Soil organic C (SOC) and soil moisture (SM) affect the agricultural productivity of soils. For sustainable food production, knowledge of the horizontal as well as vertical variability of SOC and SM at field scale is crucial. Machine learning models using depth‐related data from multiple electromagnetic induction (EMI) sensors and a gamma‐ray spectrometer can provide insights into this variability of SOC and SM. In this work, we applied weighted conditioned Latin hypercube sampling to calculate 25 representative soil profile locations based on geophysical measurements on the surveyed agricultural field, for sampling and modeling. Ten additional random profiles were used for independent model validation. Soil samples were taken from four equal depth increments of 15 cm each. These were used to approximate polynomial and exponential functions to reproduce the vertical trends of SOC and SM as soil depth functions. We modeled the function coefficients of the soil depth functions spatially with Cubist and random forests with the geophysical measurements as environmental covariates. The spatial prediction of the depth functions provides three‐dimensional (3D) maps of the field scale. The main findings are (a) the 3D models of SOC and SM had low errors; (b) the polynomial function provided better results than the exponential function, as the vertical trends of SOC and SM did not decrease uniformly; and (c) the spatial prediction of SOC and SM with Cubist provided slightly lower error than with random forests. Hence, we recommend modeling the second‐degree polynomial with Cubist for 3D prediction of SOC and SM at field scale.

中文翻译：

---

利用多个地球物理传感器和机器学习对土壤有机碳含量和土壤水分进行3D映射

土壤有机碳（SOC）和土壤水分（SM）影响土壤的农业生产力。对于可持续的食品生产，在田间尺度上了解SOC和SM的水平和垂直变异性至关重要。使用来自多个电磁感应（EMI）传感器和伽马射线光谱仪的深度相关数据的机器学习模型可以洞悉SOC和SM的这种可变性。在这项工作中，我们应用了加权条件拉丁超立方采样，根据对被调查的农田的地球物理测量结果来计算25个代表性土壤剖面位置，以进行采样和建模。十个其他随机配置文件用于独立模型验证。从每个15厘米的四个相等的深度增量中获取土壤样品。这些被用来近似多项式和指数函数，以重现SOC和SM的垂直趋势作为土壤深度函数。我们在立体空间和随机森林中对土壤深度函数的函数系数进行了建模，并将地球物理测量值作为环境协变量。深度函数的空间预测提供了场标的三维（3D）映射。主要发现是：（a）SOC和SM的3D模型误差低；（b）由于SOC和SM的垂直趋势并非均匀下降，因此多项式函数提供的结果优于指数函数；（c）Cubist对SOC和SM的空间预测所提供的误差比随机森林的误差要低一些。因此，