Skip to main content
padlock icon - secure page this page is secure

Neural network and crop residue index multiband models for estimating crop residue cover from Landsat TM and ETM+ images

Buy Article:

$61.00 + tax (Refund Policy)

Crop residues on the soil surface provide not only a barrier against water and wind erosion, but they also contribute to improving soil organic matter content, infiltration, evaporation, temperature, and soil structure, among others. In Argentina, soybean (Glycine max (L.) Merill) and corn (Zea mays L.) are the most important crops. The objective of this work was to develop and evaluate two different types of model for estimating soybean and corn residue cover: neural networks (NN) and crop residue index multiband (CRIM) index, from Landsat images. Data of crop residue were acquired throughout the summer growing season in the central plains of Córdoba (Argentina) and used for training and validating the models. The CRIM, a linear mixing model of composite soil and residue, and the NN design, included reflectance and digital numbers from a combination of different TM bands to estimate the fractional residue cover. The results show that both methodologies are appropriate for estimating the residue cover from Landsat data. The best developed NN model yielded R 2 = 0.95 when estimating soybean and corn residue cover fraction, whereas the best fit using CRIM yielded R 2 = 0.87; in addition, this index is dependent on the soil and residue lines considered.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Affiliations: Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, . Córdoba, Argentina

Publication date: May 19, 2014

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more