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Modeling gross primary production in a cornfield using solar induced fluorescence and the photochemical reflectance index
Yen-Ben
Cheng, NASA GSFC / ERT, Inc., yen-ben.cheng@nasa.gov
(Presenter)
Elizabeth
M.
Middleton, NASA GSFC, elizabeth.m.middleton@nasa.gov
Qingyuan
Zhang, GESTAR/USRA & GSFC/NASA, qingyuan.zhang-1@nasa.gov
Lawrence
A
Corp, Sigma Space, lawrence.a.corp@nasa.gov
Petya
Krasteva Entcheva
Campbell, NASA GSFC/JCET/UMBC, petya.campbell@nasa.gov
Karl
Fred
Huemmrich, NASA GSFC/UMBC, karl.f.huemmrich@nasa.gov
Bruce
Cook, NASA GSFC, bruce.cook@nasa.gov
William
Kustas, USDA BARC, bill.kustas@ars.usda.gov
Determining the health and vigor of vegetation using high spectral resolution remote sensing techniques is a critical component in monitoring productivity from both natural and managed ecosystems and their feedbacks to climate. This presentation summarizes a field campaign conducted in a USDA-ARS experimental cornfield site located in Beltsville, MD, USA over a five-year period. The site is equipped with an instrumented tower which makes continuous eddy covariance measurements of CO2 along with incoming PAR. Hyperspectral reflectance observations were acquired over corn canopies at multiple times a day at various stages through the growing season. On all field days, supporting plant information and leaf level data were acquired (e.g., CO2 gas exchange) as well as biophysical field data, including leaf area index (LAI), mid-day canopy PAR transmission, soil reflectivity, and soil moisture. The canopy optical measurements enabled retrievals of the photochemical reflectance index (PRI) and solar induced fluorescence (SIF) centered at O2-A and -B bands. These two spectrally based bio-indicators have been widely utilized in studies to assess whether vegetation is performing near-optimally or exhibiting symptoms of environmental stress (e.g., drought or nutrient deficiency, non-optimal temperatures, etc.). Both SIF and PRI expressed diurnal dynamics and seasonal changes that followed environmental conditions and physiological status of the cornfield. We further investigated the correlation between these two retrievals and the flux tower based carbon assimilation observations (i.e. gross ecosystem production, GEP). We were able to successfully model the variation of GEP (r2=0.81; RMSE=0.18 mg CO2/m2/s) by utilizing both SIF and PRI. Several cross-validation algorithms were applied to the model to demonstrate the robustness and consistency of the model. Our results suggest great potential of using SIF and PRI to monitor photosynthetic activities. Further studies are needed at various ecosystems.
Presentation Type: Poster
Session: Poster Session 1-B
(Tue 4:30 PM)
Associated Project(s):
Poster Location ID: 13
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