Alternate satellite models for estimation of sugar beet residue nitrogen credit

Satellite assessment of aboveground plant residue mass and quality is essential for agro-ecosystem management of organic nitrogen (N) because growers credit a portion of residue N towards crop requirements the following spring. Precision agriculture managers are calling for advanced satellite models to map field-scale residue mass and quality. 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 N assessment is needed. We addressed this problem by building ground-truth models for sugar beet N-credit and testing these models with alternate satellite sensor imagery. We recorded spectral reflectance and measured leaf carbon (C) and N in situ at leaf and canopy levels near the end of the growing season using 1 nm bandwidth spectroradiometer. We performed univariate correlation analyses between spectral reflectance and the variables N, C:N ratio and biomass to determine spectral signature models for leaf quality and spectral signature models for 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 created using stepwise linear regression. Biomass formulae for each sensor were applied to satellite imagery acquired at peak season, while leaf quality formulae were applied to imagery acquired just prior to harvest. August sugar beet fields in the St. Thomas, ND vicinity were identified and aboveground biomass mapped with 10-20% error, depending upon the sensor. Sugar beet leaf N was similar for all sites and varieties tested (31 mg g−1 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. The general model for each sensor maps N-credit per unit area and delineates aberrant, low leaf quality areas as zones with high C:N ratio. 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.