Precision agriculture aims to optimize field management to increase agronomic yield, reduce environmental impact, and potentially foster soil carbon sequestration. In 2015, the Copernicus mission, with Sentinel-1 and -2, opened a new era by providing freely available high spatial and temporal resolution satellite data. Since then, many studies have been conducted to understand, monitor and improve agricultural systems. This paper presents results from the SolumScire project, focusing on the prediction of the spatial distribution of soil zones and topsoil properties, such as pH, soil organic matter (SOM) and clay content in agricultural fields through random forest algorithms. For this purpose, samples from 120 fields were investigated. The zoning and soil property prediction has an accuracy greater than 90%. This is supported by a high agreement of the derived zones with farmer’s observations. The trained models revealed a prediction accuracy of 94%, 89% and 96% for pH, SOM and clay content, respectively. The obtained models for soil properties can support precision field management, the improvement of soil sampling and fertilization strategies, and eventually the management of soil properties such as SOM.

There are currently many in-field methods for estimating soil properties (e.g., pH, texture, total C, total N) available in precision agriculture, but each have their own level of suitability and only a few can be used for direct determination of plant-available nutrients. As promising approaches for reliable in-field use, this review provides an overview of electromagnetic, conductivity-based, and electrochemical techniques for estimating plant-available soil nutrients and pH. Soil spectroscopy, conductivity, and ion-specific electrodes have received the most attention in proximal soil sensing as basic tools for precision agriculture during the last two decades. Spectral soil sensors provide indication of plant-available nutrients and pH, and electrochemical sensors provide highly accurate nitrate and pH measurements. This is currently the best way to accurately measure plant-available phosphorus and potassium, followed by spectral analysis. For economic and practicability reasons, the combination of multi-sensor in-field methods and soil data fusion has proven highly successful for assessing the status of plant-available nutrients in soil for precision agriculture. Simultaneous operation of sensors can cause problems for example because of mutual influences of different signals (electrical or mechanical). Data management systems provide relatively fast availability of information for evaluation of soil properties and their distribution in the field. For rapid and broad adoption of in-field soil analyses in farming practice, in addition to accuracy of fertilizer recommendations, certification as an official soil analysis method is indispensable. This would strongly increase acceptance of this innovative technology by farmers.