Spectral Estimates of Crop Residue Cover and Density for Standing and Flat Wheat Stubble

Published in Agron. J. 104:271–279 (2012) Posted online 6 Jan 2012 doi:10.2134/agronj2011.0175 Copyright © 2012 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. C residue is an important agricultural C sink component for greenhouse gas (GHG) mitigation. According to Smith et al. (2008), there are three general mechanisms where opportunity for GHG mitigation in agriculture is viable, namely, reducing emission, enhancing removal and avoiding emission. Th e use of crop residue falls in two of these three mechanisms. Pacala and Socolow (2004) and Caldeira et al. (2004), both identifi ed crop residue as a valuable, rapidly deployable option for GHG mitigation. Th e Intergovernmental Panel for Climate Change (IPCC) detailed the large mitigation potential of agriculture for short and medium term coming from C sequestration, and to a lesser degree from biomass (from agricultural residues and dedicated energy crops) for bioenergy feedstock (Smith et al., 2007). However, measuring and validating crop residue is limited by measurement uncertainty and monitoring costs (Smith et al., 2007). In addition to the mechanistic variations in C sequestration processes, agricultural systems inherently exhibit several sources of spatial and temporal variability. Increasing the geographical extent and employing remote sensing methodologies in fi eld measurements are some of the options being considered to help address these challenges (Izaurralde and Rice, 2006; Smith et al., 2007). A review on the U.S. C sequestration research needs by Morgan et al. (2010) identifi es remote sensing as an important tool for quantifying estimated C fl uxes. Whatever the focus, accurate measurement of the amount of crop residue left in the fi eld aft er harvest is important. Th ere are several methods to estimate crop residue. One method estimates crop residue from measured yield data for a location using the harvest index (HI). For example, Johnson et al. (2006) used HI to estimate the crop residue and consequently historical C. Although this method is useful for estimating residue in the absence of additional data, accuracy is oft en questionable because this technique does not incorporate variable harvesting and management practices and may diff er depending on environmental conditions and the time of harvest. Another method of estimating the crop residue amount would be through conversion charts relating percent crop residue cover to residue amounts (Sloneker and Moldehauer, 1977; Gregory, 1982; McCool et al., 1995). Adjustment factors associated with tillage operations and implements used in the fi eld are available for wheat (Hickman and Schoenberger, 1989) and for a few other crops (McCool et al., 1995; Kline, 2000). Remote sensing techniques have been used for many years to measure various agricultural resources at regional scales. Using satellite and aerial images, landscape assessment can be quickly achieved with minimal fi eld sampling. Another advantage of using remote sensing technologies is their capability of monitoring large spatial extents in a relatively short span of time. Th ese tools off er ABSTRACT