Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2

Abstract

Accurately quantifying gross primary production (GPP) globally is critical for assessing plant productivity, carbon balance, and carbon-climate feedbacks, while current GPP estimates exhibit substantial uncertainty. Solar-induced chlorophyll fluorescence (SIF) observed by the Orbiting Carbon Observatory-2 (OCO-2) has offered unprecedented opportunities for monitoring land photosynthesis, while its sparse coverage remains a bottleneck for mapping finer-resolution GPP globally. Here, we used the global, OCO-2-based SIF product (GOSIF) and linear relationships between SIF and GPP to map GPP globally at a 0.05° spatial resolution and 8-day time step for the period from 2000 to 2017. To account for the uncertainty of GPP estimates resulting from the SIF-GPP relationship, we used a total of eight SIF-GPP relationships with different forms (universal and biome-specific, with and without intercept) at both site and grid cell levels to estimate GPP. Our results showed that all of the eight SIF-GPP relationships performed well in estimating GPP globally. The ensemble mean 8-day GPP was generally highly correlated with flux tower GPP for 91 eddy covariance flux sites across the globe (R2 = 0.74, Root Mean Square Error = 1.92 g C m−2 d−1). Our fine-resolution GPP estimates showed reasonable spatial and seasonal variations across the globe and fully captured both seasonal cycles and spatial patterns present in our coarse-resolution (1°) GPP estimates based on coarse-resolution SIF data directly aggregated from discrete OCO-2 soundings. SIF-GPP relationships with different forms could lead to significant differences in annual GPP particularly in the tropics. Our ensemble global annual GPP estimate (135.5 ± 8.8 Pg C yr−1) is between the median estimate of non-process based methods and the median estimate of process-based models. Our GPP estimates showed interannual variability in many regions and exhibited increasing trends in many parts of the globe particularly in the Northern Hemisphere. With the availability of high-quality, gridded SIF observations from space (e.g., TROPOMI, FLEX), our novel approach does not rely on any other input data (e.g., climate data, soil properties) and therefore can map GPP solely based on satellite SIF observations and potentially lead to more accurate GPP estimates at regional to global scales. The use of a universal SIF-GPP relationship versus biome-specific relationships can also avoid the uncertainty associated with land cover maps. Our novel, independent GPP product (GOSIF GPP), freely available at our data repository, will be valuable for studying photosynthesis, carbon cycle, agricultural production, and ecosystem responses to climate change and disturbances, informing ecosystem management, and benchmarking terrestrial biosphere and Earth system models.

Correcting Clear‐Sky Bias in Gross Primary Production Modeling From Satellite Solar‐Induced Chlorophyll Fluorescence Data

“…Appling these two C 3 and C 4 models, the RMSEs of estimated GPP at the daily, monthly, and yearly scales are 1.81, 1.33, and 0.89 gC/m 2 /d, respectively (Figures 3d and 4). The estimation accuracies in GPP are generally higher than previous studies, such as RMSE of 1.90 and 1.21 gC/m 2 /d at monthly and yearly scale in Li and Xiao (2019).…”

“…Owing to the physiological link between SIF and GPP, a growing number of studies have attempted to estimate GPP from satellite SIF using empirical SIF‐GPP relationships (Frankenberg et al, 2011; Li & Xiao, 2019; Zhang, Guanter et al, 2018). These empirical relationships are generally calibrated with clear‐sky GPP and SIF, which could cause uncertainties in GPP estimation under cloudy sky conditions.…”

Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations

“…Studies such as those of Zhang et al [184], Li et al [176], and Yu et al [185] demonstrated a way to improve the resolution of OCO-2 and extend the OCO-2 SIF products back to 2000. These improved SIF products have a higher resolution (0.05 • ), longer data record (from 2000 to present), and are globally contiguous; they will have more flexible and wide applications such as estimating higher-resolution GPP at local, regional, and global levels compared to the original ones [186].…”

Performance of the Remotely-Derived Products in Monitoring Gross Primary Production across Arid and Semi-Arid Ecosystems in Northwest China

“…We re-sampled it to 1 km pixel size before being extracted based on site locations. The GOSIF GPP product was developed by Li and Xiao [24], with a 0.05 • spatial resolution and an eight-day time step for the period from 2000 to 2018. The GOSIF GPP product benefited from the breakthrough of two key matters, the relationship between SIF and GPP, and SIF data with high spatial and temporal resolutions.…”

“…Therefore, the land cover maps were not taken into account when the GOSIF products are produced. Similarly, Li and Xiao [24] used a nearly universal rather than biome-specific SIF-GPP relationship to get the GOSIF GPP product. In a nutshell, the GOSIF GPP reduced the impact of land cover classification uncertainty to some extent.…”