Exploring the Feasibility of Winter Wheat Freeze Injury by Integrating Grey System Model with RS and GIS

Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model （GSM） to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing （RS） and geographic information system （GIS） technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis （GRA） was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm （GCA）, and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.

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

Net primary 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 climatevegetation 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/AVHRR 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 0lag 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.

Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

Powdery mildew （Blumeria graminis） is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis （CWA） was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features （SFs） were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression （MLR） analysis and the partial least square regression （PLSR） analysis were adopted as training methods in model mildew on leaf level were found to be closely related with the development. The spectral characteristics of the powdery spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed （R2=0.77, relative root mean square error RRMSE=0.28） the model composed by 14 optimal conventional SFs （R2---0.69, RRMSE--0.32） in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.

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

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, SAVI L=0.5 end MSAVI2） 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 MSAVI2 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.