The evaluation of soil organic carbon through VIS-NIR spectroscopy to support the soil health monitoring

Increasing the organic matter content of the soil has been presented in the:”4per1000″ proposal as a significant climate mitigation measure able to support the achievement of Sustainable Development Goal 13 - Climate Action of United Nations.

At the same time, the report of the Mission Board for Soil health and Food, "Caring for soil is caring for life," indicates that one of the targets that must be reached by 2030 is the conservation and increase of soil organic carbon stock.  De facto, the panel clearly indicates the soil organic carbon as one of the indicators that can be used to monitor soil health, and at the same time, if the current soil use is sustainable or not.

Thus it is to be expected that the monitoring of SOC will become requested to check and monitor the sustainability of agricultural practices realized in the agricultural areas. For all the above reasons, the development of a reliable and fast indirect methods to evaluate the SOC is necessary to support different stakeholders (government, municipality, farmer) to monitor SOC at different spatial scales (national, regional, local).

Over the past two decades, data mining approaches in spatial modeling of soil organic carbon using machine learning techniques and artificial neural network (ANN) to investigate the amount of carbon in the soil using remote sensing data has been widely considered. Accordingly, this study aims to design an accurate and robust neural network model to estimate the soil organic carbon using the data-based field-portable spectrometer and laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The measurements will be on two sets of the same soil samples, the first by the standard protocol of requested laboratories for soil scanning, The second set of the soil samples without any cultivation to simulate the soil condition in the sampling field emphasizes the predictive capabilities to achieve fast, cheap and accurate soil status. Carbon soil parameter will determine using, multivariate regression method used for prediction with Least absolute shrinkage and selection operator regression (Lasso) in interval way (high, medium, and low). The results will increase accuracy, precision, and cost-effectiveness over traditional ex-situ methods.

The contribution has been realized within the international EIT Food project MOSOM (Mapping of Soil Organic Matter; https://www.eitfood.eu/projects/mosom)