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Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images

文献类型：期刊论文

作者Ma, Xiao2,3; Chen, Pengfei1,2; Jin, Xiuliang4
刊名REMOTE SENSING
出版日期2022-12-01
卷号14期号:24页码:16
关键词leaf nitrogen content hybrid method UAV hyperspectral image
DOI10.3390/rs14246334
通讯作者Chen, Pengfei(pengfeichen@igsnrr.ac.cn)
英文摘要Predicting leaf nitrogen content (LNC) using unmanned aerial vehicle (UAV) images is of great significance. Traditional LNC prediction methods based on empirical and mechanistic models have limitations. This study aimed to propose a new LNC prediction method based on combining deep learning methods and mechanistic models. Wheat field experiments were conducted to make plants with different LNC values. The LNC and UAV hyperspectral images were collected during the critical growth stages of wheat. Based on these data, a method combining the deep multitask learning method and the N-based PROSAIL model was proposed and compared with traditional LNC prediction methods, including spectral index (SI), partial least squares regression (PLSR) and artificial neural network (ANN) methods. The results show that the new proposed method obtained the best LNC prediction results, with R-2, RMSE and RMSE% values of 0.79, 20.86 mu g/cm(2) and 18.63%, respectively, during calibration and 0.82, 18.40 mu g/cm(2) and 16.92%, respectively, during validation. The other methods obtained R-2, RMSE and RMSE% values between 0.29 and 0.68, 25.71 and 38.52 mu g/cm(2) and 22.95 and 34.39%, respectively, during calibration and between 0.43 and 0.74, 22.79 and 33.55 mu g/cm(2) and 20.96 and 30.86%, respectively, during validation. Thus, this study provides an accurate LNC prediction tool for precise nitrogen (N) management in the field.
WOS关键词CANOPY CHLOROPHYLL CONTENT ; AREA INDEX ; VEGETATION INDEXES ; WINTER-WHEAT ; RADIATIVE-TRANSFER ; INVERSION ; LAI ; VALIDATION ; EFFICIENCY ; RETRIEVAL
资助项目National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences[41871344] ; [XDA28040502]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000904207200001
出版者MDPI
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/188359]  
专题中国科学院地理科学与资源研究所
通讯作者Chen, Pengfei
作者单位1.Natl Sci & Technol Infrastructure China, Natl Earth Syst Sci Data Ctr, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Agr Sci, Inst Crop Sci, Beijing 100081, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Xiao,Chen, Pengfei,Jin, Xiuliang. Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images[J]. REMOTE SENSING,2022,14(24):16.
APA Ma, Xiao,Chen, Pengfei,&Jin, Xiuliang.(2022).Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images.REMOTE SENSING,14(24),16.
MLA Ma, Xiao,et al."Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images".REMOTE SENSING 14.24(2022):16.

入库方式： OAI收割

来源：地理科学与资源研究所

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