The Diurnal Dynamics of Gross Primary Productivity Using Observations From the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R Series at an Oak Savanna Ecosystem

Abstract

Gross primary productivity (GPP) is the largest flux in the global carbon cycle and satellite‐based GPP estimates have long been used to study the trends and interannual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar‐orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R series (GOES‐R) to test the ability of subdaily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We compared three methods to estimate GPP: (a) a light‐use efficiency model, (b) a linear relationship between the product of near‐infrared reflectance of vegetation and photosynthetically active radiation (LIN‐NIRvP) and EC tower GPP, and (c) a light response curve (LRC‐NIRvP) between NIRvP and EC GPP. The LRC‐NIRvP achieved the lowest mean absolute error for winter (2 µmol CO2 m−2 s−1), spring (2.51 µmol CO2 m−2 s−1), summer (1.43 µmol CO2 m−2 s−1), and fall (1.35 µmol CO2 m−2 s−1). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation toward the morning hours during the dry summers. The LRC‐NIRvP and the light‐use efficiency model were in agreement with these patterns of a shift in peak daily GPP toward the morning hours during summer. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.

Ecosystem Gross Primary Productivity After Autumn Snowfall and Melt Events in a Mountain Meadow

“…Differences are to be expected amongst RS sensors (Rossi et al., 2019) and different grassland sites (Wohlfahrt et al., 2010), and the coarse spatial scale and diurnal return interval of MODIS gave different NDVI results than the tower‐mounted instrumentation at our site (Figures 3, 7 and 13). Recent advances to geostationary satellite technology—for example, enhanced observational capacities in the visible and near infrared from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite (Khan et al., 2022)—provide new opportunities for understanding snow duration (Romanov et al., 2000; Romanov & Tarpley, 2004) and vegetation productivity after melt. Enhanced observational systems at multiple scales in space including tower‐mounted instruments, phenocams, unpiloted aerial vehicles, and satellite remote sensing will further improve our understanding of autumn phenological changes in global ecosystems (Gallinat et al., 2015).…”

New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western U.S. heatwave

“…where t is the time in decimal hours from 7 to 17 (7 a.m. to 5 p.m.) and GPP t is the GPP at hour t. The resulting C GPP is the weighted mean hour of the diurnal cycle of GPP. For example, if C GPP is greater than 12, then it indicates a shift of GPP toward afternoon, while if C GPP is less than 12, then it indicates a shift toward morning (32). The GPP peak hour (Hour peak ) was defined as the time at which GPP reached the maximum from 7 a.m. to 5 p.m.…”

“…As a result, all the diurnal metrics were aligned with the peak time of solar radiation. We also used an alternative approach that involves calculating the difference between C GPP and C SW to eliminate the effect resulting from radiation (32,67,68). Figure S4 shows an example of the spatial patterns of C GPP , Hour peak , and Ratio A/M .…”

Vegetation Index‐Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity

“…Although various studies have acknowledged the limitations of NIRv or NIRvP in capturing the physiological stress responses of vegetation, comprehensive investigations of these limitations have yet to be performed (Dechant et al., 2022; Khan et al., 2022; Liu et al., 2023). In the context of extreme climate events (e.g., droughts), meteorological constraints are potential driving factors of fluctuations in GPP and act as the main drivers of variations in terrestrial carbon sinks (Chen et al., 2013; He et al., 2021; Zhao & Running, 2010).…”