期刊文献+

棉花高光谱植被指数与LAI和地上鲜生物量的相关关系研究 被引量:4

Relationships between Hyperspectral Vegetative Index,LAI and Aboveground Fresh Biomass of Cotton
下载PDF
导出
摘要 通过测试棉花关键生育阶段350~2500nm波段的冠层高光谱数据,用近红外波段760~850nm及红光波段650—670nm的2个范围内的波段,组成了高光谱归一化植被指数(NDW)和800和670nm两个波段组成修改型二次土壤调节植被指数(MSAVI2),分别与棉花叶面积指数(LAI)和地上鲜生物量进行相关分析,结果表明,棉花NDVI和MSAVI2与LAI和地上鲜生物量两个参数均以幂指数相关关系为最佳(RNDVI—LAI=0.7291^**,RMSAVI22—LAI=0.7436^**,n=81;RNDVI-鲜生物量=0.7426^**,RMSAVI2-鲜生物量=0.7911^**,n=59),MSAVI2与LAI和地上鲜生物量的相关性均高于NDVI与LAI和地上鲜生物置的相关性,说明MSAVI2较NDVI能更好的消除土壤背景对反射光谱造成的影响,能较精确的提取反映棉花生长状况的叶面积指数和生物量信息。 This hyperspectral reflectance (350 to 2 500 nm) data were recorded at the cotton key growth stages in a field experiment, utilizing cotton leaf area index at near infrared band 760 - 850 nm and red region band 650 - 670 nm, combining the two bands range reflectance into vegetation indices NDVI (Normalized Vegetation Index), and near infrared band 800 nm and red region band 670 nm, combining two bands range reflectance into MSAVI2 (Modified Second Soil - Adjusted Vegetation Index). Analyzing the correlation between NDVI and MSAVI2 and cotton leaf area index(LAI) , aboveground fresh biomass respectively, it was found that there were all the highest power function relationship among them( RNDVI-LAI = 0. 729 1^* *, RMSAVI2-LAI = 0. 743 6^* *, n = 81; RNDVI- Fresh biomass = 0. 742 6 ^** , RMSAVI2 - Fresh biomass =0.791 1 ^** , n = 59 ), power function of MSAVI2 and LAI , aboveground fresh biomass have the higher precision than NDVI does, it shows that MSAVI2 can eliminate the effects of the soft background on the reflectance , it is better to extract cotton canopy LAI, aboveground fresh biomass.
出处 《新疆农业科学》 CAS CSCD 2008年第5期787-790,共4页 Xinjiang Agricultural Sciences
基金 国家自然科学基金项目(30460060和30060039)
关键词 棉花 高光谱植被指数 叶面积指数 生物量 cotton hyperspectral vegetation index leaf area index aboveground fresh biomass
  • 相关文献

参考文献10

  • 1Casanova D., G.F.Epema, J. Goudriaan. Monitoring rice reflectance at field level for estimating biomass and LAI[J] .Field Crops Research. 1998,55 (1 -2):83-92.
  • 2Shibayama M. ,Akiyama T.Seasonal Visible,Near - Infrared and Mid - Infrared Spectra of rice canopies in relation to LAI and above - ground dry biomass[J]. Remote Sensing of Envrironment, 1989,27(2) : 119 - 197.
  • 3Hansen P M,, and Schjoerrlng J K. Reflectance measurement of canopy biomass and nitrogen statue in wheat crops using normalized difference vegetation indices and partial least squares regession[J]. Remote Sensing Environment,2003,86(4) : 542- 553.
  • 4Hung T N and Byun W L.. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regress[J]. European Journal of Agronomy, 2006,24(4) : 349 - 356.
  • 5唐延林,王秀珍,王福民,王人潮.农作物LAI和生物量的高光谱法测定[J].西北农林科技大学学报(自然科学版),2004,32(11):100-104. 被引量:47
  • 6刘占宇,黄敬峰,吴新宏,董永平,王福民,刘朋涛.草地生物量的高光谱遥感估算模型[J].农业工程学报,2006,22(2):111-115. 被引量:60
  • 7宋开山,张柏,李方,段洪涛,王宗明.高光谱反射率与大豆叶面积及地上鲜生物量的相关分析[J].农业工程学报,2005,21(1):36-40. 被引量:87
  • 8Qi, J., Chehbouni, A., Huete, A.R., et al. Modified soil adjusted vegetation index[ J]. Remote Sensing of Enviro nment, 1994,48 (2) : 119 - 226.
  • 9Broge, N.H., and Leblanc, E.. Comparing predicting power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J]. Remote Sensing of Enviro mnent, 2001,76(2) : 156 - 172.
  • 10Prasad S. Thenkabail, Ronald B. Smith and Eddy De Pauw. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics[J]. Remote Sensing Environment, 2000,71 (2) : 158 - 182.

二级参考文献37

  • 1胡新博.草地光谱与牧草产量的相关分析[J].草食家畜,1996(4):43-47. 被引量:19
  • 2Brogea N H, Mortensen J V. Deriving green crop areaindex and canopy chlorophyll density of winter wheat from spectral reflectance data [J]. Remote Sensing of Environment, 2002,81: 45- 57.
  • 3Chen J M, Cihlar J. Retrieving leaf area index of boreal conifer forests using Landsat TM images [J]. Remote Sensing of Environment, 1996,55 : 153- 162.
  • 4Chason J W, Balsocchi D D, et al. A comparion of direct and indirect methods for estimating forest canopy leaf area [J]. Agricultural and Forest Meterology, 1991,57: 107- 128.
  • 5Gitelson A A, Merzlyak M N. Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll[J]. J Plant Physical, 1996,148:494-500.
  • 6Blachburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectaral approaches [ J ]. Remote Sensing of Environment, 1998,66: 273- 285.
  • 7Blachburn G A, Milton E J. Seasonal variations in the spectral reflectance of deciduous tree canopies [J]. Int J Remote Sensing, 1995,16(4):709-720.
  • 8Shibayama M, Akiyama Y. Estimating grain yield of maturing rice canopy using high spectral resolution reflectance measurements [ J ]. Remote Sensing of Environment, 1991,36 : 45- 53.
  • 9Ian B. Strachana, Elizabeth. Pattey, Johanne B. Boisvert. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance [J]. Remote Sensing of Environment, 2002,80 : 213- 224.
  • 10Stith T. Gower, Chris J, Kucharik, John M. Norman. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems[J]. Remote Sensing of Environment, 1999,70: 29- 51.

共引文献169

同被引文献63

引证文献4

二级引证文献85

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部