[Quantitative Identification of Yellow Rust and Powdery Mildew in Winter Wheat Based on Wavelet Feature].

Powdery mildew (Blumeria graminis) and stripe rust (Puccinia striiformis f. sp. Tritici) are two of the most prevalent and serious winter wheat diseases in the field, which caused heavy yield loss of winter wheat all over the world. It is necessary to quantitatively identify different diseases for spraying specific fungicides. This study examined the potential of quantitative distinction of powdery mildew and yellow rust by using hyperspectral data with continuous wavelet transform at canopy level. Spectral normalization was processing prior to other data analysis, given the differences of the groups in cultivars and soil environment. Then, continuous wavelet features were extracted from normalized spectral bands using continuous wavelet transform. Correlation analysis and independent t-test were used conjunctively to obtain sensitive spectral bands and continuous wavelet features of 350～1 300 nm, and then, principal component analysis was done to eliminate the redundancy of the spectral features. After that, Fisher linear discriminant models of powdery mildew, stripe rust and normal sample were built based on the principal components of SBs, WFs, and the combination of SBs & WFs, respectively. Finally, the methods of leave-one-out and 55 samples which have no share in model building were used to validate the models. The accuracies of classification were analyzed, it was indicated that the overall accuracies with 92.7% and 90.4% of the models based on WFs, were superior to those of SFs with 65.5% and 61.5%; However, the classification accuracies of Fisher 80-55 were higher but no different than leave-one-out cross validation model, which was possibly related to randomness of training samples selection. The overall accuracies with 94.6% and 91.1% of the models based on SBs & WFs were the highest; The producer' accuracies of powdery mildew and healthy samples based on SBs & WFs were improved more than 10% than those of WFs in Fisher 80-55. Focusing on the discriminant accuracy of different disease, yellow rust can be discriminated in the model based on both WFs and SBs & WFs with higher accuracy; the user' accuracy and producer' accuracy were all up to 100%. The results show great potential of continuous wavelet features in discriminating different disease stresses, and provide theoretical basis for crop disease identification in wide range using remote sensing image.

[Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat].

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.

[Estimating Winter Wheat Nitrogen Vertical Distribution Based on Bidirectional Canopy Reflected Spectrum].

The vertical distribution of crop nitrogen is increased with plant height, timely and non-damaging measurement of crop nitrogen vertical distribution is critical for the crop production and quality, improving fertilizer utilization and reducing environmental impact. The objective of this study was to discuss the method of estimating winter wheat nitrogen vertical distribution by exploring bidirectional reflectance distribution function (BRDF) data using partial least square (PLS) algorithm. The canopy reflectance at nadir, +/-50 degrees and +/- 60 degrees; at nadir, +/- 30 degrees and +/- 40 degrees; and at nadir, +/- 20 degrees and +/- 30 degrees were selected to estimate foliage nitrogen density (FND) at upper layer, middle layer and bottom layer, respectively. Three PLS analysis models with FND as the dependent variable and vegetation indices at corresponding angles as the explicative variables were. established. The impact of soil reflectance and the canopy non-photosynthetic materials, was minimized by seven kinds of modifying vegetation indices with the ratio R700/R670. The estimated accuracy is significant raised at upper layer, middle layer and bottom layer in modeling experiment. Independent model verification selected the best three vegetation indices for further research. The research result showed that the modified Green normalized difference vegetation index (GNDVI) shows better performance than other vegetation indices at each layer, which means modified GNDVI could be used in estimating winter wheat nitrogen vertical distribution

[Comparative study on remote sensing invertion methods for estimating winter wheat leaf area index].

The present study aims to explore capability of different methods for winter wheat leaf area index inversion by integrating remote sensing image and synchronization field experiment. There were four kinds of LAI inversion methods discussed, specifically, support vector machines (SVM), discrete wavelet transform (DWT), continuous wavelet transform (CWT) and principal component analysis (PCA). Winter wheat LAI inversion models were established with the above four methods respectively, then estimation precision for each model was analyzed. Both discrete wavelet transform method and principal component analysis method are based on feature extraction and data dimension reduction, and multivariate regression models of the two methods showed comparable accuracy (R2 of DWT and PCA model was 0. 697 1 and 0. 692 4 respectively; RMSE was 0. 605 8 and 0. 554 1 respectively). While the model based on continuous wavelet transform suffered the lowest accuracy and didn't seem to be qualified to inverse LAL It was indicated that the nonlinear regression model with support vector machines method is the most eligible model for estimating winter wheat LAI in the study area.

[Research on universality of least squares support vector machine method for estimating leaf area index of winter wheat].

Leaf area index (LAI) is one of the most important parameters for evaluating winter wheat growth status and forecasting its yield. Hyperspectral remote sensing is a new technical approach that can be used to acquire the instant information of vegetation LAI at large scale. This study aims to explore the capability of least squares support vector machines (LS-SVM) method to winter wheat LAI estimation with hyperspectral data. After the compression of PHI airborne data with principal component analysis (PCA), the sample set based on the measured LAI data and hyperspectral reflectance data was established. Then the method of LS-SVM was developed respectively to estimate winter wheat LAI under four different conditions, to be specific, different plant type cultivars, different periods, different nitrogenous fertilizer and water conditions. Compared with traditional NDVI model estimation results, each experiment of LS-SVM model yielded higher determination coefficient as well as lower RMSE value, which meant that the LS-SVM method performed better than the NDVI method. In addition, NDVI model was unstable for winter wheat under the condition of different plant type cultivars, different nitrogenous fertilizer and different water, while the LS-SVM model showed good stability. Therefore, LS-SVM has high accuracy for learning and considerable universality for estimation of LAI of winter wheat under different conditions using hyperspectral data.

[Inversion of LAI by considering the hotspot effect for different geometrical wheat].

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.

[Inversion of leaf area index during different growth stages in winter wheat].

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.

[Geometric distortion correction for hyperspectral image using a rotating scan reflector].

Offner imaging spectrometer is a kind of pushbroom imaging system. Hyperspectral images acquired by Offner imaging spectrometers require relative motion of sensor and scene that is translation or rotation. Via rotating scan with a reflector at the front of sensor's len, large objects can be entirely captured. But for the changes in object distances, geometric distortion occurs. A formula of space projection from an object point to an image point by one capture was derived. According to the projection relation and slit's motion curve, the object points' coordinates on a reference plan were obtained with rotation angle for a variable. A rotating scan device using a reflector was designed and installed on an Offner imaging spectrometer. Clear images were achieved from the processing of correction algorithm.

[Monitoring the chlorophyll fluorescence parameter Fv/Fm in compact corn based on different hyperspectral vegetation indices].

In order to further assess the feasibility of monitoring the chlorophyll fluorescence parameter Fv/Fm in compact corn by hyperspectral remote sensing data, in the present study, hyperspectral vegetation indices from in-situ remote sensing measurements were utilized to monitor the chlorophyll fluorescence parameter Fv/Fm measured in the compact corn experiment. The relationships were analyzed between hyperspectral vegetation indices and Fv/Fm, and the monitoring models were established for Fv/Fm in the whole growth stages of compact corn. The results indicated that Fv/Fm was significantly correlated to the hyperspectral vegetation indices. Among them, structure-sensitive pigment index (SIPI) was the most sensitive remote sensing variable for monitoring Fv/Fm with correlation coefficient (r) of 0.88. The monitoring model of Fv/Fm was established on the base of SIPI, and the determination coefficients (r2) and the root mean square errors (RMSE) were 0.8126 and 0.082 respectively. The overall results suggest that hyperspectral vegetation indices can be potential indicators to monitor Fv/Fm during growth stages of compact corn.

[Using extraction of red edge position to validate consistency of hyperspectral imaging and non-imaging data].

Using Pushbroom imaging spectrometer (PIS) and FieldSpec ProFR2500 (ASD), spectral reflectances of winter wheat and maize at different stages were collected synchronously. In order to validate the reliability of imaging spectral data, the red edge position of hyperspectral data for PIS and ASD were extracted by different algorithms, respectively. The following results were obtained: (1) The original spectrum of both instruments had high inosculation in red light region (670-740 nm); (2) With the spectra collected under laboratory condition (maize leaf), the extracted red edge position was is concentrated between 700 and 720 nm for the two instruments; (3) With the spectra collected undre field condition (wheat leaf), the extracted red edge position for PIS and ASD were different, the red edge position of PIS data was in 760 nm, while it was in 720 nm for ASD data. The main reason might be that the imaging spectral data were influenced by oxygen absorbtion; (4) the red edge rangeability of PIS and ASD were different, but the trends were the same. The above results could provide some references for hyperspectral imaging data's extensive application.