Multispectral indices and advanced classification techniques to detect percent residue cover over agricultural crops using Landsat data

Anna Pacheco; Heather McNairn; Anne M. Smith

doi:10.1117/12.694675

27 September 2006 Multispectral indices and advanced classification techniques to detect percent residue cover over agricultural crops using Landsat data

Anna Pacheco, Heather McNairn, Anne M. Smith

Author Affiliations +

Proceedings Volume 6298, Remote Sensing and Modeling of Ecosystems for Sustainability III; 62981C (2006) https://doi.org/10.1117/12.694675
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

Detecting and quantifying crop residue cover on agricultural fields is essential in identifying conservation tillage practices and estimating carbon sequestration, both of which are important goals within the Agricultural Policy Framework of Agriculture and Agri-Food Canada. Crop residue is traditionally measured using ground survey techniques such as the line-transect method or visual (drive-by) assessment but these techniques are tedious, time-consuming and subjective. With the increased number of advanced earth observation satellites, remote sensing has now become a viable option for mapping agricultural land management practices and percent crop residue cover. A wide variety of indices such as the Normalized Difference Index (NDI) and the Modified Soil Adjusted Crop Residue Index (MSACRI) were developed using multispectral data for this purpose but results have been mixed. Advanced classification techniques including linear spectral mixture analysis (SMA) and spectral angle mapper (SAM) provide an alternative to derive percent crop residue cover. Landsat-7 SLC-Off data were acquired over an agricultural study site in Eastern Ontario on May 25 2005. Simultaneous ground data were collected to characterize residue type, position, direction and percent cover. NDI, MSACRI, SMA and SAM were all computed and used to derive percent crop residue cover information. Preliminary results indicate that the SMA model predicts percent crop residue cover over agricultural fields with the most success, especially over fields of corn residue with an R² value of 0.85 (RMSE of 12.46 and D of 0.99). However, further investigation is needed where residue models are validated against a larger dataset with greater variability in percent crop residue cover.

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Anna Pacheco, Heather McNairn, and Anne M. Smith "Multispectral indices and advanced classification techniques to detect percent residue cover over agricultural crops using Landsat data", Proc. SPIE 6298, Remote Sensing and Modeling of Ecosystems for Sustainability III, 62981C (27 September 2006); https://doi.org/10.1117/12.694675

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