Site characteristics mediate the relationship between forest productivity and satellite measured solar induced fluorescence

Authors: YAZBECK, T. - The Ohio State University; BOHRER, G. - The Ohio State University; GENTINE, P. - Columbia University; YE, L. - Columbia University; ARRIGA, N. - European Commission-Joint Research Centre (JRC); BERNHOFER, C. - Dresden University; BLANKEN, P.D. - University Of Colorado; DESAI, A.R. - University Of Wisconsin; DURDEN, D. - Neon, Inc; KNOHL, A. - Goettingen University; KOWALSKA, N. - Czech Academy Of Sciences; METZGER, SA. - Neon, Inc; MOLDER, M.A. - Lund University; NOORMETS, A - West Texas A & M University; NOVICK, K. - Indiana University; Scott, Russell; SIGUIT, L. - Czech Academy Of Sciences; SOUDANI, K. - Université Paris-Saclay; UEYAMA, M. - Osaka Prefecture University; VARLAGIN, A. - Russian Academy Of Sciences

Submitted to: Frontiers in Forests and Global Change
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/5/2021
Publication Date: 12/12/2021
Citation: Yazbeck, T., Bohrer, G., Gentine, P., Ye, L., Arriga, N., Bernhofer, C., Blanken, P., Desai, A., Durden, D., Knohl, A., Kowalska, N., Metzger, S., Molder, M., Noormets, A., Novick, K., Scott, R.L., Siguit, L., Soudani, K., Ueyama, M., Varlagin, A. 2021. Site characteristics mediate the relationship between forest productivity and satellite measured solar induced fluorescence. Frontiers in Forests and Global Change. 4. Article 695269. https://doi.org/10.3389/ffgc.2021.695269.
DOI: https://doi.org/10.3389/ffgc.2021.695269

Interpretive Summary: There are new measurements from satellites that can potentially help us to better monitor forest productivity over vast regions. Therefore, this study compared one of these measures termed Solar-Induced Chlorophyll Fluorescence (SIF) with ground-based measurements of forest productivity. The results showed that SIF is a good predictor, when accounting for site-to-site differences, probably species composition and canopy structure are accounted for. Furthermore, accounting for forest water stress also helped to improve SIF-based estimates of productivity. SIF is a promising predictor for productivity among other remote sensing variables, but more focus should be placed on including information about the particular forest that the satellite is measuring.

Technical Abstract: Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the SIF:GPP relationship can be strongly influenced by both vegetation structure and physiology. At the monthly time scales, the structural response often dominates but short-term physiological variations can strongly impact the SIF:GPP relationship. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018-2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due to differences in species composition and canopy structure. Seasonally-averaged leaf area index and canopy conductance provide a predictor to the site-level effect. We show that light saturation is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely VPD, LE, and canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, water stress, and light saturation effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions.