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Quantification and Mapping of Surface Residue Cover for Maize and Soybean Fields in South Central Nebraska
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Transactions of the ASABE. 59(3): 925-939. (doi: 10.13031/trans.59.11489) @2016
Authors: Vivek Sharma, Suat Irmak, Ayse Kilic, Vasudha Sharma, John E. Gilley, George E. Meyer, Stevan Z. Knezevic, David Marx
Keywords: Crop residue cover, Landsat, Maize, Soybean, Tillage, Tillage index.
Abstract. The area cultivated under conservation tillage practices such as no-till and minimal tillage has recently increased in Midwestern states, including Nebraska. This increase, consequently, resulted in changes in some of the impacts of cropping systems on soil, such as enhancing soil and water quality, improving soil structure and infiltration, increasing water use efficiency, and promoting carbon sequestration. However, there are no methods currently available to quantify the percent crop residue cover (CRC) and the area under conservation tillage for maize and soybean at large scales on a continuous basis. This research used Landsat-7 (ETM+) and Landsat-8 (OLI) satellite data to evaluate six tillage indices [normalized difference tillage index (NDTI), normalized difference index 7 (NDI7), normalized difference index 5 (NDI5), normalized difference senescent vegetative index (NDSVI), modified CRC (ModCRC), and simple tillage index (STI)] to map CRC in eight counties in south central Nebraska. A linear regression CRC model showed that NDTI performed well in differentiating the CRC for different tillage practices at large scales, with a coefficient of determination (R2) of 0.62, 0.68, 0.78, and 0.07 for 25 March, 18 April, 28 May, and 6 June 2013 Landsat images, respectively. A minimum NDTI method was then used to spatially map the CRC on a regional scale by considering the timing of planting and tillage implementation. The measured CRC data were divided into training (calibration) and testing (validation) datasets. A CRC model was developed using the training dataset between minimum NDTI and measured CRC with an R2 of 0.89 (RMSD = 10.63%). A 3 x 3 matrix showed an overall accuracy of 0.90 with a kappa coefficient of 0.89. About 26% of the maize area and 15% of the soybean area had more than 70% CRC in south central Nebraska. This research and the procedures presented illustrate that multi-spectral Landsat images can be used to estimate and map CRC (error within 10.6%) on a regional scale and continuous basis using locally developed tillage practice versus crop residue algorithms. Further research is needed to incorporate soil and residue moisture content into the CRC versus tillage index to enhance the accuracy of the models for estimating CRC.