The mapping of trace elements on fine grids from dense data on covariates from remote sensors and high-resolution environmental predictors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027049%3A_____%2F22%3AN0000058" target="_blank" >RIV/00027049:_____/22:N0000058 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

The mapping of trace elements on fine grids from dense data on covariates from remote sensors and high-resolution environmental predictors

Original language description

The distribution of trace elements in the surface soils is complex and reflects the geochemistry of the original geological substrate modified by a variety of environmental and human-induced factors. Since the covariate datasets representing these factors are usually in a much finer resolution than the geochemical surveys, the auxiliary variables were employed for the predictive mapping of As, Cd, Ni, and Pb using a quantile random forest model. Taking into account the multi-conditional nature of topsoil geochemistry, various remote sensed or high-resolution covariates were selected for predictive mapping within the study in the Czech Republic supported by the Technology Agency of the Czech Republic – project No. SS03010364. Geological variation was conceptualised using the airborne geophysical (gravimetric, radiometric, and magnetometric) data together with local effects represented by the intensity of mineral exploration from an inventory of mining dumps. Soil conditions for the binding capacities were represented by DEM derivatives and predictions for organic carbon and clay contents from dense legacy data. Finally, the precipitation and deposition rates from continuous ground measurements, and night-time lights data covered the natural and human-induced distributional effects. Moreover, the model was tested for an increase of prediction capability when using a mosaic of bare soils from Sentinel-2 satellite data or the Euclidean buffer distance maps to better capture the spatial dependencies. The relative importance of covariates and cross-validated metrics varied across the range of elements which hinted at the controls of their distributions or the effects of underrepresented processes within the suite of auxiliary variables.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

40104 - Soil science

Project

<a href="/en/project/SS03010364" target="_blank" >SS03010364: Soil evaluation decision support system for soil quality with special reference to contents of risky substances in agricultural soils in the Czech Republic</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů