基于穗光谱指数的水稻产量预测

doi:10.3969/j.issn.1004-1524.2018.02.02

浙江农业学报 ›› 2018, Vol. 30 ›› Issue (2): 187-193.DOI: 10.3969/j.issn.1004-1524.2018.02.02

基于穗光谱指数的水稻产量预测

蒋琴素^{1, 2}, 成琪璐², 徐礼根^{1, *}, 周启发²

1.浙江大学农业与生物技术学院,浙江杭州310058;
2.浙江大学生命科学学院,浙江杭州310058

收稿日期:2016-01-19 出版日期:2018-02-20 发布日期:2018-02-11
通讯作者: 徐礼根,E-mail:xuligeng@126.com
作者简介:蒋琴素(1979—),女,浙江黄岩人,硕士研究生,实验师,研究方向为植物生理。E-mail:lucy9514@sina.com
基金资助:
国家自然科学基金(41271363)

Rice yield prediction with panicle spectral indices

JIANG Qingsu^{1, 2}, CHENG Qilu², XU Ligen^{1, *}, ZHOU Qifa²

1. College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China;
2. College of Life Sciences, Zhejiang University, Hangzhou 310058, China

Received:2016-01-19 Online:2018-02-20 Published:2018-02-11

摘要/Abstract

摘要： 为探索运用水稻穗光谱植被指数预测水稻产量的可行性,以2个水稻品种为材料,设置3个氮素水平,测定了3个时期水稻叶片和穗的高光谱反射(350~2 500 nm)和色素含量,并测定了水稻的产量构成组分和籽粒产量。结果表明:与典型的植物反射光谱相比,水稻穗的反射光谱具有“绿峰消失”的特征;与叶片光谱指数[归一化差值指数(normalized vegetation index,NDVI)和光化学反射指数(photochemical reflectance index,PRI)]相比,穗光谱指数对叶绿素更敏感,而且能更准确地区分氮素水平。水稻叶片NDVI和PRI预测产量的均方根误差(RSME)分别为873.4~1 125.0、723.3~889.4 kg·hm^-2,而穗NDVI和PRI预测产量的RSME分别为681.7~743.1、515.0~637.8 kg·hm^-2,表明水稻穗光谱指数比叶片光谱指数更适合于水稻产量预测。

关键词: 水稻, 高光谱反射, 植被指数, 穗, 产量

Abstract: This pilot study was aimed to investigate the feasibility of predicting rice yield with panicle spectral indices. A field experiment was conducted with two rice genotypes of contrasting yield potential and three contrasting nitrogen (N) levels at Hangzhou, China in 2015. Leaf and panicle hyperspectral reflectance (350-2 500 nm), chlorophyll concentrations (Chlc) and carotenoids concentrations (Carc) were measured at three different dates, and the yield components and grain yield were determined at maturing stage. It was found that the panicle spectra were distinctly different from the leaf spectra with the disappearance of the sharp green “hump”. As compared to the corresponding leaf indices, the panicle normalized vegetation index (NDVI) and photochemical reflectance index (PRI) were more reliable in differentiating the N levels, more sensitive to the chlorophyll content, and performed more accurately in predicting the rice yield. In the rice yield prediction based on best relationship fit, NDVI and PRI in the leaves at the three dates yielded a RSME of 873.4-1 125.0 and 723.3-889.4 kg·hm^-2, respectively, while NDVI and PRI in the panicles at the three dates achieved a RMSE of 681.7-743.1 and 515.0-637.8 kg·hm^-2, respectively.

Key words: rice, hyperspectral reflectance, vegetation indices, panicle, yield

中图分类号:

S511

蒋琴素, 成琪璐, 徐礼根, 周启发. 基于穗光谱指数的水稻产量预测[J]. 浙江农业学报, 2018, 30(2): 187-193.

JIANG Qingsu, CHENG Qilu, XU Ligen, ZHOU Qifa. Rice yield prediction with panicle spectral indices[J]. , 2018, 30(2): 187-193.

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基于穗光谱指数的水稻产量预测

Rice yield prediction with panicle spectral indices

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