A Vegetation Phenology Monitoring Methodology Based on Sichuan Province

Sichuan region as an important hub of western China population, its phenological change has a great influence on the western economic construction and social development, so the phenological response under the background of global warming and change, the ecological balance, scientific research and agricultural production is of great significance [1]. Taking MODIS remote sensing satellite images of Sichuan province as the data set, aiming at the inversion problem of vegetation phenology, the machine learning method--Extreme gradient boosting(XGBoost) was used to build the vegetation phenology prediction model, and the results were compared with the traditional methods. The results show that the prediction model of machine learning method has a certain accuracy. The experimental results show that the XGBoost is able to achieve an acceptable accuracy, the average root mean squared error (RMSE), mean absolute error (MAE) and coefficient of correlation (R) were 4.684/4.413, 4.353/4.297, and 0.7725/0.7812 respectively.