'Hypertemporal' remote sensing of plant function: a comparison of phenocam and geostationary operational environmental satellite NDVI data products

Abstract

Ongoing climate warming is changing the seasonality of plant canopy function, but common approaches to explore these changes via polar-orbiting satellites often miss rapid canopy transitions due to infrequent observations. I explored the ability of satellites designed for studying weather systems, namely The Geostationary Operational Environmental Satellite (GOES), to track plant canopy status on time scales of minutes. With new capabilities to remotely sense in the infrared, the GOES weather satellites now have the capability to detect photosynthetic activity. Satellite observations of the normalized difference vegetation index (NDVI) are compared against near-surface phenological camera ("PhenoCam") observations from the National Ecological Observation Network (NEON, Inc.) at six sites every 15 minutes for one week in April 2019. Diurnal trends across both observation platforms showed the expected diurnal parabolic structure in NDVI with critical differences in NDVI magnitude between PhenoCams and GOES observations. One tailed T-test results show that there is variability between methods when measuring NDVI, with P-values less than 0.05 in all cases. This was anticipated due to correction factors needed for PhenoCam NDVI observations. However, additional variability can be attributed to other areas such as cloud cover, plant type, and heterogeneity. My proof-of-concept study demonstrates that raw NDVI data from both methods are often comparable, which lends credit to the notion that NDVI can be accurately observed from space at high (up to five minute) temporal resolution. With current research underway on the topics of atmospheric corrections and further surface validation, GOES has the potential to observe land surface attributes at up to 5-minute intervals across entire hemispheres for identifying phenology, disturbance and other vegetation dynamics in real time. With two hypertemporal methods at different spatial scales recently introduced, the research is primed to move towards a real time understanding of plant canopy function across the United States.