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Leaf chlorophyll content (μg cm -2 ) is an important variable for agricultural remote sensing because of its close relationship to leaf N content. Th e objectives of this study were to develop and test a new index, based on red, green and blue bands, that is sensitive to differences in leaf chlorophyll content at leaf and canopy scales. We propose the triangular greenness index (TGI), which calculates the area of a triangle with vertices: (λr, Rr), (λg, Rg), and (λb, Rb), where λ is the wavelength (nm) and R is the refl ectance for bands in red (r), green (g), and blue (b) wavelengths. Th e TGI was correlated with chlorophyll content using a variety of leaf and plot refl ectance data. Generally, indices using the chlorophyll red-edge (710-730 nm) had higher correlations with chlorophyll content compared to TGI. However, correlations between TGI and chlorophyll content were equal to or higher than broad-band indices, when leaf area index (LAI) was >2. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, LAI, and soil type at low LAI, whereas at high LAI, TGI was only aff ected by leaf chlorophyll content. Th e TGI could be used with low-cost sensors, such as commercially-available digital cameras, for N management by remote sensing.

This study assessed whether vegetation indices derived from broadband RapidEye TM data containing the red edge region (690-730 nm) equal those computed from narrow band data in predicting nitrogen (N) status of spring wheat (Triticum aestivum L.). Various single and combined indices were computed from in-situ spectroradiometer data and simulated RapidEye TM data. A new, combined index derived from the Modified Chlorophyll Absorption Ratio Index (MCARI) and the second Modified Triangular Vegetation Index (MTVI2) in ratio obtained the best regression relationships with chlorophyll meter values (Minolta Soil Plant Analysis Development (SPAD) 502 chlorophyll meter) and flag leaf N. For SPAD, r 2 values ranged from 0.45 to 0.69 (p,0.01) for narrow bands and from 0.35 and 0.77 (p,0.01) for broad bands. For leaf N, r 2 values ranged from 0.41 to 0.68 (p,0.01) for narrow bands and 0.37 to 0.56 (p,0.01) for broad bands. These results are sufficiently promising to suggest that MCARI/MTVI2 employing broadband RapidEye TM data is useful for predicting wheat N status.

Spectral indices are useful for estimating crop yield potential and basing in-season N fertilizer applications. Th e normalized diff erence vegetation index (NDVI) is positively related to crop N status and leaf area index (LAI) under N limited conditions. However, under water limited conditions, variations in LAI may be driven by soil moisture rather than plant-available N, which will confound spectral estimates of crop N status. Th is study evaluated the performance of spectral indices for ground sensing of crop chlorophyll a+b content (C ab ) and N status in dryland wheat (Triticum aestivum L.). Sensitivity of spectral indices to C ab and LAI was assessed using refl ectance spectra simulated by the PROSPECT+SAIL model. Simulations showed the modifi ed chlorophyll absorption ratio index and second modifi ed triangular vegetation index in ratio (MCARI/MTVI2) to be sensitive to C ab and resistant to LAI. Th is conclusion was tested in dryland fi elds with measured canopy refl ectance at Zadoks growth stages 57 to 60. NDVI and other simple indices were highly correlated with LAI (r 2 ≤ 0.84) and less well correlated with chlorophyll meter readings (r 2 ≤ 0.46) and fl ag leaf N (r 2 ≤ 0.29). MCARI/MTVI2 was poorly correlated with LAI (r 2 = 0.01) and more highly correlated with chlorophyll (r 2 = 0.70) and fl ag leaf N (r 2 = 0.54), agreeing with PROSPECT+SAIL. Use of MCARI/ MTVI2 may improve ground estimates of crop N status where LAI variability is associated with water availability. Th is information is potentially useful for decisions whether to apply foliar N to enhance grain protein concentration of wheat.

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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. Spectral indices (SI) derived from crop refl ectance data are sensitive to chlorophyll a and b content (Chl). However, the SI-Chl relationship might be confounded by variation in leaf area index (LAI) and soil background refl ectance, especially in semiarid environments where water determines crop growth. Th is study evaluated the sensitivity of SI to variation in soil refl ectance and how this may aff ect overall SI performance for ground-based sensing of Chl in dryland wheat (Triticum aestivum L.). Selected SI were computed from spectra simulated by the PROSPECT-SAIL radiative-transfer model for 5 LAI values, 7 Chl values, and 121 dry soil surface refl ectance spectra. Th ese spectra represented soils across major wheat growing areas in the United States. Soil properties and refl ectance varied widely among the soils indicated by the high SI variation for LAI values < 1.5. Overall, soil background variation contributed less to the observed SI variability (<6%) than LAI (<97%). Combined indices [i.e., Normalized Diff erence Red Edge Index (NDRE)/Normalized Diff erence Vegetation Index (NDVI) and Modifi ed Chlorophyll Absorption Ratio Index (MCARI)/Second Modifi ed Triangular Vegetation Index (MTVI)] were least aff ected by soil background variation than single indices (i.e., NDVI). Results showed that ground sensing of Chl may be improved by means of combined indices that are resistant to soil background and LAI. Empirical measurements verifi ed that the modeling results were a reliable representation of the infl uence of Chl, LAI, and soil on canopy refl ectance. Further research is needed to evaluate the eff ect of soil moisture, surface roughness, residue, growth stage, and shadow on SI. Abbreviations: Chl, chlorophyll a and b content; GESAVI, Generalized soil adjusted vegetation index; LAI, leaf area index; MCARI, modifi ed chlorophyll absorption in refl ective index; NDRE, normalized diff erence red edge index; NDVI, normalized diff erence vegetation index; OSAVI, optimized soil adjusted vegetation index; SAVI, Soil Adjusted Vegetation Index; SI spectral index; MTV2, Second Modifi ed Triangular Vegetation Index; TCARI, transformed chlorophyll absorption in refl ectance index; TSAVI, Transformed Soil Adjusted Vegetation Index.