Emulation-based self-supervised SIF retrieval in the O$_2$-A absorption band with HyPlant

The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery requires accurate atmospheric compensation to correctly disentangle its small contribution to the at-sensor radiance from other confounding factors. In spectral fitting SIF retrieval approaches this compensation is estimated in a joint optimization of free variables when fitting the measured at-sensor signal. Due to the computational complexity of Radiative Transfer Models (RTMs) that satisfy the level of precision required for accurate SIF retrieval, fully joint estimations are practically unachievable with exact physical simulation. We present in this contribution an emulator-based spectral fitting method neural network (EmSFMNN) approach integrating RTM emulation and self-supervised training for computationally efficient and accurate SIF retrieval in the O$_2$-A absorption band of HyPlant imagery. In a validation study with in-situ top-of-canopy SIF measurements we find improved performance over traditional retrieval methods. Furthermore, we show that the model predicts plausible SIF emission in topographically variable terrain without scene-specific adaptations. Since EmSFMNN can be adapted to hyperspectral imaging sensors in a straightforward fashion, it may prove to be an interesting SIF retrieval method for other sensors on airborne and spaceborne platforms.