[Hyperspectral Analysis and Electrolyte Leakage Inversion of Creeping Bentgrass under Salt Stress].

Leaf electrolyte leakage is an important index of the plant cell permeability which plays an important role in the study of turfgrass salt stress. Traditional methods of measuring leaf electrolyte leakage have many disadvantages such as time-consuming, destroying the plants and being unable to monitor salt stress in large area. The aim of this study is to build a hyperspectral inversion model for leaf electrolyte leakage of creeping bentgrass under different salt concentration stresses thus to promote the application of the hyperspectral techniques in turfgrass salt stress monitoring. Creeping bentgrass was used in this study, and it was grown in water for two weeks before salt treatments. Leaves were collected at 7, 14 and 21 d under 0(CK), 100 and 200 mmol·L-1 NaCl respectively. The spectral values were gathered using Unispec-SC Spectral Analysis System (PP SYSTEMS，USA)before collecting grass leaves. Leaf electrolyte leakage was measured with electrical conductivity method. The relation and differences between salt treatments and spectral reflectance values were analyzed with EXCEL. Normalized difference vegetation index (NDVI) and difference vegetation index (DVI) were calculated using the spectral reflectance values. The first-order differential was calculated with difference method. The trilateral parameters of the blue, green and red rays were calculated at the meantime. The correlation analysis of the Leaf electrolyte leakage, spectral reflectance value, DVI and trilateral parameters was achieved by using EXCEL and Matlab software. Electrolyte leakage inversion model of the calibration set consisted of 48 high correlational samples, was built using unary linear regression, multivariate linear regression and partial least-squares regression methods. The prediction set inspection inversion model was established using the other 24 samples. The results showed that there is a positive correlation between salt stresses and 450～700 nm wave band. The leaf electrolyte leakage was positively associated with 450～732 nm band region at 0.01. The green edge amplitude and area of green edge were correlated with the foliar electrolyte leakage positively. Models based on partial least squares regression could inversion the foliar electrolyte leakage optimally. The calibration R2 reached to 0.681, and the validation R2 reached to 0.758. The calibration RMSE was 7.124, and the validation RMSE reached to 7.079. The inversion model made it possible to detect creeping bentgrass leaf electrolyte leakage under salt stress rapidly. This study also provided theoretical reference for monitoring the damage of other creeping bentgrass related plant species resulted by salt stress.

[Study on germination rate of zoysia (Zoysia japonica Steud.) seeds using near infrared reflectance spectroscopy].

With 37 zoysia seed samples with different germination rates ranging from 58.5% to 92%, harvested in different years from 2009 to 2011 and from different locations of China, a model for determining germination rate of zoysia seeds was tried to be built by near infrared reflectance spectroscopy with quantitative partial least squares (QPLS). All the seeds samples were divided into two groups: calibration set (including 28 samples) and validation set (including 9 samples). The results showed that with the spectral range from 6 000 to 7 000 cm(-1) and 6 main components, there was a better fitting between the predictive value and true value. Determination coefficients (R2) of calibration and validation sets are 90.73% and 91.80%, the coefficients of correlation are 0.986 6 and 0.987 2, the standard errors are 9.80 and 9.47, and the average absolute errors are 7.64% and 6.98% respectively. Even with different calibration samples, the models have a high determination coefficient (R2 over building of NIR model for determining 90%), low standard errors (about 10.00) and low absolute errors (about 8.00%). The building of NIR model for determining germination rate of zoysia seeds could promote the application of high quality seeds in production.

[Study on varieties identification of Kentucky bluegrass using hyperspectral imaging and discriminant analysis].

Hyperspectral images of six varieties of Kentucky bluegrass were acquired using hyperspectral imager (550-1 000 nm) and the leaf spectral properties were extracted. Wilks' lambda stepwise method was used and 9 optimal wavelengths were selected from the original 94 wavelengths and the discriminant models for varieties identification of Kentucky bluegrass were built based on Fisher' s linear discriminant function. The results showed that the Fisher' s linear discriminant model with 9 wavelengths achieved classification accuracies of 100% for both training and testing samples. While for the models with three wavelengths and six wavelengths, classification accuracies reached 83.3% and 96.7% for the testing samples, respectively. It indicates that hyperspectral images combined with discriminant analysis might be a good method to identify the varieties of Kentucky bluegrass.