Geospatial Mapping of Soil Nitrate-Nitrogen Distribution Under a Mixed-Land Use System.

Mapping the spatial distribution of soil nitrate-nitrogen (NO3-N) is important to guide nitrogen application as well as to assess environmental risk associated with NO3-N leaching into the groundwater. We employed univariate and hybrid geostatistical methods to map the spatial distribution of soil NO3-N across a landscape in northern Florida. Soil samples were collected from four depth increments (0-30, 30-60, 60-120, 120-180 cm) from 147 sampling locations identified using a stratified random and nested sampling design based on soil, land use and elevation strata. Soil NO3-N distribution in the top two layers were spatially autocorrelated, and mapped using lognormal kriging. Environmental correlation models for NO3-N prediction were derived using linear and non-linear regression methods, and employed to produce NO3-N trend maps. Land use and land use related variables derived from satellite imagery were identified as important variables to predict NO3-N using environmental correlation models. While lognormal kriging produced smoothly varying maps, trend maps derived from environmental correlation models generated spatially heterogeneous maps. Trend maps were combined with ordinary kriging predictions of trend model residuals to generate regression kriging prediction maps, which gave the best NO3-N predictions. As land use and remotely sensed data are readily available and have much finer spatial resolution compared to field sampled soils, our findings suggest the efficacy of environmental correlation models based on land use and remotely sensed data for landscape scale mapping of soil NO3-N. The methodologies implemented are transferable for mapping of soil NO3-N in other landscapes.