Abstract:

Vegetation index of each pixel in remote sensing image reflects the growth state of vegetation covered by pixels. If the vegetation index images obtained from all remote-sensing images in one year in the same area are lined up, the dynamic change of vegetation index time series can mirror the phenological rules in this area. Potato is a special kind of winter-planted crop in South China, and relevant parcels show peculiar curve characters of vegetation index time series in planting pattern–“early season rice–late season rice–winter-planted potato”. Taking Renping Peninsula in Huizhou City, Guangdong Province, as research area and the planting area of winter potato as research object, the paper makes use of those characters and employs HJ image data in 2011 as the primary data, with totaling 14 scenes’ HJ-1 A/B CCD remote sensing image data being used. After calculating the NDVI (Normalized Difference Vegetation Index) of images in each scene, NDVI time serials data set is obtained. Corresponding NDVI time serials curve of 26 training sample points is achieved for averaging, thus getting NDVI time serials character reference curve. Moreover, SAM (Spectral Angle Mapping) is used to calculate the value of spectral angle between NDVI time series curve of each pixel in the NDVI time serial data set and NDVI time series character reference curve, obtaining the image of spectral angle value. The pixel value in this image is the value of spectral angle between NDVI time serials curve of each pixel in the NDVI time serial data set and character reference curve. The 2-time standard deviation, 2.5-time standard deviation and 3-time standard deviation of the image of spectral angle value are taken as the threshold value. The spectral angle value of each pixel in the image of spectral angle value and the threshold value are compared, and if the spectral angle value is less than the threshold value, this pixel is classified as a target object, so as to separate the target pixel from pixelscovered by other types of land on the remote sensing images, for the purpose of rapidly and effectively extracting the planting area of winter potato. According to the comparative analysis, it is found that the result extracted by taking 2.5-time standard deviation as the threshold value fits best the actual planting conditions, thereby serving as the final extraction result. Research findings show that the overall extraction accuracy is 82.70% in the research area and 93.75% in key planting areas. This method, as one of the typical applications of homemade optical satellite to agriculture in South China, can extract the winter potato area effectively and will lay a firm foundation for accurately and rapidly monitoring other agricultural information (such as winter-fallowed cultivation) in South China where there is much cloud, rain and broken land.

Key words: winter potato, planting area, SAM, NDVI time series curves, phenology, remote sensing