Geoinformation Assessment of Erodibility of Arable Soils in the Republic of Tatarstan

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A methodology for automated mapping of soil erosion degrees in arable lands of the Republic of Tatarstan was developed with explicit consideration of soil typology using multi-year bare soil composites from Landsat imagery for the 1985–1995 period and machine learning algorithms. The study is based on 980 field soil-erosion survey points stratified across six soil groups: chernozems, gray forest soils, light-gray forest soils, dark-gray forest soils, sod-podzolic soils, and sod-carbonate soils. Soil type/subtype was included as a categorical predictor in both implemented models: a relief-oriented model combining fifteen spectral indices with morphometric terrain characteristics, and a spectral model using only optical surface properties. Application of CatBoost gradient boosting algorithm followed by isotonic calibration separately for each soil group achieved overall classification accuracy of 0.59–0.64, with the relief-oriented model demonstrating a determination coefficient of 0.58 versus 0.32 for the spectral model. Medium and severely eroded soils were recognized with 66–86% accuracy by both models regardless of soil type, while slightly eroded soils can't be reliably identified. The information content of the models was insufficient for reliable differentiation of uneroded and slightly eroded soils. This confirms the known limitation of medium-resolution multispectral data when recording the initial stages of erosion, when changes in the thickness of the humus horizon do not lead to a significant change in the spectral characteristics of the arable layer surface. Fifteen percent of eroded soils were detected on slopes less than 3°, indicating underestimation of erosion processes on gentle slopes or reflecting (in some cases) naturally shallow soil profiles characteristic of the region. Accounting for soil typology through categorical predictors and group-wise calibration substantially improved the quality of arable land erosion mapping.

作者简介

A. Gafurov

Kazan Federal University

Email: AMGafurov@kpfu.ru
Kazan, Russia

Zh. Buryak

Kazan Federal University

Email: buryakzh@gmail.com
Kazan, Russia

A. Avvakumova

Kazan Federal University

Email: avvakumova_alina@mail.ru
Kazan, Russia

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