Detailed knowledge of the intra-field variability of soil properties and crop characteristics is indispensable for the establishment of sustainable precision agriculture. We present an approach that combines ground-based agrogeophysical soil and aerial crop data to delineate field-specific management zones that we interpret with soil attribute measurements of texture, bulk density, and soil moisture, as well as yield and nitrate residue in the soil after potato (Solanum tuberosum L.) cultivation. To delineate the management zones, we use aerial drone-based normalized difference vegetation index (NDVI), spatial electromagnetic induction (EMI) soil scanning, and the EMI–NDVI data combination as input in a machine learning clustering technique. We tested this approach in three successive years on six agricultural fields (two per year). The field-scale EMI data included spatial soil information of the upper 0–50 cm, to approximately match the soil depth sampled for attribute measurements. The NDVI measurements over the growing season provide information on crop development. The management zones delineated from EMI data outperformed the management zones derived from NDVI in terms of spatial coherence and showed differences in properties relevant for agricultural management: texture, soil moisture deficit, yield, and nitrate residue. The combined EMI–NDVI analysis provided no extra benefit. This underpins the importance of including spatially distributed soil information in crop data interpretation, while emphasizing that high-resolution soil information is essential for variable rate applications and agronomic modeling.

中文翻译：

---

地面电磁感应和空中无人机数据划定的高分辨率农艺土壤信息和管理区

土壤特性和作物特性的田间变异性的详细知识对于建立可持续的精准农业是必不可少的。我们提出了一种方法，结合基于地面的农业地球物理土壤和空中作物数据来描绘特定领域的管理区域，我们通过土壤质地、体积密度和土壤水分的土壤属性测量结果以及土壤中的产量和硝酸盐残留量进行解释。马铃薯（Solanum tuberosumL.) 栽培。为了划定管理区域，我们使用基于无人机的归一化差异植被指数 (NDVI)、空间电磁感应 (EMI) 土壤扫描和 EMI-NDVI 数据组合作为机器学习聚类技术的输入。我们连续三年在六个农田（每年两个）上测试了这种方法。现场尺度 EMI 数据包括上部 0-50 厘米的空间土壤信息，以近似匹配为属性测量采样的土壤深度。生长季节的 NDVI 测量提供了有关作物生长的信息。从 EMI 数据划分的管理区在空间连贯性方面优于从 NDVI 得出的管理区，并显示出与农业管理相关的特性差异：质地、土壤水分亏缺、产量、和硝酸盐残留。EMI-NDVI 组合分析没有提供额外的好处。这强调了在作物数据解释中包含空间分布的土壤信息的重要性，同时强调高分辨率土壤信息对于可变速率应用和农艺建模至关重要。