Influence of parameterization strategy for parent material effects in predictive mapping of topsoil geochemistry

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027049%3A_____%2F22%3AN0000061" target="_blank" >RIV/00027049:_____/22:N0000061 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Influence of parameterization strategy for parent material effects in predictive mapping of topsoil geochemistry

Popis výsledku v původním jazyce

The distribution of trace elements in soils is complex and reflects the geochemistry of the original geological substrates modified by variety of environmental and human-induced changes of soil environment. An effective use of geological information within digital soil mapping and geochemical mapping over large require a degree of class aggregation into several broad nominal (or ordinal) classes. Nevertheless, there are several potential weaknesses of the reclassification of lithological information - lithological variation within geological units, variation in composition of individual lithological types, and inadequate description of lithology in geological map. Hence, we tested how the predictive geochemical mapping using environmental correlation will be sensitive under various complement scenarios using aggregated geological substrates and additional numeric covariates that partially represent parent material such as subsoil texture, land gravity data (gravity survey Bouguer anomaly) and other geophysical spatial data (airborne magnetic and gamma radiometric surveys). To compare various scenarios, we have used lithological classification in combinations with other numerical substrate-wise covariates in pragmatic predictive geochemical models using quantile regression forest over contrast area (approximately 11 000 km2) in the Czech Republic. Thee independent geochemical datasets for soil trace elements after the acid digestion procedure were used to train and validate the predictive models. Lithologywise covariates were iteratively combined with the joint set of other readily available covariates representing topography, land use, remotely sensed surface characterisation (using a cloudless bare soil composite assembled from Sentinel 2) and depositional inputs of trace elements into soil to compare the prediction of topsoil concentrations of trace elements under various research strategies for parametrisation of lithological information. The results enabled to select optimal covariates suite for lithology parametrisation for the complex nation-wide model for topsoil contents of trace elements.

Název v anglickém jazyce

Influence of parameterization strategy for parent material effects in predictive mapping of topsoil geochemistry

Popis výsledku anglicky

The distribution of trace elements in soils is complex and reflects the geochemistry of the original geological substrates modified by variety of environmental and human-induced changes of soil environment. An effective use of geological information within digital soil mapping and geochemical mapping over large require a degree of class aggregation into several broad nominal (or ordinal) classes. Nevertheless, there are several potential weaknesses of the reclassification of lithological information - lithological variation within geological units, variation in composition of individual lithological types, and inadequate description of lithology in geological map. Hence, we tested how the predictive geochemical mapping using environmental correlation will be sensitive under various complement scenarios using aggregated geological substrates and additional numeric covariates that partially represent parent material such as subsoil texture, land gravity data (gravity survey Bouguer anomaly) and other geophysical spatial data (airborne magnetic and gamma radiometric surveys). To compare various scenarios, we have used lithological classification in combinations with other numerical substrate-wise covariates in pragmatic predictive geochemical models using quantile regression forest over contrast area (approximately 11 000 km2) in the Czech Republic. Thee independent geochemical datasets for soil trace elements after the acid digestion procedure were used to train and validate the predictive models. Lithologywise covariates were iteratively combined with the joint set of other readily available covariates representing topography, land use, remotely sensed surface characterisation (using a cloudless bare soil composite assembled from Sentinel 2) and depositional inputs of trace elements into soil to compare the prediction of topsoil concentrations of trace elements under various research strategies for parametrisation of lithological information. The results enabled to select optimal covariates suite for lithology parametrisation for the complex nation-wide model for topsoil contents of trace elements.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

40104 - Soil science

Projekt

<a href="/cs/project/SS03010364" target="_blank" >SS03010364: Systém na podporu rozhodování při hodnocení kvality půdy z hlediska obsahu rizikových látek v zemědělských půdách České republiky</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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