Abstract: Satellite remote sensing has revolutionized our ability to observe terrestrial ecosystems by providing increasingly rich detail about their spatial, temporal, and spectral dynamics. These include critical observations of the response of the terrestrial carbon cycle to ongoing changes in climate, extreme events, and anthropogenic management. More recently, remote sensing has been integrated with ecological theory to predict tipping points and to quantify ecological resistance and resilience to disturbances, all of which require analyses of the detailed time series that many satellite platforms provide.
Such time series have recently become even richer since the launch of the Geostationary Operational Environmental Satellite - R Series (GOES-R) with its Advanced Baseline Imager (ABI) and new visible and infrared bands that provide surface reflectances every 5-10 minutes. We now have real time estimates of land surface attributes like the NDVI and variables related to ecosystem carbon uptake like the near infrared reflectance of vegetation (NIRv). Such “weather” satellites have rarely been used for ecological science, but there is a growing international community of researchers who are using them to track changes in ecosystem function, including gross primary productivity (GPP), ushering in the era of “hypertemporal” remote sensing of the land surface and carbon cycle.
Real-time estimates of ecosystem function also create unprecedented opportunities to connect remote sensing and ecological theory. By tracking ecosystem responses to disturbances as they occur, we can quantify the resistance and resilience of key ecosystem functions like carbon uptake. We do so here using observations of Hurricanes Ian and Fiona, the latter of which was the most intense cyclone to hit Canada on record and passed near the CA-RPn peatland eddy covariance site in western Newfoundland. Fiona decreased NDVI and NIRv by 10% at CA-RPn, after which vegetation did not fully recover before the onset of autumn senescence. We also describe ongoing efforts to track vegetation recovery from the Kincade Fire in California since it burned in October 2019 using the ‘resilience sensing system’ time series approach of Lenton et al. (2022, doi.org/10.1098/rstb.2021.0383). Both examples provide key insights into how remote sensing and ecological theory can intersect to improve our knowledge of how ecosystems respond to ongoing disturbances and global changes.
