Estimation of Main Parameters of Castor's Primary Productivity by Hyperspectral Remote Sensing Data
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This paper studied on the estimation of main parameters of castor's primary productivity by hyperspectral remote sensing data,using field experiment with different nitrogen and phosphorus levels.The result showed that with increasing application of nitrogen and phosphorus,LAI and ABM all increased,and castor canopy spectral reflectance was correspondently changed,especially the reflectivity of near-infrared ray(700~900 nm)was notably increased.Canopy spectral reflectance in 350~900 nm(especially 700~900 nm)was the highest in flowering and fruiting period,higher in seed maturing period,and the lowest in seedling stage.These changes were positively correlated with LAI.This suggests that the canopy spectral reflectance could be used to monitor castor's growth vigor and nutritional status.The relationships between hyperspectral vegetation index NDVI,RVI and LAI,ABM were all highly significant with determination of coefficients(R2)as 0.6115、0.6363、0.7102 and 0.6148,respectively.This suggests that both NDVI and RVI could be used to estimate castor's LAI and ABM during different phenological periods.Cite
Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soybean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (∼daily), high spatial resolution (∼5 m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8 m2/m2. The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27%; and coefficients of determination ranging between 86 and 88%. The NDVI performed best for the estimation of dry leaf biomass (CV = 27% and r2 = 090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV ≤ 20%). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependant best-fit functions. This approach eliminates/reduces the need for reflectance resampling, VIs inter-calibration and spatial resampling.
Red edge
Growing season
Enhanced vegetation index
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Estimating of Main Cultivation Physiology Parameters of Cotton by Using Hyperspectral Remote Sensing
Based on hyperspectral observation of cotton leaf in Xinjiang, the relationship between the characteristics of hyperspectral data and main cultivation physiology parameters was analyzed. The results showed that cotton have typical reflectance spectra characteristics as normal plants. The reflectance of leaf in senescence must be rise up in Red Valley Region (640-680 nm), which showed a high negative relationship with leaf photosynthesis rates. There was a high positively correlation between the first derivative spectral data and the chlorophyll content of cotton leaf in some bands, especially in 723-849nm, and the maximum value of correlation coefficient (r=0.7344) in waveband 750 nm, by using multivariate regression analyzing. NDVI (normalized difference vegetation index) was logarithmic correlation with LAI. Each area of Red Edge, Blue Edge and Yellow Edge accumulated the information of more channels, so they had larger application potential capacity to estimate total nitrogen content in leaf. And based on the relationship between them, some statistical models of the main physiology parameters have been established,such as LAI, chlorophyll content, total nitrogen content etc, which could be estimated by the hyperspectral data.
Red edge
Imaging spectrometer
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Leaf area index(LAI) is an important parameter as the indicator of optimal diagnosis for crop growing status.Research shows that there are high correlation between hyperspectral data and LAI.So hyperspectral remote sensing can be used in monitoring growth status of soybean.In this paper,hyperspectral reflectance(350 to 2 500 nm) data was obtained in four soybean key growth stages,Ratio vegetation index(RVI) was computed using average reflectance of near infrared bands of 760~850 nm and red region bands of 650-670 nm;Modified second soil-adjusted vegetation index(MSAVI2) was composed of reflectance of near infrared band of 800 nm and 670 nm.Based on RVI and MSAVI,six single variables of linear and nonlinear function models against LAI were established.All models reached 0.01 significance level,whilst,power function fitting of RVI,exponential function fitting and logarithm function fitting of MSAVI2 had comparatively higher accuracy for estimating soybean LAI;then the soybean canopy LAI was estimated according to the highest correlation coefficient of accurate logarithm model function between MSAVI2 and measured LAI,it showed that the correlation between measured LAI and estimated LAI was significant(R=0.9098**,n=46).The regression function accuracy was 84.9%,the RMSE was 0.2420,average relative error was 0.1510.It is real-time,nondestructive and quantitative for adopting vegetation indices RVI,MSAVI2 to obtain soybean LAI,it can offer an evidence to design an optimum soybean canopy and estimate soybean yield by using hyperspectral remote sensing.
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Canopy reflectance of processing tomato was measured with ASD FieldSpec at different growth stages; leaf area index (LAI) and processing tomato yields were collected. The relationship between canopy reflectance LAI and processing tomato yield was analyzed with single phase Linear Stepwise Regression and multivariate regression. The results revealed that there was a good relativity between hyperspectral data and leaf area index at green maturing period, and was not significant correlation in other period. The ideal compound regression model of compound regression between production and 4 growth periods of tomato was obtained.
Stepwise regression
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Aims Biomass and leaf area index (LAI) are important parameters for indicating crop growth potential and photosynthetic productivity in wheat. Non-destructive, quick assessment of leaf dry weight and LAI is necessary for growth diagnosis and cultural regulation in wheat production. The objectives of this study were to determine the relationships of leaf dry weight and LAI to ground-based canopy hyperspectral reflectance and spectral parameters and to derive regression equations for monitoring leaf dry weight and LAI in winter wheat (Triticum aestivum) with hyperspectral remote sensing. Methods Three field experiments were conducted with different wheat varieties and nitrogen levels for three growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and leaf dry weight and LAI during the experiments. Experiment one was conducted in 2005?2006 to construct a monitoring model with four N rates of 0, 90, 180 and 270 kg·hm–2 using cultivars ‘Ningmai9’ and ‘Yumai34’ (low and high protein types, respectively). Experiment two was un-dertaken in 2004?2005 to construct a monitoring model with four N rates of 0, 75, 150 and 225 kg·hm–2 using cultivars ‘Ningmai9’, ‘Yangmai12’ and ‘Yumai34’ (low, medium, high protein types, respec-tively). Experiment three was conducted in 2003?2004 to test a monitoring model with four N rates of 0, 75, 150, 225 and 300 kg·hm–2 using cultivars ‘Ningmai9’, ‘Huaimai20’ and ‘Xuzhou26’ (low, medium, high protein types, respectively). Important findings Leaf dry weight and LAI in wheat increased with increasing nitrogen rates and with significant differences between stages of growth. The dynamics of leaf dry weight and LAI during growth exhibited single peak patterns. The sensitive spectral bands were located mostly within red light and near infrared regions, with correlation coefficients –0.60 in 590~710 nm and 0.69 in 745~1 130 nm. The regression analyses between existing vegetation indices and leaf dry weight and LAI revealed that some key spectral parameters could accurately estimate changes in leaf dry weight and LAI across a broad range of stages of growth, nitrogen levels and growing seasons, with unified spectral parameters for each growth parameter. Among them, regression models based on RVI (810, 560), FD755, GMI, SARVI (MSS) and TC3 produced better estimation of leaf dry weight and LAI. Testing of the monitoring models with an independent dataset indicated that the spectral indices of RVI (810, 560), GMI, SARVI (MSS), PSSRb, (R750-800/R695-740)-1, VOG2 and mSR705 gave accurate growth estimation under the ex-perimental conditions. Overall, leaf dry weight and LAI in wheat could be monitored by key vegetation indices, with more reliable estimation from RVI (810, 560), GMI and SARVI (MSS).
Dry weight
Photochemical Reflectance Index
Specific leaf area
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Based on the data of the field experiments with four wheat varieties and five level of the nitrogen application,the relationships of the canopy reflectance spectra with the wheat yield and yield components were analyzed.The results showed that the correlation of the canopy multispectral reflectance between theoretical and actual yields was significant at the jointing stage.Therefore,it could be used to estimate the yield.However,the correlation of the canopy hyperspectral reflectance and yield was significant,so it couldn't be used to estimate the yield directly.The panicle number per mu was well forecasted by using canopy multispectral/ hyperspectral reflectance.The canopy multispectral and hyperspectral reflectance were linearly related to panicle number per mu at the jointing,mid-filling and maturity stage of wheat(p0.01).Thus,the estimate equations of the canopy hyperspectral reflectance A(760,850)/R550 and multispectral reflectance RVI(810,560) were constituted.The research results provided the important references for choosing appropriate canopy reflectance indexes,constituting the yield estimate model and ensuring the precision of the hyperspectral remote sensing information retrieval.
Multispectral pattern recognition
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Hyperspectral reflectance data of the two processing tomato cultivars Li Geer 87-5 and Tun He No.8 under four nitrogen fertilization treatments and three different plant patterns were recorded in a field experiment by the ASD Fieldspec non-imaging spectroradiometer at six main growth stages.Calculation of reflectance spectrum data obtained the normalized difference vegetation index(NDVI),ratio vegetation index(RVI),the second modified soil adjusted vegetation index(MSAVI2) and red edge normalized difference vegetation index(RENDVI).Regression analysis techniques were then performed to establish function models among the four indices and the measured chlorophyll density(CH.D),leaf area index(LAI),aboveground fresh biomass(AFBM) and aboveground dry biomass(ADBM),respectively.The results showed that the four vegetation indices are positively significant correlated with the four physiological parameters at 1% level.Among them,RENDVI and CH.D,RVI and LAI had the strongest linear relationship and the strongest power exponential function(RRENDVI-CH.D=0.8034**,RRVI-LAI=0.8703**,n=54,α=1%),respectively.Based on their strongest regression functions to predict CH.D and LAI,respectively.There were significant correlation at 1% between tested CH.D and estimated CH.D,tested LAI and estimated LAI(RMeasured CH.D-Estimated CH.D=0.8113**,RMeasured LAI-Estimated LAI=0.8546**,n=54,α=1%).The regression function accuracies were 85.5% and 86.3%,respectively.The study examined that CH.D,LAI,AFBM and ADBM of processing tomato canopy can be estimated by vegetation indices,and hyperspectral remote sensing can be achieved instant,nondestructive,un-touched and quantitative monitoring growth status of processing tomato.
Spectroradiometer
Red edge
Enhanced vegetation index
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