Potential Use of Spectral Reflectance Indices as a Selection Tool for Grain Yield in Winter Wheat under Great Plains Conditions

Abstract

).Abbreviations: GNDVI, green normalized diff erence vegetation index; LAI, leaf area index; NDVI, normalized diff erence vegetation index; NIR, near infrared; NWI, normalized water indices; NWI-1, normalized water index 1; NWI-2, normalized water index 2; NWI-3, normalized water index 3; NWI-4, normalized water index 4; PAR, photosynthetically active radiation; RIL, recombinant inbred lines; RNDVI, red normalized diff erence vegetation index; SR, simple ratio; SRI, spectral refl ectance indices; WI, water index. Published in Crop Sci. 47:1426-1440 (2007. doi: 10.2135/cropsci2006.07.0492 © Crop Science Society of America 677 S. Segoe Rd., Madison, WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Published online July 30, 2007Reproduced from Crop Science. Published by Crop Science Society of America. All copyrights reserved.CROP SCIENCE, VOL. 47, JULY-AUGUST 2007 WWW.CROPS.ORG 1427 or thousands of segregating populations in a breeding program (Royo et al., 2003). Reynolds et al. (1999) indicated that morphological characters such as number of grains per unit area and harvest index can be used in the visual selection of breeding lines, but those traits are diffi cult to measure in a large number of small plots in early generations due to labor intensiveness and time.Yield in a given environment is directly and indirectly infl uenced by morphological, physiological, and environmental factors. Selection of breeding lines for grain yield in advanced nurseries often needs repetition to make a selection decision because commonly used statistical procedures sometimes fail to produce suffi ciently accurate results for identifying superior genotypes (Ball and Konzak, 1993). An analytical breeding strategy is an alternative breeding approach that requires a better understanding of the factors responsible for development, growth, and yield (Richards, 1982). This strategy considers morphophysiological selection criteria that have potential to make empirical selection more effi cient (Reynolds et al., 2001). The limited application of this analytical approach is probably due to the lack of an appropriate understanding of the physiological parameters, their estimation, and their true associations with grain yield (Richards, 1996). Commonly used physiological selection criteria include stomatal conductance, canopy temperature depression, and C 13 isotope discrimination of grains (Reynolds et al., 1999). Spectral properties of the plant came into focus as a potential selection tool for grain yield in more recent years (Aparicio et al., 2002;Royo et al., 2003;Babar et al., 2006).The basic principle governing canopy spectral refl ectance is that specifi c plant traits are associated with the...

Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

“…In addition, these new inventory tools could also be applied for the construction of EU scale Agricultural Information System (AIS) integrating all national AIS Systems. (Table 6) ap, bp, cp, dp Backscatter coefficient (sigma zero) for microwave backscattering signal, which is a combined signal reflected from soil and vegetation canopies [60][61][62][63][26][27][28]. Significance levels ( = reference probability)…”

“…According to Price [53], Maas [73], Flowers et al [80] and Hadria et al [81] the reflectance signal measured by the optical satellite sensors is a combined interacting signal reflected from both soil cover and vegetation canopies. The soil/canopy reflectance ratio (SCR) similar to Spectral Reflectance Indices applied by Prasad et al [26] for winter wheat grain yield estimations is varying along with spring cereal phenological development during growing season. In spring time before cereal sowing and emergence, the bare soil reflectance is prevailing in optical SCR reflectance ratio [53,73,80].…”

“…Brown et al [1] and Henning et al [2] studied crop canopy and microwave SAR (Synthetic Aperture Radar) backscattering interactions. Prasad et al [26] evaluated the use of Spectral Reflectance Indices (SRI) similar to VGI indices applied in this study, for assessing grain yield variabil ity in winter wheat genotypes under Great Plains (USA) growing conditions. Mangiarotti et al [27] applied a similar composite approach to the one applied by McNairn et al [7].…”

Field Measurements of Canopy Spectra for Biomass Assessment of Small-Grain Cereals

“…Because of the sensitivity of NDVI and SR to external factors ─particularly the soil background at low LAI values─and the developments in the field of imaging spectrometry, a set of new vegetation indices have been developed in order to minimize the effect of disturbing elements in the capturing of the spectra (Baret & Guyot, 1991;Broge & Mortensen, 2002;Gilabert et al, 2002;Meza Diaz & Blackburn, 2003;Rondeaux et al, 1996). In order to compare the suitability of the classical vegetation indices and the new ones mentioned in the literature as being appropriate for estimating growth traits in wheat and other cereals (P. Chen et al, 2009;Haboudane et al, 2004;Li et al, 2010a;Prasad et al, 2007), 83 hyperspectral vegetation indices were tested using durum wheat data from our own research. The indices were calculated from spectral reflectance measurements taken at different growth stages in 7 field experiments each involving 20-25 durum wheat genotypes, conducted under contrasting Mediterranean conditions for 2 years.…”

Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)

“…The optical calibration procedure for different Vegetation Indices models was originally presented by Price [57] and later revised and utilized for cereals by Flowers et al [64], Prasad et al [65], Maas [66][67][68] and Maas and Dunlap [69]. Table 11 (Appendix B) presents calibration parameter values applied in optical calibration equations 3-5 (Appendix C), which convert binary digital count (dc) values from satellite optical sensors into surface reflectance values (rf).…”