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Article

The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

1
Department of Applied Ecology, Saint Petersburg State University, 199034 Saint Petersburg, Russia
2
Laboratory of Soil Science, Ufa Institute of Biology UFRC, Russian Academy of Sciences, 450054 Ufa, Russia
3
Department of Geodesy, Cartography and Geographic Information Systems, Bashkir State University, 450076 Ufa, Russia
*
Author to whom correspondence should be addressed.
Academic Editors: Panagiotis Tziachris, Dimitris Triantakonstantis and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(4), 243; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040243
Received: 16 February 2021 / Revised: 26 March 2021 / Accepted: 4 April 2021 / Published: 7 April 2021
(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)
Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes. View Full-Text
Keywords: agrochemical properties; digital soil mapping; SVM; MLR; topographic variables agrochemical properties; digital soil mapping; SVM; MLR; topographic variables
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MDPI and ACS Style

Suleymanov, A.; Abakumov, E.; Suleymanov, R.; Gabbasova, I.; Komissarov, M. The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes. ISPRS Int. J. Geo-Inf. 2021, 10, 243. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040243

AMA Style

Suleymanov A, Abakumov E, Suleymanov R, Gabbasova I, Komissarov M. The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes. ISPRS International Journal of Geo-Information. 2021; 10(4):243. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040243

Chicago/Turabian Style

Suleymanov, Azamat; Abakumov, Evgeny; Suleymanov, Ruslan; Gabbasova, Ilyusya; Komissarov, Mikhail. 2021. "The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes" ISPRS Int. J. Geo-Inf. 10, no. 4: 243. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040243

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