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28 February 2020 Assessment of unified models for estimating potato leaf area index under water stress conditions across ground-based hyperspectral data
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Abstract

Accurate simulation of potato leaf area index (LAI) under water stress is of great significance for rational selection of planting location and extraction of planting area. To obtain the simulation model of potato LAI under different water conditions [water saturation (WS), water deficiency (WD), and control check (CK)], several spectral parameters of vegetation indices (VIs), first-order differential parameters and spectral parameters of the continuum removal method were derived from canopy spectra, first-order differential spectra and continuum removal spectra for correlation with the corresponding LAI, respectively. Furthermore, three different types of parameters with high LAI correlation coefficients were used to build the simulation models and verify the accuracy. As a result, the selected modeling parameters were normalized difference VI, soil-adjusted VI, quotient of red edge area (SDr) and blue edge area (SDb),   (  SDr  −  SDb  )    /    (  SDr  +  SDb  )  , depth area ratio (W), and gross area of absorption peak (S). The most suitable models are as follows: the exponential model of W under the WS condition [coefficient of determination   (  R2  )    =  0.8262, root mean square error   (  RMSE  )    =  0.4338, and mean absolute error   (  MAE  )    =  0.3750], the power function model of S under the CK condition (R2  =  0.8133, RMSE  =  0.4695, and MAE  =  0.3798), and the power function model of SDr  /  SDb under the WD condition (R2  =  0.8407, RMSE  =  0.4459, and MAE  =  0.3882).

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2020/$28.00 © 2020 SPIE
Shanjun Luo, Yingbin He, Qian Li, Weihua Jiao, Yaqiu Zhu, Jinkuan Yu, Chang Zhao, Ruiyang Xu, Shengli Zhang, Fei Xu, Jing Sun, Zhongcai Han, and Chuang Li "Assessment of unified models for estimating potato leaf area index under water stress conditions across ground-based hyperspectral data," Journal of Applied Remote Sensing 14(1), 014517 (28 February 2020). https://doi.org/10.1117/1.JRS.14.014517
Received: 26 August 2019; Accepted: 10 February 2020; Published: 28 February 2020
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