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利用原位光谱法估算甘蔗作物冠层氮浓度

Estimating canopy nitrogen concentration of sugarcane crop using in situ spectroscopy.

作者信息

Reyes-Trujillo Aldemar, Daza-Torres Martha C, Galindez-Jamioy Carlos A, Rosero-García Esteban E, Muñoz-Arboleda Fernando, Solarte-Rodriguez Efrain

机构信息

School of Environmental & Natural Resources Engineering. Universidad del Valle, Calle 13, No.100-00, Cali, Colombia.

Physics Department, Universidad del Valle, Calle 13, No.100-00, Cali, Colombia.

出版信息

Heliyon. 2021 Mar 25;7(3):e06566. doi: 10.1016/j.heliyon.2021.e06566. eCollection 2021 Mar.

Abstract

Estimating nitrogen (N) concentration in situ is fundamental for managing the fertilization of the sugarcane crop. The purpose of this work was to develop estimation models that explain how N varies over time as a function of three spectral data transformations in two stages (plant cane and first ratoon) under variable rates of N application. A randomized complete-block experimental design was applied, with four levels of N fertilization: 0, 80, 160, and 240 kg N ha. Six sampling events were carried out during the rapid growth stage, where the canopy reflectance spectra with a hyperspectral sensor were measured, and tissue samples for N determination in plant cane and first ratoon were taken, from 60 days after emergence (DAE) and 60 days after harvest (DAH), respectively, until days 210 DAE and 210 DAH. To build the models, partial least squares regression analysis was used and was trained by three transformations of the spectral data: (i) average reflectance spectrum (R), (ii) multiple scatter correction and Savitzky-Golay filter MSC-SG) reflectance spectrum, and (iii) calculated vegetation indices (VIs).

摘要

原位估算氮(N)浓度对于甘蔗作物施肥管理至关重要。本研究的目的是建立估算模型,以解释在不同施氮量下,甘蔗作物在两个生长阶段(新植蔗和头季宿根蔗)中,氮含量如何随三种光谱数据变换而随时间变化。采用随机完全区组试验设计,设置四个施氮水平:0、80、160和240 kg N/ha。在快速生长阶段进行了六次采样,使用高光谱传感器测量冠层反射光谱,并分别在出苗后60天(DAE)和收获后60天(DAH)采集新植蔗和头季宿根蔗用于氮测定的组织样本,直至210 DAE和210 DAH。为建立模型,使用了偏最小二乘回归分析,并通过光谱数据的三种变换进行训练:(i)平均反射光谱(R),(ii)多元散射校正和Savitzky-Golay滤波(MSC-SG)反射光谱,以及(iii)计算植被指数(VIs)。

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