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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #310144

Research Project: QUANTIFYING AND MONITORING NUTRIENT CYCLING, CARBON DYNAMICS AND SOIL PRODUCTIVITY AT FIELD, WATERSHED AND REGIONAL SCALES

Location: Hydrology and Remote Sensing Laboratory

Title: Multispectral satellite mapping of crop residue cover and tillage intensity in Iowa

Author
item Beeson, Peter
item Daughtry, Craig
item Hunt, Earle - Ray
item Sadeghi, Ali
item Karlen, Douglas
item Tomer, Mark

Submitted to: Journal of Soil and Water Conservation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/4/2016
Publication Date: 8/29/2016
Citation: Beeson, P.C., Daughtry, C.S., Hunt Jr, E.R., Sadeghi, A.M., Karlen, D.L., Tomer, M.D. 2016. Multispectral satellite mapping of crop residue cover and tillage intensity in Iowa. Journal of Soil and Water Conservation. 71:385-395. doi:10.2489/jswc.71.5.385.

Interpretive Summary: Soil tillage intensity can be characterized by the proportion of the soil surface covered by crop residue shortly after planting, with residue cover <30% classified as conventional tillage and residue cover >30% as conservation tillage. Accurate assessments of crop residue cover are important for evaluating effectiveness of conservation practices. The standard technique used by USDA-NRCS, the line-point transect, is robust and accurate for assessing crop residue cover in a single field, but is impractical for monitoring crop residue cover in many fields over broad areas in a timely manner. Remote sensing may provide an efficient, timely, and objective method of obtaining information on soil tillage intensity over large areas. Multispectral images of the South Fork watershed of the Iowa River located in central Iowa were acquired by five satellites over three years. Classification accuracy for each satellite image was evaluated using crop residue cover data acquired in selected fields throughout the watershed within 10 days of a satellite overpass. Differences in the classification accuracy depended primarily on timing of satellite images with respect to field operations and the spectral bands of each sensor. The tillage intensity maps identified areas where additional conservation practices may be needed. We conclude that satellite imagery is well suited for classifying crop residue cover and tillage intensity over large geographic areas.

Technical Abstract: Accurate post-planting crop residue cover assessments are crucial for identifying dominant tillage practices across large geographic areas and evaluating effectiveness of Conservation Management Plans. Current assessment protocols are labor intensive, time consuming, and costly. We hypothesize that using satellite technology could provide this information more efficiently. Current and proposed methods for assessing crop residue cover after planting were evaluated within a 788 km2 watershed in central Iowa for three years (2009-2011). The watershed is dominated by corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] which are grown on glacial-till derived soils across 85% of the land area. For each year line-point transect data for 40 to 60 fields and roadside surveys for approximately 200 fields provided current practice estimates of crop residue cover which were compared with information derived from SPOT-5 HRG, Landsat TM, Indian ResourceSat AWiFS, and DEIMOS-1 satellite images. Conservation Technology Information Center (CTIC) criteria were used to classify tillage intensity based on post-planting surface cover. SPOT-5 and Landsat images provided similar accuracy ranging from 64 to 92%, while AWiFS and DEIMOS classifications had accuracies ranging from 61 to 73%. Differences in the accuracy of derived information depended primarily on timing of satellite images with respect to field operations and the spectral bands of each sensor. The resulting maps, however, identified tillage intensity as a function of slope, which could be useful for targeting additional conservation practices throughout the watershed. We conclude that satellite imagery is well suited for classifying crop residue cover and tillage intensity over large geographic areas.