Submitted to: American Society of Agronomy
Publication Type: Abstract only
Publication Acceptance Date: 9/20/2008
Publication Date: 10/15/2008
Citation: Daughtry, C.S., Serbin, G., Doraiswamy, P.C., Monty, J., Crawford, M.M., Vyn, T.J. 2008. Mapping crop residue cover and soil tillage intensity using remote sensing [abstract]. American Society of Agronomy. 2008 CDROM. Interpretive Summary:
Technical Abstract: Until recently crop residues were managed primarily to reduce soil erosion and increase soil organic carbon, but demands for biofuels may remove much of the residue. Current methods of measuring crop residue cover are inadequate for characterizing the temporal and spatial variability of crop residues within fields or across large regions. Our objectives were to evaluate several spectral indices for measuring crop residue cover and classifying soil tillage intensity in agricultural fields. Landsat Thematic Mapper, Hyperion Imaging Spectrometer, aircraft imaging spectrometer (SpecTIR) data were acquired over agricultural fields at test sites in Indiana and Iowa shortly after planting during 2004-2007. Crop residue cover in corn and soybean fields was measured using the line-point transect method. Spectral indices using Landsat TM bands were weakly related to crop residue cover. With the hyperspectral data, crop residue cover was linearly related to the cellulose absorption index (CAI) which measures the relative intensity of cellulose absorption features near 2100 nm. Water in the crop residue significantly attenuated CAI and crop residues covers were underestimated as scene water content increased. Spatial and temporal adjustments in the spectral estimates of crop residue cover are possible using a reflectance ratio water index. Inventories of soil tillage intensity by previous crop type were generated for each scene by combining information on the previous season’s crop classification with the current season’s crop residue cover. Regional surveys of soil management practices that affect soil C dynamics are possible using either advanced multipsectral or hyperspectral imaging systems. Hyperspectral data are not required, because the three narrow bands that are used for CAI and the scene moisture correction could be incorporated in advanced multipsectral sensors.