欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2009, Vol. 35 ›› Issue (6): 1131-1138.doi: 10.3724/SP.J.1006.2009.01131

• 耕作栽培·生理生化 • 上一篇    下一篇

油菜叶片气孔导度与冠层光谱植被指数的相关性

孙金英12，曹宏鑫2*，黄云1   

  1. 1西南大学资源与环境学院，重庆400716；2江苏省农业科学院农业资源与环境研究所/数字农业工程技术研究中心，江苏南京210014
  • 收稿日期:2009-01-05 修回日期:2009-03-17 出版日期:2009-06-12 网络出版日期:2009-04-16
  • 通讯作者: 曹宏鑫，E-岽mail:caohongxin07@yahoo.cn;Tel:025-48390125
  • 基金资助:

    本研究由江苏省“六大人才高峰”项目（06-G-169）资助。

Correlation between Canopy Spectral Vegetation Index and Leaf Stomatal Conductance in Rapeseed(Brassica napus L.)

SUN Jin-Ying12,CAO Hong-Xin2*,HUANG Yun1   

  1. 1College of Resources and environment,South West University,Chongqing 400716,China;2Institute of Agricultural Resources and Environment Research/Engineering Research Center for Digital Agriculture,Jiangsu Academy of Agricultural Sciences,Nanjing 210014,China
  • Received:2009-01-05 Revised:2009-03-17 Published:2009-06-12 Published online:2009-04-16
  • Contact: CAO Hong-Xin，E-岽mail:caohongxin07@yahoo.cn;Tel:025-48390125

PDF

1448

被引次数

21

摘要：

利用冠层光谱实时、无损和定量监测植物叶片气孔导度，对于改善作物水分利用效率以及产量和品质预测预报具有十分重要的意义。本研究采用裂区设计法(宁油18和宁油16两个品种)、两个供氮水平(N180：纯氮180 kg hm-2、P2O5 120 kg hm-2、K2O 180 kg hm-2和硼砂15 kg hm-2；N0：CK)于2007—2008年测定油菜冠层光谱反射率、叶片气孔导度以及叶面积指数(LAI)和叶片鲜、干生物量，利用各波段光谱反射率组合产生的植被指数，分析油菜叶片气孔导度的变化规律及其与光谱植被指数的相关性，从而建立光谱植被指数对叶片气孔导度的估算模型。结果表明，在整个生育期油菜叶片气孔导度呈“双峰”变化, LAI和叶片鲜、干生物量均呈单峰曲线变化；开花前光谱植被指数与油菜叶片气孔导度和油菜冠层叶片平均气孔导度均呈极显著正相关，且光谱植被指数对油菜冠层叶片气孔导度的拟合效果好于对油菜叶片气孔导度的。光谱植被指数与冠层叶片气孔导度的量化关系可为今后快速、无损、大面积的油菜作物气孔导度估算奠定一定基础。

关键词: 油菜, 气孔导度, 光谱, 植被指数, 相关性

Abstract:

It plays a very important role for improving water use efficiency of crops and predicting crop yield and quality to monitor leaf stomatal conductance with real time, non-destructively and quantitatively by using canopy spectral characteristics. In the paper, the spectral reflectance, leaf stomatal conductance, LAI (leaf area index), leaf fresh and dry biomass of two rapeseed varieties were determined in a field experiment by split-plot design with the main plot of N levels and the subsidiary plot of cultivars, 3 replications, and plot area of 4.3 m by 7.0 m in 2007–2008. The changes in leaf stomatal conductance and the correlation between leaf stomatal conductance and spectral vegetation index were analyzed based on the vegetation index combined with spectral reflectance in all kinds of bands. The estimating models for spectral vegetation index of leaf stomatal conductance were established according to the relationship between spectral vegetation index and leaf stomatal conductance. The results showed that there were two peaks in changes carve of leaf stomatal conductance, and one peak in the changes curve of LAI, leaf fresh and dry biomass in the whole growth period. There existed significantly positive correlation between spectral vegetation index and leaf stomatal conductance or canopy leaf stomatal conductance before flowering, and the spectral vegetation index better fitted into canopy average stomatal conductance than into leaf stomatal conductance. The quantitative relationships between spectral vegetation index and canopy leaf stomatal conductance laid the foundation for rapid and non-destructive stomatal conductance estimates in a large area of rapes in future.

Key words: Rapeseed(Brassica napus L.), Stomatal conductance, Spectrum, Vegetation index, Correlation


[1] Zhang J-H(张佳华), Wang C-Y(王长耀). Water deficit crop yield model based on remote sensing and crop photosynthesis. J Hydraulic Eng (水利学报), 1999, 30(8): 35-39(in Chinese with English abstract)


[2] Zhang J-H(张佳华), Fu C-B(符淙斌), Wang C-Y(王长耀). Study on stomatal conductance distribution of winter wheat in regional scale using remote sensing information. Acta Meteorol Sin (气象学报), 2000, 58(3): 347-353(in Chinese with English abstract)

[3] Eastin J D, Sullivan C J. Environmental Stress Influences on Plant Persistence, Physiology, and Production. In: Tesar M B ed. Physiological Basis of Crop Growth and Development.Madison, WI: ASA Special Publication. 1984. pp 201-238

[4]Carter G A. Reflectance wavebands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies. Remote Sens Environ, 1998, 63: 61-72


[5] Myneni R B, Ganapol B D, Asrar G. Remote sensing of vegetation canopy photosynthetic and stomatal conductance efficiencies. Remote Sens Environ, 1992, 42: 217-238


[6] Verma S B, Sellers P J, Walthall C L, Hall F G, Kim J, Goetz S J. Photosynthesis and stomatal conductance related to reflectance on the canopy scale. Remote Sens Environ, 1993, 44: 103-116


[7] Flexas J, Escalona J M, Evain S, Gulias J, Moya I, Osmond C B, Medrano H. Steady-state chlorophyll fluorescence (Fs)measurements as a tool to follow variations of net CO2 assimilation and stomatal conductance during water-stress in C3 plants. Physiol Plant, 2002, 114: 231-240


[8] Tian Y-C(田永超), Zhu Y(朱艳), Yao X(姚霞), Zhou C-J(周昌俊), Cao W-X(曹卫星). Quantitative relationships between canopy spectral reflectance and leaf stomatal conductance in rice. J Plant Ecol (植物生态学报), 2006, 30(2): 261-267(in Chinese with English abstract)


[9] Mutanga O, Skidmore A K, Wieren S. Discrimination tropical grass (Cenchrus ciliaris) canopies grown under different nitrogen treatment using spectroradiometry. J Photogram Remote Sens, 2003, 57: 263-272


[10] Jago R A, Mark E J C, Curran P J. Estimation canopy chlorophyll concentration from field and air born spectra. Remote Sens Envoron, 1999, 68: 217-224


[11] Pearson R L, Miller D L. Remote mapping of standing crop biomass for estimation of the productivity of the short grass prairie. In: Proceedings of the Eighth International Symposium on Remote Sensing of Environment. MI: ERIM Annu Arbor, 1972. pp 1357-1381


[12] Rouse J W, Haas R H, Schell J A, Deering D W, Harlan J C. Monitoring the vernal advancement of retrogradation of natural vegetation. Greenbelt, MD, USA: NASA/GSFC, Type III Final Report, 1974. pp 1-371


[13] Gitelson A A, Kaufman Y, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MO-DIS. Remote Sens Environ, 1996, 58: 289-298


[14] Wang F-M(王福民), Huang J-F(黄敬峰), Tang Y-L(唐延林), Wang X-Z(王秀珍). New vegetation index and its application in estimating leaf area index of rice. J Rice Sci (水稻科学), 2007, 21(2): 159-166(in Chinese with English abstract)


[15] Leng S-H(冷镄虎), Zhu G-R(朱耕如). Study on the origin rape grain dry matter. Acta Agron Sin (作物学报), 1992, 18(4): 250-270(in Chinese with English abstract)

[16] Fu S-Z(傅寿仲). Photosynthesis and output formation of rape. Jiangsu Agric Sci (江苏农业科学), 1980, (6): 18-21(in Chinese with English abstract)

[17] Fu S-Z(傅寿仲), Zhu G-R(朱耕如). Jiangsu Oil Crops Science (江苏油作科学). Nanjing: Nanjing Sci & Tech Press, 1995.pp 220-237(in Chinese)

[18] Liu W-D(刘伟东), Xiang Y-Q(项月琴), Zheng L-F(郑兰芬), Tong Q-X(童庆禧), Wu C-S(吴长山). Relationships between rice LAI, CHD and hyperspectra data .J Remote Sens (遥感学报), 2000, (4): 279-283 (in Chinese with English abstract)

[19] Thiemann S, Kaufmann H. Determination of chlorophyll content and trophic state of lakes using field spectrometer and IRS_1C satellite data in the Mecklenburg lake district Germany. Remote Sens Environ, 2000, 73: 227-235

[20] Tian Q, Tong Q, Guo X, Zhao C. Spectroscopic determination of wheat water status using 1650-1850 nm spectral absorption features. Intl J Remote Sens, 2001, 22: 2329-2338

[21] Jensen A, Lorenzen B. Radiometric estimation of biomass and nitrogen content of barley grown at different nitrogen levels. Intl J Remote Sens, 1990, 11: 1809-1820

[22] Filella I, Penuelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Intl J Remote Sens, 1994, 15: 1459-1470
[1] 陈松余, 丁一娟, 孙峻溟, 黄登文, 杨楠, 代雨涵, 万华方, 钱伟. 甘蓝型油菜BnCNGC基因家族鉴定及其在核盘菌侵染和PEG处理下的表达特性分析[J]. 作物学报, 2022, 48(6): 1357-1371.
[2] 秦璐, 韩配配, 常海滨, 顾炽明, 黄威, 李银水, 廖祥生, 谢立华, 廖星. 甘蓝型油菜耐低氮种质筛选及绿肥应用潜力评价[J]. 作物学报, 2022, 48(6): 1488-1501.
[3] 黄伟, 高国应, 吴金锋, 刘丽莉, 张大为, 周定港, 成洪涛, 张凯旋, 周美亮, 李莓, 严明理. 芥菜型油菜BjA09.TT8和BjB08.TT8基因调节类黄酮的合成[J]. 作物学报, 2022, 48(5): 1169-1180.
[4] 雷新慧, 万晨茜, 陶金才, 冷佳俊, 吴怡欣, 王家乐, 王鹏科, 杨清华, 冯佰利, 高金锋. 褪黑素与2,4-表油菜素内酯浸种对盐胁迫下荞麦发芽与幼苗生长的促进效应[J]. 作物学报, 2022, 48(5): 1210-1221.
[5] 石育钦, 孙梦丹, 陈帆, 成洪涛, 胡学志, 付丽, 胡琼, 梅德圣, 李超. 通过CRISPR/Cas9技术突变BnMLO6基因提高甘蓝型油菜的抗病性[J]. 作物学报, 2022, 48(4): 801-811.
[6] 袁大双, 邓琬玉, 王珍, 彭茜, 张晓莉, 姚梦楠, 缪文杰, 朱冬鸣, 李加纳, 梁颖. 甘蓝型油菜BnMAPK2基因的克隆及功能分析[J]. 作物学报, 2022, 48(4): 840-850.
[7] 黄成, 梁晓梅, 戴成, 文静, 易斌, 涂金星, 沈金雄, 傅廷栋, 马朝芝. 甘蓝型油菜BnAPs基因家族成员全基因组鉴定及分析[J]. 作物学报, 2022, 48(3): 597-607.
[8] 王瑞, 陈雪, 郭青青, 周蓉, 陈蕾, 李加纳. 甘蓝型油菜白花基因InDel连锁标记开发[J]. 作物学报, 2022, 48(3): 759-769.
[9] 赵改会, 李书宇, 詹杰鹏, 李晏斌, 师家勤, 王新发, 王汉中. 甘蓝型油菜角果数突变体基因的定位及候选基因分析[J]. 作物学报, 2022, 48(1): 27-39.
[10] 赵雪, 周顺利. 玉米抗茎倒伏能力相关性状与评价研究进展[J]. 作物学报, 2022, 48(1): 15-26.
[11] 娄洪祥, 姬建利, 蒯婕, 汪波, 徐亮, 李真, 刘芳, 黄威, 刘暑艳, 尹羽丰, 王晶, 周广生. 种植密度对油菜正反交组合产量与倒伏相关性状的影响[J]. 作物学报, 2021, 47(9): 1724-1740.
[12] 张建, 谢田晋, 尉晓楠, 王宗铠, 刘崇涛, 周广生, 汪波. 无人机多角度成像方式的饲料油菜生物量估算研究[J]. 作物学报, 2021, 47(9): 1816-1823.
[13] 王艳花, 刘景森, 李加纳. 整合GWAS和WGCNA筛选鉴定甘蓝型油菜生物产量候选基因[J]. 作物学报, 2021, 47(8): 1491-1510.
[14] 张骁, 闫岩, 王文辉, 郑恒彪, 姚霞, 朱艳, 程涛. 基于小波分析的水稻籽粒直链淀粉含量高光谱预测[J]. 作物学报, 2021, 47(8): 1563-1580.
[15] 李金敏, 陈秀青, 杨琦, 史良胜. 基于高光谱的水稻叶片氮含量估计的深度森林模型研究[J]. 作物学报, 2021, 47(7): 1342-1350.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2