Submitted to: Agriculture Ecosystems and the Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/18/2004
Publication Date: 3/1/2005
Citation: Beeri, O., Phillips, R., Carson, P., Liebig, M.A. 2005. Alternate satellite models for estimation of sugar beet residue nitrogen credit. Agric. Ecosys. Environ. 107:21-35. Interpretive Summary: Use of remote sensing for precision agriculture has the potential improve nitrogen (N) management in agroecosystems. Using a combination of ground-truth data and satellite imagery, residue N credits were developed for sugar beet fields in the Red River Valley of North Dakota. Data collected within sugar beet fields included leaf carbon (C) and N, spectral reflectance of leaf C and N, and aboveground biomass. Data collected with satellite imagery was generated by Landsat 5, SPOT 5, Quick-Bird 2, and Ikonos 2. Variability of aboveground biomass in sugar beet fields was identified and mapped with 84 to 94% accuracy, depending on the sensor. Sugar beet leaf N was similar for all sites and varieties tested, so aboveground biomass primarily influenced N-credit estimates. Measured C:N ratio variability was also identified and mapped to allow for the identification of aberrant, low-leaf quality field zones. Spectral models for N-credit and leaf quality developed in this study have the potential to improve N management for sugar beet fields in the Red River Valley of North Dakota.
Technical Abstract: Remote sensing has proven useful for assessing the concentration of foliar biochemicals under controlled laboratory conditions, but field-scale satellite model validation for quantitative, landscape-scale nitrogen (N) assessment is needed. We addressed this problem by building ground-truth models for sugar beet (Beta vulgaris L.) N-credit and tested these models with alternate satellite sensor imagery. Spectral reflectance and leaf carbon (C) and N in situ at leaf and canopy levels were assessed near the end of the growing season using a 1-nm bandwidth spectroradiometer. Univariate correlation analyses between spectral reflectance and leaf N, C:N ratio, and aboveground biomass were performed to determine spectral signature models for leaf quality and plant biomass. The 1-nm hyperspectral data were convolved to fit Landsat 5, SPOT 5, Quick-Bird 2, and Ikonos 2 multi-spectral satellite bands and models were created using stepwise linear regression. Field-scale variability for aboveground biomass was identified and mapped, with 84 to 94% accuracy, depending on the sensor. Sugar beet leaf N was similar for all sites and varieties tested (31 mg/g dw), so biomass primarily influenced N-credit estimates. Measured C:N ratio variability was identified and mapped to delineate areas where C:N ratio was outside the normal distribution, thereby allowing for the identification of aberrant, low-leaf quality field zones. In summary, we provide separate spectral models for N-credit and leaf quality applicable to available multi-spectral sensors for precision sugar beet N management.