georesГеоресурсыGeoresources1608-50431608-5078Georesursy LLC10.18599/grs.2022.1.8geores-157Research ArticleГЕОЭКОЛОГИЧЕСКИЕ ИССЛЕДОВАНИЯGEOECOLOGICAL STUDIESЦифровое картографирование показателей, определяющих сорбционные свойства почв по отношению к поллютантам, по данным дистанционного зондирования Земли с применением машинного обученияDigital mapping of indicators that determine the sorption properties of soils in relation to pollutants, according to remote sensing data of the Earth using machine learningГиниятуллинК. Г.GiniyatullinK. G.

Камиль Гашикович Гиниятуллин – канд. биол. наук, доцент

420008, Казань, ул. Кремлевская, д. 18

Kamil G. Giniyatullin – PhD (Biology), Associate Professor

18, Kremlevskaya st., Kazan, 420008

ginijatullin@mail.ruСахабиевИ. А.SahabievI. A.

Ильназ Алимович Сахабиев – старший преподаватель

420008, Казань, ул. Кремлевская, д. 18

Ilnas A. Sahabiev – Senior Lecturer

18, Kremlevskaya st., Kazan, 420008

СмирноваЕ. В.SmirnovaE. V.

Елена Васильевна Смирнова – канд. биол. наук, зав. кафедрой, доцент

420008, Казань, ул. Кремлевская, д. 18

Elena V. Smirnova – PhD (Biology), Associate Professor

18, Kremlevskaya st., Kazan, 420008

УразметовИ. А.UrazmetovI. A.

Ильдар Анварович Уразметов – канд. пед. наук, доцент

420008, Казань, ул. Кремлевская, д. 18

Ildar A. Urazmetov – PhD (Pedagogic), Associate Professor

18, Kremlevskaya st., Kazan, 420008

ОкуневР. В.OkunevR. V.

Родион Владимирович Окунев – канд. биол. наук, доцент

420008, Казань, ул. Кремлевская, д. 18

Rodion V. Okunev – PhD (Biology), Associate Professor

18, Kremlevskaya st., Kazan, 420008

ГордееваК. А.GordeevaK. A.

Карина Андреевна Гордеева – аспирант

420008, Казань, ул. Кремлевская, д. 18

Karina A. Gordeeva – PhD student

18, Kremlevskaya st., Kazan, 420008

Казанский (Приволжский) федеральный университетРоссияKazan Federal UniversityRussian Federation2022140420242418492Copyright © Гиниятуллин К.Г., Сахабиев И.А., Смирнова Е.В., Уразметов И.А., Окунев Р.В., Гордеева К.А., 20242024Гиниятуллин К.Г., Сахабиев И.А., Смирнова Е.В., Уразметов И.А., Окунев Р.В., Гордеева К.А.Giniyatullin K.G., Sahabiev I.A., Smirnova E.V., Urazmetov I.A., Okunev R.V., Gordeeva K.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.geors.ru/jour/article/view/157

По данным дистанционного зондирования Земли (ДЗЗ) проведено сравнение точности пространственного прогноза почвенных показателей, определяющих сорбционные свойства по отношению к поллютантам. Для построения пространственных карт изменения свойств почвы использовались методы машинного обучения на основе моделей регрессии опорных векторов (SVMr – support vector machine regression) и случайного леса (RF – random forest). Показано, что методы машинного моделирования с использованием ДЗЗ могут быть успешно использованы для пространственного прогноза содержания гранулометрических фракций, органического вещества, рН и емкости катионного обмена почв на участках небольшой площади. Выявлено, что пространственный прогноз содержания фракции пыли наилучшим образом моделируется с помощью алгоритма RF, тогда как остальные свойства почв, способные определить их собрционный потенциал по отношению к поллютантам, лучше моделируются с помощью метода SVMr. В целом, оба метода машинного обучения дают близкие результаты пространственного прогноза.

According to the data of remote sensing of the Earth, the accuracy of the spatial prediction of soil indicators determining sorption properties in relation to pollutants was compared. To build spatial maps of changes in soil properties, machine learning methods based on support vector regression models (SVMr) and random forest (RF) were used. It was shown that the methods of machine modeling using remote sensing can be successfully used for spatial prediction of the content of particle size fractions, organic matter, pH and the capacity of cation exchange of soils in small areas. It is shown that the spatial prediction of the content of silt fraction is best modeled using the RF algorithm, while the other properties of soils that can determine their sorption potential in relation to pollutants are better modeled using the SVMr method. In general, both machine learning methods have similar spatial prediction results.

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The authors declare that there are no conflicts of interest present.