Quantification of phytoplankton primary production from space: A revisit based on high-frequency observations with the aid of Himawari-8/AHI

Synoptic quantification of phytoplankton depth-integrated primary production (IPP) has advanced significantly over recent decades by leveraging satellite observations and sophisticated IPP models. However, monthly mean upstream products from polar-orbiting satellites, e.g. , the Moderate Resolution Imaging Spectroradiometer (MODIS), are commonly used to generate IPP products, raising a concern about whether neglecting diurnal or daily IPP variabilities may compromise the accuracy of monthly- and annual-scale quantifications. Here, we aim to investigate this concern by comparing IPP quantified using high-frequency data at multiple timescales. A theoretical time-resolved model (TPM) was utilized for IPP modeling, driven by either diurnal photosynthetically available radiation (PAR) from the Advanced Himawari Imager (AHI) onboard Himawari-8 (H8) or daily PAR from MODIS. Preliminary evaluation against in situ measurements corroborated the superiority of AHI-based daily IPP estimation over MODIS, attributed to the robustness of AHI PAR data across varying sky conditions. Satellite IPP products were generated in the full-disk area of H8 between 2016 and 2019 under “daily-to-monthly-to-annual” (DtA) and “monthly-to-annual” (MtA) scenarios for comparison, using other requisite daily and gap-free biogeochemical products. Our analysis unveiled moderate spatiotemporal discrepancies between DtA-based IPP products from AHI and MODIS, confirming an overestimation in MODIS-derived monthly (< 8%) and annual total IPP (∼5%). In contrast, under the MtA scenario, MODIS substantially overestimated monthly (∼14–30%) and annual total IPP (∼20%) and gave biased temporal trends (∼1.3–1.6 times higher) compared to DtA-based IPP estimates of AHI. The discrepancies between IPP products were largely subject to the cloud-induced variabilities in daily PAR products and ocean color data coverage. By upscaling our results to the global ocean, it is anticipated that the annual total IPP previously estimated from MODIS with TPM-like models is overestimated by at least 19%. This study emphasizes the necessity of modeling IPP at finer timescales using high-frequency observations and provides insights for improving IPP quantification with the aid of geostationary satellites. • AHI provides more accurate daily PAR observations than MODIS. • Daily IPP modeling driven by AHI PAR data is more robust than MODIS. • IPP quantification with satellite monthly mean products is overestimated. • Daily-to-annual IPP products achieve more realistic quantification from space.