【摘要】 树莓有“第三代黄金水果”的美誉，对于红树莓之乡——尚志而言，准确获取树莓的种植面积对该区域的农作物种植结构调整、产业发展有着重大的意义。以黑龙江省尚志市苇河镇周家营子村为研究区，利用Sentinel-2数据较高的空间和时间分辨率，获得研究区的时序数据，基于该影像剖析研究区作物成长中每个时期的光谱特性和归一化植被指数的时序变化，采用CART算法开展了研究区树莓种植面积估计的研究。与仅依据多时相遥感影像得到的种植区域结果对比，探究NDVI时序数据的参与给区域提取精度带来的变化，并与基于最佳时相数据的面向对象分类和支持向量机分类两种分类算法所得的结果相比较。实验结果表明：基于时序CART算法的两种方法提取树莓种植面积较另外两种分类算法都有更理想的效果，可以得到作物种植面积与空间分布，能够满足作物监测的需求；在利用多时相数据分类的基础上，NDVI时序数据的加入，使作物之间的光谱差异得以放大，分类精度得到提升，相比仅依据Sentinel-2多时相数据分类精度提高了1.67%,Kappa系数提高了0.02。更多还原

【Abstract】 Raspberry has the reputation of being "the third generation of gold fruits." Obtaining accurate data on the planting area of raspberries is of great significance for adjusting the crop planting structure and industrial development in Shangzhi, the red raspberry country. Taking Zhoujiayingzi village, Weihe town, Shangzhi city, Heilongjiang province as the study area, a high spatial and temporal resolution of Sentinel-2 data was used to obtain time series data of the study area. Using time-series changes in terms of spectral characteristics and normalized vegetation index, the CART algorithm was used to estimate the raspberry planting area in the study area. A comparison with the results of planting areas obtained based only on multi-temporal remote sensing images was performed to explore any differences due to the participation of NDVI time-series data on the area extraction accuracy, and to compare the object-oriented classification and the support vector machine classification based on optimal time-phase data. The experimental results show that the two methods based on the time series CART algorithm obtain better results than the other two classification algorithms in extracting the planting area of raspberries and that they can obtain the planting area and spatial distribution of crops with a higher accuracy, which meets the needs of crop monitoring.NDVI time series data were then added to the multi-temporal data classification so that the spectral difference between crops could be enlarged, and the classification accuracy improved. Compared with only using Sentinel-2 multi-temporal data, the classification accuracy is improved by 1. 67% and the Kappa coefficient is improved by 0. 02.更多还原