SIMULATION OF SPACEBORNE HYPERSPECTRAL REMOTE SENSING TO ASSIST CROP NITROGEN CONTENT MONITORING IN AGRICULTURAL CROPS

The proper management of nitrogen (N) is a pre-requisite for sustainable fertilization in modern agriculture. Methods for N -retrieval from Earth Observation (E. O.) data have been mainly based on empirical algorithms. In the present study, two methods (physically based / hybrid) for the assessment of crop nitrogen content (Narea) and concentration (Nmass) were tested. Data from a hyperspectral field campaign in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP) were exploited using a recalibrated PROSPECT model coupled with the canopy reflectance model 4SAIL. The physically based algorithm achieved relative errors (rRMSE) of 72% for Narea with R (2) = 0.92. The hybrid approach obtained higher accuracies with rRMSE lower than 16% for the retrieval of Nmass. Uncertainties of the predictor variables have to be taken into account. Both algorithms represent interesting techniques for global agricultural monitoring from hyperspectral satellite data but further analysis is required.