Solar-induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite-observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer resolution SIF (1.3 km × 2.25 km) measured from the Orbiting Carbon Observatory-2 (OCO-2) provides the first opportunity to examine the SIF–GPP relationship at the ecosystem scale using flux tower GPP data. However, it remains unclear how strong the relationship is for each biome and whether a robust, universal relationship exists across a variety of biomes. Here we conducted the first global analysis of the relationship between OCO-2 SIF and tower GPP for a total of 64 flux sites across the globe encompassing eight major biomes. OCO-2 SIF showed strong correlations with tower GPP at both midday and daily timescales, with the strongest relationship observed for daily SIF at the 757 nm (R2 = 0.72, p < 0.0001). Strong linear relationships between SIF and GPP were consistently found for all biomes (R2 = 0.57–0.79, p < 0.0001) except evergreen broadleaf forests (R2 = 0.16, p < 0.05) at the daily timescale. A higher slope was found for C4 grasslands and croplands than for C3 ecosystems. The generally consistent slope of the relationship among biomes suggests a nearly universal rather than biome-specific SIF–GPP relationship, and this finding is an important distinction and simplification compared to previous results. SIF was mainly driven by absorbed photosynthetically active radiation and was also influenced by environmental stresses (temperature and water stresses) that determine photosynthetic light use efficiency. OCO-2 SIF generally had a better performance for predicting GPP than satellite-derived vegetation indices and a light use efficiency model. The universal SIF–GPP relationship can potentially lead to more accurate GPP estimates regionally or globally. Our findings revealed the remarkable ability of finer resolution SIF observations from OCO-2 and other new or future missions (e.g., TROPOMI, FLEX) for estimating terrestrial photosynthesis across a wide variety of biomes and identified their potential and limitations for ecosystem functioning and carbon cycle studies.
Climate warming and drying is associated with increased wildfire disturbance and the emergence of megafires in North American boreal forests. Changes to the fire regime are expected to strongly increase combustion emissions of carbon (C) which could alter regional C balance and positively feedback to climate warming. In order to accurately estimate C emissions and thereby better predict future climate feedbacks, there is a need to understand the major sources of heterogeneity that impact C emissions at different scales. Here, we examined 211 field plots in boreal forests dominated by black spruce (Picea mariana) or jack pine (Pinus banksiana) of the Northwest Territories (NWT), Canada after an unprecedentedly large area burned in 2014. We assessed both aboveground and soil organic layer (SOL) combustion, with the goal of determining the major drivers in total C emissions, as well as to develop a high spatial resolution model to scale emissions in a relatively understudied region of the boreal forest. On average, 3.35 kg C m−2 was combusted and almost 90% of this was from SOL combustion. Our results indicate that black spruce stands located at landscape positions with intermediate drainage contribute the most to C emissions. Indices associated with fire weather and date of burn did not impact emissions, which we attribute to the extreme fire weather over a short period of time. Using these results, we estimated a total of 94.3 Tg C emitted from 2.85 Mha of burned area across the entire 2014 NWT fire complex, which offsets almost 50% of mean annual net ecosystem production in terrestrial ecosystems of Canada. Our study also highlights the need for fine-scale estimates of burned area that represent small water bodies and regionally specific calibrations of combustion that account for spatial heterogeneity in order to accurately model emissions at the continental scale.