What question did this study set out to answer?

The study aims to predict key soil structure parameters using machine learning and remote sensing data.

January 14, 2026Soil Systems0 citationsOpen Access

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

ASAzamat Suleymanov SOShin‐ichiro OguraTohoku University RSRuslan SuleymanovUfa Institute of Chemistry

Key Points

The study aims to predict key soil structure parameters using machine learning and remote sensing data.
Utilized machine learning methods with environmental variables and remote sensing data from Landsat 8.
Studied different soil types including Chernozems, Solonchaks, and Solonetzes in semi-arid conditions.
Evaluated soil aggregate stability and structural coefficients for topsoil up to 25 cm.
Achieved a best model prediction for soil aggregate stability coefficient with R2 = 0.50 and RMSE 0.17.
Identified spectral indices as key predictors for soil structure parameters.
Showed a high proportion of agronomically valuable aggregates in the studied soils.

Abstract

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 (<0.25 mm, microaggregates (MIA)) ones. In particular, the average share of MIA, MEA, and MAA in Chernozem was 7.63, 83.20, and 11.73%, and in Solonchak, 4.24, 87.91, and 9.74%, respectively. After wet sifting, the water-resistant macroaggregates (WSMAA) were not identified (they were destroyed by water) in all studied soils; the proportion of water-stable mesoaggregates (WSMEA) in Chernozems was 65.92 and microaggregates (WSMIA)—39.67; Solonchaks—74.95 and 22.54; Solonetz soil—66.77 and 33.22%; respectively. Under the ML framework, the best model was achieved for Ksas predictions (R2 = 0.50 and RMSE 0.17), where spectral indices (NDWI, EVI, SAVI, and NDVI) were the main predictors. Other ML techniques explained 22-30% variance of the remaining properties. The findings of this study can be valuable in further endeavors for soil water-physical mapping and accelerate the adoption of measures for land management/reclamation planning for landscapes with similar (arid and semi-arid) natural climatic conditions.

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