Везде

RAS PresidiumИсследование Земли из космоса Earth Research from Space

  • ISSN (Print) 0205-9614
  • ISSN (Online) 3034-5405

Digital mapping of organic carbon content and stocks in soils of Cis-Salair drained plain using the Google Earth Engine online platform and the random forest algorithm

You can
PII
S30345405S0205961425020015-1
DOI
10.7868/S3034540525020015
Publication type
Article
Status
Published
Authors
N. V. Gopp
  • Affiliation: Institute of Soil Science and Agrochemistry SB RAS
Volume/ Edition
Volume / Issue number 2
Pages
3-17
Abstract
For a key site on the Cis-Salair drained plain, digital mapping of the content of soil organic carbon (SOC) in the topsoil (0–30 cm) was conducted using the random forest algorithm implemented on the Google Earth Engine cloud platform. The following were used as predictors in the random forest model: 1) 19 bioclimatic variables from WorldClim; 2) 5 climatic variables calculated based on WorldClim data and soil-climate atlas data; 3) 8 vegetation indices calculated based on Landsat 8 OLI images; 4) 26 morphometric characteristics of the terrain calculated based on the ALOS DEM; 5) 2 variables describing the spatial location. The correlation coefficients (R) between the content of SOC and the values of the predictors were taken into account when forming sets of predictors: 1) BIO11+RVI; 2) Longitude+CNBL; 3) SAT10+CC+Texture; 4) 60 predictors; 5) 42 (without relief curvatures, vegetation indices and predictors with zero values); 6) 37 (all with R > ±0.5); 7) 32 (all with R > ±0.3 without vegetation indices); 8) 27 (all with R > ±0.5 without vegetation indices); 9) 23 (without BIO1–19, relief curvatures, vegetation indices and predictors with zero values). The result of modeling the content of SOC based on 32 predictors and a training dataset (n=42) with a lower RMSE (0.72) was chosen as the best. Based on this model, a soil bulk density map was compiled using a pedotransfer function. This data, together with a map of the SOC content, was used to create a map of SOC stock. The SOC content in the arable layer (0–30 cm) varied from 1.3 to 6.1%, according to the actual data. The SOC stocks ranged from 84 t/ha to 203 t/ha. The highest levels of SOC content and stocks were found in the soils in the upper part of the slope. A gradual decrease in these values was noted as one moved downhill. The soil bulk density ranged from 1.20 g/cm³ to 1.36 g/cm³ and increased as one moved downhill, indicating a reverse trend compared to the SOC content and stocks. The total SOC stock in the arable layer (0–30 cm) of the soils of the studied territory with an area of 225 hectares amounted to 28.7 kt.
Keywords
климат рельеф черноземы серые лесные почвы машинное обучение
Date of publication
01.04.2025
Year of publication
2025
Number of purchasers
0
Views
55

References

  1. 1. Аринушкина Е.В. Руководство по химическому анализу почв. М.: Изд-во МГУ, 1970. 487 с.
  2. 2. Афанасьев В.Н., Цыпин А.П. Эконометрика в пакете STATISTICA: учебное пособие по выполнению лабораторных работ. Оренбург: ГОУ ОГУ, 2008. 204 с.
  3. 3. Гопп Н.В. Агроэкологический потенциал западной части Кузнецко-Салаирской геоморфологической провинции: методика цифрового картографирования, геопространственный анализ, корреляция с содержанием органического углерода в почвах // Почвы и окружающая среда. 2023. 6(3). e224. https://doi.org/10.31251/pos.v6i3.224
  4. 4. Сляднев A.П. Почвенно-климатический атлас Новосибирской области. Новосибирск: Наука, 1978. 122 с.
  5. 5. Шишов Л.Л., Тонконогов В.Д., Лебедева И.И., Герасимова М.И. Классификация и диагностика почв России. Смоленск: Ойкумена, 2004. 342 с.
  6. 6. Abdelbaki A.M. Evaluation of pedotransfer functions for predicting soil bulk density for U.S. soils. Ain Shams Engineering Journal. 2018. 9(4). P. 1611–1619. https://doi.org/10.1016/j.asej.2016.12.002
  7. 7. Breiman L. Random forests. Machine learning. 2001. 45(1). P. 5–32. https://doi.org/10.1023/A:1010933404324
  8. 8. Conrad O., Bechtel B., Bock M., Dietrich H., Fischer E., Gerlitz L., Wehberg J., Wichmann V., Böhner J. System for automated geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development. 2015. 8(7). P. 1991–2007. https://doi.org/10.5194/gmd-8-1991-2015
  9. 9. Cutler D.R., Edwards T.C. Jr., Beard K.H., Cutler A., Hess K.T., Gibson J., Lawler J.J. Random forests for classification in Ecology. Ecology. 2007. 88. P. 2783–2792. https://doi.org/10.1890/07-0539.1
  10. 10. Dharumarajan S., Hegde Rajendra, Singh S.K. Spatial prediction of major soil properties using Random Forest techniques — A case study in semi-arid tropics of South India // Geoderma Regional. 2017. 10. P. 154‒162. https://doi.org/10.1016/j.geodrs.2017.07.005
  11. 11. FAO. Standard operating procedure for soil organic carbon: Tyurin spectrophotometric method. Rom: FAO, 2021. https://www.fao.org/3/cb4757en/cb4757en.pdf
  12. 12. FAO and ITPS. Global Soil Organic Carbon Map V1.5: Technical report. Rome, FAO, 2020. 169 p.
  13. 13. Fick S.E., Hijmans R.J. WorldClim 2: new 1 km spatial resolution climate surfaces for global land areas. International Journal of Climatology. 2017. 37(12). P. 4302–4315. https://doi.org/10.1002/joc.5086
  14. 14. Gandhi U. JavaScript and the Earth Engine API. In: Cardille J.A., Crowley M.A., Saah D., Clinton N.E. (eds) Cloud-Based Remote Sensing with Google Earth Engine. Springer, Cham, 2024. https://doi.org/10.1007/978-3-031-26588-4_1
  15. 15. Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D., Moore R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. 2017. 202. P. 18–27. https://doi.org/10.1016/j.rse.2017.06.031
  16. 16. Grimm R., Behrens T., Märker M., Elsenbeer H. Soil organic carbon concentrations and stocks on Barro Colorado Island – Digital soil mapping using Random Forests analysis // Geoderma. 2008. 146(1–2). P. 102–113. https://doi.org/10.1016/j.geoderma.2008.05.008
  17. 17. Hengl T.A. Practical guide to geostatistical mapping of environmental variables. EC JRC, Ispra (Italy), 2007. 165 p.
  18. 18. IUSS. Working Group WRB. World Reference Base for Soil Resources 2014. International soil classification system for naming soils and creating legends for soil maps. Update 2015. World Soil Resources Reports No. 106. FAO, Rome, 2015. https://www.fao.org/3/i3794en/I3794en.pdf
  19. 19. McBratney A.B., Mendonça Santos M.L., Minasny B. On digital soil mapping. Geoderma. 2003. 117. P. 3–52. https://doi.org/10.1016/S0016-7061 (03)00223-4
  20. 20. Singh J., Knapp H.V., Demissie M. Hydrologic modelling of the Iroquois River watershed using HSPF and SWAT. Illinois State Water Survey Contract Report 2004-08. Illinois State Water Survey, Champaign. 2004. http://www.isws.illinois.edu/pubdoc/CR/ISWSCR2004-08.pdf
  21. 21. Sreenivas K., Dadhwal V.K., Kumar S., Harsha G.Sri, Mitran T., Sujatha G., Janaki Rama Suresh G., Fyzee M.A., Ravisankar T. Digital mapping of soil organic and inorganic carbon status in India // Geoderma. 2016. 269. P. 160–173. https://doi.org/10.1016/j.geoderma.2016.02.002
  22. 22. Suleymanov A., Abakumov E., Alekseev I., Nizamutdinov T. Digital mapping of soil properties in the high latitudes of Russia using sparse data. Geoderma Regional. 36. 2024. e00776. https://doi.org/10.1016/j.geodrs.2024.e00776
  23. 23. Vågen T.G., Winowiecki L.A., Tondoh J.E., Desta L.T., Gumbricht T. Mapping of soil properties and land degradation risk in Africa using MODIS reflectance // Geoderma. 2016. 263. P. 216–225. https://doi.org/10.1016/j.geoderma.2015.06.023
QR
Translate

Индексирование

Scopus

Scopus

Scopus

Crossref

Scopus

Higher Attestation Commission

At the Ministry of Education and Science of the Russian Federation

Scopus

Scientific Electronic Library