Red Edge Parameters Research Articles

Local detrended fluctuation analysis for spectral red-edge parameters extraction

Nowadays, spectrum technology makes it possible to nondestructively monitor crop and provide real-time diagnostic advice. Spectral red-edge parameters are important detection target for the growth of rapeseed. In this work, we propose a new method to acquire the rapeseed’s spectral red-edge parameters based on local detrended fluctuation analysis (L-DFA). In practice, by using the L-DFA, local Hurst exponent of the spectrum is calculated firstly. And then, six traditional red-edge parameter extraction methods, namely maximum first derivative method, adjusted linear extrapolation method, linear four-point interpolation method, inverted Gaussian method, Lagrangian interpolation method, and polynomial fitting interpolation, are employed to act on the estimated local Hurst exponent, and thus four red-edge parameters, i.e., red-edge position, red-edge amplitude, and left and right red-edge area, are obtained. In our experiments, by using the above four local Hurst exponent-based red-edge parameters as argument, a prediction model is developed to forecast the SPAD values (soil and plant analyzer development, often used as a parameter to indicate the chlorophyll content) for the five rapeseed’s growth periods based on random decision forest method. The results show that the local Hurst exponent-based red-edge feature can produce better model effect than the original spectrum-based one for the all phenological periods. Significance test for the four kinds of plant patterns shows that there is huge difference of the proposed four-dimensional red-edge features between the transplant and direct plant in the whole process of rapeseed’s growth. The differences between the other three groups of plant factors (two planting densities, two fertilizer types, and two weed controls) are not significant in certain specific growth periods of rapeseed. These findings provide a basis for studying difference of rapeseed yield and oil differences between different planting patterns.

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

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

Early assessment of the yield loss in rice due to the brown planthopper using a hyperspectral remote sensing method

ABSTRACTThe brown planthopper (BPH) is an important pest in rice. Rice losses due to BPH's damage are often more than 10% of yield. Assessments on loss rates from BPH are now basically dependent on experiential indices. However, early assessments on rice yield losses using hyperspectral data are still rare. In this study, reflectance from rice canopy was measured in net cages after different densities of BPH release. Results showed that reflectance in the near-infrared region (750–1000 nm) from milk grain stage, and in 400–531 nm and 567–705 nm from mature grain stage was closely related to BPH density. These spectral indices: red-edge parameters (λr, Dλr, Sλr), ratio of the maximal reflectance in green (490–560 nm) to minimal reflectance (640–740 nm) in red (RGREEN/RRED), ratio of the near-infrared peak (RNIR) to RRED, normalized difference vegetation index between RNIR and RRED and soil-adjusted vegetation index (SAVI) from both milk and mature grain stages, indicated BPH densities well. As expected, rate of loss in rice grain was higher with increasing BPH density. SAVI, Dλr, Sλr and reflectance at 760 nm (R760) from milk grain stage were significantly correlated with rates of loss in panicle, and 1000-grain weight and multiple-linear regression models for detecting loss rate were established. The hyperspectral reflectance from rice at milk grain stage can be used to assess rice yield losses due to BPH and improve management policies.

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

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

Response of winter wheat (Triticum aestivum L. ) hyperspectral characteristics to low temperature stress

The simple winter wheat variety was conducted under the low temperature treatment at -2, -4, and -6 °C, the canopy reflectance was measured and the red edge parameters were extracted to study the winter wheat canopy spectral characteristics effected by the low temperature stress and the hyperspectral response to the low temperature stress of winter wheat at jointing stage. The results showed that the canopy reflectance decreased in visible region and increases at near infrared band with the high intensively low temperature stress, and "green peak" was weakened and "red well" was not distinctive. Moreover, the derivate spectrum had the trend of shift to short wavelength direction with the strengthening of low temperature stress and the red edge presented the blue shift. The area of red edge and red edge amplitude exhibit increase. It indicated that the canopy spectrum of winter wheat is sensitive to the low temperature stress, and the hyperspectral technology can be used to monitor the low temperature stress of winter wheat at jointing stage.

Comparison of red-edge parameters for spring wheat chlorophyll content at different growth stages in irrigated and dry-land regions

通过分析水浇地与不同类型旱地春小麦红边参数变化规律, 利用与春小麦叶绿素含量相关性最好的红边位置, 分别建立水浇地与旱地春小麦叶绿素含量最佳估测模型。结果表明: 春小麦的红边位置表现出水浇地>阴坡旱地>半阴/阳坡旱地>阳坡旱地的特点。起身期到扬花期, 各地类春小麦红边位置均偏向长波方向, 扬花至乳熟期, 又偏向短波。水浇地与阴坡旱地春小麦的红边在起身期和乳熟期存在"单峰"现象, 而在其他各期均为"双峰"; 半阴/阳坡旱地在抽穗和扬花期"双峰"明显, 在起身、拔节和乳熟期表现出"单峰"现象; 阳坡地在各生育期均为"单峰"现象。整个生育期, 水浇地春小麦的红边面积最大, 阳坡地的最小。起身期至乳熟期, 水浇地与旱地春小麦叶绿素含量线性模型的拟合<i>R</i>²和检验<i>R</i>²均大于0.81, <i>RMSE</i>均小于1.70, 说明可以利用红边位置预测各地春小麦叶绿素含量。

Chlorophyll content retrieve of vegetation using Hyperion data based on empirical models

Modeling using empirical methods based on Hyperion is a fast and accurate way to retrieve vegetation chlorophyll content. In this paper,the measured spectra and simulated Hyperion spectra were analyzed,the correlation between chlorophyll content and reflectance with its change forms and the relation between chlorophyll content and red edge parameters as well as vegetation indexes were calculated to obtain the most accurate modeling method. The vegetation index of modified simple ratio( MSR) has a significant correlation with chlorophyll content, and its regression model can retrieve chlorophyll concentration accurately. Using MSR and measured chlorophyll content,the authors built the regression model based on Hyperion data and then established the chlorophyll concentration profile. The chlorophyll concentration profile of Zhangye City was computed and a high- accuracy was achieved,with its relative error less than 5%.

Analysis of the relationship between the spectral characteristics of maize canopy and leaf area index under drought stress

Maize is one of the most widespread grain crops in the world; however, more than 70% of corn in China suffers some degree of drought disaster every year. Leaf area index (LAI) is an important biophysical parameter of the vegetation canopy and has important significance for crop yield estimation. Using the data of canopy spectral reflectance and leaf area index (LAI) for maize plants experiencing different levels of soil moisture from 2011 to 2012, the characteristics of the canopy reflective spectrum and its first derivative, and their relationships to leaf area index, were analyzed. Soil moisture of the control group was about 75% while that of the drought stress treatment was about 45%. In addition, LAI retrieval models for maize were established using vegetation indices (VIs) and principal component analysis (PCA) and the models were tested using independent datasets representing different soil water contents and different developmental stages of maize. The results showed that canopy spectral reflectances were in accordance with the characteristics of green plants, under both drought stress and at different developmental stages. In the visible band, canopy reflectance for both healthy and damaged vegetation had a green-wavelength peak and a red-wavelength valley; reflectance under drought stress, especially in the green peak (about 550nm) and the red valley (about 676nm) was higher than in the control group. In the near-infrared band, the canopy spectral reflectance decreased substantially between 780 and 1350nm under drought stress. Moreover, the red edge of the spectrum was shifted toward blue wavelengths. The first derivative spectrum showed a double peak phenomenon at the edge of the red band at different developmental stages: the main peak appeared between 728 and 732nm and the minor peak at about 718nm. The double peaks become more obvious through the growth and development of the maize, with the most notable effect during the silking and milk stages, after which it gradually decreased. During maize growth, the LAI of all plants, regardless of soil moisture conditions, increased, and the largest LAI also occurred during the silking and milk stages. During those stages, the LAI of plants under different drought stress levels was significantly lower (by 20% or more) than in normal plants with sufficient water supplies. The LAI was highly significantly correlated with canopy spectral reflectance in the bands from 350nm to 510nm, from 571nm to 716nm, and from 1450nm to 1575nm. Also, the LAI was significantly correlated with red edge parameters and several VIs. The Perpendicular Vegetation Index (PVI) had the best correlation with LAI, with a coefficient of determination (R2) of 0.726 for the exponential correlation. Using dependent data, a LAI monitoring model for the maize canopy was constructed using PCA and VI methods. The test results showed that both the VI and PCA methods of monitoring maize LAI could provide robust estimates, with the predicted values of LAI being significantly correlated with the measured values. The model based on PVI showed higher precision under the drought stresses, with a correlation coefficient of 0.893 (n=27), while the model based on PCA was more precise under conditions of adequate soil moisture, with a correlation coefficient of 0.877 (n=32). Therefore, a synthesis of the models based on both VI and PCA could be more reliable for precisely predicting LAI under different levels of drought stresses in maize.

Estimating Regional Winter Wheat Leaf N Concentration with MERIS by Integrating a Field Observation-Based Model and Histogram Matching

<abstract><title><italic>Abstract.</italic></title> Leaf nitrogen status is a key indicator for evaluating crop growth, yield potential, and grain quality. Non-destructive and rapid assessment of leaf nitrogen is required for improving nitrogen management in wheat production. The Medium-Resolution Imaging Spectrometer (MERIS), one of the payloads on ENVISAT, has an average 20 nm spectral resolution in the red edge region, a 300 m spatial resolution, and a 3-day repeat cycle, making MERIS a potentially valuable sensor for fast measurement and monitoring of crop N status at regional to global scales. The red edge can provide information related directly to the N concentration of vegetation. To develop a model for estimating crop N from field observations, winter wheat (Triticum aestivum L.) leaf nitrogen concentration (LNC) and spectral reflectance data in the North China Plain were collected. A comparison between spectral reflectance, its first derivative, and red edge parameters for estimating wheat LNC found that the red well position (RWP) is the best indicator of LNC (R² = 0.60). A model was developed for LNC estimation with RWP, and the model was calibrated to the satellite imagery using a histogram matching method. A geo-rectified LNC map of Shandong and Henan provinces in the North China Plain was then generated from MERIS data. Validation with independent field observation data showed that the accuracy was higher than 95%, with root mean square error (RMSE) of 0.11 g N g^-1 DW and a coefficient of determination (R²) of 0.90. This research will contribute to improving N fertilizer usage by mapping large-area winter wheat N status.

Soil TPH Concentration Estimation Using Vegetation Indices in an Oil Polluted Area of Eastern China

Assessing oil pollution using traditional field-based methods over large areas is difficult and expensive. Remote sensing technologies with good spatial and temporal coverage might provide an alternative for monitoring oil pollution by recording the spectral signals of plants growing in polluted soils. Total petroleum hydrocarbon concentrations of soils and the hyperspectral canopy reflectance were measured in wetlands dominated by reeds (Phragmites australis) around oil wells that have been producing oil for approximately 10 years in the Yellow River Delta, eastern China to evaluate the potential of vegetation indices and red edge parameters to estimate soil oil pollution. The detrimental effect of oil pollution on reed communities was confirmed by the evidence that the aboveground biomass decreased from 1076.5 g m−2 to 5.3 g m−2 with increasing total petroleum hydrocarbon concentrations ranging from 9.45 mg kg−1 to 652 mg kg−1. The modified chlorophyll absorption ratio index (MCARI) best estimated soil TPH concentration among 20 vegetation indices. The linear model involving MCARI had the highest coefficient of determination (R 2 = 0.73) and accuracy of prediction (RMSE = 104.2 mg kg−1). For other vegetation indices and red edge parameters, the R2 and RMSE values ranged from 0.64 to 0.71 and from 120.2 mg kg−1 to 106.8 mg kg−1 respectively. The traditional broadband normalized difference vegetation index (NDVI), one of the broadband multispectral vegetation indices (BMVIs), produced a prediction (R 2 = 0.70 and RMSE = 110.1 mg kg−1) similar to that of MCARI. These results corroborated the potential of remote sensing for assessing soil oil pollution in large areas. Traditional BMVIs are still of great value in monitoring soil oil pollution when hyperspectral data are unavailable.

Monitoring Chlorophyll and Nitrogen Contents in Cotton Leaf Infected by Verticillium wilt with Spectra Red Edge Parameters

The relationship between chlorophyll (Chl) content, leaf nitrogen content (LNC) and red edge parameters were analyzed, and diagnose models of spectra red edge parameters were established for cotton leaf infected by Verticillium wilt. Results showed that: (1) Chl a, Chl b, Chl a+b content, and LNC decreased with increasing in severity level (SL) of Verticillium wilt in cotton leaves, in which Chl a showed the highest and Chl b showed the lowest decrement rate, respectively. (2) Spectrum reflectance increased with increasing severity of Verticillium wilt in the visible region (400–700 nm), near-infrared region (700–1300 nm) and short infrared region (1300–2500 nm), and significantly higher increment was detected in 525–680 nm region (P0.01). Spectrum absorption decreased significantly with increasing SL of Verticillium wilt in the visible region and short infrared region (P0.01), and which increased first and then decreased in near-infrared region. (3) Decrease of REP, Dr, Lo, Depth672, Area672 and increase of Lwidth was detected among red edge parameters, in which Area672 showed the highest and Dr showed the Lowest decrement rate, respectively. (4) There was significantly positive correlation between Chl a, Chl b, Chl a+b, LNC of cotton leaves and REP, Lo, Depth672, Area672 of red edge parameters, significantly negative correlation was found for Lwidth of red edge parameter, while no significant correlation was found for Dr of red edge parameter. (5) Diagnose models of Chl a, Chl a+b,edge parameter, while no significant correlation was found for Dr of red edge parameter. (5) Diagnose models of Chl a, Chl a+b, and LNC for Verticillium wilt in cotton leaves with the independent variables Area672, and Chl b with the independent variables Lo reached the best estimated precision (P0.01). This could diagnose severity level of Verticillium wilt in cotton leaves effectively.

Research on the relationship between the fractional coverage of the submerged plant Vallisneria spiralis and observed spectral parameters

The present paper discusses the relationship between the coverage fraction of submerged plants and the observed spectral characteristics. The purpose of this paper is to validate a remote sensing technology to monitor the change in the plant composition of a water body. In the current study, the reflectance spectra of the submerged plant Vallisneria spiralis at different fraction coverages of the wetland in Hangzhou Bay were measured. The relationships between the fraction coverage of V. spiralis and simulated Quickbird normalized difference vegetation index (NDVI), red edge, and other spectral characteristic parameters were established. The results showed that the spectral reflectance characteristics of submerged plant V. spiralis were mainly in the visible light (490-650nm) and near infrared (700-900nm). The rate of change of the blue band curve and simulated Quickbird NDVI showed a higher correlation with the V. spiralis coverage, so estimation models of the fraction coverage were constructed using these parameters. The estimated fraction coverage of V. spiralis with different models were validated with ground data, and the accuracy of estimation models was assessed. The most suitable estimated fraction coverage of V. spiralis was obtained using the rate of change of the blue band curve and simulated Quickbird NDVI. The present work demonstrated a method to monitor the distribution and dynamical variation of submerged plants at the large scale.

Effect of water condition on hyperspectral and red-edge characteristics of tobacco leaf

采用ASD Fieldspec HH光谱仪, 测定了不同水分处理下两个烟草品种叶片的光谱特性, 分析其红边特征参数的变化规律及其与烟叶生理指标的相关性。结果表明: 不同水分处理之间的烟草叶片光谱反射率差异明显, 但两个品种的变化规律一致。烟草伸根期和旺长期, 近红外光区的光谱反射率随土壤水分的增加而升高, 成熟期则呈相反趋势, 且光谱反射率在旺长期达到最高值。不同水分处理下烟草叶片一阶微分光谱趋势总体一致, 但红边一阶导数光谱差异显著。红边位置随土壤水分减少向长波方向移动, 发生“红移”现象。红边幅值和红边面积在伸根期和旺长期均随土壤含水量的增加而发生“红移”现象, 成熟期则发生“蓝移”现象。烟草叶片的红边位置同其生理指标的相关性要优于其他参数, 同叶绿素a含量、叶绿素b含量、类胡萝卜素含量、叶绿素总量、叶片鲜重、叶片干重、叶面积和叶片含水率均呈极显著相关关系; 红边幅值和红边面积与叶绿素a含量、叶绿素b含量、类胡萝卜素含量、叶绿素总量和叶片含水率之间均呈极显著性正相关, 红边面积同叶片干重呈极显著性负相关关系。

The Red Edge Parameters Diversification Disciplinarian and Its Application for Nutrition Diagnosis

Red edge refers to the abrupt change of canopy reflectance in the spectrum band of 680~750nm,it is formed by strong absorption of red band and strong reflection of NIR band by pant canopy. The dynamics of red edge parameters (REP) for phenological stages of wheat canopy are studied, it indicated that the winter wheat growth dynamic can be evaluated by the red edge parameters with the study on the red edge parameters diversification disciplinarian. The red edge position moved to the longer wavelength from erecting stage to heading stage, and returned to the original wavelength direction from grain\|filling stage to maturity stage. The correlation analysis of REP and the components was also discussed. The red edge position can be used to predict the soluble sugar and the chlorophyll concentration; the red edge swing for the inversion of total nitrogen content; the near\|infrared platform for starch; the red vale for the leaf area index and the regression equation was also established. For this purpose red edge parameters can play a vital role in providing time\|specific and time\|critical information for precision farming.

A Review on the Principles and Basic Methods of Remote Sensing Spectral Characteristic Parameters

The principles and methods of remote sensing spectral characteristic parameters including derivative spectrum,red edge parameter,spectrum absorption characteristics and spectrum reflectance characteristics,national and international development of hyperspectral remote sensing and its application in extracting biophysical and biochemical information of vegetation,and the feasibility of vegetation indices application are reviewed.The potentiality of further application of remote sensing technology in extracting vegetation information in order to promote remote sensing was put forward.Remote sensing spectral parameter provides a powerful tool for extracting vegetation biophysical and biochemical information.

基于HJ 星高光谱数据红边参数的冬小麦叶面积指数反演

This study intends to examine the applicability of HJ-1A HSI data to estimation of leaf area index for crop monitoring. Four red edge indices (red vale position, red edge position, red edge slop and red edge swing) were used to extract the required information from the HJ-1A HSI data with inverse Gauss model. Combined with ground measurements data in April and May in 2009, regression analyses were used to establish red edge parameters model for LAI. Red edge position, red edge slop and red edge swing were highly correlated with LAI. R 2 were 0.5592, 0.7796 and 0.8107 respectively. Validation results showed that the HJ-1A HSI data have great potential in LAI inversion.

Biomass estimation of alpine grasslands under different grazing intensities using spectral vegetation indices

We measured and analyzed the spectral reflectance characteristics and biophysical properties such as biomass and vegetation cover of purple feather-grass (Stipa purpurea) alpine steppe grassland under different grazing intensities (no grazing, light, moderate, and heavy grazing) during the growing season. We investigated the effects of grazing intensities on the spectral reflectance and red edge parameters of S. purpurea alpine steppe grassland and established potential models of biomass estimation based on the relationship of spectral vegetation indices and measured alpine grassland biomass in Northern Tibet, China. The results indicated that the spectral reflectance of alpine grassland is similar to the spectral characteristics of typical green vegetation. Reflectance in the near-infrared region of the no grazing treatment was higher than the grazing treatments, while the reflectance of the no grazing treatment in the visible light region was weaker than the grazing treatments. With enhanced grazing intensity, the spectral reflectance of grass canopy followed an increasing trend; the red edge position moved to longer wavelengths, and the amplitude of the red edge decreased in alpine grassland. A significant relationship of measured aboveground biomass and vegetation indices was caculated based on spectral reflectance. Hence, the optimized soil adjusted vegetation index is prefered for the estimation of aboveground biomass of S. purpurea alpine steppe in Northern Tibet, China.

The vegetation classification in coal mine overburden dump using canopy spectral reflectance

The canopy spectral characteristics of typical plants in the overburden of the Fuxin coal mine dump were measured and analyzed. The reflectance of Leymus chinensis was affected by the soil, with a slight shift from green (550 nm) to the near infrared (NIR) region. Changes in chlorophyll and water absorption were not significant in the red (670 nm) and NIR bands, respectively. The reflectance curve trend for Artemisia lavandulaefolia was similar to those of Sophora japonica and Ulmus pumila, while the reflectance of S. japonica and U. pumila fluctuated in the NIR region (760–1200 nm), especially with greater water absorption around 930 and 1120 nm. In contrast, the reflectance of A. lavandulaefolia fluctuated slightly around 930 nm and a significant peak appeared at 1127 nm. In addition, the spectral reflectance of S. japonica was lower than for the other species in the visible band (400–700 nm). However, it was higher than for L. chinensis in the NIR region (780–1200 nm). Three classifiers, the self-organizing map (SOM), learning-vector quantization (LVQ), and a probabilistic neural network (PNN), were used to classify the vegetation and the results of all classifiers were compared based on total spectral reflectance data from 400 to 1200 nm. The PNN was the best classifier in terms of training and testing accuracy. The first difference reflectance was calculated, and the red edge parameter was able to classify the herbs ( L. chinensis and A. lavandulaefolia) and the arbores ( S. japonica and U. pumila) with an accuracy of 77 and 84%, respectively, although it did not perform as well for detail species. A mixing parameter matrix was built based on the sensitive wavelengths (550, 674, 810, 935, and 1125 nm), the vegetation indices (SAVI and NDGI), and the water absorption slope. High classification accuracy was obtained by applying the mixing parameter matrix. This method could be used for revegetation monitoring and in decision making.

Estimating Leaf Chlorophyll Content Using Red Edge Parameters

Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content (LCC). This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed ( Brassica napus L.) and wheat ( Triticum aestivum L.) crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC. This finding allowed the construction of a new red edge parameter, defined as red edge symmetry (RES). Compared to the commonly used red edge parameters (red edge position, red edge amplitude, and red edge area), RES was a better predictor of LCC. Furthermore, RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm ( R 675 and R 755) and reflectance of red edge center wavelength at 718 nm ( R 718), with the equation RES = ( R 718 -R 675) /( R 755 - R 675). In addition, RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion. The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data. This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources, as well as ground-based spectral reflectance data.

Estimation of winter wheat biomass based on remote sensing data at various spatial and spectral resolutions

Biomass can indicate plant growth status, so it is an important index for plant growth monitoring. This paper focused on the methodology of estimating the winter wheat biomass based on hyperspectral field data, including the LANDSAT TM and EOS MODIS images. In order to develop the method of retrieving the wheat biomass from remote sensed data, routine field measurements were initiated during periods when the LANDSAT satellite passed over the study region. In the course of the experiment, five LANDSAT TM images were acquired respectively at early erecting stage, jointing stage, earring stage, flowering stage and grain-filling stage of the winter wheat, and the wheat biomass was measured at each stage. Based on the TM and MODIS images, spectral indices such as NDVI, RDVI, EVI, MSAVI, SIPI and NDWI were calculated. At the same time, the hyperspectral field data was used to compute the normalized difference in spectral indices, red-edge parameters, spectral absorption, and reflection feature parameters. Then the correlation coefficients between the wheat biomass and spectral parameters of the experiment sites were computed. According to the correlation coefficients, the optimal spectral parameters for estimating the wheat biomass were determined. The bestfitting method was employed to build the relationship models between the wheat biomass and the optimal spectral parameters. Finally, the models were used to estimate the wheat biomass based on the TM and MODIS data. The maximum RMSE of estimated biomass was 66.403 g/m2.