基于Sentinel-2的UNVI植被指数及性能对比研究

作物精准识别和分类是农业遥感检测的重要内容，对作物长势监测以及估产十分重要。以美国混合农业带为研究区，基于Sentinel-2时间序列影像，根据其传感器响应函数计算了针对Sentinel-2的通用归一化植被指数（Universal Normalized Vegetation Index，UNVI），并通过两个对比实验，分析UNVI等6个指数在作物精准分类中的性能。实验一以JM（Jeffries-Matusita）距离为指标对不同作物类别之间的可分性进行分析，结果表明UNVI优于NDVI、EVI、WDRVI、NDre1和NDWI指数，在玉米和棉花、玉米和水稻、玉米和水稻的区分上，UNVI优于其他指数区分能力相当，但在其余的作物组合上如棉花和水稻，NDVI等指数则无法将其很好的区分，此时UNVI指数依然可以表现出较好的区分能力；实验二对6种时间序列指数特征分别使用随机森林和支持向量机进行作物分类，结果表明UNVI指数的总体精度和Kappa系数最高，其次是NDre1指数和WDRVI指数，EVI的总体精度和Kappa系数最低，这表明UNVI比其他6个指数更好地区分了研究区大豆、玉米、棉花和水稻等4种主要作物。综上，基于Sentinel-2时间序列的UNVI指数在进行作物分类时与其他5种遥感植被指数相比，具有较大的优势，UNVI可为农作物长势分析和作物估产研究等农业研究和应用的可选植被指数。

Comparative Study on UNVI Vegetation Index and Performance based on Sentinel-2

Accurate crop identification and classification is an important part of agricultural remote sensing detection， which is very important for crop growth monitoring and yield estimation. In this paper， based on the Sentinel-2 time series images of the United States mixed agricultural belt as the research area， the Universal Normalized Vegetation Index （UNVI） for Sentinel-2 is calculated according to its sensor response function， and two comparisons are made. Experiment to analyze the performance of UNVI and other six indexes in the accurate classification of crops. Experiment 1 uses the JM （Jeffries-Matusita） distance as an indicator to analyze the separability between different crop categories. The results show that UNVI is better than NDVI， EVI， WDRVI， NDre1 and NDWI index. In corn and cotton， corn and rice， In terms of distinguishing between corn and rice， UNVI is better than other indexes in distinguishing ability， but in other crop combinations such as cotton and rice， NDVI and other indexes cannot distinguish them well. At this time， UNVI index can still perform better Distinguishing ability of experiment； Experiment 6 uses random forests and support vector machines to classify crops of the six time series index features. The results show that the UNVI index has the highest overall accuracy and Kappa coefficient， followed by the NDre1 index and the WDRVI index， and the EVI overall accuracy and The Kappa coefficient is the lowest， which indicates that UNVI distinguishes the four main crops of soybean， corn， cotton and rice in the study area better than the other five indexes. In summary， the UNVI index based on the Sentinel-2 time series has greater advantages in crop classification than other remote sensing vegetation indexes studied in this paper. UNVI can be used for agricultural research and application such as crop growth analysis and crop yield research Optional vegetation index.