Research on remote sensing classification of fruit trees based on Sentinel-2 multi-temporal imageries

作者：Xinxing Zhou, Yangyang Li, Yuan-Kai Luo, Yawei Sun, Yijun Su, Changwei Tan, Yaju Liu · 发表于：Scientific Reports · 年份：2022 · DOI：10.1038/s41598-022-15414-0 · 被引用次数：46 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Remote Sensing and LiDAR Applications

Accurately obtaining the spatial distribution information of fruit tree planting is of great significance to the development of fruit tree growth monitoring, disease and pest control, and yield estimation. In this study, the Sentenel-2 multispectral remote sensing imageries of different months during the growth period of the fruit trees were used as the data source, and single month vegetation indices, accumulated monthly vegetation indices (∑VIs), and difference vegetation indices between adjacent months (∆VIs) were constructed as input variables. Four conventional vegetation indices of NDVI, PSRI, GNDVI, and RVI and four improved vegetation indices of NDVIre1, NDVIre2, NDVIre3, and NDVIre4 based on the red-edge band were selected to construct a decision tree classification model combined with machine learning technology. Through the analysis of vegetation indices under different treatments and different months, combined with the attribute of Feature_importances_, the vegetation indices of different periods with high contribution were selected as input features, and the Max_depth values of the decision tree model were determined by the hyperparameter learning curve. The results have shown that when the Max_depth value of the decision tree model of the vegetation indices under the three treatments was 6, 8, and 8, the model classification was the best. The accuracy of the three vegetation index processing models on the training set were 0.8936, 0.9153, and 0.8887, and the acc...