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Journal Article

On the impact of soil texture on local scale organic carbon quantification: From airborne to spaceborne sensing domains

Authors

Khosravi,  Vahid
External Organizations;

Gholizadeh,  Asa
External Organizations;

Žížala,  Daniel
External Organizations;

Kodešová,  Radka
External Organizations;

/persons/resource/saberioo

Saberioon,  Mohammadmehdi
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Agyeman,  Prince Chapman
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Vokurková,  Petra
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Juřicová,  Anna
External Organizations;

Spasić,  Marko
External Organizations;

Borůvka,  Luboš
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Citation

Khosravi, V., Gholizadeh, A., Žížala, D., Kodešová, R., Saberioon, M., Agyeman, P. C., Vokurková, P., Juřicová, A., Spasić, M., Borůvka, L. (2024): On the impact of soil texture on local scale organic carbon quantification: From airborne to spaceborne sensing domains. - Soil and Tillage Research, 241, 106125.
https://doi.org/10.1016/j.still.2024.106125


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5025790
Abstract
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.