Current agriculture practices demand the implementation of modern technological approaches that allow accurate remote monitoring of crop and soil status. So far, the large number of agronomic and environmental variables that could be estimated from remote sensing as well as the accuracy of estimation constituted limiting factors for precision agriculture and environmental assessments and forecasting. Seeking to address this issue, the EU-funded Funreso project aimed to develop a novel remote sensing approach for improving the accuracy of estimation of crop and soil variables. To this end, the project developed a functional structural plant model (FSPM) capable of taking into account particular water and nitrogen stress responses at the canopy level. Accuracy improvements in remote sensing information retrieval were achieved by including prior knowledge of vegetation properties and through improved simulation models of canopy reflectance. Such detailed three-dimensional (3D) canopy models were used to retrieve the canopy biophysical and biochemical properties. Remote sensing information was fed into dynamic crop functioning to estimate crop and soil agronomic and environmental variables which are unattainable by direct estimation from remote sensing. Funreso-developed tools will help remote sensing of crop and soil status and hopefully lead to improved planning and management of agricultural activities.