Feasibility assessment on use of proximal geophysical sensors to support precision management

This data was used in the analysis of the paper, "Feasibility assessment on use of proximal geophysical sensors to support precision management", which was submitted to Vadose Zone Journal. A study was conducted at three sites in North Dakota, United States to strengthen understanding of the usefulness of different proximal geophysical data types in agricultural contexts of varying pedology. This study hypothesizes that electro-magnetic induction (EMI), gamma-ray sensor (GRS), cosmic-ray neutron sensor (CRNS), and elevation data layers are all useful in multiple linear regression (MLR) predictions of soil properties that meet expert criteria at three agricultural sites. In addition to geophysical data collection with vehicle-mounted sensors, 15 soil samples were collected at each site and analyzed for nine soil properties of interest. Included is the data from proximal geophysical sensors for each site (one file for each site) and a file containing all the sampled soil property data. The sampled soil properties include pH, electrical conductivity, cation exchange capacity, organic matter, available water holding capacity, bulk density, percent sand, percent silt, and percent clay.本数据集用于支撑投稿至《Vadose Zone Journal（渗流带期刊）》的论文《近地表地球物理传感器支撑精准农业管理的可行性评估》的数据分析工作。研究团队于美国北达科他州的三处农田开展田间实验，以深化对不同类型近地表地球物理传感器（proximal geophysical sensors）数据在不同土壤学背景农业场景中应用价值的认知。本研究提出如下假设：电磁感应（electro-magnetic induction, EMI）、伽马射线传感器（gamma-ray sensor, GRS）、宇宙射线中子传感器（cosmic-ray neutron sensor, CRNS）以及高程数据图层，均可用于开展符合专家判定标准的三处农业点位土壤属性的多元线性回归（multiple linear regression, MLR）预测。除借助车载传感器采集地球物理数据外，研究团队在每个点位采集15份土壤样品，并针对9项预设目标土壤属性开展实验室分析。本数据集包含各点位的近地表地球物理传感器采集数据（每个点位对应一个独立数据文件），以及一份整合了所有采样土壤属性数据的汇总文件。本次采样分析的土壤属性包含pH值、电导率、阳离子交换量、有机质含量、有效持水量、容重、砂粒百分比、粉粒百分比与黏粒百分比。