晚播条件下基于高光谱的小麦叶面积指数估算方法

doi:10.3964/j.issn.1000-0593(2019)04-1199-08

Abstract
References
Related Citation (15)

Download: PDF (2189 KB)
Export: BibTeX | EndNote (RIS)

Abstract In this study, hyperspectral remote sensing technology was used to measure the changes of leaf and canopy characteristics with leaf area index (LAI) of wheat leaves under late sowing conditions, and the LAI estimation method suitable for late sowing wheat was established. The results show that: (1) Correlation analysis between chlorophyll spectral reflectance vegetation index (CSRVI) is extracted from the red and blue bands (420～663 nm) to analyze the correlation between SPAD value and CSRVI of the leaf mode under normal sowing and late sowing treatment with R² being 0.963^* and 0.997^** reached significant and extremely significant level, respectively. (2) It is concluded that the correlation coefficients of LAI and SPAD values for the two sowing dates are 0.847^* and 0.813^* by using correlation analysis, respectively, and both reaching significant levels. The SPAD value is correlated with LAI and CSRVI indices, and the CSRVI index can be used to establish the LAI estimation model. (3) Analysis of the spectral curves of characteristics of leaf pattern and canopy patter shows that the reflectance of leaf pattern increases sharply at 680～780 nm. There are two distinct absorption troughs at 446 nm, 680 nm in visible light band and 1 440 and 1 925 nm in near-infrared wave band. There is a clear reflection peak at 540-600 nm band. There are two distinct reflective peaks at 1 660 and 2 210 nm, and the spectral reflectance of the three canopy modes is the highest in the three canopy modes. (4) Correlation analysis between the reflectance of each band and the leaf area index shows that the spectral reflectance has a negative correlation with the overall LAI in the visible light range, and there is a peak at 500～600 nm. (5) Correlation analysis of the equivalent vegetation index and LAI in the three canopy modes (the angle of incidence of the instrument with the ground at 30°, 60°, and 90° respectively) is obtained: there was no significant correlation between 8 vegetation indices and the LAI under the late sowing condition of 60° canopy mode. And a significant and extremely significant the 6 vegetation indices (normalized vegetation index (NDVI), enhanced vegetation index (EVI), re-normalized vegetation index (RDVI), Soil-adjusted vegetation index (SAVI) and modified Soil-adjusted vegetation index (MSAVI) ) under the late sowing condition of 60° canopy mode; the CSRVI indices in the 90° canopy mode were significantly correlated with the LAI of the normal sowing date. NDVI index is significantly correlated with LAI in late sowing treatment; the correlation between the 8 vegetation indices in the 30° canopy mode and the LAI in the two sowing dates was not relevant. Comprehensive analysis of the CSRVI index, NDVI index is the most relevant, and these two indices have the most potential to estimate LAI. (6) The LAI model was estimated by the vegetation index calculated by the three canopy models. The results show that under the normal sowing date, the best estimation model is the Linear function model established by the 90° canopy model with CSRVI index Y=Y=-7.873 6+6.223 8X; The best model under late sowing conditions is the power function model Y=30 221 333.33X^{17.679 1} established by the 60° canopy mode RDVI index, with R² being 0.950^* and 0.974^** in the two treatments, respectively. Studies have shown that the CSRVI index extracted from the test can reflect the chlorophyll content of flag leaf. The chlorophyll content of wheat during the growth period can be monitored by the leaf pattern of the spectroscopy instrument; LAI estimation model based on CSRVI index and RDVI index calculated by canopy model can be used to observe wheat LAI without damage.

Key words：Hyperspectral; LAI; Vegetation index; Mhlorophyll; Model

Received: 2018-08-03 Accepted: 2018-12-25

ZTFLH:

TP79

Corresponding Authors: WANG Xiao-yan, XIONG Qin-xue E-mail: wamail_wang@163.com；xiongqinxue@qq.com

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)04-1199-08 OR https://www.gpxygpfx.com/EN/Y2019/V39/I04/1199

[1] LÜ Xiao, YIN Hong, JIANG Chun-ji, et al(吕晓, 殷红, 蒋春姬. 等). Chinese Journal of Agrometeorology(中国农业气象)，2016, 37(6): 720.
[2] HE Jia, LIU Bing-feng, LI Jun(贺佳, 刘冰峰, 李军). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2014, 30(24): 141.
[3] XIN Ming-yue, YIN Hong, CHEN Long, et al(辛明月, 殷红, 陈龙. 等). Chinese Journal of Agrometeorolog(中国农业气象)，2015, 36(6): 762.
[4] FENG Su-wei, LI Gan, JIANG Xiao-ling, et al(冯素伟, 李淦, 姜小苓. 等). Journal of Henan Institute of Science and Technology(河南科技学院学报)，2012, 40(1): 7.
[5] HUANG Wen-jiang, WANG Ji-hua, LIU Liang-yun, et al(黄文江, 王纪华, 刘良云. 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2015, 21(6): 82.
[6] MENG Yu-chi，HOU Xue-hui，WANG Meng(孟禹弛, 侯学会, 王猛). Jiangsu Agricultural Sciences(江苏农业科学), 2017, 45(5): 211.
[7] YANG Feng, FAN Ya-ming, LI Jian-long, et al(杨峰, 范亚明, 李建龙，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2010, 26(2): 237.
[8] HOU Xue-hui, NIU Zheng, HUANG Ni, et al(侯学会, 牛铮, 黄妮, 等). Remote Sensing For Land ＆ Resources(国土资源遥感)，2012, 95(4): 30.
[9] LI Zi-yang, QIAN Yong-gang, SHEN Qing-feng, et al(李子扬, 钱永刚, 申庆丰, 等). Infrared and Laser Engineering(红外与激光工程)，2014, 43(3): 944.
[10] ZHANG Chun-lan, YANG Gui-jun, LI He-li, et al(张春兰, 杨贵军, 李贺丽, 等). Chinese Agricultural Science Bulletin(中国农业科学)，2018, 51(5): 855.
[11] WANG Kai-long, XIONG Hei-gang, ZHANG Fang(王凯龙, 熊黑钢, 张芳). Journal of Arid Land Resources and Environment(干旱区资源与环境), 2013, 27(11): 45.
[12] XIA Tian, WU Wen-bin, ZHOU Qing-bo, et al(夏天, 吴文斌, 周清波, 等). Scientia Agricultura Sinica(中国农业科学)，2012, 45(10): 2085.