Advancing hourly gross primary productivity mapping over East Asia using Himawari-8 AHI and artificial intelligence: Unveiling the impact of aerosol-induced radiation dynamics

Abstract

Advancements in geostationary satellites allow monitoring of terrestrial photosynthesis at sub-daily scales, offering unprecedented opportunities for understanding vegetation productivity. However, at short temporal scales, photosynthesis is highly influenced by illumination conditions, particularly diffuse radiation (Ddif). Existing empirical models often overlook Ddif's impact, leading to uncertainties in hourly gross primary productivity (GPP) mapping. We determined that incorporating Ddif effects into GPP modeling improves clear-sky GPP mapping using Himawari-8. We employed a light gradient boosting machine (LGBM) considering Ddif and compared it with other empirical models: parametric, regression, and machine learning. The LGBM outperformed others, achieving an R2 of 0.8146 and root mean square error of 2.848 mu mol CO2/m2/s against ground observations from 2020 to 2021. To investigate input variable contributions in LGBM predictions, we performed SHapely Additive exPlanation (SHAP) analysis. Results confirmed that aerosol optical depth (AOD) had a greater impact during morning and evening when Ddif influence increased due to solar path length. Hourly GPP maps over East Asia from 2020 to 2021 using the LGBM demonstrated that diurnal patterns differ by landcover, with variations observed in latitudinal profiles. This underscores the need to examine GPP spatial distribution at high frequency. We confirmed that the spatial distribution of AOD SHAP values varied over time, highlighting temporal dynamics of aerosol effects. Our findings demonstrate the necessity of GPP mapping using geostationary satellites and suggest various impact studies can use our proposed framework. This approach provides a valuable tool for understanding vegetation's rapid response to atmospheric aerosols, contributing to more accurate ecosystem flux modeling.