Articles | Volume 8, issue 1
https://doi.org/10.5194/soil-8-113-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-8-113-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Content of soil organic carbon and labile fractions depend on local combinations of mineral-phase characteristics
Malte Ortner
CORRESPONDING AUTHOR
Soil Science Department, University of Trier, 54296 Trier, Germany
Michael Seidel
Geoinformatics and Remote Sensing, Institute for Geography, Leipzig
University, 04103 Leipzig, Germany
Sebastian Semella
Geoinformatics and Remote Sensing, Institute for Geography, Leipzig
University, 04103 Leipzig, Germany
present address: Deutsches Biomasseforschungszentrum GmbH, 04347
Leipzig, Germany
Thomas Udelhoven
Department of Remote Sensing & Geoinformatics, University of Trier,
54296 Trier, Germany
Michael Vohland
Geoinformatics and Remote Sensing, Institute for Geography, Leipzig
University, 04103 Leipzig, Germany
Soil Science Department, University of Trier, 54296 Trier, Germany
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Biogeosciences, 21, 473–511, https://doi.org/10.5194/bg-21-473-2024, https://doi.org/10.5194/bg-21-473-2024, 2024
Short summary
Short summary
We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
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Short summary
Soil organic carbon (SOC) and its labile fractions are influenced by soil use and mineral properties. These parameters interact with each other and affect SOC differently depending on local conditions. To investigate the latter, the dependence of SOC content on parameters that vary on a local scale depending on parent material, soil texture, and land use as well as parameter combinations was statistically assessed. Relevance and superiority of local models compared to total models were shown.
Soil organic carbon (SOC) and its labile fractions are influenced by soil use and mineral...