Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop g...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2017-04, Vol.9 (4), p.309
Main Authors: Yuan, Huanhuan, Yang, Guijun, Li, Changchun, Wang, Yanjie, Liu, Jiangang, Yu, Haiyang, Feng, Haikuan, Xu, Bo, Zhao, Xiaoqing, Yang, Xiaodong
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Chlorophyll
Crop growth
Crop yield
Crops
Datasets
Detection
Estimation
Leaf area
Leaf area index
Learning theory
Least squares method
Leaves
Neural networks
Optimization
Plant growth
Regression analysis
Remote monitoring
Remote sensing
Sampling
Sampling methods
Soybeans
Stability
Support vector machines
Unmanned aerial vehicles
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Summary:Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R2, SDR2, V-RMSE, and SDRMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period).
ISSN:2072-4292
2072-4292
DOI:10.3390/rs9040309