Mapping soil organic carbon (SOC) stock

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RESEARCH


Free image/jpeg, Resolution: 4272x2848, File size: 3.87Mb, plowed field and rapeseed field

Mapping soil organic carbon (SOC) stock

Mapping soil organic carbon (SOC) stock

The research project SOCmonit focuses on development of methods to standardize the spatio-temporal monitoring of soil organic carbon (SOC) within long-term field experiment (LTFE) sites using spectral measurements from remote sensing and proximal soil detection. LTFEs investigate, among other things, the influence of soil cultivation and fertilization on the content of SOC. Spectrometric methods are suitable for the quantitative determination of all soil properties that have an influence on soil color, including SOC. Modelling approaches establish the relation between the target quantity and the measured spectra.

Innovative mapping toolbox

Currently, there is no modeling approach that takes into account the partly complex data processing steps for spectrometric data from remote sensing and proximal soil acquisition as well as the influence of in situ "disturbance variables". With this in mind, SOCmonit is developing an automated data processing and modeling toolbox. A comparison of several spectral sensors and carrier platforms for remote sensing and proximal soil detection will be conducted. Methods employed will generate different data formats with various information contents in relation to the target SOC: image formats with different spatial resolution versus point spectral measurement, multispectral versus hyperspectral measurement, laboratory versus field measurement, aboveground versus underground measurement. Different data processing steps as well as the involved processing steps of predicting SOC content are implemented in open source code software. Software is automated, standardized and comprehensibly documented with the aim to make the procedure accessible to a broad group of users. The collected datasets will be published and thus allow a direct comparison with future methodological innovations.

RSS RESPONSIBILITIES

  • Development and testing of algorithms, validation
  • Development of open-source Sentinel-2 download and processing software
  • Development of drone data processing software

Potential of application

A cost-effective, time saving method for the spatio-temporal monitoring of soil parameters, such as SOC, is attractive for authorities, farmers and governments responding to current and future-planned climate initiatives. With an exact knowledge of the spatio-temporal variability of SOC within agriculturally cultivated fields, hotspot identification can take place and cultivation can be adapted so as to preserve and increase SOC content in order to reduce greenhouse gas emissions.

Research partners

Helmholtz-Zentrum für Umweltforschung GmbH (UFZ), Halle (Saale)

Funding source

Federal Ministry of Food and Agriculture – BMEL via the Federal Office for Agriculture and Food, BLE