Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia

Chinilin, Andrey; Savin, I. Yu.

doi:10.1016/j.ejrs.2023.07.007

Cited by 4 publications

(2 citation statements)

References 53 publications

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“…The random forest regression (RFR) algorithm is a machine learning algorithm to estimate the ecological indicators [39][40][41]. In this study, 70% of the soil samples were utilized as the training set and 30% of the soil samples were utilized as the testing set to estimate the SBD and SOC with different land types, respectively.…”

Section: Ssa-rfr Model and Accuracy Evaluationmentioning

confidence: 99%

How Land Use Transitions Contribute to the Soil Organic Carbon Accumulation from 1990 to 2020

Zhang,

Xia,

Zhao

et al. 2024

Remote Sensing

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Soil organic carbon stock (SOCS) changes caused by land use changes are still unclear, and understanding this response is essential for many environmental policies and land management practices. In this study, we investigated the temporal–spatial and vertical distribution characteristics of the SOCS in the Western Sichuan Plateau (WSP) using the sparrow search algorithm–random forest regression (SSA-RFR) models with excellent model applicability and accuracy. The temporal–spatial variations in the SOCS were modeled using 1080 soil samples and a set of nine environmental covariates. We analyzed the effect of land use changes on the SOCS in the WSP. The total SOCS increased by 18.03 Tg C from 1990 to 2020. The results of this study confirmed a significant increase in the SOCS in the study area since 2010. There was a 27.88 Tg C increase in the SOCS in 2020 compared to the total SOCS in 2010. We found that the spatial distribution of the SOCS increased from southeast to northwest, and the vertical distribution of the SOCS in the study area decreased with increasing soil depth. Forests and grasslands are the main sources of SOCS the total SOCS in the forest and grassland accounted for 37.53 and 59.39% of the total soil organic carbon (SOC) pool in 2020, respectively. The expansion of the wetlands, forest, and grassland areas could increase the SOCS in the study area. A timely and accurate understanding of the dynamics of SOC is crucial for developing effective land management strategies to enhance carbon sequestration and mitigate land degradation.

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Section: Ssa-rfr Model and Accuracy Evaluationmentioning

confidence: 99%

How Land Use Transitions Contribute to the Soil Organic Carbon Accumulation from 1990 to 2020

Zhang,

Xia,

Zhao

et al. 2024

Remote Sensing

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show abstract

“…Much progress has also been made in mapping soils on a national scale. Examples of countries with soil maps using DSM methods include Russia (Mukhortova et al, 2021;Chinilin & Savin, 2023), China (Liang et al, 2019;Liu et al, 2020), the United States (Hempel et al, 2014b;Ramcharan et al, 2018;Chaney et al, 2019), Brazil (Gomes et al, 2019), Australia (Grundy et al, 2015;Viscarra Rossel et al, 2015), India (Dharumarajan et al, 2019(Dharumarajan et al, , 2020, Iran (Taghizadeh-Mehrjardi et al, 2020;Zeraatpisheh et al, 2020), Nigeria (Akpa et al, 2014), Chile (Padarian et al, 2017), France (Mulder et al, 2016a,b), Scotland (Poggio & Gimona, 2014, 2017b and Denmark (Adhikari et al, 2013(Adhikari et al, , 2014a.…”

Section: Globalsoilmapmentioning

confidence: 99%

BIS-4D: a high-resolution soil modelling and mapping platform for the Netherlands in 3D space and time

Helfenstein

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Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India

Koley,

Kumar

2024

Environ Monit Assess

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Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia

Cited by 4 publications

References 53 publications

How Land Use Transitions Contribute to the Soil Organic Carbon Accumulation from 1990 to 2020

How Land Use Transitions Contribute to the Soil Organic Carbon Accumulation from 1990 to 2020

BIS-4D: a high-resolution soil modelling and mapping platform for the Netherlands in 3D space and time

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India

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