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Article

Unmanned Aerial Vehicles Applicability to Mapping Soil Properties Under Homogeneous Steppe Vegetation

by
Azamat Suleymanov
1,2,*,
Mikhail Komissarov
2,
Mikhail Aivazyan
2,
Ruslan Suleymanov
2,3,4,
Ilnur Bikbaev
2,3,
Arseniy Garipov
1,2,
Raphak Giniyatullin
2,
Olesia Ishkinina
5,
Iren Tuktarova
5 and
Larisa Belan
5,6
1
Laboratory of Artificial Intelligence in Environmental Research, Decarbonisation Technologies Center, Ufa State Petroleum Technological University, 450064 Ufa, Russia
2
Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia
3
Decarbonisation Technologies Center, Ufa State Petroleum Technological University, 450064 Ufa, Russia
4
Department of Geodesy, Cartography and Geographic Information Systems, Ufa University of Science and Technology, 450076 Ufa, Russia
5
Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia
6
Department of Geology, Hydrometeorology and Geoecology, Ufa University of Science and Technology, 450076 Ufa, Russia
*
Author to whom correspondence should be addressed.
Land 2025, 14(5), 931; https://doi.org/10.3390/land14050931
Submission received: 21 March 2025 / Revised: 23 April 2025 / Accepted: 23 April 2025 / Published: 25 April 2025
(This article belongs to the Special Issue Digital Soil Mapping for Soil Health Monitoring in Agricultural Lands)
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Abstract

Unmanned aerial vehicles (UAVs) are rapidly becoming a popular tool for digital soil mapping at a large-scale. However, their applicability in areas with homogeneous vegetation (i.e., not bare soil) has not been fully investigated. In this study, we aimed to predict soil organic carbon, soil texture at several depths, as well as the thickness of the AB soil horizon and penetration resistance using a machine learning algorithm in combination with UAV images. We used an area in the Eurasian steppe zone (Republic of Bashkortostan, Russia) covered with the Stipa vegetation type as a test plot, and collected 192 soil samples from it. We estimated the models using a cross-validation approach and spatial prediction uncertainties. To improve the prediction performance, we also tested the inclusion of oblique geographic coordinates (OGCs) as covariates that reflect spatial position. The following results were achieved: (i) the predictive models demonstrated poor performance using only UAV images as predictors; (ii) the incorporation of OGCs slightly improved the predictions, whereas their uncertainties remained high. We conclude that the inability to accurately predict soil properties using these predictor variables (UAV and OGC) is likely due to the limited access to soil spectral signatures and the high variability of soil properties within what appears to be a homogeneous site, particularly in relation to soil-forming factors. Our results demonstrated the limitations of UAVs’ application for modeling soil properties on a site with homogeneous vegetation, whereas including spatial autocorrelation information can benefit and should be not ignored in further studies.
Keywords: unmanned aerial vehicles; digital soil mapping; machine learning; drones; random forest; spatial modeling unmanned aerial vehicles; digital soil mapping; machine learning; drones; random forest; spatial modeling

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MDPI and ACS Style

Suleymanov, A.; Komissarov, M.; Aivazyan, M.; Suleymanov, R.; Bikbaev, I.; Garipov, A.; Giniyatullin, R.; Ishkinina, O.; Tuktarova, I.; Belan, L. Unmanned Aerial Vehicles Applicability to Mapping Soil Properties Under Homogeneous Steppe Vegetation. Land 2025, 14, 931. https://doi.org/10.3390/land14050931

AMA Style

Suleymanov A, Komissarov M, Aivazyan M, Suleymanov R, Bikbaev I, Garipov A, Giniyatullin R, Ishkinina O, Tuktarova I, Belan L. Unmanned Aerial Vehicles Applicability to Mapping Soil Properties Under Homogeneous Steppe Vegetation. Land. 2025; 14(5):931. https://doi.org/10.3390/land14050931

Chicago/Turabian Style

Suleymanov, Azamat, Mikhail Komissarov, Mikhail Aivazyan, Ruslan Suleymanov, Ilnur Bikbaev, Arseniy Garipov, Raphak Giniyatullin, Olesia Ishkinina, Iren Tuktarova, and Larisa Belan. 2025. "Unmanned Aerial Vehicles Applicability to Mapping Soil Properties Under Homogeneous Steppe Vegetation" Land 14, no. 5: 931. https://doi.org/10.3390/land14050931

APA Style

Suleymanov, A., Komissarov, M., Aivazyan, M., Suleymanov, R., Bikbaev, I., Garipov, A., Giniyatullin, R., Ishkinina, O., Tuktarova, I., & Belan, L. (2025). Unmanned Aerial Vehicles Applicability to Mapping Soil Properties Under Homogeneous Steppe Vegetation. Land, 14(5), 931. https://doi.org/10.3390/land14050931

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