Interpretable Machine Learning-Derived Spectral Indices for Vegetation Monitoring

ArXi:2512.21948v2 Announce Type: replace Spectral indices such as NDVI have driven vegetation monitoring for decades, yet their design remains largely manual and ad hoc. Their usefulness stems not only from their empirical performance, but also from algebraic forms that remain compact and biologically interpretable. However, the space of possible algebraic expressions relating spectral bands is effectively infinite, making systematic search impractical without structural constraints. We