Оценка спектрально-отражательных свойств эродированных агропочв Республики Татарстан

Zhanna A. Buryak; Artur M. Gafurov

doi:10.52575/2712-7443-2025-49-3-517-532

Authors

Zhanna A. Buryak Kazan (Volga Region) Federal University
Artur M. Gafurov Kazan (Volga Region) Federal University

DOI:

https://doi.org/10.52575/2712-7443-2025-49-3-517-532

Keywords:

soil erosion, arable land, remote sensing of the Earth, Landsat, GEE, bare soil, spectral indices

Abstract

Soil water erosion is the most widespread form of cultivated land degradation in Russia, yet operational information on its extent is fragmented, outdated, and difficult to access. This study addresses that gap by integrating multitemporal Landsat 4/5 imagery (30 m; 1985-1995) within Google Earth Engine to generate a seamless median composite of bare fields for the Republic of Tatarstan, one of the country’s most heavily farmed and erosion-prone regions. After cloud, shadow and vegetation masking (NDVI ≤ 0.2) and restriction to cropland masks, the composite was enriched with a suite of soil-oriented spectral indices (SAVI, BITM, BIXS, BaI, NDSoilI, DBSI, NSDS) calculated via the open-source “spectral” library. Zonal statistics were extracted for 63.5 thousand ha of soils delineated from 1:10 000 erosion-survey maps and for 416 precisely geolocated sites with eroded soils identified on very-high- resolution Maxar imagery. One-factor ANOVA applied to 694 000 raster observations revealed statistically significant differences (p ≪ 0.0001) among chernozems, dark-grey forest soils, grey forest, light-grey forest and sod-podzolic soils; the greatest intergroup separation was delivered by the red and near-infrared bands and their derivative indices SAVI and BIXS. Compared with the composite signature of their parent soils, eroded patches exhibited a systematic 11-19 % increase in reflectance, peaking in the NIR and in brightness indices, while maintaining soil-specific spectral ordering. The findings demonstrate that reliable discrimination of erosion-affected pixels is conditional on prior stratification by genetic soil type; failure to account for inherent colour contrast can misclassify naturally light-toned full-profile soils as eroded counterparts of darker chernozems. The decade-scale bare-soil composite proves a robust, vegetation-independent baseline that captures both spatial and statistical variability of arable soils and provides transferable thresholds for automated, region-wide erosion mapping. Integrating this workflow into the national land-monitoring system would greatly enhance the temporal currency, spatial detail and scientific underpinning of soil-conservation planning across Russia’s key agricultural zones.

Acknowledgements: The study was funded from the grant of the Republic of Tatarstan to support scientific research conducted by young scientists and youth research teams in the Republic of Tatarstan (contract No. 08-24/МГ dated 12/25/2024).

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Author Biographies

Zhanna A. Buryak, Kazan (Volga Region) Federal University

Candidate of Geographical Sciences, Leading Researcher of the Research Laboratory “Balance C”, Kazan (Volga Region) Federal University, Kazan, Russia
E-mail: buryakzh@gmail.com

Artur M. Gafurov, Kazan (Volga Region) Federal University

Candidate of Geographical Sciences, Associate Professor of the Department of Landscape Ecology, Kazan (Volga Region) Federal University, Kazan, Russia
E-mail: gafurov.kfu@gmail.com

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