Paper Titles

Effects of Surfactants on the Characteristics of Y₂O₃ Dispersion Phase through Hydrothermal Synthesis
p.1574

Empirical Analysis about the Relationship between Economic Growth and Financial Development: Evidence from China
p.1578

Effects of Tin on Aluminum – Zinc Alloy as Sacrificial Anode to Protect Underground Oil Pipeline in Al-Mahawil Regional
p.1585

Empirical Study on the Independent Director System' Effectiveness on the Corporate Performance of Listed Companies in Shenzhen Stock Exchange
p.1595

Estimation of Wheat Chlorophyll Content Based on HJ Satellite CCD
p.1599

Embedded System Based Target Tracking
p.1605

Electronic Scale Automatic Calibration System with Image Identification
p.1609

Evaluating the Performance of Training Algorithms in Active Noise Control Using MLP Neural Network
p.1613

Employment Evaluation for University Student Based on Analytic Hierarchy Process
p.1618

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 468-471Estimation of Wheat Chlorophyll Content Based on...

Estimation of Wheat Chlorophyll Content Based on HJ Satellite CCD

Article Preview

Abstract:

Chlorophyll content is an important indicator for assessing crop health and predicting crop yield. It is possible that chlorophyll content (CC) was quickly and non-destructively estimated by remote sensing. The objective of the experiment was to develop precision agricultural practices for predicting CC of wheat. In this study, we compared some spectral parameters (SPs) and CC with the determination coefficient (R2), and combined these SPs by stepwise regression methods. The results indicated that the 1.45SIPI-1.05PSRI, the R2 value was 0.6589 and corresponding the root mean square error (RMSE) was 1.463, and it can be used to improve the prediction accuracy of CC.

You might also be interested in these eBooks

Automation Equipment and Systems

Info:

Periodical:

Advanced Materials Research (Volumes 468-471)

Pages:

1599-1604

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.468-471.1599

Citation:

Cite this paper

Online since:

February 2012

Authors:

Xiu Liang Jin, Chang Wei Tan, Jun Chan Wang, Lu Tong, Fen Tuan Yang, Xin Kai Zhu, Wen Shan Guo

Keywords:

Chlorophyll Content (CC), Spectral Parameters, Stepwise Regression Method, Wheat

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] C. Buschmann, E. Nagel, In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation, Int. J. Remote Sens. 14 (1993) 711-722.

DOI: 10.1080/01431169308904370

[2] A.A. Gitelson, M.N. Merzlyak, Quantitative estimation of chlorophyll a using reflectance spectra: experiments with autumn chestnut and maple leaves, J. Photochem Photobiol (B). 22 (1994 a) 247-252.

DOI: 10.1016/1011-1344(93)06963-4

[3] A.A. Gitelson, M.N. Merzlyak, Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum andAcer platanoides leaves. Spectral features and relation to chlorophyll estimation,J. Plant Physiol. 143 (1994 b) 286-292.

DOI: 10.1016/s0176-1617(11)81633-0

[4] J. Markwell, J. C. Osterman, J. L. Mitchell, Calibration of the Minolta SPAD-502 leaf chlorophyll meter, Photosynth Res. 46 (1995) 467-472.

DOI: 10.1007/bf00032301

[5] J.A. Gamon, J.S. Surfus, Assessing leaf pigment content and activity with a reflectometer. New Phytol. 143 (1999) 105-117.

DOI: 10.1046/j.1469-8137.1999.00424.x

[6] A.A. Gitelson, M.N. Merzlyak, O.B. Chivkunova, Optical properties and non-destructive estimation of anthocyanin content in plant leaves. Photochem Photobiol, Photochem Photobiol. 74 (2001) 38-45.

DOI: 10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2

[7] A.A. Gitelson,Y. Zur, O.B. Chivkunova M.N. Merzlyak, Assessing carotenoid content in plant leaves with reflectance spectroscopy, Photochem Photobiol. 75 (2002) 272-281.

DOI: 10.1562/0031-8655(2002)075<0272:accipl>2.0.co;2

[8] P.J. Curran, J.L. Dungan, H.L. Gholz, Exploring the relationship between reﬂectance red edge and chlorophyll content in slash pine, Tree Physiology. 7 (1990) 33-48.

DOI: 10.1093/treephys/7.1-2-3-4.33

[9] A.A. Gitelson, M.N. Merzlyak,Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll, J. Plant Physiol. 148 (1996) 494-500.

DOI: 10.1016/s0176-1617(96)80284-7

[10] G.A. Blackburn,Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluationof some hyper-spectral approaches, Remote Sens. Environ. 66 (1998) 273-285.

DOI: 10.1016/s0034-4257(98)00059-5

[11] B. Datt, Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves, Remote Sens. Environ. 66 (1998) 111-121.

DOI: 10.1016/s0034-4257(98)00046-7

[12] B. Datt, Visible/near infrared reﬂectance and chlorophyll content in Eucalyptus leaves, Int. J. Remote Sens. 20 (1999) 2741-2759.

DOI: 10.1080/014311699211778

[13] M.L. Adams, W.D. Philpot, W.A. Norvell, Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. Int. J. Remote Sens. 20 (1999) 3663-3675.

DOI: 10.1080/014311699211264

[14] G.A. Carter, A.K. Knapp,Leaf optical properties in higher plants:linking spectral characteristics to stress and chlorophyll concentration, Am. J. Bot. 84 (2001) 677-684.

DOI: 10.2307/2657068

[15] G. Le Maire, C. Francois, E. Dufrene, Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reﬂectance measurements, Remote Sens. Environ. 89 (2004) 1-28.

DOI: 10.1016/j.rse.2003.09.004

[16] A.A. Gitelson, A. Vina, V. Ciganda, D.C. Rundquist, T.J. Arkebauer, J. Geophys, Remote estimation of canopy chlorophyll content in crops, J. Geophysi. Res. Let. Res. Lett. 32:L08403 (2005) doi:10.1029/2005 GL022688.

DOI: 10.1029/2005gl022688

[17] A.A. Gitelson, A. Vina, S.B. Verma, D.C. Rundquist, T.J. Arkebauer, G. Keydan, B. Leavitt, V. Ciganda, G.G. Burba, A.E. Suyker, Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity, J.Geophysi.Res.Lett. 111: D08S11(2006).

DOI: 10.1029/2005jd006017

[18] Y.Peng, A.A. Gitelson,G.Keydan D.C. Rundquist,W.Moses, Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content, Remote Sens. Environ. 115(2011) 978-989.

DOI: 10.1016/j.rse.2010.12.001

[19] A.A. Gitelson, O.B. Chivkunova, M.N. Merzlyak,Nondestrucitve estimation of anthocyanins and chlorophylls in anthocyanic leaves, Am.J.Botany. 96(2009) 1861-1868.

DOI: 10.3732/ajb.0800395

[20] M.Steele, A.A. Gitelson, D.C. Rundquist, Nondestructive estimation of leaf chlorophyll content in Grapes,Am.J.Enol.Vitic. 59(2008) 299-305.

DOI: 10.5344/ajev.2008.59.3.299

[21] J.H. Zhang,J.Wang,H.Chao, Prediction of Chlorophyll Concentration of Rice Based on Spectral Absorption,Transmittance and Reflectance, Chinese Agr. Sci.Bull.26 (2010) 78-83.　(in chinese)

[22] F.J. Adamsen P.J. Pinter Jr, E.M. Barnes R.L. Lamorte G.W. Wall S.W. Leavitt, A.Kimball, Measuring wheat senescence with a digital camera.Crop Sci. 39 (1999)719-724.

DOI: 10.2135/cropsci1999.0011183x003900030019x

[23] Y.Manetas, G. Grammatikopoulus, A.Kyparissis, The use of the portable,non-destructive, SPA D-502 (Minolta) chlorophyll meter with leaves of varying trichome density and anthocyanin content, J.Plant Physi.153(1998) 513-516.

DOI: 10.1016/s0176-1617(98)80182-x

[24] Information on http: //www.shznews.com/ Category_145/ Index.aspx

[25] J.U.H. Eitel, D.S. Long, P.E. Gessler, E.R. Hunt, Combined Spectral Index to Improve Ground-Based Estimates of Nitrogen Status in Dryland Wheat,Agron. J. 100 (2008) 1694-1702.

DOI: 10.2134/agronj2007.0362