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
- Авторлар: Gopp N.V.1, Nechaeva T.V.1
-
Мекемелер:
- Institute of Soil Science and Agrochemistry SB RAS
- Шығарылым: № 2 (2025)
- Беттер: 3-17
- Бөлім: МЕТОДЫ И СРЕДСТВА ОБРАБОТКИ И ИНТЕРПРЕТАЦИИ КОСМИЧЕСКОЙ ИНФОРМАЦИИ
- URL: https://bakhtiniada.ru/0205-9614/article/view/306986
- DOI: https://doi.org/10.31857/S0205961425020015
- EDN: https://elibrary.ru/ejdwqo
- ID: 306986
Дәйексөз келтіру
Аннотация
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.
Негізгі сөздер
Авторлар туралы
N. Gopp
Institute of Soil Science and Agrochemistry SB RAS
Email: gopp@issa-siberia.ru
Novosibirsk, Russia
T. Nechaeva
Institute of Soil Science and Agrochemistry SB RAS
Хат алмасуға жауапты Автор.
Email: gopp@issa-siberia.ru
Novosibirsk, Russia
Әдебиет тізімі
- 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
- Arinushkina E.V. Rukovodstvo po himicheskomu analizu pochv [Guidelines for chemical analysis of soils]. M.: Izd-vo MGU, 1970. 487 p. (In Russian).
- Afanas’ev V.N., Tsypin A.P. Ekonometrika v pakete STATISTICA: uchebnoe posobie po vypolneniyu laboratornyh rabot [Econometrics in STATISTICA Software: Manual for Laboratory Practicum]. Orenburg, Orenburg State Univ., 2008 204 p. (In Russian).
- Breiman L. Random forests. Machine learning. 2001. 45(1). P. 5–32. https://doi.org/10.1023/A:1010933404324
- 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
- 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
- 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
- FAO. Standard operating procedure for soil organic carbon: Tyurin spectrophotometric method. Rom: FAO, 2021. https://www.fao.org/3/cb4757en/cb4757en.pdf
- FAO and ITPS. Global Soil Organic Carbon Map V1.5: Technical report. Rome, FAO, 2020. 169 p.
- 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
- 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
- Gopp N.V. Agroekologicheskij potencial zapadnoj chasti Kuznecko-Salairskoj geomorfologicheskoj provincii: metodika cifrovogo kartografirovaniya, geoprostranstvennyj analiz, korrelyaciya s soderzhaniem organicheskogo ugleroda v pochvah [Аgroecological potential of the western part of the Kuznetsk-Salair geomorphological province: digital mapping methodology, geospatial analysis, correlation with soil organic carbon content] // Počvy i okružaûŝаâ sreda. 2023. 6(3). e224. https://doi.org/10.31251/pos.v6i3.224 (In Russian).
- 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
- 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
- Hengl T.A. Practical guide to geostatistical mapping of environmental variables. EC JRC, Ispra (Italy), 2007. 165 p.
- 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
- 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
- Shishov L.L., Tonkonogov V.D., Lebedeva I.I., Gerasimova M.I. Klassifikaciya i diagnostika pochv Rossii [Classification and Diagnostic System of Russian Soils]. Smolensk, Oikumena, 2004. 342 p. (In Russian).
- 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
- Slyadnev A.P. Pochvenno-klimaticheskij atlas Novosibirskoj oblasti [Soil and climate atlas of the Novosibirsk region]. Novosibirsk: Nauka, 1978. 122 p. (In Russian).
- 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
- 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
- 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
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