Soil physical properties influence vineyard behavior, therefore the knowledge of their spatial variability is essential for making vineyard management decisions. This study aimed to model and map selected soil properties by means of knowledge-based digital soil mapping approach. We used a Random Forest (RF) algorithm to link environmental covariates derived from a LiDAR flight and satellite spectral information, describing soil forming factors and ten selected soil properties (particle size distribution, bulk density, dispersion ratio, Ksat, field capacity, permanent wilting point, fast drainage pores and slow drainage pores) at three depth intervals, namely 0–20, 20–40, and 40–60 cm at a systematic grid (60 × 60 m²). The descriptive statistics showed low to very high variability within the field. RF model of particle size distribution, and bulk density performed well, although the models could not reliably predict saturated hydraulic conductivity. There was a better prediction performance (based on 34% model validation) in the upper depth intervals than the lower depth intervals (e.g., R² of 0.66; nRMSE of 27.5% for clay content at 0–20 cm and R² of 0.51; nRMSE of 16% at 40–60 cm). There was a better prediction performance in the lower depth intervals than the upper depth intervals (e.g., R² of 0.49; nRMSE of 23% for dispersion ratio at 0–20 cm and R² of 0.81; nRMSE of 30% at 40–60 cm). RF model overestimated areas with low values and underestimated areas with high values. Further analysis suggested that Topographic position Index, Topographic Wetness Index, aspect, slope length factor, modified catchment area, catchment slope, and longitudinal curvature were the dominant environmental covariates influencing prediction of soil properties.

土壤物理特性会影响葡萄园的行为，因此，了解其空间变异性对于制定葡萄园管理决策至关重要。本研究旨在通过基于知识的数字土壤测绘方法对选定的土壤特性进行建模和测绘。我们使用随机森林（RF）算法链接了从LiDAR飞行和卫星光谱信息得出的环境协变量，描述了土壤形成因素和十种选定的土壤特性（粒径分布，堆积密度，分散比，Ksat，田间持水量，永久枯萎）点，快速排水孔和慢速排水孔）在三个深度间隔处，即系统网格（60×60 m ^2）的0–20、20–40和40–60 cm）。描述性统计数据显示该字段中的变化范围从低到很大。尽管模型不能可靠地预测饱和的水力传导率，但颗粒尺寸分布和堆积密度的RF模型表现良好。在较高的深度区间中，有一个更好的预测性能（基于34％的模型验证），而在较低的深度区间中，则有更好的预测性能（例如，R ²为0.66； nRMSE为27.5％，粘土含量为0-20 cm，R ²为0.51；在40-60厘米处nRMSE为16％）。在较低的深度间隔中，其预测效果要好于较高的深度间隔（例如，R ²为0.49；对于0-20 cm和R ^2的色散比，nRMSE为23％为0.81; 在40-60厘米处nRMSE为30％）。RF模型高估低值区域，而低估高值区域。进一步的分析表明，地形位置指数，地形湿度指数，坡向，坡长因子，集水面积，集水坡度和纵向曲率是影响土壤特性预测的主要环境协变量。