Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments.

ABSTRACT: The performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study. Traditional univariate regression models involving narrowband NDVI with optimized band combinations as well as linear multivariate calibration partial least squares regression models were also evaluated for comparison. A four year field-collected dataset was used to test the robustness of LAI estimation models against temporal variation. The partial least squares regression and three machine learning methods were built on the raw hyperspectral reflectance and the first derivative separately. Two different rules were used to determine the models' key parameters. The results showed that the combination of

SUBMITTER: Wang L 

PROVIDER: S-EPMC6281281 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature