Water and Nitrogen Effects on Active Canopy Sensor Vegetation Indices

Published in Agron. J. 103:1815–1826 (2011) Posted online 28 Sept 2011 doi:10.2134/agronj2011.0199 Copyright © 2011 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. 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. I N management for corn using active crop canopy sensors (ACS) relies on the use of algorithms that can trigger on-the-go N fertilization in the field based on crop canopy reflectance. Optical sensing equipment that employs this approach is commercially available and these sensors rely on some version of a vegetation index to express crop reflectance (Shanahan et al., 2008; Eitel et al., 2008) and prescribe N rate application. There are different approaches and vegetation indices used to determine N rate based on these sensors, but the majority of algorithms use the nitrogen sufficiency index (NSI) approach previously proposed for chlorophyll meter readings (Varvel et al., 1997). For example, when the ratio between a targeted region in the field and a well-fertilized reference in the same field reaches a certain level, N fertilizer is needed according to a function that describes the relationship between yield and NSI readings (Bausch and Duke, 1996). Some N rate recommendation algorithms use yield potential that is determined by growing degree days and an estimate of biomass at the day of sensing (Raun et al., 2002). Several additional vegetation indices have been used to calculate N rate for corn and wheat using active canopy sensors, such as the green normalized difference vegetation index (GNDVI) (Dellinger et al., 2008), and the CI (Solari et al., 2008). Regardless of the approach used, an understanding of how these indices may be influenced by water stress and previous crop is needed. Previous work by Eitel et al. (2008) investigated the impact of water availability and N stress on leaf area index (LAI) in wheat using a multispectral radiometer and a chlorophyll meter. They showed that the ratio of the modified chlorophyll absorption ratio index to the second modified triangular vegetation index (MCARI/MTVI2) is sensitive to N and less susceptible to variable LAI caused by water stress. Another example of interaction between water and N stress in corn using remote sensing was the work done by Clay et al. (2006), where broad band widths were used to calculate different indices (NDVI, GNDVI, normalized difference water index [NDWI], and nitrogen reflectance index [NRI]), with the major conclusion being that water and N had additive effects on yield and optimum N rates (100–120 kg N ha–1) were similar across different water levels. There are other examples of indices used specifically to detect water stress (Zygielbaum et al., 2009), to determine chlorophyll content, and to estimate gross primary productivity (Lemaire et al., ABSTRACT