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Drought is one of the major natural disasters in China, it has extremely affected national food security. In this study, Normalized Difference Vegetation Index (NDVI) and surface temperature (Ts) were calculated by using 8-day composite Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance product data MOD09A1 and MOD11A2, then NDVI-Ts feature space was obtained and dry edge and wet edge equation was fit. According to coefficients of dry edge and wet edge equation, Temperature Vegetation Drought Index (TVDI) will be calculated and refer it as a drought monitoring indicator. In addition, drought monitoring and classification of Shandong province (China) was completed by TVDI from February to May,2011. Furthermore, the drought classification diagram was made and the drought area in each period was counted. The results showed revealed that: NDVI-Ts feature was roughly a triangular shape in the two-dimensional plane, and drought conditions could be better monitored through TVDI. Finally, the desktop demonstration system of drought monitoring was designed and some general functions were realized based on ArcGis Engine.

Considering spatial heterogeneity of LAI and nonlinearity of its inversion model, a new spatial scaling method based on Gaussian distribution theory was proposed, aiming to quantitatively analyze scale effects and reveal scaling rules. In this method, higher spatial resolution data, obeying Gaussian distribution when the volume is large enough, were taken as baseline. Statistical parameters and Gaussian distribution forms were integrated into the process of spatial scaling. Barley was selected as experimental object. Firstly, multi-resolution data at 10m, 15m, 20m and 30m were constructed based on 5m resolution data through up-scaling algorithms. Secondly, extracting statistics of these data and constructing scaling data based on Gaussian distribution theory. Finally, quantitatively analyzing scale effects of LAI by introducing sensitive bands ordering and multivariate linear regression. Number of effective bands, R2 of observations and estimations, and MRA could fully confirm the feasibility and validity of this proposed method.

The hyperspectral bands sensitive to the disease severity levels of wheat powdery mildew was elucidated in this study. The disease severity levels of wheat powdery mildew were also inverted by the extracting characteristic parameters, which provided a basis for detecting the wheat powdery mildew using hyperspectral data. The spectral data of single leaves was obtained at heading stage, the spectral characteristic parameters and sensitivity of wheat leaves were analyzed qualitatively and quantitatively. The result showed that spectral reflectivity within the visible wavebands (400—760 nm) was increased with the aggravating disease severity levels. The spectral sensitivity reached the maximum value within visible wavebands and the optimal sensitive bands for detecting disease severity levels was 630—680nm. After the spectrum was continuum removal-treated, the absorption position moved to longer wavelength with the aggravating disease severity levels and the disease severity levels showed extremely significant negative correlations with the absorption height, absorption width and absorption area. The linear regression equation has high determination coefficient and low root mean square error using the right AAI as independent variable to establish the model. Moreover, the precision verification test also showed that the model performed best in monitoring wheat powdery mildew. In conclusion, disease severity levels of wheat powdery mildew could be inverted effectively by hyperspectral technology, which provides the foundation for detecting wheat powdery mildew.