Canopy reflectance response to plant nitrogen accumulation in rice

Tools to quantify the nitrogen (N) status of a rice canopy during inter-nodal elongation (IE) would be valuable for mid-season N management because N accounts for the largest input cost. The objective of this paper was to study canopy reflectance as a potential tool for assessing the mid-season status of N in a rice crop. Three field plot experiments were conducted in 2002 and 2003 on cultivars Wells and Cocodrie to study the canopy reflectance response of rice to plant N accumulation (PNA) during IE and to identify the wavelengths and vegetation indices that are good indicators of PNA. Each experiment included six pre-flood N treatments of 0, 33.6, 67.2, 100.8, 133.4 and 168 kg N ha−1. Rice canopy reflectance, biomass, tissue N concentration and PNA were measured weekly during IE. The wavelengths most strongly correlated to PNA at the beginning of IE were 937 and 718 nm. Several vegetation indices were examined to determine which were strongly correlated (>0.7) with PNA at the beginning of IE. Multiple linear regression models of PNA on selected vegetation indices explained 53–85% of the variation in PNA during the first week of IE. This study identifies the best combinations of vegetation indices for estimating PNA in rice.

[1]  G. Guyot,et al.  Utilisation de la Haute Resolution Spectrale pour Suivre L'etat des Couverts Vegetaux , 1988 .

[2]  N. H. Brogea,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2022 .

[3]  Ronald W. McNew,et al.  Comparison of plant measurements for estimating nitrogen accumulation and grain yield by flooded rice , 1999 .

[4]  S. G. Bajwa,et al.  Effect of N Availability on Vegetative Index of Cotton Canopy: A Spatial Regression Approach , 2007 .

[5]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[6]  Daniel Rodriguez,et al.  Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts , 2006 .

[7]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[8]  Qifa Zhou,et al.  Leaf and spike reflectance spectra of rice with contrasting nitrogen supplemental levels , 2003 .

[9]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[10]  C. W. Smith,et al.  Rice: origin, history, technology and production. , 1999 .

[11]  R. Norman,et al.  Management of fertiliser nitrogen in dry-seeded, delayed-flood rice , 1994 .

[12]  R. Norman,et al.  Nitrogen Application Timing Effects on Nitrogen Efficiency of Dry-Seeded Rice , 1998 .

[13]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[14]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[15]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[16]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[17]  Byun-Woo Lee,et al.  Using canopy reflectance and partial least squares regression to calculate within-field statistical variation in crop growth and nitrogen status of rice , 2006, Precision Agriculture.

[18]  W. E. Larson,et al.  Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. , 2000 .

[19]  Sreekala G. Bajwa MODELING RICE PLANT NITROGEN EFFECT ON CANOPY REFLECTANCE WITH PARTIAL LEAST SQUARE REGRESSION (PLSR) , 2006 .

[20]  A. Gitelson,et al.  Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll , 1996 .

[21]  Ronnie W. Heiniger,et al.  Quantitative Approaches for Using Color Infrared Photography for Assessing In‐Season Nitrogen Status in Winter Wheat , 2003 .

[22]  A. Gitelson Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation. , 2004, Journal of plant physiology.

[23]  Weixing Cao,et al.  Monitoring Leaf Nitrogen Status in Rice with Canopy Spectral Reflectance , 2004, Agronomy Journal.

[24]  Kuo-Wei Chang,et al.  A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy , 2008 .

[25]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[26]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[27]  Linzhang Yang,et al.  Recommendations for nitrogen fertiliser topdressing rates in rice using canopy reflectance spectra , 2008 .

[28]  C. Jordan Derivation of leaf-area index from quality of light on the forest floor , 1969 .