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

Azamat Suleymanov (), Mikhail Komissarov, Mikhail Aivazyan, Ruslan Suleymanov, Ilnur Bikbaev, Arseniy Garipov, Raphak Giniyatullin, Olesia Ishkinina, Iren Tuktarova and Larisa Belan
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Azamat Suleymanov: Laboratory of Artificial Intelligence in Environmental Research, Decarbonisation Technologies Center, Ufa State Petroleum Technological University, 450064 Ufa, Russia
Mikhail Komissarov: Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia
Mikhail Aivazyan: Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia
Ruslan Suleymanov: Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia
Ilnur Bikbaev: Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia
Arseniy Garipov: Laboratory of Artificial Intelligence in Environmental Research, Decarbonisation Technologies Center, Ufa State Petroleum Technological University, 450064 Ufa, Russia
Raphak Giniyatullin: Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia
Olesia Ishkinina: Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia
Iren Tuktarova: Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia
Larisa Belan: Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia

Land, 2025, vol. 14, issue 5, 1-16

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 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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