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ARS Home » Pacific West Area » Tucson, Arizona » SWRC » Research » Publications at this Location » Publication #155268
Title: EVALUATION OF OPTICAL REMOTE SENSING MODELS FOR CROP RESIDUE COVER ASSESSMENT 1544

Author
item Thoma, David
item GUPTA, S. - UNIVERSITY OF MINNESOTA
item BAUER, M. - UNIVERSITY OF MINNESOTA

Submitted to: Journal of Soil and Water Conservation Society
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/21/2004
Publication Date: 9/19/2004
Citation: Thoma, D., Gupta, S.C., Bauer, M.E. 2004. Evaluation of optical remote sensing models for crop residue cover assessment. Journal of Soil and Water Conservation Society 59(5): 224-233.

Interpretive Summary: Measurement of crop residue cover over large areas is difficult and time consuming, but such information is needed for monitoring conservation tillage adoption, assessing carbon sequestration potential and erosion modeling. Currently a 'drive-by' survey is made in most Midwest agricultural counties where observers make educated guesses about the abundance of residue cover in a sampling of fields. This study was designed to test the accuracy of this method for residue cover estimation, and to test the feasibility of predicting crop residue cover based on satellite imagery. The findings indicate that the 'drive-by' survey correctly classified fields into two cover categories (< 30% or > 30%) 77% of the time. Several of the remote sensing techniques achieved similar or slightly better classification accuracies, but none far surpassed the classification accuracy of observers in the field. Even though the remote sensing techniques tested in this project were no more accurate than field observations, several advantages became apparent. Remote sensing techniques are more cost effective, sample every field rather than selected fields, and eliminate inherent human bias in the observations.

Technical Abstract: Measurement of crop residue cover over large areas is useful for monitoring conservation tillage adoption, assessing carbon sequestration potential and erosion modeling. This study was designed to test the accuracy of crop residue estimates in current Tillage Transect Surveys (TTS), and to test the feasibility of predicting crop residue cover based on data recorded by Landsat Enhanced Thematic Mapper Plus (ETM+) satellite scenes. A total of 468 corn plus soybean fields in 11 Minnesota counties were characterized for residue cover in the course of 3 sampling campaigns coinciding in time with satellite scene acquisition. Results showed that TTS estimates were correct for 49% to 74% of fields when either 5 or 2 categories were used in classification respectively. Regression analysis showed a strong positive relationship between percent soybean residue cover and ETM+ bands 1, 3 and 7 (r2 =0.66) and between percent corn residue and ETM+ bands 4, 5 and 7 (r2 = 0.44). Three additional indices based on satellite digital numbers, the Tillage Index (TI), Normalized Difference Index (NDI), and Normalized Difference Tillage Index (NDTI) had coefficients of determination between 0.02 and 0.56 for corn and soybean residues. The Crop Residue Index Multiband (CRIM) model, a more physically based model, correctly predicted residue cover categories for 30 to 64% of fields when 5 or 2 categories were used in classification respectively. We conclude that remote sensing techniques had accuracy as good or better than TTS estimates when residue cover classifications were decreased to two categories (0-30%, and >30%). Since residue cover information is primarily needed to assess the extent of two categories, conservation and conventional tillage, remote sensing with Landsat imagery provides a means of sampling every field with an efficient, economical and uniform methodology.