On the impact of soil texture on local scale organic carbon quantification: from hyper to multispectral sensing domains

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027049%3A_____%2F24%3AN0000085" target="_blank" >RIV/00027049:_____/24:N0000085 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0167198724001260?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0167198724001260?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.still.2024.106125" target="_blank" >10.1016/j.still.2024.106125</a>

Result language

angličtina

Original language name

On the impact of soil texture on local scale organic carbon quantification: from hyper to multispectral sensing domains

Original language description

Soil organic carbon (SOC) distribution and interaction with light is influenced by soil texture parameters (clay, silt and sand), which makes SOC prediction complicated, especially in samples with considerable pedological variability. Hence, understanding the relationship between SOC and soil texture is important within the context of SOC prediction using remote sensing data. The main objective of this study was to find the impact of soil texture on the performance of local SOC prediction models that were developed on Sentinel-2 (S2) multispectral and CASI/SASI (CS) hyperspectral airborne data as the main predictor variables. One approach to that objective was to lowering the texture variance by stratification of the samples. Therefore, soil samples collected from four agricultural sites in the Czech Republic were segregated based on the i) site-based and ii) texture-based stratification strategies. Random forest (RF) models were then developed on all stratified classes with and without considering the soil texture parameters as predictor variables and results were compared with those obtained by the RF models developed on the non-stratified (NS) samples. Both stratification strategies provided more homogeneous classes, which enhanced the accuracy of SOC prediction, compared to using the NS samples. In addition, the texture-based RF models yielded higher accuracy predictions than the site-based ones. Except sand, adding texture parameters to the main predictors improved accuracy of the models, so that the highest prediction performance was obtained by a texture-based model developed on clay added CS data. Overall, texture-based stratification could significantly enhance the SOC prediction, when the texture parameters were added to the S2 and CS data as the main predictor variables.

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

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OECD FORD branch

40104 - Soil science

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Soil & Tillage Reasearch

ISSN

0167-1987

e-ISSN

1879-3444

Volume of the periodical

241

Issue of the periodical within the volume

2024

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

16

Pages from-to

106125

UT code for WoS article

001234646200001

EID of the result in the Scopus database

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