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Title: Integrating eddy fluxes and multiple remote sensing products in a rotational grazing native pasture

item Wagle, Pradeep
item ZHOU, YUTING - Oklahoma State University
item Gowda, Prasanna

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 7/31/2018
Publication Date: 12/10/2018
Citation: Wagle, P., Zhou, Y., Gowda, P.H. 2018. Integrating eddy fluxes and multiple remote sensing products in a rotational grazing native pasture [abstract]. American Geophysical Union. Available at:

Interpretive Summary: Abstract only

Technical Abstract: Eddy covariance (EC) provides integrated fluxes only at the scale of the tower footprint. Thus, satellite remote sensing and eddy fluxes have been integrated for upscaling observations at larger spatial and longer temporal scales. However, spatial heterogeneity of the upscaled areas and spatio-temporal mismatches of eddy flux data with remote sensing products jeopardize the performance of most predictive models. In addition, EC footprint is highly variable due to several factors such as height of the tower and vegetation, speed and direction of wind, and homogeneity of the fetch. This study combines different satellite products (e.g., MODIS, Landsat, and Sentinel) and EC footprint estimates for a 60-ha native tallgrass pasture at the USDA-ARS, Grazinglands Research Laboratory. This pasture was divided into 4 paddocks for rotational grazing. High spatial resolution images will identify the spatial heterogeneity, while high temporal resolution images will identify the temporal dynamics of vegetation. The EC footprint model will quantify the contributions of different paddocks and satellite pixels to the measured eddy fluxes. The study will also test the applicability of recent Orbiting Carbon Observatory 2 (OCO-2) satellite-derived sun induced fluorescence (SIF) product at around 1.3 km x 2.25 km spatial resolutions to examine the seasonal variations of photosynthesis and greenness. The findings of this study will be helpful to improve remote sensing-based gross primary production (GPP) and evapotranspiration (ET) models in heterogeneous ecosystems.