Red Edge Position Research Articles

STRESS MONITORING OF MULBERRY PLANTS BY FINDING REP USING HYPERSPECTRAL DATA

Abstract. Stressed on crop can be monitored using different indices. Red Edge position is good estimator for stress monitoring. The red edge position (REP) is strongly correlated with foliar chlorophyll content. Strong chlorophyll absorption causes the abrupt change in the 680–800 nm region of reflectance spectra of green vegetation. The red edge consist the point of maximum slope between red and near-infrared wavelengths. REP can be used to recognize green zone of the observation area. The REP is present in spectra for vegetation recorded by remote sensing methods. REP is clearer and significant in hyper spectral data as hyper spectral consist of more and continuous bands data. In this paper experiments were carried out for mulberry crop using USGS EO-1 Hyperion data. Atmospheric corrected data is used for classification. Classification is carried out on small cluster of 14 field samples. Ground truth is verified and classified by comparing with Hyperion data. REP is different for different stressed condition of crops and shows healthy and diseased crop condition. Nutritional stresses, diseases, drought of plants are detected using REP. Stressed and healthy field of mulberry are estimated by calculating REP using maximum first derivative, linear interpolation, linear extrapolation method. Finally REP is compared using above methods. It is noticeable difference of REP for healthy and stressed crop. The research indicates that overall accuracy using maximum first derivative was 92.85 % and it was more compared to other methods. Linear extrapolation gives less accuracy compared to linear interpolation method.

Assessing and characterizing oilseed rape freezing injury based on MODIS and MERIS data

Abstract: The oilseed rape growing in the lower reaches of Yangtze River in China belongs to winter varieties and suffers the risk of freezing injury. In this research, a typical freezing injury event occurred in Anhui Province was taken as a case study, the freezing damage degree of oilseed rape was assessed, and its development characteristics based on the vegetation metrics derived from MODIS and MERIS data were investigated. The oilseed rape was mapped according to the decline of greenness from bud stage to full-bloom period, with the phenological phases identified adopting time-series analyses. NDVI was more sensitive to freezing injury compared with other commonly used vegetation indices (VIs) calculated using MODIS bands, e.g., EVI, GNDVI and SAVI. The freezing damage degree employing the difference between post-freeze growth and the baseline level in adjacent damage-free growing seasons was determined. The remote sensing-derived damage levels were supported by their correlation with the cold accumulated temperatures at the county level. The performance of several remote sensing indicators of plant biophysical and biochemical parameters was also investigated, i.e., the photosynthetic rate, canopy water status, canopy chlorophyll content, leaf area index (LAI) and the red edge position (REP), in response to the advance of the freezing damage. It was found that the photosynthetic rate indicator—Photochemical Reflectance Index (PRI) responded strongly to freezing stress. Freezing injury caused canopy water loss, which could be detected though the magnitude was not very large. MERIS-LAI showed a slow and lagging response to low temperature and restored rapidly in the recovery phase; additionally, REP and the indicator of canopy chlorophyll content—MERIS Terrestrial Chlorophyll Index (MTCI), did not appear to be influenced by freezing injury. It was concluded that the physiological functions, canopy structure, and organic content metrics showed a descending order of vulnerabilities to freezing injury. Keywords: Brassica napus, oilseed rape, freezing injury, crop monitoring, MODIS, MERIS DOI: 10.3965/j.ijabe.20171003.2721 Citation: She B, Huang J F, Zhang D Y, Huang L S. Assessing and characterizing oilseed rape freezing injury based on MODIS and MERIS data. Int J Agric & Biol Eng, 2017; 10(3): 143–157.

Deriving a light use efficiency estimation algorithm using insitu hyperspectral and eddy covariance measurements for a maize canopy in Northeast China.

We estimated the light use efficiency (LUE) via vegetation canopy chlorophyll content (CCC canopy) based on in situ measurements of spectral reflectance, biophysical characteristics, ecosystem CO 2 fluxes and micrometeorological factors over a maize canopy in Northeast China. The results showed that among the common chlorophyll‐related vegetation indices (VIs), CCC canopy had the most obviously exponential relationships with the red edge position (REP) (R 2 = .97, p < .001) and normalized difference vegetation index (NDVI) (R 2 = .91, p < .001). In a comparison of the indicating performances of NDVI, ratio vegetation index (RVI), wide dynamic range vegetation index (WDRVI), and 2‐band enhanced vegetation index (EVI2) when estimating CCC canopy using all of the possible combinations of two separate wavelengths in the range 400−1300 nm, EVI2 [1214, 1259] and EVI2 [726, 1248] were better indicators, with R 2 values of .92 and .90 (p < .001). Remotely monitoring LUE through estimating CCC canopy derived from field spectrometry data provided accurate prediction of midday gross primary productivity (GPP) in a rainfed maize agro‐ecosystem (R 2 = .95, p < .001). This study provides a new paradigm for monitoring vegetation GPP based on the combination of LUE models with plant physiological properties.

WREP: A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops

Red edge position (REP), defined as the wavelength of the inflexion point in the red edge region (680–760nm) of the reflectance spectrum, has been widely used to estimate foliar chlorophyll content from reflectance spectra. A number of techniques have been developed for REP extraction in the past three decades, but most of them require data-specific parameterization and the consistence of their performance from leaf to canopy levels remains poorly understood. In this study, we propose a new technique (WREP) to extract REPs based on the application of continuous wavelet transform to reflectance spectra. The REP is determined by the zero-crossing wavelength in the red edge region of a wavelet transformed spectrum for a number of scales of wavelet decomposition. The new technique is simple to implement and requires no parameterization from the user as long as continuous wavelet transforms are applied to reflectance spectra. Its performance was evaluated for estimating leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) of cereal crops (i.e. rice and wheat) and compared with traditional techniques including linear interpolation, linear extrapolation, polynomial fitting and inverted Gaussian.Our results demonstrated that WREP obtained the best estimation accuracy for both LCC and CCC as compared to traditional techniques. High scales of wavelet decomposition were favorable for the estimation of CCC and low scales for the estimation of LCC. The difference in optimal scale reveals the underlying mechanism of signature transfer from leaf to canopy levels. In addition, crop-specific models were required for the estimation of CCC over the full range. However, a common model could be built with the REPs extracted with Scale 5 of the WREP technique for wheat and rice crops when CCC was less than 2g/m2 (R2=0.73, RMSE=0.26g/m2). This insensitivity of WREP to crop type indicates the potential for aerial mapping of chlorophyll content between growth seasons of cereal crops. The new REP extraction technique provides us a new insight for understanding the spectral changes in the red edge region in response to chlorophyll variation from leaf to canopy levels.

Relationship between crop nutritional status, spectral measurements and Sentinel 2 images

In order to monitor the nutritional status of some crops based on plant spectroscopy and Sentinel 2 satellite images in Colombia, spectral reflectance data were taken between 350 and 2,500 nm with a FieldSpec 4 spectrometer in rubber, rice, sugar cane, maize, soybean, cashew, oil palm crops, pastures and natural savanna. Furthermore contents of mineral nutrients in leaves were determined. Several vegetation indexes and red edge positions were calculated using various methods from spectral data and Sentinel 2 satellite images and were correlated with leaf nutrient content. The results showed correlations between spectral indices, mainly those involving a spectral response in the red-edge range with the N, P, K and Cu although the best correlation coefficients were for N. First reflectance derivatives, transformations by the State Normal Variate and second reflectance derivatives showed great potential to monitor N content in crops. The green model index and the red-edge model computed from Sentinel 2 images had the best performance to monitor N content, although in the study area, presence of clouds affected the use of these images. The Sentinel 2 images allowed calculating some vegetation indexes obtained with other images, such as Landsat or SPOT, but additionally other indexes and calculations based on the bands of the red-edge, which is a great contribution to obtain more information of crops on their spatial and temporal variability.

A new narrow band vegetation index for characterizing the degree of vegetation stress due to copper: the copper stress vegetation index (CSVI)

ABSTRACTThis study proposed a new narrow band index to characterize the Cu (Copper) stress degree on vegetation (Copper Stress Vegetation Index, CSVI). Firstly, the spectral reflectance and biochemical data of wheat, pea, locust and ash were analysed using Pearson correlation coefficient (r) to select wavelengths sensitive to Cu stress. The calculated Pearson correlation coefficients suggested that the reflectance near 550 nm and 700 nm correlated positively with Cu contents in leaves and solutions, and negative correlation was present in the range of 800–900 nm. Secondly, the selected wavelengths of 550 nm, 700 nm, and 850 nm were used to establish CSVI, and it was compared with existing popular vegetation indices (VIs) related to heavy metal stress (Normalized Difference Vegetation Index (NDVI), Red-Edge Position (REP), Difference Vegetation Index (DVI), Photochemical Reflectance Index (PRI)) by calculating Pearson correlation coefficient between VIs and Cu contents in leaves and solutions. Thirdly, verifications of CSVI on other vegetations were conducted, and the performance of CSVI was also compared with that of NDVI, REP, DVI, and PRI. The results suggested that CSVI showed significant correlation with Cu stress degree, and the correlation of CSVI was much stronger than that of other VIs for all the tested vegetations. The proposed CSVI characterizes the Cu stress degree on vegetation with advantages of better effectiveness, straightforward calculation, and robustness for different vegetations. This study focused on the spectral reflectance at the leaf scale, so it is expected that future work extends it to canopy scale and mixed-pixel scale.

Using Red Edge Position Shift to Monitor Grassland Grazing Intensity in Inner Mongolia

Red edge parameters including wavelength, amplitude and area of the red edge peak are important remote sensing-based descriptors of vegetation which have been widely used for evaluating plant chlorophyll content, biomass and even vegetation stress. This paper investigates the possibility of monitoring grazing intensity based on red edge position (REP) shift. At an experimental site located in the Inner Mongolia Grassland Research Station (E116°42′, N43°38′), five methods were used to extract REP, which ranged from 703 to 725 nm depending on the extraction method chosen and the degree of grazing intensity (GI). The results showed that grazing can cause REP to shift toward shorter wavelengths, and the amplitude of the shift is related to GI. The likely reason for the REP shift is the “green loss” of grass leaf caused by the grazer. Based on the use of broadband satellite remote sensing data, linear extrapolation is recommended as a relatively optimum method for extracting REP.

Assessment of Red-Edge Position Extraction Techniques: A Case Study for Norway Spruce Forests Using HyMap and Simulated Sentinel-2 Data

Systematic quantification and monitoring of forest biophysical and biochemical variables is required to assess the response of ecosystems to climate change and gain a deeper understanding of the carbon cycle. Red-Edge Position (REP) is a hyperspectrally detectable parameter, which is sensitive to Chlorophyll (Chl) content. In the current study, REP was modelled for Norway spruce Forest canopy Reflectance and Transmittance (FRT) using Radiative Transfer Modelling (RTM) (resampled to HyMap and Sentinel-2 spectral resolution) as well as calculated from the real HyMap and simulated Sentinel-2 image data. Different REP extraction methods (PF, LE, 4PLI and its optimized versions for HyMap and Sentinel-2 spectral resolution) were assessed. The lowest differences in REP values calculated from image-extracted spectra and from the theoretical RTM simulations were found for the 4PLI method including its HyMap and Sentinel-2 optimized versions (4PLIH and 4PLIS). Despite its simplicity, the 4PLI REP extraction technique demonstrated its potential usefulness for estimating canopy chlorophyll (Chl × LAI) content using both airborne hyperspectral (HyMap) data as well as space-borne Sentinel-2 image data.

Analysis of the correlation between the red EDGE vegetation indices and the gross primary productivity of winter wheat crop according to gas and spectrometric measurements in Baryshevsky district of Kiev region

The results of studies of the correlation between the red edge vegetation indices, defined from the ground spectrometric survey, and gross primary productivity of winter wheat crops in Baryshevsky district of Kiev region in 2015, which was determined using gasometric measurements of CO2 fluxes over vegetation cover are presented. In this work the calculations and analysis of following vegetation indices of red edge were carried out: the red edge position (REP), MERIS Terrestrial Chlorophyll Index (TCI), a modified red Edge Normalised Difference Index NDVI705, as well as canopy chlorophyll index (CCI) and the index of Double Difference (DD). According the results of the research the index REP showed the better correlation with gross primary productivity of vegetation cover for the studied sample of vegetation indices (correlation coefficient using the formula Pearson at r = 0.68). To build a more accurate statistical models and valid conclusions it is advisable to carry out further research of this issue. The results will be used for evaluation of the cropland productivity and for determining of the balance of CO2 fluxes over vegetation cover based on satellite data, which include the red edge bands (RapidEye, Sentinel-2 and so on).

The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: a simulation study

ABSTRACTIn this study we use ground reference data from 962 forest plots to demonstrate the potential of Sentinel-2 (S2) bands in estimating canopy biophysical properties in boreal forests in Finland. We simulated canopy bidirectional reflectance factors (BRFs) using the PARAS model, which applies photon recollision probability. Results showed that the highest correlation between simulated S2 BRFs and fraction of absorbed photosynthetically active radiation (fPAR) was for the band combination band 7/band 9 (wavelengths 773–793 nm and 935–955 nm, respectively) (the coefficient of determination (R2) was 0.93). For effective leaf area index (LAIe) the best band combination was band 8/band 4 (wavelengths 785–900 nm and 650–680 nm, respectively) (R2 = 0.93). Based on this study, the above-ground biomass (AGB) and S2 band combinations did not show strong relationships (R2 = 0.24). The new inverted red-edge chlorophyll index (IRECI) and Sentinel-2 red-edge position – index (S2REP) showed moderate relationships with fPAR (R2 = 0.61 and R2 = 0.45, respectively) and LAIe (R2 = 0.56 and R2 = 0.30, respectively). This study demonstrated the potential of the S2 data to estimate canopy biophysical properties.

Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice

This study systematically analyzed the performance of multivariate hyperspectral vegetation indices of rice (Oryza sativa L.) in estimating the arsenic content in agricultural soils. Field canopy reflectance spectra was obtained in the jointing-booting growth stage of rice. Newly developed and published multivariate vegetation indices were initially calculated to estimate soil arsenic content. The well-performing vegetation indices were then selected using successive projections algorithm (SPA), and the SPA selected vegetation indices were adopted to calibrate a multiple linear regression model for estimating soil arsenic content. Results showed that a three-band vegetation index (R716−R568)/(R552−R568) performed best in the newly developed vegetation indices in estimating soil arsenic content. The photochemical reflectance index (PRI) and red edge position (REP) performed well in the published vegetation indices. Moreover, the linear combination of two vegetation indices ((R716−R568)/(R552−R568) and REP) selected using SPA improved the estimation of soil arsenic content. These results indicated that the newly developed three-band vegetation index (R716−R568)/(R552−R568) might be recommended as an indicator for estimating soil arsenic content in the study area. PRI and REP could be used as universal vegetation indices for monitoring soil arsenic contamination.

Extraction of REPs from leaves reflectance spectrum for estimation of chlorophyll content

Abstract: The red edge parameters which include red edge amplitude, red edge position, red edge area and the reflectance of red edge position can be used as an indicator of stress and senescence of vegetation. Among these parameters, the red edge position (REP) is the best one to estimate tomato chlorophyll content according to statistical analysis. The REP is defined as the point of maximum slope at the 680 ~ 740nm region in a vegetation reflectance spectrum. In this research, the 6 algorithms could be used to extract the REP, and the quantitative relationships between REP on various algorithms and leaf chlorophyll status were analyzed, and then the prediction models of the chlorophyll content for each REP extraction algorithm were developed using Linear regression, Logarithmic regression, Power regression, Exponential regression and Quadratic polynomial regression. The result showed that the Logarithmic model of the Linear Extrapolation had the best accuracy and reliability. The calibration R-Square was 0.62 and the validation R-Square was 0.77 and the root mean squared error (RMSE) was 8.36.

Research of foliar dust content estimation by reflectance spectroscopy of Euonymus japonicus Thunb

To seek effective protective measures for fog and haze weather to establish an inversion model for leaf dust content based on spectrum data, 120 leaf samples of Euonymus japonicus were collected in 12 regions with uniform distribution in Beijing City. The variation characteristics of reflectance spectra of E. japonicus and foliar dust content were noted, and the differences of leaf reflectance and first derivative spectrum between leaves with and without dust were analyzed. The correlation between foliar dust content and leaf spectral reflectance was established. The results suggest that foliar dust increased the reflectance in specific ranges of the visible light spectrum, decreased the reflectance at near infrared regions, and had no change in red edge position, blue edge slope, yellow and blue edge area. Furthermore, the yellow edge position and yellow edge slope were decreased, the blue edge position and red edge slope and red edge area were increased. This research calculated leaf surface dust content inversion models, with the model fitting only within spectral parameters and foliar dust content. This study led to the discovery that the fitting index of the area of red edge as a variable is relatively high, and grading the foliar dust content to obtain a variety of model fitting with four spectral parameters enabled the conclusion that model (2) is the final relatively good inversion model of E. japonicus foliar dust content.“Project supported by Central Public Welfare Research Institute of basic research funds (CAFYBB2014MA006)”.

The dynamic simulation of rice growth parameters under cadmium stress with the assimilation of multi-period spectral indices and crop model

Obtaining precise information regarding the levels of heavy metal stress in crops is vital for food security, agricultural production, and ecological protection. In this study, we realized the dynamic simulation of rice growth parameters in three experiment fields that were exposed to varying levels of soil Cd (cadmium) concentration, intending to monitor the stress-induced changes of growth parameters on time scale. To simulate the growth parameters Leaf Area Index (LAI), Weight of Storage Organs (WSO) and Total Above Ground Production (TAGP) more accurately, we imbedded a Cd stress factor fCd into the initial World Food Study (WOFOST) model. Then, as the spectral sensing technology is a potentially promising method to monitor crop stress conditions, an optimized methodology of assimilating multi-period spectral indices into the coupled WOFOST+PROSPECT+SAIL model was adopted to obtain the optimum value of stress factor; next, the dynamic simulation of growth parameters was adjusted. Particularly, based on the specific sensibility to contamination levels at different growth stages, TCARI (Transformed Chlorophyll Absorption in Reflectance Index), REP (Red Edge Position) and RH (Reflectance Height) were selected as the multi-period spectral indices, serving as the compared targets of the cost function in the process of assimilation. The growth parameters simulated by the modified WOFOST model preferably reflected the variations of rice growth status on a time scale with R2 over 94% at all of the three levels. This study indicates that the optimized methodology of assimilating multi-period spectral indices into the crop model is applicable for simulating growth parameters under Cd stress, which provides a reference for dynamically monitoring heavy metal contamination in farmland environments.

Monitoring of bacterial leaf blight in rice using ground-based hyperspectral and LISS IV satellite data in Kurnool, Andhra Pradesh, India

Bacterial leaf blight (BLB) is one of the most common diseases of rice in India, which may lead to partial or total crop loss based on the time of infestation. Hence, on-time identification of disease and its distribution over the affected region could provide useful information for minimizing the crop loss. In the present study, a comprehensive approach has been adopted to monitor the BLB-affected rice crop using hyperspectral data at filed scale and to upscale the observations at village-level using multispectral satellite data. The spectro-radiometer data at 350–2500 nm wavelength range was collected, along with other crop parameters, viz. chlorophyll and moisture content. The step-wise discriminate analysis (SDA) revealed that only four wavebands, i.e. 760, 990, 680 and 540 nm, could significantly discriminate diseased crop from healthy one. The selected wavebands were used to compute 12 narrowband vegetation indices, whereas according to SDA plant senescence index (PSRI), pigment-specific simple ratio (PSSRb) and red-edge position were only found to be effective. PSRI and PSSRb could be successfully re-computed using resolution simulation of hyperspectral data corresponding to linear imaging self-scanner (LISS) IV sensor. The equations were deployed on LISS IV satellite data to generate geospatial maps of PSRI and PPSRb. The geospatial maps could differentiate different degrees of stressed crop very effectively. Hence, the proposed approach can be adopted for in-season monitoring and assessment of diseased crops at regional level for better agricultural planning and management.

Chlorophyll content in Phyllostachys violascens related to hyper-spectral vegetation indices and development of an inversion model

Reflectance data and relative chlorophyll content for Phyllostachys violascens at leaf scale were measured during the growth period from April, 5th to June, 18th using a portable Analytical Spectral Devices （ASD） field spectrometer and a hand-held Chlorophyll Content Meter （CCM）-200. Correlation analyses were conducted between hyper-spectral vegetation indices and chlorophyll content based on the data. Then individual univariate linear inversion models were developed for chlorophyll content and hyper-spectral vegetation indices, such as red edge indexes GM, Vog3, double difference index DD, modified normalized differential vegetation index mND705, modified simple ratio mSR705, and Red-edge positions （REP）. Also multivariate linear models for selected hyper-spectral vegetation indices and chlorophyll content were tested. Multivariate linear models are designed in two methods, strategy A is based on the 20 Phyllostachys violascens samples, and each data for the sample is the average for all the 14 times. On the contrary, strategy B is based on the data of 14 times, which average the 20 samples for each time. Results over the entire growth period showed （1） significant （P＜0.01） correlations between chlorophyll content and hyper-spectral vegetation indices, GM （r = 0.866 3）, Vog3 （r = 0.927 4 ）, DD （r = 0.880 6）, mND705 （r = 0.917 9）, mSR705 （r = 0.924 9）, and REP （r =0.895 4）. At the end of the growth period, all vegetation indices had a favorable relationship with chlorophyll content, showing as the high correlation coefficients, although some indexes perform bad in most other time periods；（2） Using the univariate linear model, correlation for hyper-spectral vegetation indices and chlorophyll content showed r ＞ 0.85. The multivariate linear models of the six hyper-spectral vegetation indices listed above and chlorophyll content using two strategies, both accurately predicted chlorophyll content of Phyllostachys violascens ［with correlation coefficients between predicted values and measured values that were all above r = 0.89］. The multivariate linear models can be used to predicte chlorophyll content in the leaf of Phyllostachys violascens. Considering the calculate method, strategy B is more fit for the dynamic change of chlorophyll content for Phyllostachys violascens at leaf scale. ［Ch, 4 fig. 2 tab. 45 ref.］

A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

Abstract. Water deficits can cause chlorophyll degradation which decreases the total concentration of chlorophyll a and b (Chls). Few studies have investigated the effectiveness of spectral indices under water-stressed conditions. Chlorophyll meters have been extensively used for a wide variety of leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter works by sensing leaves absorptance and transmittance, the reading of chlorophyll concentration will be affected by changes in transmittance as if there were a water deficit in the leaves. The overall objective of this paper was to develop a novel and reliable reflectance-based model for estimating Chls of fresh and water-stressed leaves using the reflectance at the absorption bands of chlorophyll a and b and the red edge spectrum. Three independent experiments were designed to collect data from three leaf sample sets for the construction and validation of Chls estimation models. First, a reflectance experiment was conducted to collect foliar Chls and reflectance of leaves with varying water stress using the ASD FieldSpec spectroradiometer. Second, a chlorophyll meter (SPAD-502) experiment was carried out to collect foliar Chls and meter readings. These two data sets were separately used for developing reflectance-based or absorptance-based Chls estimation models using linear and nonlinear regression analysis. Suitable models were suggested mainly based on the coefficient of determination (R2). Finally, an experiment was conducted to collect the third data set for the validation of Chls models using the root mean squared error (RMSE) and the mean absolute error (MAE). In all of the experiments, the observations (real values) of the foliar Chls were extracted from acetone solution and determined by using a Hitachi U-2000 spectrophotometer. The spectral indices in the form of reflectance ratio/difference/slope derived from the Chl b absorption bands (ρ645 and ρ455) provided Chls estimates with RMSE around 0.40–0.55 mg g−1 for both fresh and water-stressed samples. We improved Chls prediction accuracy by incorporating the reflectance at red edge position (ρREP) in regression models. An effective chlorophyll indicator with the form of (ρ645–ρ455)/ρREP proved to be the most accurate and stable predictor for foliar Chls concentration. This model was derived with an R2 of 0.90 (P &lt; 0.01) from the training samples and evaluated with RMSE 0.35 and 0.38 mg g−1 for the validation samples of fresh and water-stressed leaves, respectively. The average prediction error was within 14% of the mean absolute error.

Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China

Evaluating vegetation phenology is crucial for a better understanding of the effects of climate change on the terrestrial ecosystem. The scientific community has used various vegetation index data sets from different sensors to quantify vegetation phenology from regional to global scales. The normalized difference vegetation index (NDVI) related to photosynthetic activities is the most widely used index. Recently, a number of published articles have used the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) to measure vegetation phenology. MTCI can closely represent the red-edge position (REP). Unlike NDVI, MTCI is more sensitive to high values of chlorophyll content. However, the consistency of vegetation phenological metrics derived from MTCI and NDVI needs to be further explored. This study compared two phenological metrics, i.e. onset of greenness (OG) and end of senescence (ES), extracted from MERIS MTCI data and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) first generation NDVI (NDVIg) data, which has the longest time records, at nine regions in China from 2003 to 2006. The results showed that the differences of OG and ES vary between different vegetation types, regions, and years, although both NDVI and MTCI time series capture the growth patterns well for most vegetation types. Compared to ES, the OG estimates are more consistent. NDVI yields in general later ES estimates than MTCI.

Evaluating the feasibility of multitemporal hyperspectral remote sensing for monitoring bioremediation

In recent years, several studies focused on the detection of hydrocarbon pollution in the environment using hyperspectral remote sensing. Particularly the indirect detection of hydrocarbon pollution, using vegetation reflectance in the red edge region, has been studied extensively. Bioremediation is one of the methods that can be applied to clean up polluted sites. So far, there have been no studies on monitoring of bioremediation using (hyperspectral) remote sensing. This study evaluates the feasibility of hyperspectral remote sensing for monitoring the effect of bioremediation over time. Benzene leakage at connection points along a pipeline was monitored by comparing the red edge position (REP) in 2005 and 2008 using HyMap airborne hyperspectral images. REP values were normalized in order to enhance local variations caused by a change in benzene concentrations. 11 out of 17 locations were classified correctly as remediated, still polluted, or still clean, with a total accuracy of 65%. When only polluted locations that were remediated were taken into account, the (user's) accuracy was 71%.

RESEARCH ОF HYPERSPECTRAL RED EDGE INDІCES FOR VEGETATION COVER CHANGE DETECTION OVER THE OIL FIELD USING SPECTROMETRIC SURVEY DATA

The variations of hyperspectral vegetation indices such as red edge position, MERIS terrestrial chlorophyll (TCI) and modified normalized difference vegetation index (NDVI705) using terrestrial cover spectrometry data over the Pryozerne oil field (Crimea) by the spectroradiometer FieldSpec® 3 FR to receive the searching indices are studied. As far as the points with the vegetation cover there are the limitation for NDVI705 ≥ 0.169 that distinguishes them from the other points in case of heterogeneous terrestrial cover. The lower magnitudes for all indices of interest relative to their background values out of field boundaries are observed for all points with vegetation cover over the oil field excepting the points near the oil boreholes. The strong correlation between REP and NDVI705 (r = 0.87) as well as TCI and NDVI705 (r = 0.88) and decreasing their values over the field shows the possibility of every index to respond to the changes of vegetation cover over hydrocarbon fields and give the informative index values in the prospecting goals only for the areas with the proper limitations of NDVI705.