Impact of subsurface chlorophyll maxima on satellite-based Arctic spring primary production estimates - Matthes, L. C.; Bélanger, Simon; Raulier, B.; Babin, Marcel (Remote Sensing of Environment, 2023)
In recent years several regional and pan-Arctic ocean color algorithms, which consider the unique bio-optical properties of the Arctic Ocean, have been developed to accurately extract surface chlorophyll a (Chl a), a key input into net primary production (NPP) algorithms, from spectral remote sensing reflectance (Rᵣₛ). However, most satellite derived NPP (NPPꜱᴀᴛ) algorithms used in the Arctic do not account for production at deep subsurface chlorophyll maxima (SCMs), a prominent feature in the Arctic Ocean during the summer months, leading to underestimations of NPP during the post-bloom period. In this study, a shallow SCM was observed in early summer (June/July 2016) in Baffin Bay at the time of the sea ice edge bloom, raising the question to what extent this SCM contributes to Rᵣₛ and how it is captured in NPPꜱᴀᴛ estimates. Radiative transfer simulations based on in situ data of inherent and apparent optical properties and Chl a concentration in the water column were used to examine the effects of heterogeneous vertical Chl a profiles of various strength and depth on spectral reflectance, algorithm-derived surface Chl aꜱᴀᴛ and NPPꜱᴀᴛ. NPPꜱᴀᴛ estimates for varying Chl a profile shapes were then compared to NPPꜱᴀᴛ estimates of reference simulations for homogenous Chl a profiles, and to measured NPP calculated using observed Chl a profiles. Results show a significant impact of shallow SCMs on Rᵣₛ with the depth of maximum Chl a < 30 m, causing an increase in Chl aꜱᴀᴛ by 17 ± 6% relative to a vertically homogenous ocean. Interestingly, SCMs significantly influencing Rᵣₛ were found down to the fifth light penetration depth. The increase in Chl aꜱᴀᴛ reduced the difference between predicted and measured NPP estimates to -25 ± 12% at stations with a shallow SCM. Furthermore, due to the significant contribution of these shallow SCM stations to regional NPP, regional predicted NPPꜱᴀᴛ was only 16% lower than measured NPP. Our results demonstrate that the partial representation of a shallow and very productive SCM in ...