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Plant leaves, as a vital organ of the plant photosynthesis, directly reflects the plant growth and nutrition status. Internal physicochemical property model, based on the reflection and transmission of plant leaf, indirectly reflects the information of matter and energy exchange in the process of plant growth information, which can be the premise and foundation of the plant growth process of fine management. The optical properties of plant leaves plays a great significant role in the field of crop nutrition diagnosis and plant morphological structure based on remote sensing, in the field of virtual plant light transmission simulation and computer graphics et al. BRDF (Bidirectional Reflectance Distribution Function) mainly focused on spatial distribution and spectral characteristics of the reflected light on the surface of the object characteristics. It can characterize crop growth parameters accurately and efficiently by acquiring optical properties information of plant leaves, which also has great advantages in the research and application of remote sensing in agriculture. In order to better apply BRDF technology in the agricultural remote sensing, this paper will focus on BRDF measurement and device, model development and classification and its application in the field of plants remote sensing. Eventually, although BRDF technology is still in early exploration stage and is facing many tough challenges, it has a promising future in terms of digital agriculture development.

Hyperspectral imaging feature on potato leaves stressed by late blight was studied in the present paper. The experiment used 60 potato leaves. Among those 60 potato leaves, 48 leaves were vitro inoculated with pathogen of potato late blight, the rest 12 leaves were used as control samples. The leaves were observed for 7 continuous days before and after inoculated and samples including healthy and infested were acquired. Hyperspectral data of healthy and infected potato samples of different disease severity were obtained by the hyperspectral imaging system from 374 to 1,018 nm and then extract spectral data of region of interest (ROI) from those hyperspectral data by the ENVI software. In order to improve the signal-to-noise ratio, the spectral data were preprocessed using different pretreatment methods such as moving average smoothing, normalization, derivative, baseline etc. The least squares-support vector machine(LS-SVM) models were developed based on the raw and those preprocessed data. Among the nine models, the model that used the raw data and the data after the spectroscopic transformation performed best with the discrimination of 94.87%. It was demonstrated that it is realized to determine the potato late blight disease of different disease severity using hyperspectral imaging technique.

Determination of color values on tomato leaves stressed by the high temperature using hyperspectral imaging technique was studied in this paper. Hyperspectral images of sixty healthy and sixty unhealthy tomato leaves in the wavelengths of 380-1023 nm were acquired by the hyperspectral imaging system. Simultaneously, three color parameters (L*, a* and b*) were measured by a colorimeter. Reflectance of all pixels in the region of interest (ROI) was extracted from the corrected hyperspectral image. Partial Least Squares (PLS) models were established based on different preprocessing methods. Successive Projections Algorithm (SPA) was identified to select effective wavelengths. Finally, Partial Least Squares-Discriminant Analysis (PLS-DA) models were built to classify different types of samples. The results showed that the determination coefficient (R²) were 0. 818, 0. 109 and 0. 896 in the prediction sets of PLS modes; 0.591, 0.244 and 0.673 in the prediction sets of SPA-PLS models. The overall classification accuracy in the prediction sets of PLS-DA models were over 77.50%. It demonstrated that it is feasible to measure color values on tomato leaves and identify different types of samples using hyperspectral imaging technique.

Visual and Near-infrared (VIS-NIR) reflectance spectroscopy had been used widely in monitoring agricultural pollution in recent years, however, it was rarely applied in monitoring the contamination of heavy metal in orchards. In the present paper, Newhall navel orange (Citrus sinensis [L.] Osbeck cv. Newhall) were cultivated in the potted soil contaminated with cadmium (Cd) at different levels, and the spectral reflectance and Cd content in the leaves were measured simultaneously at different growing seasons, which then were used to establish the prediction model by partial least squares regression (PLSR) based on spectral reflectance and by linear regression based on spectral index. The results showed that Cd was more easily transferred to and cumulated in the new leaves, and this phenomenon was more obvious in heavily contaminated soils with Cd. Blue shift in red edge was found in the band of 700-730 nm in the new leaves, however, no such phenomenon was found in the old leaves. The coefficient of determination (R²) of linear regression model based on spectral index was nearly 0. 8, while the PLSR model had a better result in predicting Cd content in the new leaves than the linear regression with R²CV of approximately 0.9. Furthermore, the standard normal variate transformation(SNV) in spectral preprocessing can improve the precision significantly in PLSR model. These results suggest that the VIS-NIR method has a great potential in monitoring heavy metal pollution in the navel orange.

Aiming at SPAD values of living plant leaf chlorophyll content affected easily by the blade thickness, water content, etc, a fine retrieval method of chlorophyll content based on multiple parameters of neural network model is presented. The SPAD values and water index (WI) of leaves were obtained by the leaf transmittance under the irradiation of light central wavelength in 650 nm, 940 nm, 1450 nm respectively. Meanwhile, the corresponding blade thickness is got by micrometer and the chlorophyll content is measured by spectrophotometric method. To modeling samples, the single parameter model between SPAD values and chlorophyll content was built and the nonlinear model between WI, thickness, SPAD values and chlorophyll content was established based on BP neural network. The predicted value of chlorophyll content of test samples were calculated separately by two models, and the correlation and relative errors were analyzed between predicted values and actual values. 340 samples of three different plant leaves were tested by the method described above in experiment. The results showed that compared with single parameter model, the prediction accuracy of three different plant samples were improved in different degrees, the average absolute relative error of chlorophyll content of all pooled samples predicted by BP neural network model reduced from 7.55% to 5.22%. The fitting determination coefficient is increased from 0.83 to 0.93. The feasibility were verified in this paper that the prediction accuracy of living plant chlorophyll content can improved effectively using multiple parameter BP neural network model.

Existing methods for the identification of pummelo cultivars are usually time-consuming and costly, and are therefore inconvenient to be used in cases that a rapid identification is needed. This research was aimed at identifying different pummelo cultivars by hyperspectral imaging technology which can achieve a rapid and highly sensitive measurement. A total of 240 leaf samples, 60 for each of the four cultivars were investigated. Samples were divided into two groups such as calibration set (48 samples of each cultivar) and validation set (12 samples of each cultivar) by a Kennard-Stone-based algorithm. Hyperspectral images of both adaxial and abaxial surfaces of each leaf were obtained, and were segmented into a region of interest (ROI) using a simple threshold. Spectra of leaf samples were extracted from ROI. To remove the absolute noises of the spectra, only the date of spectral range 400~1000 nm was used for analysis. Multiplicative scatter correction (MSC) and standard normal variable (SNV) were utilized for data preprocessing. Principal component analysis (PCA) was used to extract the best principal components, and successive projections algorithm (SPA) was used to extract the effective wavelengths. Least squares support vector machine (LS-SVM) was used to obtain the discrimination model of the four different pummelo cultivars. To find out the optimal values of σ2 and γ which were important parameters in LS-SVM modeling, Grid-search technique and Cross-Validation were applied. The first 10 and 11 principal components were extracted by PCA for the hyperspectral data of adaxial surface and abaxial surface, respectively. There were 31 and 21 effective wavelengths selected by SPA based on the hyperspectral data of adaxial surface and abaxial surface, respectively. The best principal components and the effective wavelengths were used as inputs of LS-SVM models, and then the PCA-LS-SVM model and the SPA-LS-SVM model were built. The results showed that 99.46% and 98.44% of identification accuracy was achieved in the calibration set for the PCA-LS-SVM model and the SPA-LS-SVM model, respectively, and a 95.83% of identification accuracy was achieved in the validation set for both the PCA-LS-SVM and the SPA- LS-SVM models, which were built based on the hyperspectral data of adaxial surface. Comparatively, the results of the PCA-LS-SVM and the SPA-LS-SVM models built based on the hyperspectral data of abaxial surface both achieved identification accuracies of 100% for both calibration set and validation set. The overall results demonstrated that use of hyperspectral data of adaxial and abaxial leaf surfaces coupled with the use of PCA-LS-SVM and the SPA-LS-SVM could achieve an accurate identification of pummelo cultivars. It was feasible to use hyperspectral imaging technology to identify different pummelo cultivars, and hyperspectral imaging technology provided an alternate way of rapid identification of pummelo cultivars. Moreover, the results in this paper demonstrated that the data from the abaxial surface of leaf was more sensitive in identifying pummelo cultivars. This study provided a new method for to the fast discrimination of pummelo cultivars.

In order to improve the accuracy of wheat yellow rust disease severity using remote sensing and to find the optimum inversion model of wheat diseases, the canopy reflectance and disease index (DI) of winter wheat under different severity stripe rust were acquired. The three models of PLS (Partial Least Square), BP neural network using seven hyperspectral vegetation indices which have significant relationship with the occurrence of disease and vegetation index (PRI) were adopted to build a feasible regression model for detecting the disease severity. The results showed that PLS performed much better. The inversion accuracy of PLS method is best than of the VI (PRI, Photochemical Reflectance Index) and BP neural network models. The coefficients of determination (R2) of three methods to estimate disease severity between predicted and measured values are 0.936, 0.918 and 0.767 respectively. Evaluation was made between the estimated DI and the measured DI, indicating that the model based on PLS is suitable for monitoring wheat disease. In addition, to explore the different contributions of diverse types of vegetation index to the models, the paper attempts to use NDVI, GNDVI and MSR which on behalf of vegetation greenness and NDWI and MSI that represents the moisture content to be input variables of PLS model. The results showed that, for the wheat yellow rust disease, changes in chlorophyll content is more sensitive to the disease severity than the changes in water content of the canopy. However, the accuracy of the two models are both lower than predicted when participating in all seven vegetation indices, namely using several species of vegetation indices tends to be more accurate than that using single category. It indicated that it has great potential for evaluating wheat disease severity by using hyper-spectral remote sensing.

Leaf water content is a fundamental physiological characteristic parameter of crops, and plays an important role in the study of the ecological environment. The aim of the work reported in this paper was to focus upon the retrieval of leaf water content from leaf-scale reflectance spectra by developing a physical inversion model based on the radiative transfer theory and wavelet analysis techniques. A continuous wavelet transform was performed on each of leaf component specific absorption coefficients to pick wavelet coefficients that were identified as highly sensitive to leaf water content and insensitive to other components. In the present study, for identifying the most appropriate wavelet, the six frequently used wavelet functions available within MATLAB were tested. Two biorl. 5 wavelet coefficients observed at the scale of 200 nm are provided with good performance, their wave-length positions are located at 1 405 and 1 488 nm, respectively. Two factors (α and Δ) of the predictive theoretical models based on the biorl. 5 wavelet coefficients of the leaf-scale reflectance spectra were determined by leaf structure parameter N. We built a database composed of thousands of simulated leaf reflectance spectra with the PROSPECT model. The entire dataset was split into two parts, with 60% the calibration subset assigned to calibrating two factors (α and Δ) of the predictive theoretical model. The remaining 40% the validation subset combined with the LOPEX93 experimental dataset used for validating the models. The results showed that the accuracy of the models compare to the statistical regression models derived from the traditional vegetation indices has improved with the highest predictive coefficient of determination (R2) of 0. 987, and the model becomes more robust. This study presented that wavelet analysis has the potential to capture much more of the information contained with reflectance spectra than previous analytical approaches which have tended to focus on using a small number of optimal wavebands while discarding the majority of the spectrum.

In the present paper we discussed the methods of classification of citrus greening and extraction of spectral features based on the spectral reflectance of four different statuses of citrus leaves (healthy, HLB, iron deficiency and nitrogen deficiency). Between two classes of classification, the values of discriminability of different spectra were calculated to extract spectral features. The greater value of discriminability showed a bigger difference of the two spectra, which means it would be easier to distinguish the two classes. By the Fisher linear discriminant analysis, three classification models (HLB & healthy, HLB & iron deficiency and HLB & nitrogen deficiency) based on the spectral features yielded more than 90% accuracies, which were better than expected. And at last, we discussed the application of the classification tree in multi-class discriminant analysis and spectral features extraction. The models trained based on the original reflectance spectra, first derivative and selected spectral features yielded more than 88% average accuracy, and especially the model based on the spectral features yielded more than 94% average accuracies, which verified the feasibility of detection of citrus greening in multi-class discriminant analysis and the importance of the spectral feature extraction. The results were compared based on classification tree, k-NN and Bayesian classifiers. Adoption of spectral features as input variables was significantly superior to using the original spectrum, which confirmed the validity of spectral feature selection. Spectral features could be used well for developing a multi-spectral imaging system to detect the citrus greening.

Ground-based remote sensing system is a significant way to understand the growth of corn and provide accurate and scientific data for precision agriculture. The vehicle-borne system is one of the most important tools for corn canopy monitoring. However, the vehicle-borne growth monitoring system cannot maintain steady operations due to the row spacing of corn. The reflectance of corn canopy, which was used to construct the model for the chlorophyll content, was disturbed by the reflectance of soil background. The background interference with the reflectance could not be removed effectively, which would result in a deviation in the growth monitoring. In order to overcome this problem, a novel vegetation index named MPRI was developed in the present paper. The tests were carried out by the vehicle-borne system on the cornfield. The sensors which configured the vehicle-borne system had 4 bands, being respectively 550, 650, 766 and 850 nm. It would obtain the spectral data while the vehicle moved along the row direction. The sampling rate was about 1 point per second. The GPS receiver obtained the location information at the same rate. MPRI was made up by the reflectance ratio of 660 and 550 nm. It was very effective to analyze the information about the reflectance of the canopy. The results of experiments showed that the MPRI of soil was the positive value and the MPRI of canopy was the negative value. So it is easier to distinguish the spectral information about soil and corn canopy by MPRI. The results indicated that: it had satisfactory forecasting accuracy for the chlorophyll content by using the MPRI on the moving monitoring. The R2 of the prediction model was about 0.72. The R2 Of the model of NDVI, which was used to represent the chlorophyll content, was only 0.24. It indicates that MPRI had good measurement results for the dynamic measurement process. It provided the novel measurement way to get the canopy reflectance spectra and the better vegetation index to construct the prediction model of the contents of chlorophyll.

In order to detect the freeze injury stress level of winter wheat growing in natural environment fast and accurately, the present paper takes winter wheat as experimental object. First winter wheat canopy hyperspectral data were treated with resampling smooth Second hyperspectral data were analyzed based on principal components analysis (PCA), a freeze injury inversion model was established, stems survival rate was dependent, and principal components of spectral data were chosen as independent variables. Third, the precision of the model was testified. The result showed that the freeze injury inversion model based on 6 principal components can estimate the winter wheat freeze injury accurately with the coefficient of determination (R2) of 0. 697 5, root mean square error (RMSE) of 0. 184 2, and the accuracy of 0. 697 5. And the model was verified. It can be concluded that the PCA technology has been shown to be very promising in detecting winter wheat freeze injury effectively, and provide important reference for detecting other stress on crop.

Identification of early blight on tomato leaves by using hyperspectral imaging technique based on different effective wavelengths selection methods (successive projections algorithm, SPA; x-loading weights, x-LW; gram-schmidt orthogonaliza-tion, GSO) was studied in the present paper. Hyperspectral images of seventy healthy and seventy infected tomato leaves were obtained by hyperspectral imaging system across the wavelength range of 380-1023 nm. Reflectance of all pixels in region of interest (ROI) was extracted by ENVI 4. 7 software. Least squares-support vector machine (LS-SVM) model was established based on the full spectral wavelengths. It obtained an excellent result with the highest identification accuracy (100%) in both calibration and prediction sets. Then, EW-LS-SVM and EW-LDA models were established based on the selected wavelengths suggested by SPA, x-LW and GSO, respectively. The results showed that all of the EW-LS-SVM and EW-LDA models performed well with the identification accuracy of 100% in EW-LS-SVM model and 100%, 100% and 97. 83% in EW-LDA model, respectively. Moreover, the number of input wavelengths of SPA-LS-SVM, x-LW-LS-SVM and GSO-LS-SVM models were four (492, 550, 633 and 680 nm), three (631, 719 and 747 nm) and two (533 and 657 nm), respectively. Fewer input variables were beneficial for the development of identification instrument. It demonstrated that it is feasible to identify early blight on tomato leaves by using hyperspectral imaging, and SPA, x-LW and GSO were effective wavelengths selection methods.

In order to improve accuracy of quantitative analysis model for the greenhouse tomato nitrogen, phosphorus and potassium nutrient stress, and explore the advantages of polarization non-destructive detection in single-leaf plants scale, polarized reflectance characteristics of greenhouse nutrient deficiency tomato leaves in different growing seasons and different deficiency extents were both examined via means of polarized reflectance spectroscopy system, which was self-developed by the research group. The main factors with effects on the polarized reflectance characteristics of tomato leaves were discussed, such as incident zenith angle, azimuth angle, detection zenith angle, light source polarizer degree, and detector polarizer degree. Experiments were carried out to verify the optimum level of above five parameters by means of range analysis of orthogonal experiments, through that way we can know the best angle combination of five parameters. Based on the above analysis, the angle combination and sorting of detecting tomato nutrients deficiency leaves via means of polarization spectroscopy system were obtained as follows: incident zenith angle 60 degrees, light source polarizer degree 0 degrees, detection zenith angle 45 degrees, detector polarizer degree 45 degrees and azimuth angle 180 degrees. At the same time, both the spectra of nitrogen, phosphorus and potassium deficiency leaves in different growth stages and different deficiency extent leaves were compared with each other. Results show that there is a positive correlation between the greenhouse nutrient deficiency tomato leaves growth cycle and tomato leaves polarized reflectance spectra. Nutrient excess or nutrient deficiency can both lead to polarized reflectance decline and polarized reflectance decline extent of greenhouse tomato leaves is more obvious during the fruiting and harvest period. This paper has a certain theoretical and practical significance in the research on nutrition rapid detection on the plant single leaf scale by means of polarized reflectance spectrum.

Aimed to deal with the limitation of canopy geometry to crop LAI inversion accuracy a new LAI inversion method for different geometrical winter wheat was proposed based on hotspot indices with field-measured experimental data. The present paper analyzed bidirectional reflectance characteristics of erective and loose varieties at red (680 nm) and NIR wavelengths (800 nm and 860 nm) and developed modified normalized difference between hotspot and dark-spot (MNDHD) and hotspot and dark-spot ratio index (HDRI) using hotspot and dark-spot index (HDS) and normalized difference between hotspot and dark-spot (NDHD) for reference. Combined indices were proposed in the form of the product between HDS, NDHD, MNDHD, HDRI and three ordinary vegetation indices NDVI, SR and EVI to inverse LAI for erective and loose wheat. The analysis results showed that LAI inversion accuracy of erective wheat Jing411 were 0.9431 and 0.9092 retrieved from the combined indices between NDVI and MNDHD and HDRI at 860 nm which were better than that of HDS and NDHD, the LAI inversion accuracy of loose wheat Zhongyou9507 were 0.9648 and 0.8956 retrieved from the combined indices between SR and HDRI and MNDHD at 800 nm which were also higher than that of HDS and NDHD. It was finally concluded that the combined indices between hotspot-signature indices and ordinary vegetation indices were feasible enough to inverse LAI for different crop geometrical wheat and multiangle remote sensing data was much more advantageous than perpendicular observation data to extract crop structural parameters.

Nitrogen is the necessary element in life activity of vegetation, which takes important function in biosynthesis of protein, nucleic acid, chlorophyll, and enzyme etc, and plays a key role in vegetation photosynthesis. The technology about inversion of vegetation nitrogen concentration by hyperspectral remote sensing has been the research hotspot since the 70s of last century. With the development of hyperspectral remote sensing technology in recent years, the advantage of spectral bands subdivision in a certain spectral region provides the powerful technology measure for correlative spectral characteristic research on vegetation nitrogen. In the present paper, combined with the newest research production about monitoring vegetation nitrogen concentration by hyperspectral remote sensing published in main geography science literature in recent several years, the principle and correlated problem about monitoring vegetation nitrogen concentration by hyperspectral remote sensing were introduced. From four aspects including vegetation nitrogen spectral index, vegetation nitrogen content inversion based on chlorophyll index, regression model, and eliminating influence factors to inversion of vegetation nitrogen concentration, main technology methods about inversion of vegetation nitrogen concentration by hyperspectral remote sensing were detailedly introduced. Correlative research conclusions were summarized and analyzed, and research development trend was discussed.

Being orientated to the low prescion of crop leaf area index (LAI) inversion using the same spectral vegetation index during different crop growth stages, the present paper analyzed the precision of LAI inversion by employing NDVI(normalized difference vegetation index). Ten vegetation indices were chosen including six broad-band vegetation indices and four narrow-band vegetation indices responding to vegetation cover to inverse LAI in different growth stages. Several conclusions were drawn according to the analysis. The determinant coefficient (R2) and root mean square error (RMSE) between LAI inversion value and true value were 0.5585 and 0.3209 respectively during the whole growth duraton. The mSR (modified simple ratio index) index was appropriate to inverse of LAI during earlier growth stages (before jointing stage) in winter wheat. The R2 and RMSE between LAI inversion value and true value were 0.7287 and 0.2971 respectively. The SR (simple ratio index) index was suitable enough to inverse of LAI during medium growth stages (from joingting stagess to heading stages). The R2 and RMSE between LAI inversion value and true value were 0.6546 and 0.3061 respectively. The NDVI (normalized difference vegetation index) index was proven to be fine to inverse LAI during later growth stages(from heading stage to ripening stage). The R2 and RMSE between LAI inversion value and true value were 0.6794 and 0.3164 respectively. Therefore it was indicated that the results of LAI inversion was much better inverse of winter wheat LAI choosing different vegetation indices during differen growth stages for winter wheat according to the change of vegetation cover and canopy reflectance than merely with NDVI to inverse LAI in the whole growth stages. It was concluded that the precision of LAI inversion was significantly improved with segmented models based on different vegetation indices.

The present paper put forward the technology route for feature images extraction of grey mold sick on tomato leaves based on SIMCA--combination image extraction based on MLR-grey mold sick information extraction based on minimum distance method. Firstly, through the 680-740 nm band's variance image and the discrimination power parameter, the feature band images was found, then the feature bands information was used as the input of MLR analysis, and under the 0.5 accuracy threshold value, 99% accuracy was obtained, which showed the discrimination power of the features bands for grey mold sick tomato leaf detection, and using the MLR regression coefficient to extract a band combination image, and through the minimum distance method, tomato grey mold sick information was found. The result shows that the proposed method has a very good prediction ability and greatly reduces the hyperspectral data processing time.

Leaf area index (LAI) is an important structural parameter of vegetation canopy, the correct estimation of which has been the focus in the remote sensing community. As a kind of hyperspectral and multi-angle remote sensing data with higher resolution (17 m), PROBA/CHRIS has significant application value in LAI inversion. In the present paper, the analytical two-layer canopy reflectance model (ACRM) was used to simulate a series of reflectances with different LAI values. Based on this, a new vegetation index was built and successfully applied to LAI inversion of PROBA/CHRIS image data. Our results indicated that: compared with the spectral index NDVI and multi-angle index HDS, the new index could make better use of spectrum and multi-angle messages and have a better correlation with LAI of the study area; moreover, the correlation coefficient R2 reached up to 0.734 7. And in order to obtain the figure of LAI distribution of the study area, we used the optimal fit equation between LAI and HDVI to estimate LAI, and the accuracy of the RMSE was 0.619 8.

The present paper takes Chuzhou in Anhui Province as the research area, and deciduous broad-leaved forest as the research object. Then it constructs the recognition model about deciduous broad-leaved forest was constructed using NDVI difference rate between leaf expansion and flowering and fruit-bearing, and the model was applied to HJ-CCD remote sensing image on April 1, 2012 and May 4, 2012. At last, the spatial distribution map of deciduous broad-leaved forest was extracted effectively, and the results of extraction were verified and evaluated. The result shows the validity of NDVI difference rate extraction method proposed in this paper and also verifies the applicability of using HJ-CCD data for vegetation classification and recognition.

Proper vegetation indices have decisive influences on the precision of hyperspectral estimation models for surface parameters. In the present paper, in order to find the proper hyperspectral indices for cotton canopy water content estimation, two water parameters for cotton canopy water content (EWT(canopy), equivalent water thickness; VWC, vegetation water content) and corresponding hyperspectra data were analyzed. A rigorous search procedure was used to determine the best index predictors of cotton canopy water. In the procedure, all possible ratio indices and normalized difference indices were derived from the canopy hyperspectra, involving all the two-band combinations between 350 nm and 2500 nm. Then the correlation between two water parameters and all combination indices were analyzed, and the best indices which produced maximum correlation coefficients were determined. Finally, the indices were compared with the published water indices for their performances in estimation of cotton canopy water content. The results showed that for the estimation of EWT(canopy), the new developed ratio index R1 475/R1 424 and normalized difference index (R1 475 -R1 424)/(R1 475 + R1 424) was the most proper one, and the correlation coefficient of the estimated and measured EWT(canopy) reached 0.849. For the estimation of VWC, the performance of published index was better than new developed index, the best suitable water indices for VWC estimation were (R835 - R1 650)/(R835 + R1 650), and the correlation coefficient of the estimated and measured VWC was 0.849.

The leaf area index (LAI) is a very important parameter affecting land-atmosphere exchanges in land-surface processes; LAI is one of the basic feature parameters of canopy structure, and one of the most important biophysical parameters for modeling ecosystem processes such as carbon and water fluxes. Remote sensing provides the only feasible option for mapping LAI continuously over landscapes, but existing methodologies have significant limitations. To detect LAI accurately and quickly is one of tasks in the ecological and agricultural crop yield estimation study, etc. Emerging hyperspectral remote sensing sensor and techniques can complement existing ground-based measurement of LAI. Spatially explicit measurements of LAI extracted from hyperspectral remotely sensed data are component necessary for simulation of ecological variables and processes. This paper firstly summarized LAI retrieval method based on different level hyperspectral remote sensing platform (i. e., airborne, satelliteborne and ground-based); and secondly different kinds of retrieval model were summed up both at home and abroad in recent years by using hyperspectral remote sensing data; and finally the direction of future development of LAI remote sensing inversion was analyzed.

Hyperspectral imaging feature of chlorophyll content (SPAD) in tomato leaves stressed by grey mold was studied in the present paper. Hyperspectral imagings of healthy and infected tomato leaves were obtained by hyperspectral imaging system from 380 to 1 030 nm and diffuse spectral response of region of interest (ROI) from hyperspectral imaging was extracted by EN-VI software, then different preprocessing methods were used including smoothing and normalization etc. The partial least squares regress (PLSR) and principal component regress (PCR) models were developed for the prediction of SPAD value in tomato leaves based on normalization preprocessing method, then the back-propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) models were built based on the four variables suggested by PLSR model. Among the four models, LS-SVM model was the best to predict SPAD value and the coefficient of determination (R2) was 0.901 8 with the root mean square error of prediction (RMSEP) of 2.599 2. It was demonstrated that chlorophyll content (SPAD) in healthy and infected tomato leaves can be effectively detected by the hyperspectral imaging technique.

Studying vegetation infrared radiation character is the base of developing infrared camouflage and concealment technology of ground military target. Accurate fusion of target and background can be achieved by simulating formation mechanism of vegetation infrared radiation character. Leaf transpiration is characteristic physiological mechanism of vegetation and one of the main factors that influence its infrared radiation character. In the present paper, physical model of leaf energy balance is set up. Based on this model the influence of plant transpiration on leaf temperature is analyzed and calculated. The daily periodic variation of transpiration, leaf temperature and infrared radiation character of typical plants such as camphor tree and holly is actually measured with porometer and infrared thermal imaging system. By contrasting plant leaf with dryness leaf, experimental data indicates that plant transpiration can regulate leaf energy balance effectively and control leaf temperature in a reasonable range and suppress deep range variation of leaf infrared radiation character.

With the global climate warming, reducing greenhouse gas emissions becomes a focused problem for the world. The carbon capture and storage (CCS) techniques could mitigate CO2 into atmosphere, but there is a risk in case that the CO2 leaks from underground. The objective of this paper is to study the chlorophyll contents (SPAD value), relative water contents (RWC) and leaf spectra changing features of beetroot under CO2 leakage stress through field experiment. The result shows that the chlorophyll contents and RWC of beetroot under CO2 leakage stress become lower than the control beetroot', and the leaf reflectance increases in the 550 nm region and decreases in the 680nm region. A new vegetation index (R550/R680) was designed for identifying beetroot under CO2 leakage stress, and the result indicates that the vegetation index R550/R680 could identify the beetroots after CO2 leakage for 7 days. The index has strong sensitivity, stability and identification for monitoring the beetroots under CO2 stress. The result of this paper has very important meaning and application values for selecting spots of CCS project, monitoring and evaluating land-surface ecology under CO2 stress and monitoring the leakage spots by using remote sensing.

In the present paper, broad bean rust, fusarium rhizome rot, broad bean zonate spot, yellow leaf curl virus and normal leaves were studied using Fourier transform infrared spectroscopy combined with chemometrics. The results show that the spectra of samples were similar, only with minor differences in absorption intensity of several peaks. Second derivative analyses show that the significant difference of all samples was in the range of 1200-700 cm(-1). The data in the range of 1 200-700 cm(-1) were selected to evaluate correlation coefficients, hierarchical cluster analysis (HCA) and principal component analysis (PCA). Results showed that the correlation coefficients are larger than 0.928 not only between the healthy leaves, but also between the same diseased leaves. The values between healthy and diseased leaves, and among diseased leaves, are all declined. HCA and PCA yielded about 73.3% and 82.2% accuracy, respectively. This study demonstrated that FTIR techniques might be used to detect crop diseases.

In this paper, Duobao Mt. and Tong Mt. were taken as examples. The spectra of the crown or leaf of the vegetation were collected. Fourteen metal elements in the rock, soil (A, B, C) and vegetation (root, trunk, leaf), and biological chemical parameters were measured. It was indicated that different metal elements were selected and enriched in different vegetation. The red edge position (REP) and the absorbing depth are related to biological chemical parameters. A multivariable regression equation was built between the absorption depths and the contents of metal elements. The relative coefficients between the absorbing depths and chemical elements, including Co, Cu, N, Mo, Ag, Sb, W, Pb and As, are greater than 0.75. Thus, it is important to analyze and measure the contents of metal elements by hyper-spectral remote sensing of vegetation stress spectrum.

The wheat leaf area index (LAI) was inverted using hyperspectral remote sensing technology in the present paper. Eighteen kinds of hyperspectral indices were comparatively analyzed, and the index OSAVI, which could reflect wheat LAI most sensitively, was screened out. The models for wheat LAI inversion were built using the field spectra as the training samples. The study showed that the calibration R-square and prediction R-square of the inversion model which were built by hyperspectral index OSAVI were 0.823 and 0.818, respectively, higher than that of other indices, indicating that the accuracy was highest. The inversion model was spatially quantitatively expressed in OMIS image, and then the inversion value and measured value was compared by the method of regression fitting. The R-square and RMSE of the fitting model were 0.756 and 0.500, respectively, indicating that the similarity between the inversion value and measured value was high. The result showed that it was feasible to invert the wheat LAI by hyperspectral indices, and OSVAI was an optimal one.

Insufficiency of phosphorus could greatly effect rice production, thus it is significant to adopt quick and nondestructive diagnosis of phosphorus content. The present paper focused on first expanded leaves with different phosphorus fertilization levels, comprehensively extracted 26 features' spectral information such as color, texture and shape etc. Single feature index analysis was conducted. Then features were collected to integrate CfsSubsetEval + Scattersearch method for optimizing, evaluation and choosing. Based on the feature selection for different leave positions, leaves in different phosphorus fertilization levels were finally classified into three grades (extremly insufficient, significant insufficient and normal) according to rough set theory. Results showed that the accuracy of recognition was very high while few phosphorus contained in the leaves. Moreover, the third expanded leaf is the best part for phosphorus-nutrient diagnosis.

The accurate inversion of leaf area index (LADI) in canopy is very important for guiding crop management and assessing crop yield. Sixty samples belonging to corn in four different areas of Jilin City were scanned by ASD field pro3 and LAI-2000 for optical data and LAI. A new vegetation index, the normalized composite Vegetation index (NCVI), containing the factor of canopy water content, is proposed in the present paper for a better quantitative estimation of LAI than with the remotely sensed normalized difference vegetation index (NDVI), especially in the arid and semi-arid areas. A model was built for inversion of LAI with NCVI, and experience validation. The results showed that there was a good linear correlation between the simulation LAI inversed from NCVI model and the real LAI values. The model breaking the limitations of the traditional empirical models for LAI inversion has a good result for estimating LAI of the dense canopy whose LAI value was greater than 3. In addition, NCVI model was very sensitive to the water environment of soil, and the inversion result in the arid and semi-arid areas was superior to the general area.

The purpose of the present paper is to model a physical polarimetric bidirectional reflectance distribution function (pBRDF), which can character not only the non-Lambertian but also the polarized features in order that the pBRDF can be applied to analyze the relationship between the degree of polarization and the physiological and biochemical parameters of leaves quantitatively later. Firstly, the bidirectional polarized reflectance distributions from several leaves surfaces were measured by the polarized goniometer developed by Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences. The samples of leaves include two pieces of zea mays L. leaves (young leaf and mature leaf) and a piece of E. palcherrima wild leaf. Non-Lambertian characteristics of directional reflectance from the surfaces of these three leaves are obvious. A Cook-Torrance model was modified by coupling the polarized Fresnel equations to simulate the bidirectional polarized reflectance properties of leaves surfaces. The three parameters in the modified pBRDF model, such as diffuse reflectivity, refractive index and roughness of leaf surface were inversed with genetic algorithm (GA). It was found that the pBRDF model can fit with the measured data well. In addition, these parameters in the model are related with both the physiological and biochemical properties and the polarized characteristics of leaves, therefore it is possible to build the relationships between them later.

The spectral features of ground objects are not only the brief contents of mechanism of remote sensing, but also the important basis in remote sensing application. As one of main components of terrestrial ecosystems, urban forest plays a key role in maintaining urban ecosystem balance. In the present paper, the authors adopted FieldSpec 3 portable spectroscope made by American ASD Company, and investigated or examined some spots in the Kangjian park of Shanghai, China. The spectra of euonymus japonicus L. cv, hypericum monogynum, sabina chinensis, ophiopogon japonicus, viburnum awabuki, and buxus sinica were measured. According to the actual conditions, the authors analyzed the data noise characteristic of the spectrum and got rid of the noise with Savitzky Golay method. Meanwhile, differential spectrum technology was used to remove the environmental background influence. Then the authors analyzed their features and variation of these spectral curves from the vegetation canopy and leaf level respectively. The research on spectral reflectance characteristics for urban vegetations is very significant. And the result of this research can be used for the study of physical chemistry performances of urban vegetation, remote sensing retrieval, vegetation classification, vegetation survey and environmental monitoring in urban area.

Weeds grow scatteredly in fields, where many insentient objects exist, for example, withered grasses, dry twig and barriers. In order to improve the precision level of spraying, it is important to study green plant detecting technology. The present paper discussed detecting method of green plant by using spectral recognizing technology, because of the real-time feature of spectral recognition. By analyzing the reflectivity difference between each of the two sides of the "red edge" of the spectrum from plants and surrounding environment, green plant discriminat index (GPDI) is defined as the value which equals the reflectivity ratio at the wavelength of 850 nm divided by the reflectivity ratio at the wavelength of 650 nm. The original spectral data of green plants and the background were measured by using the handhold FieldSpec 3 Spectroradiometer manufactured by ASD Inc. in USA. The spectral data were processed to get the reflectivity of each measured objects and to work out the GPDI thereof as well. The classification model of green plant and its background was built up using decision tree method in order to obtain the threshold of GPDI to distinguish green plants and the background. The threshold of GPDI was chosen as 5.54. The detected object was recognized as green plant when it is GPDI>GPDITH, and vice versa. Through another test, the accuracy rate was verified which was 100% by using the threshold. The authors designed and developed the green plant detector based on single chip microcomputer (SCM) "AT89S51" and photodiode "OPT101" to realize detecting green plants from the background. After passing through two optical filters, the center wavelengths of which are 650 and 850 nm respectively, the reflected light from measured targets was detected by two photodiodes and converted into electrical signals. These analog signals were then converted to digital signals via an analog-to-digital converter (ADS7813) after being amplified by a signal amplifier (OP400). The converted digital signal of reflected light was eventually sent to the SCM (AT89S51) and was calculated and processed there. The processing results and the control signals were given out to actuate executive device to open or close the solenoid valve. The test results show that the level of detectivity of the designed detector was affected by the species, size, and density of weeds. The detectivity of broad-leaf species is higher than that of narrow-leaf species. Broad-leaf species are more easily detected than those narrow-leaf ones; the bigger the plants and the denser the leaves are, the higher the level of detectivity is.

The objective of the present paper is to identify healthy wheat and disease wheat by using hyeprspectral remote sensing as soon as possible. The canopy spectral reflectance of winter wheat infected by different severity yellow rust was measured and the disease indices (DI) were investigated in the field respectively. Smoothing the canopy spectra and calculating the first derivative values, the two methods were used to calculate the red edge position (REP) and yellow edge position (YEP) of the first derivative values: (a) maximum of the first derivative value; (b) Cho and Skidmore method. The result showed that REP gradually shifted to short-wave band, and the YEP gradually shifted to long-wave band with disease severity increasing, however, REP-YEP quickly became smaller. Analyzing and comparing the prediction precision of REP, YEP and REP-YEP for DI, the result indicated that the model REP-YEP as variable has the best estimation precision for DI than REP and YEP, the model estimation error is 6.22, and relative error is 14.3%, and it could identify healthy and disease wheat 12 days before the disease symptom apparently appeared. Therefore, this study not only can provide theory and technology for large areas monitoring of wheat disease by using hyperspectral remote sensing in the future, but also has the important meaning and practical application value for implementing precision agriculture.

Vis/NIR spectroscopy technology is capable of analyzing the content of biochemical parameter in folium rapidly and nondestructively. In the process of spectrum analysis, the variations in path-length between different samples exist, with the random light scattering and leaf thickness perturbations, which influence the precision of quantitative analysis model. In order to resolve this problem, an improved path-length correction method based on Extended Multiplicative Scattering Correction is presented. In this paper, firstly the theory of EMSC algorithm is deduced. EMSC method incorporates both chemical terms and wavelength functions to help realize the efficient separation of path-length and interest concentration. Secondly two experiments were implemented to demonstrate the validity of the method. In Experiment 1, sixteen samples of different thickness but almost the same chlorophyll content were selected, and how the path-length affects the spectrum was compared, after EMSC preprocessing, the variable coefficient of spectrum could approach the repeatability error of spectrometer. In Experiment 2, thirty-two samples of different thickness and chlorophyll content were selected. PLS model established using cross validation was employed to evaluate the efficiency of the presented algorithm. Before the preprocessing, the root mean squared error of prediction is 3.9 SPAD with 5 principal components. After preprocessing, the predicted root mean squared error is 2.2 SPAD with 12 principal components. The results indicate that the improved EMSC preprocessing method could exactly eliminate the spectrum difference caused by the path-length variations between different foliums, enhance the sensitivity of concentration and spectral data, and increase the precision of calibrated model.

Accurate estimation of leaf water content (LWC) from remote sensing can assist in determining vegetation physiological status, and further has important implications for drought monitoring and fire risk evaluation. This paper focuses on retrieving LWC from canopy spectra of winter wheat measured with ASD FieldSpec Pro. The experimental plots were treated with five levels of irrigation (0, 200, 300, 400 and 500 mm) in growing season, and each treatment had three replications. Canopy spectra and LWC were collected at three wheat growth stages (booting, flowering, and milking). The temporal variations of LWC, spectral reflectance, and their correlations were analyzed in detail. Partial least square regression embedded iterative feature-eliminating was designed and employed to obtain diagnostic bands and build prediction models for each stage. The results indicate that LWC decreases quickly along with the winter wheat growth. The mean values of LWC for the three stages are respectively 338.49%, 269.65%, and 230.90%. The spectral regions correlated strongly with LWC are 1 587-1 662 and 1 692-1 732 nm (booting), 617-687 and 1 447-1 467 nm (flowering), and 1 457-1 557 nm (milking). As far as the LWC prediction models are concerned, the optimum modes of spectral data are respectively logarithmic, 1st order derivative and plain reflectance. The diagnostic bands detected by PLS are from SWIR, NIR, and SWIR. Retrieval accuracy at the flowering stage is the highest (R2(cv) = 0.889) due to the enhancement of leaf water information at canopy scale via multiple scattering. At the booting and milking stage, accuracies are relatively lower (R2(cv) = 0.750, 0.696), because the retrieval of LWC is negatively affected by soil background and dry matter absorption respectively. This research demonstrated clearly that the spectral response and retrieval of LWC has distinct temporal characteristics, which should not be neglected when developing remote sensing product of crop water content in the future.

Acid rain is a worldwide environmental problem. Serious acid rain pollution in subtropical China has constituted a potential threat to the health of the local forest. In the present paper, the changing properties of the chlorophyll concentration and spectral reflectance at the visible wavelengths for the six subtropical broad-leaved tree species leaves under simulated acid rain (SAR) treatment with different pH levels were studied. With the increasing strength of the SAR, the chlorophyll concentrations of the experimental species under pH 2.5 and pH 4.0 treatment were higher than that under pH 5.6; the spectral reflectance at the visible wavelengths for pH 2.5 and pH 4.0 were lower than that for pH 5.6 in general; while there weren't significant differences between pH 2.5 and pH 4.0. After the treatment with different levels of SAR, the differences in spectral reflectance at the visible wavelengths mainly focused around the green peak and red edge on the reflectance curve. The subtropical broad-leaved tree species studied were relatively not sensitive to acid rain stresses; some stronger acid rain may accelerate the growth of the tree species used here to some extent.

Chlorophyll content is an important indicator of photosynthesis activity, stress and nutritional state. In the present paper, the hyperspectral data, foliar chlorophyll content and heavy metal contents in foliar and soil were measured for the maize growing in three natural fields. In most previous research, the contamination stress was controlled artificially in laboratory by adding chromium, zinc or copper pollutant etc. to the soil, and the pollutant concentration added was much higher than that in natural environment. The three sample fields were under different heavy mental contamination level, but all located at the Changchun region, Northeast China, where is called Golden Maize Belts in the world. After continuum removal (400-800 nm), ten spectral indices were computed including max absorption position, normalized reflectance at max absorption position, absorption depth, green peak, normalized reflectance at green peak, red edge, normalized reflectance at red edge, red peak, absorption width, and asymmetry degree. The physics meaning of the above indices and their correlation with maize foliar chlorophyll content were analyzed. It was found that there were close relationships between these indices and foliar chlorophyll content except max absorption position, green edge and asymmetry degree. Besides the asymmetry degree, five indices were selected in the stepwise multiple linear regression for estimating chlorophyll content and its determination coefficient (R2) is 0.7027. Furthermore, in order to measure the weak change information of foliar chlorophyll content under the contamination stress, the BP artificial neural network (ANN-BP) was used. Several ANN-BP models were built and tried with different structure, namely five nodes, seven nodes or ten nodes in input layer, one hidden layer or two hidden layer, and different nodes number in hidden layers. It was found that the highest accuracy of estimates was obtained by the model with two hidden layers, ten nodes in input layer, seven nodes in first hidden layer and 4 nodes in second hidden layer (R2 = 0.9758).

In the present paper a method based on Vis/NIR spectral analysis technology was applied to the nondestructive measurement of plant chlorophyll content. Firstly, the Vis/NIR spectra in the wavelength range from 500 to 900 nm of the plant leaves were acquired from 35 samples by transmittance and reflectance method, and then three different mathematical treatments were used in original spectra data pretreatment to decrease the noise: moving average smoothing with the segment size 5, first Savitzky-Golay derivative, and wavelet transform (WT) way. Secondly, a total of 35 samples were examined in the test, in which 23 samples were selected randomly for model building and the other 12 for model prediction, then partial least squares (PLS) method was used to develop the quantitative analysis model for chlorophyll content with absorbance spectroscopy, and 7 principal components (PCs) were selected. Finally, this model was used to predict the chlorophyll content of 12 unknown leaf samples in prediction collection. The experiment result indicated that the better prediction performance was achieved with the correlation coefficient between the prediction values and the truth values being 0.93, and the root mean squared error of prediction is about 1.1 SPAD. It could be concluded that it is feasible to measure plant chlorophyll content based on visible/near infrared (Vis/NIR) spectroscopy. And it is also significant in realizing rapid and nondestructive measurement of chlorophyll content in the future.

Moderate-resolution imaging spectrometer (MODIS) and multi-angle imaging spectroradiometer (MISR) are two important sensors on TERRA satellite. The authors can have more spectral and multi-angular observations on the land surface objects by combining these two datasets. In the present paper, both MODIS and MISR observations were combined to estimate leaf area index (LAI) of land surface. The adjoining model and trust-region optimal algorithm were introduced into the framework of physical model inversion to speed up the running of the model inversion algorithm. And the algorithm allows the prior knowledge on the retrieved parameters to be input into the inversion procedure. The uncertainty and sensitivity matrix (USM) based analysis is helpful for selecting the observed data subset with more information and less noise to retrieve LAI. The measured LAI in situ and estimated LAI from ETM data were scaling-up to MODIS/MISR LAI product scale, and were taken as the ground truth to evaluate the new approach. The result suggests that combining two sensors datasets can improve the accuracy of LAI estimation, and the developed inversion method in this paper can be applied to the large area remote sensed image data effectively.

Many researches have developed models to estimate chlorophyl content at leaf and canopy level, but they were species-specific. The objective of the present paper was to develop a new model. First, canopy reflectance was simulated for different species and different canopy architecture using radiative transfer models. Based on the simulated canopy reflectance, the relationship between canopy reflectance and canopy chlorophyll content was studied, and then a chlorophyll estimation model was built using the method of spectral index. The coefficient of determination (R2) between spectral index based model and canopy chlorophyll content reached 0.75 for simulated data. To investigate the applicability of this chlorophyll model, the authors chose a field sample area in Gansu Province to carry out the measurement of leaf chlorophyll content, canopy reflectance and other parameters. Besides, the authors also ordered the synchronous Hyperion data, a hyperspectral image with a spatial resolution of 30 m. Canopy reflectance from field measurment and reflectance from Hyperion image were respectively used as the input parameter for the chlorophyll estimation model. Both of them got good results, which indicated that the model could be used for accurate canopy chlorophyll estimation using canopy reflectance. However, while using spaceborne hyperspectral data to estimate canopy chlorophyll content, good atmospheric correction is required.

The level and form of nitrogen fertilizer could significantly influence the growth and development of plant. The present paper studied the content of chlorophyll by the instrument SPAD-502 after treated with different nitrogen fertilizer level and different nitrogen fertilizer form. The results showed that the contents of chlorophyll in the last expanding leaf of maize seedling treated by levels of 0, 100 and 200 kgN x hm(-2) respectively had no significant difference, with the value of SPAD ranging from 43.3 to 43.7, but when the nitrogen fertilizer level got to 400 kgN x hm(-2), the content of chlorophyll in the last expanding leaf of maize seedling increased significantly, which can be caused by other components in the nitrogen fertilizer, which needs to be further studied. The experiments of nitrogen form showed that maize seedling treated by ammonia nitrogen ((NH4)2SO4) contained more chlorophyll than that treated by saltpeter nitrogen (Ca(NO3)2), and the statistical analysis was significant. The reason for the effect of nitrogen form on the content of chlorophyll of maize seedling leaf could be: (1) it is easier for plants to absorb ammonia nitrogen ((NH4)2SO4) than saltpeter nitrogen (Ca(NO3)2); (2) ammonia nitrogen ((NH4)2SO4) contains more trace elements which can promote the growth and development of plants.

Arecanut is a sort of palm that is important economic crop for the farmers in Hainan province of China, wherein there are many kinds of metal elements such as K, Ca, Na, Mg, Fe, Mn, Cu, Zn etc. These elements are important nutrition for the growth of arecanut. It is very valuable to study on the content of these metal elements in arecanut leaf in terms of plant nutriology of arecanut. The arecanut leaf in Wangling county, Hainan province of China was sampled by diagonal-field-sampling method. Refering to other plant sample determination by FAAS, the detailed studies are done with different digestion and determination methods. In the present paper the effects of mixed acid of HNO3-HClO4 digestion method on determining the amount of metal elements in the arecanut leaf by FAAS is reported, and another one is incineration digestion method. FAAS method was established for the determination of K, Ca, Mg, Na, Fe, Mn, Cu and Zn The samples were incinerated or heated with HNO3-HClO4 (4:1). In the meantime, the optimum parameters of FAAS and effects of different digestion methods on the results were discussed. The recovery rate of standard addition is 98.36%-102.38% in the first method; RSD is 0.42%-2.328% (n=6); The recovery rate of standard addition is 99.22%-103.72% in the second method; RSD is 0.58%-1.283 (n=6). The metal amount determined by the first method is lower than the second method, the ratio is 0.9703-0.9934. The two methods are satisfied, but the latter is better. It is precise enough to common experiment to use flame atomic absorption spectrophotometry with digestion by incineration If the especially precise experiment is required, the digestion methods with mixed acid of HNO3-HClO4 may be introduced. The paper introduced methods dependable for determination of some metal elements in order to study on some nutrient effects of these metal elements in arecanut.

Huperzia serrata is an important medicinal plant. This species is rich in inner-specific variation with various closely related species, and their individuals are also small with few identification characters. In the present paper, the method of Fourier transform infrared spectrometer with an OMNI collector was applied to obtaining the infrared spectra of 16 leaf samples including Huperzia serrata and its five closely related species (Huperzia sutchueniana, Phlegmariurus mingchegensis, Lycopodium japonicum, Selaginella doederleinii, Selaginella heterostachys). Based on the indices of wave number-absorbance, the differences of the 16 infrared spectra were compared by the method of Principal Component Analysis (PCA). The results showed that there is good correspondence between the position relationship of PCA three-dimensional plot of the samples based on the indices of wave number-absorbance of FTIR spectra and their phenetic relationship. Therefore, the infrared spectra could be applied to identifying the samples of Huperzia serrata and its closely related species.