Evaluation of Solar-Induced Chlorophyll Fluorescence as a Prediction of Gross Primary Production using Multiple Eddy Covariance Sites
Abstract
Solar-Induced Chlorophyll Fluorescence (SIF) provides information about the state of photosynthesis and offers an exciting prospect of improving remote sensing observations of Gross Primary Production (GPP). The relationship between SIF and GPP has been extensively investigated, while ongoing studies research the influence of different environmental factors on this relationship. We study the relationship between SIF and GPP as a function of environmental variables and compare SIF to two standard optical remote sensing metrics: leaf area index, and photosynthetic active radiation (PAR). We estimated the effects of water stress, represented by soil moisture observations and modelled stomata conductance, on the site-level empirical SIF-GPP relationships. We used eddy-covariance observations from 26 mid-latitude forest sites, mainly evergreen needleleaf, deciduous broadleaf, and mixed forests, in United States and Europe, accessed through Ameriflux and European Fluxes Database Cluster. Half-hourly GPP were estimated from EC data using the neural network technique, while instantaneous SIF data were provided by TROPOMI satellite under clear sky conditions at solar noon on a daily basis. We limited our analysis period to the growing season of each site in year 2018, which was estimated using carbon-flux phenology approach. Results showed that in most sites, SIF had a stronger relationship with GPP than PAR and LAI. SIF observations were good predictor of the daily GPP, but not of the hourly. Weekly GPP showed a stronger correlation with weekly SIF than at narrower timeframes. Soil moisture content and stomata conductance influenced the slope of GPP vs SIF but the change was not consistent among sites.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMB031.0007Y
- Keywords:
-
- 0430 Computational methods and data processing;
- BIOGEOSCIENCES;
- 0439 Ecosystems;
- structure and dynamics;
- BIOGEOSCIENCES;
- 0466 Modeling;
- BIOGEOSCIENCES;
- 0480 Remote sensing;
- BIOGEOSCIENCES