Comparing Different Methods for Wheat LAI Inversion Based on Hyperspectral Data
文献类型：期刊论文
| 作者 | Ma, Junwei2,3; Wang, Lijuan2; Chen, Pengfei1,3 |
| 刊名 | AGRICULTURE-BASEL
 |
| 出版日期 | 2022-09-01 |
| 卷号 | 12期号:9页码:14 |
| 关键词 | leaf area index gaussian process regression artificial neural networks partial least squares regression hyperspectral wheat |
| DOI | 10.3390/agriculture12091353 |
| 通讯作者 | Chen, Pengfei(pengfeichen@igsnrr.ac.cn) |
| 英文摘要 | Gaussian process regression (GPR) can effectively solve the problem of high-dimensional modeling with a small sample size. However, there is a lack of studies comparing GPR with other methods for leaf area index (LAI) inversion using hyperspectral data. In this study, winter wheat was used as the research material to evaluate performance of different methods for LAI inversion, i.e., GPR, an artificial neural network (ANN), partial least squares regression (PLSR) and the spectral index (SI). To this end, a 2-year water and nitrogen coupled experiment was conducted, and canopy hyperspectral and LAI data were measured at the critical growth stages of wheat. Based on these data, calibration and validation datasets were obtained, and the LAI prediction model was constructed using the above four methods and validated. The results showed that the LAI inversion models of the SI were the least effective compared with other methods, with R-2 and RMSE ranging from 0.42-0.76 and 0.80-1.04 during calibration and R-2 and RMSE ranging from 0.37-0.55 and 0.94-1.09 during validation. The ANN and GPR had the best results, with R-2 of 0.89 and 0.85 and RMSE of 0.46 and 0.53 during calibration and R-2 of 0.74 and 0.71 and RMSE of both 0.74 during validation. The PLSR had intermediate LAI inversion results, with R-2 and RMSE values of 0.80 and 0.61 during calibration and R-2 and RMSE values of 0.67 and 0.80 during validation. Thus, the ANN and GPR methods were recommended for LAI inversion of winter wheat. |
| WOS关键词 | LEAF-AREA INDEX ; GAUSSIAN-PROCESSES ; VEGETATION ; DERIVATIVES ; REGRESSION ; RETRIEVAL ; NITROGEN |
| 资助项目 | National Natural Science Foundation of China[41871344] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA28040500] |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000856178500001 |
| 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/311030/184673]  |
| 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.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China 2.Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Jiangsu, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resource & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Junwei,Wang, Lijuan,Chen, Pengfei. Comparing Different Methods for Wheat LAI Inversion Based on Hyperspectral Data[J]. AGRICULTURE-BASEL,2022,12(9):14. |
| APA | Ma, Junwei,Wang, Lijuan,&Chen, Pengfei.(2022).Comparing Different Methods for Wheat LAI Inversion Based on Hyperspectral Data.AGRICULTURE-BASEL,12(9),14. |
| MLA | Ma, Junwei,et al."Comparing Different Methods for Wheat LAI Inversion Based on Hyperspectral Data".AGRICULTURE-BASEL 12.9(2022):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。