英文论文集-光谱

1、The red edge parameters as indicators of rice nitrogen levels

作者：Jingfeng Huang; Xiuzhen Wang; Renchao Wang

期刊：Multispectral and Hyperspectral Remote Sensing Instruments and Applications

Proc. SPIE 4897 16 June 2003

摘要：The canopy spectra of rice under different nitrogen levels were studied. Some red edge parameters in the first derivative reflectance curve (wavelength, amplitude and area of the red edge peak) were used to evaluate rice leaf chlorophyll, LAI. Red edge positions move to longer wave bands till booting stage and move to short bands after booting stage. A high correlation was found between chlorophyll content of top leaves and the wavelength of the red edge position and between LAI and the red edge parameter. Then, the red edge was found valuable for assessment of carotenoid or albumen-nitrogen or non-albumen-nitrogen and the wavelength of the red parameters. Some red edge parameters are one of the best remote sensing descriptors.

2、Relationship between Narrow Band Normalized Deference Vegetation Index and Rice Agronomic Variables

作者：Jingfeng Huang, Fumin Wang, Xiuzhen Wang, Yanlin Tang,Renchao Wang The objective of this article is to determine spectral bands that are best suited for characterizing rice agronomic variables. The data for this study comes from ground-level hyperspectral reflectance measurements of rice during different stages of the 2002 growing period. Reflectance was measured in discrete narrow bands between 350 and 2500 nm. Observed rice agronomic variables included wet biomass and leaf-area index. Narrow band normalized difference vegetation index (NBNDVI) involving all possible two-band combinations of discrete channels was tested. A rigorous search procedure to identify the best NBNDVI predictors of rice agronomic variables was described. Special narrow-band lambd a (λ 1) vs. lambda (λ 2) plots of R 2 values illustrates the most effective wavelength combinations (λ 1 and λ 2) and band width (Δλ 1 and Δλ 2) for predicting rice agronomic variables at different development stages. The best of the NBNDVI models explained 53–83% variability of rice agronomic variables at different development stages. A strong relationship with rice agronomic variables is located in red-edge (700–750 nm), in the longer portion of red (650–700 nm), in moisture-sensitive NIR (950–1000 nm), in the longer portion of the blue band (450–500 nm), in the longer portion of the green (550–600 nm), in the intermediate portion of short-wave infrared (SWIR) (1600–1700 nm), and in the longer portion of SWIR (2150–2250 nm).

JOURNAL：Communications in Soil Science and Plant Analysis V olume 35, Issue 19-20, 2004

3、A modified chlorophyll absorption continuum index for chlorophyll estimation

作者：Xiao-hua Yang, Jing-feng Huang, Fu-min Wang, Xiu-zhen Wang, Qiu-xiang Yi, Yuan Wang 期刊：Journal of Zhejiang University SCIENCE A December 2006, V olume 7, Issue 12, pp 2002-2006

摘要：There is increasing interest in using hyperspectral data for quantitative characterization of vegetation in spatial and temporal scopes. Many spectral indices are being developed to improve vegetation sensitivity by minimizing the background influence. The chlorophyll absorption continuum index (CACI) is such a measure to calculate the spectral continuum on which the

analyses are based on the area of the troughs spanned by the spectral continuum. However, different values of CACI were obtained in this method because different positions of continuums were determined by different users. Furthermore, the sensitivity of CACI to agronomic parameters such as green leaf chlorophyll density (GLCD) has been reduced because the fixed positions of continuums are determined when the red edge shifted with the change in GLCD. A modified chlorophyll absorption continuum index (MCACI) is presented in this article. The red edge inflection point (REIP) replaces the maximum reflectance point (MRP) in near-infrared (NIR) shoulder on the CACI continuum. This MCACI has been proved to increase the sensitivity and predictive power of GLCD.

3、Comparison of Vegetation Indices and Red-edge Parameters for Estimating Grassland Cover from Canopy Reflectance Data

作者：Zhan-Yu Liu, Jing-Feng Huang, Xin-Hong Wu andYong-Ping Dong

期刊：Journal of Integrative Plant Biology Volume 49, Issue 3, pages 299–306, March 2007

There has been a great deal of interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover (PVC), aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer (MODIS) data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices (DVI, RVI, NDVI, RDVI, SA VIL = 0.5 and MSA VI2) were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSA VI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.

4、Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network

作者：Y ANG Xiao-hua HUANG Jing-feng WANG Jian-wen WANG Xiu-zhen LIU Zhan-yu

期刊：Journal of Zhejiang University SCIENCE A May 2007, Volume 8, Issue 6, pp 883-895

摘要：Hyperspectral reflectance (350～2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of traini ng algorithms has been tested for training RBF networks. In

this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSA VI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSA VI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSA VI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSA VI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.

5、Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression

作者：Zhan-yu Liu, Jing-feng Huang, Jing-jing Shi, Rong-xiang Tao, Wan Zhou, Li-li Zhang

期刊：Journal of Zhejiang University SCIENCE B September 2007, V olume 8, Issue 10, pp 738-744

摘要：Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

6、Comparison between back propagation neural network and regression models for the estimation of pigment content in rice leaves and panicles using hyperspectral data

作者：Chen, L.; Huang, J. F.; Wang, F. M.; Tang, Y. L.

期刊：International Journal of Remote Sensing V olume 28, Number 16, 2007, pp. 3457-3478(22)

摘要：Two nitrogen experiments on rice were conducted in 2002, and the reflectances (350 to 2500 nm) and pigment contents (chlorophylls a and b, total chlorophylls and carotenoids) for leaf and panicle samples at different growth stages were measured in the laboratory. After performing

an outlier analysis, the number of samples were 843 for leaves and 188 for panicles. Absorption features at 430, 460, 470, 640 and 660 nm for different pigments, and the relative reflectance of the green peak around 550 nm calculated by the continuum-removed method, as well as the red edge position (REP) of rice leaves and panicles were selected as the independent variables, and measured pigment contents were selected as the dependent variables. Then, back propagation neural network (BPN) models, a kind of artificial neuron network (ANN), and multivariate linear regression models (MLR) were trained and tested. The main objective of this study was to compare the predictive ability of the ANN models to that of the MLR models in estimating the content of pigments in rice leaves and panicles. Results showed that all BPN models gave higher coefficients of determination (R2) and lower absolute errors (ABSEs) and root mean squared errors (RMSEs) than the corresponding MLR models, in both calibration and validation tests. Further significance tests by paired t tests and bootstrapping algorithms indicated that most of the BPN models outperformed the MLR models. When trained by combination data that did not meet the assumption of normal distribution, the BPN models appeared to not only have a better learning ability, but also had a more accurate predictive power than the MLR models. The estimation of leaf pigments was more accurate than that of panicle pigments, independent of which model was used.

7、Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. 作者：Qiu-Xiang Yi ,Jing-Feng Huang ,Fu-Min Wang ,Xiu-Zhen Wang ,and Zhan-Yu Liu

期刊：Environmental Science & Technology 2007, 41 (19), pp 6770–6775 摘要：Over use of nitrogen fertilization can result in groundwater pollution. Tools that can rapidly quantify the nitrogen status are needed for efficient fertilizer management and would be very helpful in reducing the environmental pollution caused by excessive nitrogen application. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties. In this study, the MLR (multiple linear regression) and ANN (artificial neural network) modeling methods were applied to the monitoring of rice N (nitrogen concentration, mg nitrogen g-1 leaf dry weight) status using leaf level hyperspectral reflectance with two different input variables, and as a result four estimation models were proposed. RMSE (root-mean-square error), REP (relative error of prediction), R2 (coefficient of determination), as well as the intercept and slope between the observed and predicted N were used to test the performance of models. Very good agreements between the observed and the predicted N were obtained with all proposed models, which was especially true for the R-ANN (artificial neural network based on reflectance selected using MLR) model. Compared to the other three models, the R-ANN model improved the results by lowering the RMSE by 14.2%, 32.1%, and 31.5% for the R-LR (linear regression based on reflectance) model, PC-LR (linear regression based on principal components scores) model, and PC-ANN (artificial neural network based on principal components scores) model, respectively. It was concluded that the ANN algorithm may provide a useful exploratory and predictive tool when applied on hyperspectral reflectance data for nitrogen status monitoring. Besides, although the performance of MLR was superior to PCA used for ANN inputs selection, the encouraging results of PC-based models indicated the promising potential of ANN combined with PCA application on hyperspectral reflectance analysis.

8、Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

作者：Qiu-xiang Yi, Jing-feng Huang, Fu-min Wang, Xiu-zhen Wang

期刊：Journal of Zhejiang University SCIENCE B May 2008, Volume 9, Issue 5, pp 378-384

摘要：To further develop the methods to remotely sense the biochemical content of plant canopies, we report the results of an experiment to estimate the concentrations of three biochemical variables of corn, i.e., nitrogen (N), crude fat (EE) and crude fiber (CF) concentrations, by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed, and a set of estimation models were established using curve-fitting analyses. Coefficient of determination (R 2), root mean square error (RMSE) and relative error of prediction (REP) of estimation models were calculated for the model quality evaluations, and the possible optimum estimation models of three biochemical variables were proposed, with R 2 being 0.891, 0.698 and 0.480 for the estimation models of N, EE and CF concentrations, respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation, and that the first derivative reflectances at 759 nm, 1954 nm and 2370 nm were most suitable to develop the estimation models of N, EE and CF concentrations, respectively. In addition, the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained, especially for nitrogen (r=0.948).

9、Empirical Line Method Using Spectrally Stable Targets to Calibrate IKONOS Imagery

作者：Jun-Feng Xu,Jing-Feng Huang

期刊：Pedosphere V olume 18, Issue 1, February 2008, Pages 124–130

摘要：By using spectrally stable targets, the empirical line (EL) method was tested to correct the multispectral IKONOS imagery acquired over Putuo Mountain, Zhejiang, China. A series of calibration targets, which were spectrally stable over time, were selected to establish the linear predicted equation. Subsequently, a series of spectrally stable validation targets were selected to assess the accuracy of the equations. And, validation targets, which were spectrally unstable over time, were used to test the feasibility of using the EL method to calibrate the archival remotely sensed data. Ground reflectance measurements for each target were made using an ASD FieldSpec spectroradiometer. A Trimble GeoXT? GPS unit with sub-meter accuracy was used to estimate the target position accurately. Linear regression equations for four IKONOS bands were derived. The coefficients of determination for the blue, green, and red bands were all greater than 0.9800 and it was 0.9697 for the near infrared band. It was concluded that reasonable results could be obtained by using spectrally stable targets.

10、Identification of Optimal Hyperspectral Bands for Estimation of Rice Biophysical Parameters 作者：Fu-Min Wang, Jing-Feng Huang, Xiu-Zhen Wang

期刊：Journal of Integrative Plant Biology Volume 50, Issue 3, pages 291–299, March 2008

摘要：The present study aims to identify the narrow spectral bands that are most suitable for characterizing rice biophysical parameters. The data used for this study come from ground-level hyperspectral reflectance measurements for five rice species at three levels of nitrogen fertilization during the growing period. Reflectance was measured in discrete narrow bands between 350 and 2 500 nm. Observed rice biophysical parameters included leaf area index (LAI), wet biomass and

dry biomass. The stepwise regression method was applied to identify the optimal bands for rice biophysical parameter estimation. This research indicated that combinations of four narrow bands in stepwise regression models explained 69% to 83% variability for LAI, 56% to 73% for aboveground wet biomass and 70% to 83% for leaf wet biomass. An overwhelming proportion of rice information was in a particular portion of near infrared (NIR) (1100–1150 nm), red-edge (700–750 nm), and a longer portion of green (550–600nm). These were followed by the moisture-sensitive NIR (950–1 000 nm), the intermediate portion of shortwave infrared (SWIR) (1 650–1 700 nm), and another portion of NIR (1000–1050 nm).

11、ptimal waveband identification for estimation of leaf area index of paddy rice

作者：Fu-min Wang, Jing-feng Huang, Qi-fa Zhou, Xiu-zhen Wang

期刊：Journal of Zhejiang University SCIENCE B December 2008, V olume 9, Issue 12, pp 953-963

摘要：The objectives of the study were to select suitable wavebands for rice leaf area index (LAI) estimation using the data acquired over a whole growing season, and to test the efficiency of the selected wavebands by comparing them with feature positions of rice canopy spectra. In this study, the field experiment in 2002 growing season was conducted at the experimental farm of Zhejiang University, Hangzhou, China. Measurements of hyperspectral reflectance (350～2500 nm) and corresponding LAI were made for a paddy rice canopy throughout the growing season. And three methods were employed to identify the optimal wavebands for paddy rice LAI estimation: correlation coefficient-based method, vegetation index-based method, and stepwise regression method. This research selected 15 wavebands in the region of 350～2 500 nm, which appeared to be the optimal wavebands for the paddy rice LAI estimation. Of the selected wavebands, the most frequently occurring wavebands were centered around 554, 675, 723, and 1 633 nm. They were followed by 444, 524, 576, 594, 804, 849, 974, 1 074, 1 219, 1 510, and 2 194 nm. Most of them made physical sense and had their counterparts in spectral known feature positions, which indicates the promising potential of the 15 selected wavebands for the retrieval of paddy rice LAI.

12、Assessing the Response of Seasonal Variation of Net Primary Productivity to Climate Using Remote Sensing Data and Geographic Information System Techniques in Xinjiang

作者：Dai-Liang Peng, Jing-Feng Huang, Cheng-Xia Cai, Rui Deng andJun-Feng Xu

期刊：Journal of Integrative Plant Biology V olume 50, Issue 12, pages 1580–1588, December 2008

摘要：Net pdmary productivity (NPP) is a key component of energy and matter transformation in the terrestrial ecosystem, and the responses of NPP to global change locally and regionally have been one of the most important aspects in climate-vegetation relationship studies. In order to isolate causal climatic factors, it is very important to assess the response of seasonal variation of NPP to climate. In this paper, NPP in Xinjiang was estimated by NOAA/A VHRR Normalized Difference Vegetation Index (NDVI) data and geographic information system (GIS) techniques. The impact of climatic factors (air temperature, precipitation and sunshine percentage) on seasonal variations of NPP was studied by time lag and serial correlation ageing analysis. The results showed that the NPP for different land cover types have a similar correlation with any one of the three climatic factors, and precipitation is the major climatic factor influencing the seasonal variation of NPP in Xinjiang. It was found that the positive correlation at 0 lag appeared between

NPP and precipitation and the serial correlation ageing was 0 d in most areas of Xinjiang, which indicated that the response of NPP to precipitation was immediate. However, NPP of different land cover types showed significant positive correlation at 2 month lag with air temperature, and the impact of which could persist 1 month as a whole. No correlation was found between NPP and sunshine percentage.

13、Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

作者：Yang XiaoHua; Wang FuMin; Huang JingFeng; Wang JianWen; Wang RenChao; Shen ZhangQuan; Wang XiuZhen

期刊：Pedosphere 2009 V ol. 19 No. 2 pp. 176-188

摘要：The radial basis function (RBF) emerged as a variant of artificial neural network. Generalized regression neural network (GRNN) is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance (350 to 2 500 nm) data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density (45 plants m-2). Stepwise multivariable regression model (SMR) and RBF were used to compare their predictability for the leaf area index (LAI) and green leaf chlorophyll density (GLCD) of rice based on reflectance (R) and its three different transformations, the first derivative reflectance (D1), the second derivative reflectance (D2) and the log-transformed reflectance (LOG). GRNN based on D1 was the best model for the prediction of rice LAI and GLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.

14、A new quantitative model of ecological compensation based on ecosystem capital in Zhejiang Province, China

作者：Yan Jin, Jing-feng Huang, Dai-liang Peng

期刊：Journal of Zhejiang University SCIENCE B April 2009, V olume 10, Issue 4, pp 301-305

摘要：Ecological compensation is becoming one of key and multidiscipline issues in the field of resources and environmental management. Considering the change relation between gross domestic product (GDP) and ecological capital (EC) based on remote sensing estimation, we construct a new quantitative estimate model for ecological compensation, using county as study unit, and determine standard value so as to evaluate ecological compensation from 2001 to 2004 in Zhejiang Province, China. Spatial differences of the ecological compensation were significant among all the counties or districts. This model fills up the gap in the field of quantitative evaluation of regional ecological compensation and provides a feasible way to reconcile the conflicts among benefits in the economic, social, and ecological sectors.

15、Mapping paddy rice with multi-date moderate-resolution imaging spectroradiometer (MODIS) data in China

作者：Hua-sheng Sun, Jing-feng Huang, Alfredo R. Huete, Dai-liang Peng, Feng Zhang

期刊：Journal of Zhejiang University SCIENCE A October 2009, Volume 10, Issue 10, pp 1509-1522

摘要：The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderateresolution imaging spectroradiometer (MODIS) data in China. Paddy rice fields were extracted by identifying the unique characteristic of high soil moisture in the flooding and transplanting period with improved algorithms based on rice growth calendar regionalization. The characteristic could be reflected by the enhanced vegetation index (EVI) and the land surface water index (LSWI) derived from MODIS sensor data. Algorithms for single, early, and late rice identification were obtained from selected typical test sites. The algorithms could not only separate early rice and late rice planted in the same fields, but also reduce the uncertainties. The areal accuracy of the MODIS-derived results was validated by comparison with agricultural statistics, and the spatial matching was examined by ETM+ (enhanced thematic mapper plus) images in a test region. Major factors that might cause errors, such as the coarse spatial resolution and noises in the MODIS data, were discussed. Although not suitable for monitoring the inter-annual variations due to some inevitable factors, the MODIS-derived results were useful for obtaining spatial distribution maps of paddy rice on a large scale, and they might provide reference for further studies.

16、Validation of artificial neural network techniques in the estimation of nitrogen concentration in rape using canopy hyperspectral reflectance data.

作者：Wang Yuan; Wang FuMin; Huang JingFeng; Wang XiuZhen; Liu ZhanYu

期刊：International Journal of Remote Sensing 2009 Vol. 30 No. 17/18 pp. 4493-4505

摘要：A systematic comparison of two types of method for estimating the nitrogen concentration of rape is presented: the traditional statistical method based on linear regression and the emerging computationally powerful technique based on artificial neural networks (ANN). Five optimum bands were selected using stepwise regression. Comparison between the two methods was based primarily on analysis of the statistic parameters. The rms. error for the back-propagation network (BPN) was significantly lower than that for the stepwise regression method, and the T-value was higher for BPN. In particular, for the first-difference of inverse-log spectra (log 1/R)′, T-values performed with a 127.71% success rate using BPN. The results show that the neural network is more robust to training and estimating rape nitrogen concentrations using canopy hyperspectral reflectance data.

17、Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification

作者：Zhan-yu Liu, Jing-jing Shi, Li-wen Zhang, Jing-feng Huang

期刊：Journal of Zhejiang University SCIENCE B January 2010, V olume 11, Issue 1, pp 71-78

摘要：Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy

panicles, empty panicles caused by Nilaparvata lugens St&Oal, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.

18、DEVELOPMENT OF A VEGETATION INDEX FOR ESTIMATION OF LEAF AREA INDEX BASED ON SIMULATION MODELING

作者：Wang Fumin; Huang Jingfeng; Chen La

期刊：Journal of Plant Nutrition V olume 33, Number 3, February 2010, pp. 328-338(11)

摘要：Leaf area index (LAI) is an important structural variable for quantitative analysis of the energy and mass exchange characteristics of a terrestrial ecosystem. The objective of the research was to use the Scattering by Arbitrarily Inclined Leaves (SAIL) model to develop a new vegetation index for estimating LAI based on the Ratio Vegetation Index (RVI) and Perpendicular Vegetation Index (PVI). In the study, RVIs and PVIs were derived from the SAIL-simulated reflectance, and several potential limitations of RVI and PVI in LAI estimation were identified. First, for a given LAI level, a dark soil background resulted in higher RVI values and overestimated LAI values. The reverse was true for light colored soils. On the contrary, the PVI tended to underestimate LAI for dark soil background and overestimate LAI for light soil background. The RVI behaves oppositely to PVI in LAI estimation for same soil background. Based on these results, a new vegetation index (RMPVI: RVI Multiplied by PVI Vegetation Index) was constructed, and the sensitivity of this index to LAI was then evaluated and the performance of RMPVI in LAI estimation was compared with those of other vegetation indices. The results show that the RMPVI can greatly minimize the soil background influences, and is more sensitive to LAI than other indices, especially when LAI is greater than 2. As for LAI estimation, RMPVI can yield highest R2 than other vegetation indices used in the study, with a root mean square error (RMSE) of 0.16, which shows RMVPI is an efficient index for LAI estimation.

19、Evaluating the performance of PC-ANN for the estimation of rice nitrogen concentration from canopy hyperspectral reflectance

作者：Qiuxiang Yi; Jingfeng Huang; Fumin Wang; Xiuzhen Wang

期刊：International Journal of Remote Sensing V olume 31, Number 4, 2010, pp. 931-940(10)

摘要：In this study, a wide range of leaf nitrogen concentration levels was established in field-grown rice with the application of three fertilizer levels. Hyperspectral reflectance data of the rice canopy through rice whole growth stages were acquired over the 350 nm to 2500 nm range. Comparisons of prediction power of two statistical methods (linear regression technique (LR) and

artificial neural network (ANN)), for rice N estimation (nitrogen concentration, mg nitrogen g-1 leaf dry weight) were performed using two different input variables (nitrogen sensitive hyperspectral reflectance and principal component scores). The results indicted very good agreement between the observed and the predicted N with all model methods, which was especially true for the PC-ANN model (artificial neural network based on principal component scores), with an RMSE = 0.347 and REP = 13.14%. Compared to the LR algorithm, the ANN increased accuracy by lowering the RMSE by 17.6% and 25.8% for models based on spectral reflectance and PCs, respectively.

20、Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis

作者：Zhan-Yu Liua,Hong-Feng Wu,Jing-Feng Huang

期刊：Computers and Electronics in Agriculture Volume 72, Issue 2, July 2010, Pages 99–106

摘要：Detecting plant health condition is an important step in controlling disease and insect stress in agricultural crops. In this study, we applied neural network and principal components analysis techniques for discriminating and classifying different fungal infection levels in rice (Oryza sativa L.) panicles. Four infection levels in rice panicles were used in the study: no infection condition, light and moderate infection caused by rice glume blight disease, and serious infection caused by rice false smut disease. Hyperspectral reflectance of rice panicles was measured through the wavelength range from 350 to 2500 nm with a portable spectroradiometer in the laboratory. The spectral response characteristics of rice panicles were analyzed, and principal component analysis (PCA) was performed to obtain the principal components (PCs) derived from different spectra processing methods, namely raw, inverse logarithmic, first, and second derivative reflectance. A learning vector quantization (LVQ) neural network classifier was employed to classify healthy, light, moderate, and serious infection levels. Classification accuracy was evaluated using overall accuracy and Kappa coefficient. The overall accuracies of LVQ with PCA derived from the raw, inverse logarithmic, first, and second derivative reflectance spectra for the validation dataset were 91.6%, 86.4%, 95.5%, and 100% respectively, and the corresponding Kappa coefficients were 0.887, 0.818, 0.939 and 1. Our results indicated that it is possible to discriminate different fungal infection levels of rice panicles under laboratory conditions using hyperspectral remote sensing data.

21、Detection of nitrogen-overfertilized rice plants with leaf positional difference in hyperspectral vegetation index

作者：Qi-fa Zhou, Zhan-yu Liu, Jing-feng Huang

期刊：Journal of Zhejiang University SCIENCE B June 2010, Volume 11, Issue 6, pp 465-470

摘要：The main objective of this work was to compare the applicability of the single leaf (the uppermost leaf L1 and the third uppermost leaf L3) modified simple ratio (mSR705 index) and the leaf positional difference in the vegetation index between L1 and L3 (mSR705L1-mSR705L3) in detecting nitrogen (N)-overfertilized rice plants. A field experiment consisting of three rice genotypes and five N fertilization levels (0, 75, 180, 285, and 390 kg N/ha) was conducted at Xiaoshan, Hangzhou, Zhejiang Province, China in 2008. The hyperspectral reflectance (350–2500

nm) and the chlorophyll concentration (ChlC) of L1 and L3 were measured at different stages. The mSR705L1 and mSR705L3 indices appeared not to be highly sensitive to the N rates, especially when the N rate was high (above 180 kg N/ha). The mean mSR705L1-mSR705L3 across the genotypes increased significantly (P<0.05) or considerably from 180 to 285 kg N/ha treatment and from 285 to 390 kg N/ha treatment at all the stages. Also, use of the difference (mSR705L1-mSR705L3) greatly reduced the influence of the stages and genotypes in assessing the N status with reflectance data. The results of this study show that the N-overfertilized rice plants can be effectively detected with the leaf positional difference in the mSR705 index.

22、Detection and estimation of mixed paddy rice cropping patterns with MODIS data

作者：Dailiang Peng, Alfredo R. Huete, Jingfeng Huang, Fuming Wang, Huasheng Sun

期刊：International Journal of Applied Earth Observation and Geoinformation V olume 13, Issue 1, February 2011, Pages 13–23

摘要：In this paper, we developed a more sophisticated method for detection and estimation of mixed paddy rice agriculture from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Previous research demonstrated that MODIS data can be used to map paddy rice fields and to distinguish rice from other crops at large, continental scales with combined Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) analysis during the flooding and rice transplanting stage. Our approach improves upon this methodology by incorporating mixed rice cropping patterns that include single-season rice crops, early-season rice, and late-season rice cropping systems. A variable EVI/LSWI threshold function, calibrated to more local rice management practices, was used to recognize rice fields at the flooding stage. We developed our approach with MODIS data in Hunan Province, China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns. We further mapped the aerial coverage and distribution of early, late, and single paddy rice crops for several years from 2000 to 2007 in order to quantify temporal trends in rice crop coverage, growth and management systems. Our results were validated with finer resolution (2.5 m) Satellite Pour l’Observation de la Terre 5 High Resolution Geometric (SPOT 5 HRG) data, land-use data at the scale of 1/10,000 and with county-level rice area statistical data. The results showed that all three paddy rice crop patterns could be discriminated and their spatial distribution quantified. We show the area of single crop rice to have increased annually and almost doubling in extent from 2000 to 2007, with simultaneous, but unique declines in the extent of early and late paddy rice. These results were significantly positive correlated and consistent with agricultural statistical data at the county level (P < 0.01).