Using an innovative bivariate colour scheme to infer spatial links and patterns between prediction and uncertainty: an example based on an explainable soil CN ratio model

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027073%3A_____%2F23%3AN0000004" target="_blank" >RIV/00027073:_____/23:N0000004 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s40808-022-01493-5" target="_blank" >https://link.springer.com/article/10.1007/s40808-022-01493-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s40808-022-01493-5" target="_blank" >10.1007/s40808-022-01493-5</a>

Result language

angličtina

Original language name

Using an innovative bivariate colour scheme to infer spatial links and patterns between prediction and uncertainty: an example based on an explainable soil CN ratio model

Original language description

Although valuable for discovering geographical relationships and spatial statistics, bivariate maps or colour schemes are rarely employed in soil-related studies. For high-resolution mapping of the spatial relationships and patterns between the carbon-to-nitrogen (CN) ratio and its uncertainty throughout the Czech Republic, we assessed the application of a bivariate colour scheme. A random forest (RF) model was used to forecast CN ratio levels derived from the LUCAS topsoil dataset (n = 440 topsoil samples) using a stack of 22 environmental covariates. Of these covariates, Landsat 8 predictors (i.e. b6—SWIR 1 and b2—BLUE) had the highest relative value in the RF model. Additionally, partial dependence plots (PDPs) revealed that the aforementioned predictors had a comparable marginal impact on the CN model prediction. The 30 m × 30 m pixels CN ratio and uncertainty maps (at 0–20 cm) were able to distinguish the level contents evenly across the entire country while displaying distinct spatial features for each map. The uncertainty map and CN ratio prediction were both utilized to logically construct a bivariate colour scheme at 60 m × 60 m pixels, which enabled a once-off visualization of the two maps. The approach was deemed promising and proved generalizable for large-scale geographical evaluations in the focus area. Based on the output of the bivariate map visual, the spatial relationships and patterns between the CN ratio prediction and its uncertainty could be studied.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10511 - Environmental sciences (social aspects to be 5.7)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Modeling Earth Systems and Environment

ISSN

2363-6203

e-ISSN

2363-6211

Volume of the periodical

9

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

8

Pages from-to

1417-1424

UT code for WoS article

000843260600001

EID of the result in the Scopus database

2-s2.0-85136542008