基于哨兵2时间序列组合植被指数的作物分类研究

时间序列是一种常用的物候研究方法。为充分利用哨兵2数据在红边波段的丰富信息，本文利用多种植被指数组合成时间序列进行作物分类。将NDVI、EVI、红边NDVI三种植被指数进行组合，构建时序植被指数图像，然后使用支持向量机、随机森林、CART决策树和最大似然4种不同的算法对四种作物、三种林草、裸露地表、水体进行分类。原始分类结果中，总体精度最高的随机森林为87.92%，最低的最大似然为80.07%，在分类细节上，随机森林和支持向量机的边界最清晰，4种分类结果中，农作物的分类精度均高于其他地类，仅次于水体的精度，误差主要来自三种林草的混分，表明时间序列组合植被指数用于农作物分类是可行的。

Research on Crop Classification Method based on Sentinel-2 Time Series Combined Vegetation Index

Time series is a widely used phenological research method. A new time series vegetation indices which takes full advantage of the red edge information of Sentinel 2 data were used for crop classification to improve the classification accuracy. The NDVI, EVI, and red edge NDVI were combined to construct a time series vegetation index image. Then, four different algorithms (support vector machine, random forest, CART decision tree and maximum likelihood) were used to classify four crops, three forest grasses, bare land, and water bodies. Among the original classification results, the random forest with the highest overall accuracy is 87.92%, and the maximum likelihood with the lowest overall accuracy is 80.07%. In the classification details, the boundaries of random forest and support vector machine are the clearest. Among the four classification results, the classification accuracy of crops is higher than other land types, just smaller than water body. The error mainly comes from the mixture of three forests. It indicates that the time series combined vegetation index is feasible and accurate for crop classification.