The Basic Soil Structure Parameters and Their Spatial Prediction Using Machine Learning and Remote Sensing Data in Semi-Arid Trans-Ural Steppe Zone, Russia

Soil structure is one of the key soil water-physical properties that determine the water–air regime and ultimately affect soil fertility. This study aimed to test different machine learning (ML) methods in combination with environmental variables (soil and climate) and remote sensing data derived from Landsat 8 for prediction of key structure parameters of topsoil (0–25 cm) in semi-arid areas (Trans-Ural steppe zone, Republic of Bashkortostan, Russia). The all studied soil types (Chernozems (n = 24), Solonchaks (n = 9)) and Solonetzes (n = 12)) characterized by “excellent” aggregate state (the average structural coefficient (Ks) was 6.52, 11.23 and 5.70) and “good” resistance of aggregates to destruction by water (soil aggregate stability coefficient (Ksas)—0.67, 0.65 and 0.70, respectively). The soils had a high proportion of agronomically valuable aggregates (0.25–10 mm, mesoaggregates (MEA)), and a low proportion of blocky/lumpy (>10 mm, macroaggregates (MAA)) and fine/dusty (