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Open AccessArticle

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

by Jibo Yue 1,2,3,†, Haikuan Feng 1,4,*,†, Guijun Yang 1,3,4,* and Zhenhai Li 1,3,4
1
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2
International Institute for Earth System Science, Nanjing University, Nanjing 210023, 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
*
Authors to whom correspondence should be addressed.
Both authors contributed equally to this work.
Remote Sens. 2018, 10(1), 66; https://doi.org/10.3390/rs10010066
Received: 16 November 2017 / Revised: 25 December 2017 / Accepted: 2 January 2018 / Published: 5 January 2018
Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models. View Full-Text
Keywords: regression techniques; biomass; vegetation indexes; sampling methods; noise immunity; biomass estimation model; hyperspectral; multi-collinearity regression techniques; biomass; vegetation indexes; sampling methods; noise immunity; biomass estimation model; hyperspectral; multi-collinearity
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MDPI and ACS Style

Yue, J.; Feng, H.; Yang, G.; Li, Z. A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy. Remote Sens. 2018, 10, 66.

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