  This study introduces a framework for forecasting soil nitrogen content,
leveraging multi-modal data, including multi-sensor remote sensing images and
advanced machine learning methods. We integrate the Land Use/Land Cover Area
Frame Survey (LUCAS) database, which covers European and UK territory, with
environmental variables from satellite sensors to create a dataset of novel
features. We further test a broad range of machine learning algorithms,
focusing on tree-based models such as CatBoost, LightGBM, and XGBoost. We test
the proposed methods with a variety of land cover classes, including croplands
and grasslands to ensure the robustness of this approach. Our results
demonstrate that the CatBoost model surpasses other methods in accuracy. This
research advances the field of agricultural management and environmental
monitoring and demonstrates the significant potential of integrating
multisensor remote sensing data with machine learning for environmental
analysis.
