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Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data

文献类型： 外文期刊

作者： Tao, Huilin 1 ; Feng, Haikuan 1 ; Xu, Liangji 3 ; Miao, Mengke 1 ; Long, Huiling 1 ; Yue, Jibo 1 ; Li, Zhenhai 1 ; Yang, 1 ;

作者机构： 1.Minist Agr & Rural Affairs, Key Lab Quantitat Remote Sensing Agr, Beijing, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Anhui Univ Sci & Technol, Sch Geodesy & Geomat, Huainan 232001, Peoples R China

4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

5.Beijing Engn Res Ctr Agr Internet Things, Beijing 100097, Peoples R China

关键词： vegetation index; red-edge parameters; above-ground biomass; leaf area index; stepwise regression; partial least squares regression

期刊名称：SENSORS （ 影响因子：3.576； 五年影响因子：3.735 ）

ISSN：

年卷期： 2020 年 20 卷 5 期

页码：

收录情况： SCI

摘要： Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.

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