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Remote Sens. 2019, 11(3), 361; https://doi.org/10.3390/rs11030361

Effects of Growth Stage Development on Paddy Rice Leaf Area Index Prediction Models

1
College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, China
2
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, Guangdong, China
*
Author to whom correspondence should be addressed.
Received: 10 January 2019 / Revised: 30 January 2019 / Accepted: 8 February 2019 / Published: 11 February 2019
(This article belongs to the Special Issue Leaf Area Index (LAI) Retrieval using Remote Sensing)
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Abstract

A in situ hyperspectral dataset containing multiple growth stages over multiple growing seasons was used to build paddy rice leaf area index (LAI) estimation models with a special focus on the effects of paddy rice growth stage development. The univariate regression method applied to the vegetation index (VI), the traditional multivariate calibration method of partial least squares regression (PLSR), and modern machine learning methods such as support vector regression (SVR), random forests (RF), and artificial neural networks (ANN) based on the original and first-derivative hyperspectral data were evaluated in this study for paddy rice LAI estimation. All the models were built on the whole growing season and on each separate vegetative, reproductive and ripening growth stage of paddy rice separately. To ensure a fair comparison, the models of the whole growing season were also validated on data for each separate growth stage of the standalone validation dataset. Moreover, the optimal band pairs for calculating narrowband difference vegetative index (DVI), normalized difference vegetation index (NDVI) and simple ratio vegetation index (SR) were determined for the whole growing season and for each separate growth stage separately. The results showed that for both the whole growing season and for each single growth stage, the red-edge and near-infrared band pairs are optimal for formulating the narrowband DVI, NDVI and SR. Among the four multivariate calibration methods, SVR and RF yielded more accurate results than the other two methods. The SVR and RF models built on first-derivative spectra provided more accurate results than the corresponding models on the original spectra for both whole growing season models and separate growth stage models. Comparing the prediction accuracy based on the whole growing season revealed that the RF and SVR models showed an advantage over the VI models. However, comparing the prediction accuracy based on each growth stage separately showed that the VI models provided more accurate results for the vegetative growth stages. The SVR and RF models provided more accurate results for the ripening growth stage. However, the whole growing season RF model on first-derivative spectra could provide reasonable accuracy for each single growth stage. View Full-Text
Keywords: LAI; paddy rice; hyperspectral; machine learning; vegetation index; growth stage LAI; paddy rice; hyperspectral; machine learning; vegetation index; growth stage
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Wang, L.; Chang, Q.; Li, F.; Yan, L.; Huang, Y.; Wang, Q.; Luo, L. Effects of Growth Stage Development on Paddy Rice Leaf Area Index Prediction Models. Remote Sens. 2019, 11, 361.

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