POSTER SESSION 2
10:30–12:30, Tuesday, September 15
Poster Session | 1 | 2 | 3 | 4 | Schedule at a Glance
ABSTRACT 1319 | POSTER 134
QUANTIFICATION OF PHYTOPLANKTON PRIMARY PRODUCTION FROM SPACE: A REVISIT 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. 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. 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. 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.
Zhaoxin Li, Xiamen University, China, [email protected], https://orcid.org/0000-0002-6054-9383
Wei Yang, Center for Environmental Remote Sensing, Chiba University, Japan, [email protected]
Fang Shen, State Key Laboratory of Estuarine and Coastal Research, East China Normal University, China, [email protected]
Chong Shi, State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, China, [email protected]
Poster Session | 1 | 2 | 3 | 4 | Schedule at a Glance
