Digital mapping of soil organic carbon using remote sensing data: A systematic review

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F23%3A97356" target="_blank" >RIV/60460709:41210/23:97356 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0341816223005003" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0341816223005003</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.catena.2023.107409" target="_blank" >10.1016/j.catena.2023.107409</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Digital mapping of soil organic carbon using remote sensing data: A systematic review

Popis výsledku v původním jazyce

Soil organic carbon (SOC) has attracted a lot of attention in the soil science community. Freely available remote sensing data combined with advanced digital soil mapping (DSM) techniques has led to a better understanding and management of SOC. This paper has considered the published literature with a focus on digital mapping of SOC using remote sensing data within 2010 to 2023 intervals. The objective was to consider all the important aspects of SOC prediction and mapping, including different land-use types, DSM algorithms, environmental variables, and remote sensing data sources. According to this review conducted on the 217 papers, cropland was the most popular type of land use. Regarding the DSM algorithms, random forest (RF) appeared in the largest number of studies. The terrain and spectral variables derived from the digital elevation model (DEM) and remote sensing images, were the highest demanding among all those used as input predictors. In addition, satellite platforms provided the largest portion of the remote sensing data used for the calibration of DSM models. This review provides quantitative insight into recent trends of SOC digital mapping using remote sensing technology while suggesting some directions for future development of the topic.

Název v anglickém jazyce

Digital mapping of soil organic carbon using remote sensing data: A systematic review

Popis výsledku anglicky

Soil organic carbon (SOC) has attracted a lot of attention in the soil science community. Freely available remote sensing data combined with advanced digital soil mapping (DSM) techniques has led to a better understanding and management of SOC. This paper has considered the published literature with a focus on digital mapping of SOC using remote sensing data within 2010 to 2023 intervals. The objective was to consider all the important aspects of SOC prediction and mapping, including different land-use types, DSM algorithms, environmental variables, and remote sensing data sources. According to this review conducted on the 217 papers, cropland was the most popular type of land use. Regarding the DSM algorithms, random forest (RF) appeared in the largest number of studies. The terrain and spectral variables derived from the digital elevation model (DEM) and remote sensing images, were the highest demanding among all those used as input predictors. In addition, satellite platforms provided the largest portion of the remote sensing data used for the calibration of DSM models. This review provides quantitative insight into recent trends of SOC digital mapping using remote sensing technology while suggesting some directions for future development of the topic.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Catena

ISSN

0341-8162

e-ISSN

0341-8162

Svazek periodika

232

Číslo periodika v rámci svazku

NOV 2023

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

001046820400001

EID výsledku v databázi Scopus

2-s2.0-85166027232