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Remote Sens. 2017, 9(4), 309; doi:10.3390/rs9040309

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

1
Beijing Research Center for Information Technology in Agriculture, Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture, Beijing 100097, China
2
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
3
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
4
Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China
*
Author to whom correspondence should be addressed.
Academic Editors: Zhenhong Li, Clement Atzberger and Prasad S. Thenkabail
Received: 28 December 2016 / Revised: 15 March 2017 / Accepted: 21 March 2017 / Published: 25 March 2017
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
View Full-Text   |   Download PDF [2660 KB, uploaded 30 March 2017]   |  

Abstract

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). View Full-Text
Keywords: LAI retrieval; hyperspectral remote sensing; sampling method; random forests; artificial neural networks; support vector machine LAI retrieval; hyperspectral remote sensing; sampling method; random forests; artificial neural networks; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yuan, H.; Yang, G.; Li, C.; Wang, Y.; Liu, J.; Yu, H.; Feng, H.; Xu, B.; Zhao, X.; Yang, X. Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models. Remote Sens. 2017, 9, 309.

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