数字农科院2.0

Assessment Of Unified Models For Estimating Potato Leaf Area Index Under Water Stress Conditions Across Ground-Based Hyperspectral Data

文献类型： 外文期刊

作者： Luo, SJ; He, YB; Li, Q; Jiao, WH; Zhu, YQ; Yu, JK; Zhao, C; Xu, RY; Zhang, SL; Xu, F; Sun, J; Han, ZC; Li, C

作者机构：

关键词： Leaf Area Index; Water Stress; Vegetation Index; First-Order Differential; Continuum Removal Method; Hyperspectral Data

期刊名称： JOURNAL OF APPLIED REMOTE SENSING

ISSN： 1931-3195

年卷期： 2020 年 14 卷 1 期

页码：

收录情况： JCR（2021版） ; EI（2021版）

摘要： 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 (R-2) = 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 (R-2 = 0.8133, RMSE = 0.4695, and MAE = 0.3798), and the power function model of SDr/SDb under the WD condition (R-2 = 0.8407, RMSE = 0.4459, and MAE = 0.3882). (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

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