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

S.LAMSAL; C.M.BLISS; D.A.GRAETZ

摘要

Mapping the spatial distribution of soil nitrate-nitrogen (NO3-N) is important to guide nitrogen application as well as to assess environmental risk of 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 northeast Florida.Soil samples were collected from four depth increments (0-30,30-60,60-120 and 120-180 era) from 147 sampling locations identified using a stratified random and nested sampling design based on soil,land use and elevation strata.Soil NOa-N distributions 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 develop NO3-N trend maps.Land use and its 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 develop 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 suggested 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.

机译：绘制土壤硝态氮（NO3-N）的空间分布图对于指导氮素的应用以及评估NO3-N渗入地下水的环境风险非常重要。我们采用单变量和混合地统计方法绘制了土壤的空间分布图使用分层随机和嵌套抽样设计，从147个采样点的四个深度增量（0-30、30-60、60-120和120-180年代）收集了土壤样品，采集了来自佛罗里达东北部景观的NO3-N利用对数正态克里金法对前两层的土壤NOa-N分布进行空间自相关并作图绘制。使用线性和非线性回归方法得出NO3-N预测的环境相关模型，并将其用于绘制NO3-N趋势图，将土地利用及其相关变量从卫星图像中识别出来，作为使用环境相关模型预测NO3-N的重要变量。 l克里金法生成平滑变化的图，从环境相关模型得出的趋势图生成空间异质图。将趋势图与趋势模型残差的普通克里金法预测相结合以开发回归克里金法预测图，从而给出最佳的NO3-N预测。以及与野外采样土壤相比，遥感数据易于获得且具有更高的空间分辨率，我们的发现表明，基于土地利用的环境相关模型和遥感数据可用于土壤NO3-N的景观尺度制图。可用于在其他景观中绘制土壤NO3-N的图。

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

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