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Abstract. In this landscape-scale study we explored the potential for multitemporal 10-day composite data from the Vegetation sensor to characterize land cover types, in combination with Landsat TM image and agricultural census data. The study area (175 km by 165 km) is located in eastern Jiangsu Province, China. The Normalized DiVerence Vegetation Index (NDVI ) and the Normalized DiVerence Water Index (NDWI ) were calculated for seven 10-day composite ( VGT-S10) data from 11 March to 20 May 1999. Multi-temporal NDVI and NDWI were visually examined and used for unsupervised classi cation. The resultant VGT classi cation map at 1 km resolution was compared to the TM classi cation map derived from unsupervised classi cation of a Landsat 5 TM image acquired on 26 April 1996 at 30 m resolution to quantify percent fraction of cropland within a 1 km VGT pixel; resulting in a mean of 60% for pixels classi ed as cropland, and 47% for pixels classi ed as cropland/natural vegetation mosaic. The estimates of cropland area from VGT data and TM image were also aggregated to county-level, using an administrative county map, and then compared to the 1995 county-level agricultural census data. This landscape-scale analysis incorporated image classi cation (e.g. coarse-resolution VGT data, neresolution TM data), statistical census data (e.g. county-level agricultural census data) and a geographical information system (e.g. an administrative county map), and demonstrated the potential of multi-temporal VGT data for mapping of croplands across various spatial scales from landscape to region. This analysis also illustrated some of the limitations of per-pixel classi cation at the 1 km resolution for a heterogeneous landscape.

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Both symmetric and asymmetric airfoils are widely used in aircraft design and manufacture, and they have different aerodynamic characteristics. In order to improve flight performance and ensure flight safety, the aerodynamic coefficients of these airfoils must be obtained. Various methods are used to generate aerodynamic coefficients. The prediction model is a promising method that can effectively reduce cost and time. In this paper, a graphical prediction method for multiple aerodynamic coefficients of airfoils based on a convolutional neural network (CNN) is proposed. First, a transformed airfoil image (TAI) was constructed by using the flow-condition convolution with the airfoil image. Next, TAI was combined with the original airfoil image to form a composite airfoil image (CAI) that is used as the input of the CNN prediction model. Then, the structure and parameters of the prediction model were designed according to CAI features. Finally, a sample set that was generated on the basis of the deformation of symmetrical airfoil NACA 0012 was used to train and test the prediction model. Simulation results showed that the proposed method based on CNN could simultaneously predict the pitch-moment, drag, and lift coefficients, and prediction accuracy was high.