Jonas Ardö

Gross primary production (GPP) of the vegetation is the largest carbon exchange process of the global carbon cycle. Currently, within satellite-based remote sensing, GPP is generally modelled using linear light use efficiency models where GPP is related to photosynthetically active radiation absorbed by the green vegetation (APAR). These models work well on moderate to low spatiotemporal averaging resolutions. However, the relationship has been shown to rather follow an asymptotic curve at high spatiotemporal resolutions. The main aim of this study was to investigate at which spatial and temporal scale the GPP-APAR relationship converts from being asymptotic to linear. We used field data and satellite observations from the Dahra field site, a semi-arid savanna grassland in West Africa. At half-hourly to daily temporal resolution an asymptotic relationship gives the better fit, whereas for monthly and weekly data a linear relationship is preferred. A linear relationship was best when working with low spatial resolutions (>two and four Ha for daily and sub-daily GPP estimates, respectively), whereas if working with smaller pixel sizes, the asymptotic relationship was preferred. Hence, if studying GPP variability with satellite sensors such as AVHRR, MODIS, and Sentinel-3, a linear light use efficiency approach works well, whereas if using sensors such as Landsat and Sentinel-2, an asymptotic relationship is recommended. If we aim to improve our understanding of the GPP variability and its role within the carbon cycle, increasing the spatial and temporal resolution of Earth observation-based products is vital. This study provides an initial step toward the impact this may have, and future research across diverse ecosystems and over longer timescales is essential to expand upon these findings.