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

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 21; p. 2563
• Main Authors: Li, Xing, Xiao, Jingfeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 31.10.2019
• Subjects: Agricultural ecosystems; Agricultural production; Bias; Biosphere; Carbon; Carbon cycle; carbon fluxes; Chlorophyll; Climate change; Climatic data; data collection; earth system models; Ecosystem management; ecosystem models; Ecosystems; eddy covariance; Estimates; Fluorescence; fluxnet; gross primary productivity; Land cover; Mapping; meteorological data; model benchmarking; monitoring; Northern Hemisphere; orbiting carbon observatory-2; Photosynthesis; Primary production; Radiation; Regression analysis; remote sensing; Satellite observation; satellites; seasonal variation; Seasonal variations; Soil properties; Spatial discrimination; Spatial resolution; sun-induced chlorophyll fluorescence; Tropical environments; tropics; Uncertainty; Variables
• DOI: 10.3390/rs11212563
• ISSN: 2072-4292

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.