Abstract
Ecosystem productivity models rely on regional climatic information to estimate temperature and moisture constraints influencing plant growth. However, the productivity response to these environmental factors is uncertain at the global scale and has largely been defined using limited observations from sparse monitoring sites, including carbon flux towers. Recent studies have shown that satellite observations of Solar-Induced chlorophyll Fluorescence (SIF) are highly correlated with ecosystem Gross Primary Productivity (GPP). Here, we use a relatively long-term global SIF observational record from the Global Ozone Monitoring Experiment-2 (GOME-2) sensors to investigate the relationships between SIF, used as a proxy for GPP, and selected bio-climatic factors constraining plant growth at the global scale. We compared the satellite SIF retrievals with collocated GPP observations from a global network of tower carbon flux monitoring sites and surface meteorological data from model reanalysis, including soil moisture, Vapor Pressure Deficit (VPD), and minimum daily air temperature (Tmin). We found strong correspondence (R2 > 80%) between SIF and GPP monthly climatologies for tower sites characterized by mixed, deciduous broadleaf, evergreen needleleaf forests, and croplands. For other land cover types including savanna, shrubland, and evergreen broadleaf forest, SIF showed significant but higher variability in correlations between sites. In order to analyze temperature and moisture related effects on ecosystem productivity, we divided SIF by photosynthetically active radiation (SIFp) and examined partial correlations between SIFp and the climatic factors across a global range of flux tower sites, and over broader regional and global extents. We found that productivity in arid ecosystems is more strongly controlled by soil water content to an extent that soil moisture explains a higher proportion of the seasonal cycle in productivity than VPD. At the global scale, ecosystem productivity is affected by joint climatic constraint factors so that VPD, Tmin, and soil moisture were significant (p < 0.05) controls over 60, 59, and 35 percent of the global domain, respectively. Our study identifies and confirms dominant climate control factors influencing productivity at the global scale indicated from satellite SIF observations. The results are generally consistent with climate response characteristics indicated from sparse global tower observations, while providing more extensive coverage for verifying and refining global carbon and climate model assumptions and predictions.
Highlights
Satellite observations have been used for studying global vegetation growth and seasonal phenology, ranging from the use of vegetation greenness indices from optical-infrared (IR) sensors to monitor photosynthetic canopy cover [1,2,3,4] to vegetation optical depth and backscatter retrievals from microwave sensors to monitor canopy biomass changes [5,6]
We conducted a global assessment of bioclimatic controls affecting ecosystem productivity using both globally comprehensive productivity observations from satellite Solar-Induced chlorophyll Fluorescence (SIF) retrievals and in situ Gross Primary Productivity (GPP) observations from sparse tower sites representing a diverse set of global biomes
The satellite based SIF observations were adjusted by photosynthetically active radiation (PAR) (SIFP ) to distinguish temperature and moisture related controls on productivity apart from solar radiation effects
Summary
Satellite observations have been used for studying global vegetation growth and seasonal phenology, ranging from the use of vegetation greenness indices from optical-infrared (IR) sensors to monitor photosynthetic canopy cover [1,2,3,4] to vegetation optical depth and backscatter retrievals from microwave sensors to monitor canopy biomass changes [5,6]. In addition to remote sensing of canopy physical properties, bio-climatic indices related to light, temperature, and water-related environmental constraints to plant photosynthesis have been used to predict vegetation growth and phenology metrics including growing season timing and length [7,8,9] These climate indices, in conjunction with satellite observations of the vegetation Fraction of Photosynthetically Active Radiation (FPAR) and other environmental inputs, have been used in process based models for estimating vegetation Gross. The TCF model uses a LUE approach driven by VPD and SM inputs that define respective atmospheric moisture demand and soil water supply constraints to GPP, while Tmin is used to define low temperature constraints to growth In these LUE models, the global representation of bioclimatic factors influencing productivity has largely been derived empirically using a limited number of globally distributed carbon (CO2 ) flux towers [7,16,17,18]. A generalized additive model was used to define the best-fit curvilinear relationships between SIF and underlying environmental constraints at the global scale
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