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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Modeling Earth Systems and Environment

ISSN

2363-6203

e-ISSN

2363-6211

Svazek periodika

9

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

1417-1424

Kód UT WoS článku

000843260600001

EID výsledku v databázi Scopus

2-s2.0-85136542008