Developing machine learning based models for soil parameters prediction and mapping using Vis-NIR spectroscopic data

The application of Vis-NIR spectroscopy for physico-chemical soil properties estimations, like soil organic carbon, and digital soil mapping for the scopes of enhancing precision agriculture, promote soil carbon sequestration and improve soil health is fastly developing thanks to the use of machine learning algorithms and big data handling.

With our Subterra Green device, developed by S4 Mobile laboratories, a mobile field unit equipped with a visible and near infrared (VNIR) spectrometer and a load cell for measuring probe insertion force, we are able to collect spectroscopic data until 90 cm underground, down to a 1 cm resolution.

As part of the EU founded PHENET project, among many others, one specific scope is to conduct soil surveys among various soil types, including highly fertile chernozems, to less productive gleyic or cambisols. Samples collection for training and testing of machine learning based models, takes place from the humid continental zones of Austria to the temperate oceanic climate of Portugal. Ground-truthing data is verified with laboratory biochemical analysis of the selected soil samples. The ultimate goal would be to estimate important soil parameters in-situ and provide digital soil maps on larger scales (several hectares), providing this with the highest accuracy possible by using pre-processing techniques such as external parameter orthogonalization or direct standardization to correct detrimental effects caused by varying water content, bulk density, soil texture etc.

Developing precise machine learning based models using Vis-NIR spectroscopy and subsequently generating high-resolution digital soil maps leads us to fast, non-destructive and cost-effective monitoring of soil physico-chemical properties over space and time.