关键词: Sentinel-2A, 时序数据, NDVI, 面向对象, 决策树, 农作物, 种植结构, 北安市

Abstract:

Cropping pattern is a fundamental aspect of land use. Obtaining accurate and timely crop spatial distribution information is very important to guide agricultural production, rational allocation of resources, and help solve the problem of food security. NDVI (Normalized Difference Vegetation Index) time series have been widely used in collecting vegetation information. Identification and information extraction of different crops can be effectively achieved by analyzing the growth period of crops and the time-series spectrum characteristics of NDVI. Most existing studies on NDVI time series are limited to moderate or low resolution remote sensing imagery, which affects the accuracy of extracting crop spatial distribution information. With the successful launch of the satellite Sentinel-2A, more opportunities have emerged for the construction of NDVI time series with high temporal and spatial resolution. In this paper, by use of typical phase Sentinel-2A imagery for Beian city, a NDVI dataset with a spatial resolution of 10 m covering the whole growth period of April-October was generated based on the Savitaky-Golay filtering smoothing method, and crop identification was implemented based on decision tree model and object-oriented classification. By analyzing NDVI time series curves of crop samples, we concluded that NDVI time series was able to clearly distinguish crop phenological differences and capture crop specific features in the study area. Furthermore, we also discussed the classification accuracy based on the typical phase data by the methods of object-oriented classification and support vector machine. Taking the field sample survey datum as true value, we analyzed the results of the two classification methods. The results show that the processed NDVI time series with high resolution over the entire crop growth cycle represent different crop phenological characteristics appropriately. It is able to reflect the crops growth condition accurately and distinguish different crops effectively. The decision tree model integrated with the object-oriented classification method had the highest accuracy in crop classification as compared to other classification methods, with its overall accuracy and kappa coefficient being 96.2% and 0.892, respectively. This research show that Sentinel-2A NDVI with high resolution can be used for crop mapping, and can be applied to crop classification over large areas, thanks to Sentinel-2A imagery's wide coverage. Furthermore, the Savitzky Golay (S-G) method was used for NDVI time series smoothing, and the results indicate that the processed NDVI time series can better represent crop phenological characteristics. Then the decision tree model integrated with the object-oriented classification method was used for crop classification based on typical phase multi-spectral imagery and the smoothed NDVI time series, which improved the overall accuracy by 7.7% and the kappa coefficient by 0.055. The approach proposed in this paper provides important reference for crop mapping in other agricultural regions.

Key words: Sentinel-2A, time-series data, NDVI, object-oriented, decision tree, crops, planting structure, Bei'an