Introducing the Potential of the New Enmap-Box Hybrid Retrieval Workflow for Quantifying Non-Photosynthetic Vegetation

In this study, the potential of the newly developed Hybrid Retrieval Workflow, integrated into the EnMAP-Box, is demonstrated by quantifying NPV using hyperspectral data. Due to its diverse functions in agricultural and natural ecosystems, NPV mapping is becoming increasingly important. The emergence of new-generation spaceborne spectrometers like PRISMA and EnMAP, providing largescale and potentially multi-temporal hyperspectral data, opens up new possibilities for NPV mapping on a global scale. The EnHyR facilitates NPV mapping using hybrid machine learning approaches that use simulated spectral training data optimized via Active Learning supported by in-situ data. Preliminary results for Slovakia and Southern Germany highlight the potential of the EnHyR using PRISMA and EnMAP data for global NPV mapping.