• ISSN 1004-0323     CN 62-1099/TP
• 联合主办：中国科学院遥感联合中心
• 中国科学院兰州文献情报中心
• 中国科学院国家空间科学中心
 遥感技术与应用  2021, Vol. 36 Issue (2): 411-419    DOI: 10.11873/j.issn.1004-0323.2021.2.0411
 农业遥感专栏

1.西安科技大学 测绘科学与技术学院，陕西 西安 710054
2.中国科学院遥感与数字地球研究所 数字地球重点实验室，北京 100094
GBRT Model for Detecting the Severity of Wheat Stripe Rust by Remote Sensing
Hang Jin1,2(),Xia Jing1(),Yuan Gao1,2,Liangyun Liu2
1.College of Geomatics，Xi’an University of Science and Technology，Xi’an 710054，China
2.Key Laboratory of Digital Earth Science，Institute of Remote Sensing and Digital Earth，Chinese Academy of Sciences，Beijing 100094，China
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Abstract:

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.

Key words: GBRT    Solar-induced chlorophyll fluorescence    Reflectance spectrum    Wheat stripe rust    Disease severity

 ZTFLH: TP79