关键词: 面向对象, 决策树, 物候, 油菜, 种植面积, Sentinel-2, 江汉平原

Abstract:

Accurate and timely access to crop planting information is important for agricultural production management and national food security. Increased access to free satellite data, has allowed for further crop classification and physiological parameter inversion. Launched in June 2015, the Sentinel-2 satellite provides 13 spectral bands with high temporal, spatial and spectral resolution. This provides an opportunity to improve the operational accuracy and efficiency of rapid extraction of crop characteristics and large-scale crop planting area. The Sentinel-2 satellite provides free images at a high spatial resolution (10~30 m). Therefore, it is now possible to develop the next generation of agricultural product at both national and regional levels. Time series vegetation indices (VIs) are derived from this satellite data and are widely used to identify crop characteristics for classification. The Jianghan Plain (located in the middle and lower reaches of Yangtze River) was used as a case study area to evaluate and analyze the effect of the Sentinel-2 satellite images on rape planting area, based on an object-oriented method and differences in crop phenology. Firstly, the best estimate of rapeseed planting area was identified by using crop spectral information and normalized ve getation index at different growth stages. Secondly, a decision tree was formulated according to the difference in object features after multi-scale segmentation. Finally, the rapeseed planting area was calculated after the removal of non-vegetation areas, forests, winter wheat crops and other interferences based on the decision tree. It was found that image segmentation based on the Sentinel-2 images could identify various crop types effectively. The flowering characteristic of rapeseed is a key factor in distinguishing it from other crops. The difference in object features can effectively eliminate the effects of other land-use types on rapeseed classification and improve the accuracy and efficiency of crop classification. Results showed that the greatest classification contributors to the decision tree for distinguishing rapeseed were the normalized vegetation index (NDVI), near infrared (NIR), and brightness (Brightness). A total of 162 rapeseed sample points were used to calculate the confusion matrix. The overall classification accuracy of the rapeseed planting area was more than 98%, and the Kappa coefficient was 0.95. This shows that the Sentinel-2 data, combined with phenology information has great potential to extract large-scale crop planting area data, and can improve the accuracy and efficiency of calculating rape planting areas at both national and regional scales.

Key words: object oriented, decision tree, phenology, rapeseed, planting area, Sentinel-2, Jianghan plain