遥感探测小麦条锈病严重度的GBRT模型研究

为了提高小样本数据模型的稳定性,构建具有更高精度和鲁棒性的小麦条锈病遥感探测模型.首先基于辐亮度和反射率荧光指数方法提取了冠层日光诱导叶绿素荧光(SIF)数据,然后结合对小麦条锈病病情严重度敏感的反射率光谱指数并基于改进的分类与回归树(CART)——梯度提升回归树(GBRT)算法,构建了融合反射率和冠层SIF数据的小麦...

GBRT Model for Detecting the Severity of Wheat Stripe Rust by Remote Sensing

In order to improve the stability of the small sample data model， a remote sensing detection model of wheat stripe rust with higher accuracy and better robustness was constructed. Firstly， the data of canopy solar-Induced chlorophyll Fluorescence （SIF） were extracted based on radiance and reflectance fluorescence index method， and then combined with reflectance spectral index sensitive to severity of wheat stripe rust， the Gradient Boost Regression Tree （GBRT） was used to detect wheat stripe rust. By comparing GBRT model with CART and Multiple Linear Regression （MLR） model， the results showed that： （1） Reflectivity derivative fluorescence index D705/D722， short-wave infrared Valley reflectance and reflectance ratio fluorescence index R740/R800 were the main factors affecting the accuracy of remote sensing detection of wheat stripe rust. The importance of chlorophyll fluorescence data was higher than that of reflectance spectrum data， and canopy SIF could reflect wheat stripe rust information more sensitively than reflectance spectrum. （2） Compared with CART model and MLR model， the Root Mean Square Error （RMSE） of GBRT model was reduced by 15.50% and 13.49%， and the determination coefficient （R²） was increased by 6.16% and 11.57% respectively. The estimated DI value of GBRT model is closer to the measured value， and the fluctuation of the estimated result is low， and the robustness of CART model is high. In small sample data， it is easy to divide data sets with different features into subsets of the same feature， and the prediction results fluctuate greatly. The prediction results of MLR model are relatively stable， but its prediction accuracy is low.