Soil aggregate stability is crucial for maintaining
the arrangement of solid particles and pore space in the soil,
even under mechanical stresses. Traditional direct measurements
of soil aggregate stability are time-consuming and expensive.
This study aimed to spatially predict the soil aggregate stability
indices, including the mean weight diameter of aggregates, the
geometric mean diameter of aggregates, and the percentage of
water stable aggregates, using five machine learning models and
environmental covariates in the framework of digital soil mapping.
A total of 100 samples were collected from the surface
layer (0-15 cm) of soils in the Aji-Chai watershed, northwestern
Iran, and their SAS indices were determined by standard laboratory
methods. Four scenarios (S) were employed to evaluate the
most influencing auxiliary variables, including (S1): topographic
attributes, (S2): topographic attributes + remote sensing data, (S3):
S2 + thematic maps (geology, land use/cover maps), and (S4):
S3 + selected soil properties. Among the various machine learning
models, the random forest showed exceptional performance
and reduced uncertainty for S4, compared to the other machine
learning models and desired scenarios. The coefficient of determination,
concordance correlation coefficient, and normalized
root mean squared error values of the random forest model were
0.86, 0.87, and 31.42% for mean weight diameter; 0.80, 0.84, and
31.59% for geometric mean diameter; and 0.54, 0.68, and 20.75%
for water stable aggregates, respectively. Additionally, properties
such as soil organic matter and clay, followed by remote sensing
data, demonstrated the highest relative importance when
compared to the other covariates in predicting the soil aggregate
stability indices. In conclusion, the random forest ML-based model
seems to be able to accurately predict soil aggregate stability
indices at the watershed scale. The generated maps can serve as
a valuable baseline for land use planning and decision-making.
These findings contribute to the scientific understanding of soil
physical quality indicators and their application in sustainable
land management practices.
