A FUEL MOISTURE CONTENT MONITORING METHODOLOGY BASED ON OPTICAL REMOTE SENSING

Quickly and accurately obtaining fuel moisture content information is of great significance for diagnosing vegetation growth, improving agricultural irrigation efficiency, guiding agricultural production, monitoring the drought conditions of natural communities, and forecasting forest fires. Used the measured fuel moisture content in the southern California sample points and various vegetation indices extracted from MODIS remote sensing satellite images as the dataset for the fuel moisture content retrieving model. In this study, three machine learning methods--extreme learning machine (ELM), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) were used for the fuel moisture content retrieving model. The results show that these three methods can achieve better accuracy than the traditional machine learning method support vector machine (SVM). 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 0.1552, 0.1243, and 0.7423 respectively, which is much better than the other models.