Comparing Different Methods for Wheat LAI Inversion Based on Hyperspectral Data

Junwei Ma, Lijuan Wang and Pengfei Chen ()
Additional contact information
Junwei Ma: State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Lijuan Wang: School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
Pengfei Chen: State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Agriculture, 2022, vol. 12, issue 9, 1-14

Abstract: 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.

Keywords: leaf area index; gaussian process regression; artificial neural networks; partial least squares regression; hyperspectral; wheat (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/9/1353/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/9/1353/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:9:p:1353-:d:903648

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().