognition of urban plant species and characterization of growth conditions using ultra-dense LiDAR point clouds and provides an objective comparison with the ALS-based methods. Firstly, to solve the extremely intensive computational burden caused by the classification of ultra-dense MLS data, a new method for the semantic labeling of LiDAR data in the urban road environment is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. These priors encode geometric primitives found in the scene through sample consensus segmentation. Then, single trees are segmented from the labelled tree points using the 3D graph cuts algorithm. Multinomial logistic regression classifiers are used to determine the fine deciduous urban tree species of conversation concern and their growth vitality. Finally, the weight-of-evidence (WofE) based decision fusion method is applied to combine the probability outputs of classification results from the MLS and ALS data. The experiment results obtained in city road corridors demonstrated that point cloud data acquired from the airborne platform achieved even slightly better results in terms of tree detection rate, tree species and vitality classification accuracy, although the tree vitality distribution in the test site is less balanced compared to the species distribution. When combined with MLS data, overall accuracies of 78% and 74% for tree species and vitality classification can be achieved, which has improved by 5.7% and 4.64% respectively compared to the usage of airborne data only.

Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images

Knowledge of tree species composition is obligatory in forest management. Accurate tree species maps allow for detailed analysis of a forest ecosystem and its interactions with the environment. The research presented here focused on developing methods of tree species identification using aerial hyperspectral data. The research area is located in Southwestern Poland and covers the Karkonoski National Park (KNP), which was significantly damaged by acid rain and pest infestation in the 1980s. High-resolution (3.35 m) Airborne Prism Experiment (APEX) hyperspectral images (288 spectral bands in the range of 413 to 2440 nm) were used as a basis for tree species classification. Beech (Fagus sylvatica), birch (Betula pendula), alder (Alnus incana), larch (Larix decidua), pine (Pinus sylvestris), and spruce (Picea abies) were classified. The classification algorithm used was feed-forward multilayered perceptron (MLP) with a single hidden layer. To simulate such a network, we used the R programming environment and the nnet package. To provide more accurate measurement of accuracy, iterative accuracy assessment was performed. The final tree species maps cover the whole area of KNP; a median overall accuracy (OA) of 87% was achieved, with median producer accuracy (PA) for all classes exceeding 68%. The best-classified classes were spruce, beech, and birch, with median producer accuracy of 93%, 88% and 83%, respectively. The pine class achieved the lowest median producer and user accuracies (68% and 75%, respectively). The results show great potential for the use of hyperspectral data as a tool for identifying tree species locations in diverse mountainous forest.

A New Vegetation Index Based on Multitemporal Sentinel-2 Images for Discriminating Heavy Metal Stress Levels in Rice

Heavy metal stress in crops is a worldwide problem that requires accurate and timely monitoring. This study aimed to improve the accuracy of monitoring heavy metal stress levels in rice by using multiple Sentinel-2 images. The selected study areas are in Zhuzhou City, Hunan Province, China. Six Sentinel-2 images were acquired in 2017, and heavy metal concentrations in soil were measured. A novel vegetation index called heavy metal stress sensitive index (HMSSI) was proposed. HMSSI is the ratio between two red-edge spectral indices, namely the red-edge chlorophyll index (CI_red-edge) and the plant senescence reflectance index (PSRI). To demonstrate the capability of HMSSI, the performances of CI_red-edge and PSRI in discriminating heavy metal stress levels were compared with that of HMSSI at different growth stages. Random forest (RF) was used to establish a multitemporal monitoring model to detect heavy metal stress levels in rice based on HMSSI at different growth stages. Results show that HMSSI is more sensitive to heavy metal stress than CI_red-edge and PSRI at different growth stages. The performance of a multitemporal monitoring model combining the whole growth stage images was better than any other single growth stage in distinguishing heavy metal stress levels. Therefore, HMSSI can be regarded as an indicator for monitoring heavy metal stress levels with a multitemporal monitoring model.