Using canopy reflectance and partial least squares regression to calculate within-field statistical variation in crop growth and nitrogen status of rice

Using canopy reflectance and partial least squares regression to calculate within-field... For the site-specific prescription of fertilizer topdressing in rice cultivation, a non-destructive diagnosis of the rice growth and nutrition status is necessary. Three experiments were done to develop and test a model using canopy reflectance for the non-destructive diagnosis of plant growth and N status in rice. Two experiments for model development were conducted, one in 2000 and another in 2003 in Suwon, Korea, including two rice varieties and four nitrogen (N) rates in 2000 and four rice varieties and 10 N treatments in 2003. Hyperspectral canopy reflectance (300–1,100 nm) data recorded at various growth stages before heading were used to develop a partial least squares regression (PLS) model to calculate plant biomass and N nutrition status. The 342 observations were split for model calibration (75%) and validation (25%). The PLS model was then tested to calculate within-field statistical variation of four crop variables: shoot dry weight (SDW), shoot N concentration (SN), shoot N density (SND) and N nutrition index (NNI) using measured canopy reflectance data from a field of 6,500 m2 in 2004. Results showed that PLS regression using logarithm reflectance had better performance than both the PLS and multiple stepwise linear regression (MSLR) models using original reflectance data to calculate the four plant variables in year 2000 and 2003. It produced values with an acceptable model coefficient of determination (R 2) and relative error of calculation (REC). The model R 2 and REC ranged from .83 to .89 and 13.4% to 22.8% for calibration, and .76 to .87 and 14.0% to 24.4% for validation, respectively. The PLS regression model R 2 was reduced in the test data of year 2004 but the root mean square error of calculation (RMSEC) was smaller, suggesting that the PLS regression model using canopy reflectance data could be a promising method to calculate within-field spatial variation of rice crop growth and N status. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Using canopy reflectance and partial least squares regression to calculate within-field statistical variation in crop growth and nitrogen status of rice

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Publisher
Kluwer Academic Publishers-Plenum Publishers
Copyright
Copyright © 2006 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-006-9010-0
Publisher site
See Article on Publisher Site

Abstract

For the site-specific prescription of fertilizer topdressing in rice cultivation, a non-destructive diagnosis of the rice growth and nutrition status is necessary. Three experiments were done to develop and test a model using canopy reflectance for the non-destructive diagnosis of plant growth and N status in rice. Two experiments for model development were conducted, one in 2000 and another in 2003 in Suwon, Korea, including two rice varieties and four nitrogen (N) rates in 2000 and four rice varieties and 10 N treatments in 2003. Hyperspectral canopy reflectance (300–1,100 nm) data recorded at various growth stages before heading were used to develop a partial least squares regression (PLS) model to calculate plant biomass and N nutrition status. The 342 observations were split for model calibration (75%) and validation (25%). The PLS model was then tested to calculate within-field statistical variation of four crop variables: shoot dry weight (SDW), shoot N concentration (SN), shoot N density (SND) and N nutrition index (NNI) using measured canopy reflectance data from a field of 6,500 m2 in 2004. Results showed that PLS regression using logarithm reflectance had better performance than both the PLS and multiple stepwise linear regression (MSLR) models using original reflectance data to calculate the four plant variables in year 2000 and 2003. It produced values with an acceptable model coefficient of determination (R 2) and relative error of calculation (REC). The model R 2 and REC ranged from .83 to .89 and 13.4% to 22.8% for calibration, and .76 to .87 and 14.0% to 24.4% for validation, respectively. The PLS regression model R 2 was reduced in the test data of year 2004 but the root mean square error of calculation (RMSEC) was smaller, suggesting that the PLS regression model using canopy reflectance data could be a promising method to calculate within-field spatial variation of rice crop growth and N status.

Journal

Precision AgricultureSpringer Journals

Published: Jul 1, 2006

References

  • Monitoring rice reflectance at field level for estimating biomass and LAI
    Casanova, D.; Epema, G. F.; Goudriaan, J.
  • Yield gap analysis in relation to soil properties in direct-seeded flooded rice
    Casanova, D.; Goudriaan, J.; Bouma, J.; Epema, G. F.
  • A NIR technique for rapid determination of soil mineral nitrogen
    Ehsani, M. R.; Upadhyaya, S. K.; Slaughter, D.
  • Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data
    Grossman, Y. L.; Ustin, S. L.; Jacquemound, S.; Sanderson, E. W.; Schmuck, G.; Verdebout, J.
  • Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression
    Hansen, P. M.; Schjoerring, J. K.
  • Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis
    Huang, Z.; Turner, B. J.; Dury, S. J.; Wallis, I. R.; Foley, W. J.

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