|Table of Contents|

Models for Estimating Soybean Leaf Area Index Using Hyperspectral Data(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2008年02期
Page:
228-232
Research Field:
Publishing date:

Info

Title:
Models for Estimating Soybean Leaf Area Index Using Hyperspectral Data
Author(s):
HUANG Chun-yan1; LIU Sheng-li2; WANG Deng-wei1; ZHAN Yong2; ZHANG Heng-bin2; YUAN jie1; MA Qin-jian1; CHEN Yan1; ZHAO Peng-ju1
1Key Laboratory of Oasis Ecology Agriculture of Xinjiang Bingtuan/College of Agronomy，Shihezi University，Shihezi 832003，Xinjiang;
2Crop Research Institute，Xinjiang Academy of Agricultural Reclamation，Shihezi 832000，Xinjiang，China
Keywords:
Soybean; hyperspectral vegetation indices; Leaf area index; Estimating models
PACS:
S565.1
DOI:
10.11861/j.issn.1000-9841.2008.02.0228
Abstract:
Leaf area index（LAI) is an important parameter as the indicator of optimal diagnosis for crop growing status.Research shows that there are high correlation between hyperspectral data and LAI.So hyperspectral remote sensing can be used in monitoring growth status of soybean.In this paper，hyperspectral reflectance（350 to 2 500 nm) data was obtained in four soybean key growth stages，Ratio vegetation index（RVI) was computed using average reflectance of near infrared bands of 760~850 nm and red region bands of 650-670 nm;Modified second soil-adjusted vegetation index（MSAVI2) was composed of reflectance of near infrared band of 800 nm and 670 nm.Based on RVI and MSAVI，six single variables of linear and nonlinear function models against LAI were established.All models reached 0.01 significance level，whilst，power function fitting of RVI，exponential function fitting and logarithm function fitting ofMSAVI2had comparatively higher accuracy for estimating soybean LAI;then the soybean canopy LAI was estimated according to the highest correlation coefficient of accurate logarithm model function between MSAVI2 and measured LAI，it showed that the correlation between measured LAI and estimated LAI was significant（R=0.9098**，n=46).The regression function accuracy was 84.9%，the RMSE was 0.2420，average relative error was 0.1510.It is real-time，nondestructive and quantitative for adopting vegetation indices RVI，MSAVI2 to obtain soybean LAI，it can offer an evidence to design an optimum soybean canopy and estimate soybean yield by using hyperspectral remote sensing.

References:

[1] Hansen P M，Schjoerring J K.Reflectance measurement of canopy biomass and nitrogen statue in wheat crops using normalized difference vegetation indices and partial least squares regression［J］.Remote Sensing Environment，2003,86(4)：542-553.

[2]Hung T N，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.
[3]Filella I，Pe uelas J.The red edge position and shape as indictors of plant chlorophyll content，biomass and hydric status［J］.International Journal Remote Sensing，1994,15(7):1459-1470.
[4]刘伟东，项月琴，郑兰芬，等.高光谱数据与水稻叶面积指数及叶绿素密度的相关分析［J］.遥感学报，2000,4(4):279-283.(Liu W D，Xiang Y Q，Zheng L F，et al.Relationships between rice LAI，CH.D and hyperspectra data［J］.Journal of Remote Sensing，2000,4(4):279-283.)
[5]Pu R L，Gong P，Biging G S，et al.Extraction of red edge optical parameters from hyperion data for estimation of forest leaf area index［J］.Ieee Transactions on Geoscience and Remote Sensing，2003,4(41):916-921.
[6]王秀珍，黄敬峰，李云梅，等.水稻叶面积指数的高光谱遥感估算模型［J］.遥感学报，2004,8(1):81-88.(Wang X Z，Huang J F，Li Y M，et al.The Study on hyperspectral remote sensing estimation models about LAI of rice［J］.Journal of Remote Sensing，2004,8(1):81-88.)
[7宋开山，张柏，王宗明，等.玉米和大豆LAI高光谱遥感估算模型研究［J］.中国农学通报，2005,21(1):318-322.(Song K S，Zhang B，and Wang Z M，et al.Study on hyperspectral remote sensing estimation models for the corn LAI of canopy reflectance［J］.Chinese Agricultural Science Bulletin，2005,21(1):318-322.)
[8]浦瑞良，宫鹏.高光谱遥感及其应用［M］.北京：高等教育出版社，2000：205，185-228，124-144.(Pu R L，Gong P.Hyperspectral remote sensing and its applications［M］.Beijing:Higher Education Press，2000：205，185-228，124-144.)
[9]Qi J，Chehbouni A，Huete A R，et al.Modified soil adjusted vegetation index［J］.Remote Sensing of Environment，1994，48(2)：119-226.
[10] BrogeN H,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 Environment，2001,76(2):156-172.
[11]宋开山，张柏，王宗明，等.基于人工神经网络的大豆叶面积高光谱反演研究［J］.中国农业科学，2006,39(6):1138-1145.(Song K S，Zhang B，Wang Z M，et al.Soybean LAI estimation with in-situ collected hyperspectral data based on BP-neural networks［J］.Scientia Agricultura Sinica，2006,39(6):1138-1145.)
[12]Broge N H，Mortensen J V.Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data［J］.Remote Sensing Environment，2002，81(1):45-57.

Memo

Memo:
-
Last Update: 2014-10-10