TY - CHAP

T1 - The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

AU - Suleymanov, Azamat

AU - Abakumov, Evgeny

AU - Suleymanov, Ruslan

AU - Gabbasova, Ilyusya

AU - Komissarov, Mikhail

N1 - 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://doi.org/10.3390/ijgi10040243

PY - 2023/1/10

Y1 - 2023/1/10

N2 - 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, so-dium, 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, eleva-tion, slope, and MMRTF (multiresolution ridge top flatness) index are the most important varia-bles. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

AB - 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, so-dium, 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, eleva-tion, slope, and MMRTF (multiresolution ridge top flatness) index are the most important varia-bles. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

KW - agrochemical properties

KW - digital soil mapping

KW - SVM

KW - MLR

KW - topographic variables

UR - https://www.researchgate.net/publication/350713147_The_Soil_Nutrient_Digital_Mapping_for_Precision_Agriculture_Cases_in_the_Trans-Ural_Steppe_Zone_of_Russia_Using_Topographic_Attributes

M3 - Chapter

SN - 978-3-0365-5977-3

SP - 299

EP - 311

BT - Integrating GIS and Remote Sensing in Soil Mapping and Modeling

PB - MDPI AG

ER -