Global carbon budget calculations exclude intertidal mudflats, despite the fact that their contribution is expected to be high, and may account for up to 20% of global ocean production. As such, estimation of the true contribution of intertidal mudflats to the overall carbon budget is needed, and remote sensing is a promising tool to reach this goal. The main innovation in this study is the constraint of a set of new and existing radiometric indices, achieved by coupling hyperspectral remote sensing (hundreds of spectral bands with half maximum length, FWHM <10 nm) and the gross primary production (GPP, i.e., sediment-air carbon dioxide (CO2) fluxes) of microphytobenthos (MPB), based on pigment changes caused by photophysiological responses (i.e., xanthophyll cycle (XC) and Chl a activities) and photosynthetic efficiency (PAM-fluorometry). The ultimate goal is to develop mapping algorithms that may be implemented to estimate tidal flat GPP at various scales (from cm2 to global). Twenty-three radiometric indices were primarily screened using the reflectance (ref), the absorption coefficient (alpha) and their respective second derivative spectra obtained from hyperspectral images of MPB biofilms and corresponding GPP, under controlled conditions at 9 levels of light intensity (~50 - 2250 μmol photons m-2 s-1) and 3 temperatures (15°C, 25°C and 40°C), for each of the four seasons. Of the 23 indices, 11 have been selected to map GPP at the mesoscale, which is a first step in mapping MPB GPP at such a large scale, allowing for predictions to be made regarding the impact of tidal ecosystems in the context of global climate change.