The potential of a stepwise fusion of proximally sensed portable X-ray fluorescence (pXRF) spectra and electromagnetic induction (EMI) with remote Sentinel-2 bands and a digital elevation model (DEM) was investigated for predicting soil physicochemical properties in pedons and across a heterogeneous 80-ha crop field in Wisconsin, USA. We found that pXRF spectra with partial least squares regression (PLSR) models can predict sand, total nitrogen (TN), organic carbon (OC), silt contents, and clay with validation R² of 0.81, 0.74, 0.73, 0.68, and 0.64 at the pedon scale but performed less well for soil pH (R² = 0.51). A combination of EMI, Sentinel-2, and DEM data showed promise in mapping sand, silt contents, and TN at two depths and Ap horizon thickness and soil depth across the field. A clustering analysis using combinations of mapped soil properties or proximal and remote sensing data suggested that data fusion improved the characterization of field-scale variability of soil properties. The cost-benefit analysis showed that the most accurate management zones (MZs) for topsoil can be generated only using estimated soil property maps while it was the most costly as compared to other data sources. For an intermediate-high (for topsoil) and high (subsoil) accuracy and a moderate economic budget, the combination of sensors (proximal + remote sensing + DEM) might be a better approach for effective MZs generation than collecting soil samples for laboratory analysis while the latter produced the most accurate maps for topsoil. It can be concluded that pXRF spectra can be useful for predicting key soil properties (e.g., sand, TN, OC, silt, clay) at different soil depths, and a combination of proximal and remote sensing provides an effective way to delineate soil MZs that are useful for decision-making.

研究了近距离感应的便携式X射线荧光（pXRF）光谱和电磁感应（EMI）与远距离Sentinel-2波段和数字高程模型（DEM）逐步融合的潜力，以预测和整个土壤中的土壤理化特性。美国威斯康星州80公顷的异质作物田。我们发现，具有偏最小二乘回归（PLSR）模型的pXRF光谱可以预测砂，总氮（TN），有机碳（OC），粉砂含量和黏土，验证R ^2分别为0.81、0.74、0.73、0.68和0.64在pedon规模上，但对土壤pH值（R ² = 0.51）。EMI，Sentinel-2和DEM数据的组合显示了在绘制两个深度的沙，淤泥含量和TN以及整个田地的Ap水平厚度和土壤深度方面的前景。使用映射的土壤特性或近距离和遥感数据的组合进行的聚类分析表明，数据融合改善了土壤特性的田间尺度变化的特征。成本效益分析表明，只有使用估算的土壤特性图才能生成最精确的表土管理区（MZs），而与其他数据源相比成本最高。对于中等高（表土）和高（底土）精度以及适度的经济预算，与收集土壤样本进行实验室分析相比，传感器（近端+遥感+ DEM）的组合可能是一种有效生成MZ的更好方法，而后者可以为表土生成最准确的地图。可以得出结论，pXRF光谱可用于预测不同土壤深度下的关键土壤特性（例如，沙子，TN，OC，粉砂，粘土），并且近距离和遥感的结合提供了一种有效的方法来描绘土壤MZ对决策很有用。