Quantitative assessment of the scale conversion from instantaneous to daily GPP under various sky conditions based on MODIS local overpassing time

Abstract

With the increasing frequency of extreme events, daily gross primary productivity (GPP) is necessary to be accurately assessed to determine when and how much it is affected. Fluctuating environmental conditions contribute to diverse diurnal photosynthetic patterns influenced by changing atmospheric factors, including solar radiation, CO2 concentrations, and leaf temperature. This complexity underscores the challenge of accurately estimating daily GPP. We quantitatively assessed three temporal upscaling approaches - cosine of the solar zenith angle, extraterrestrial solar irradiance, and photosynthetic active radiation(PAR) - under varying sky conditions. Additionally, we introduced a novel correction method for universal temporal upscaling ratios. Instantaneous GPP values from the FLUXNET2015 dataset, acquired around the MODIS overpassing time, were utilized. The upscaled daily GPP exhibited minimal deviation from the half-hourly GPPs collected between 11 am and 1 pm, with increased discrepancies for GPPs later in the afternoon. The PAR-based approach demonstrated superior accuracy in the afternoon, effectively capturing incoming radiation changes due to clouds. Instant environmental variables-GPP relationships were weak under clear skies but exhibited moderate-to-high positive correlations under cloudy conditions. Analyzing the impact of the diffuse ratio of incoming photosynthetic active radiation on instantaneous GPP revealed limited enhancement at short time scales. On a daily scale, all three temporal upscaling approaches, under different ranges of clearness index, consistently underestimated daily GPP. We proposed a novel correction method, normalizing the difference between maximum and minimum air temperature in a day, notably reduced errors by approximately 10.69 and 21.07 gC m-2 d-1 for cosine of the solar zenith angle and extraterrestrial solar irradiance-based temporal upscaling approaches. We recommend adopting the corrected temporal upscaling factor globally due to its simplicity and improved accuracy.