| dc.description.abstract | 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,
permanentwilting point, fast drainage pores and slowdrainage pores) at three depth intervals, namely 0–20, 20–
40, and 40–60 cmat a systematic grid (60 × 60m2). The descriptive statistics showed lowto 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,
TopographicWetness Index, aspect, slope length factor, modified catchment area, catchment slope, and longitudinal
curvature were the dominant environmental covariates influencing prediction of soil properties. | |
