Development of a benchmark eddy flux ET dataset for evaluation of remote sensing ET models over the CONUS

Authors: VOLK, J. - Desert Research Institute; HUNTINGTON, J. - Desert Research Institute; MELTON, F. - California State University; ALLEN, R. - Kimberly Research And Extension Center; Anderson, Martha; FISHER, J. - Jet Propulsion Laboratory; KILIC, A. - University Of Nebraska; SENAY, G. - Eros National Center; HALVERSON, G. - Jet Propulsion Laboratory; Knipper, Kyle; MINOR, B. - Desert Research Institute; PEARSON, C. - Desert Research Institute; WANG, T. - California State University; YANG, YUN - US Department Of Agriculture (USDA); Evett, Steven - Steve; French, Andrew; JASONI, R. - Desert Research Institute; Kustas, William - Bill

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/1/2023
Publication Date: 3/15/2023
Citation: Volk, J., Huntington, J., Melton, F., Allen, R.G., Anderson, M.C., Fisher, J., Kilic, A., Senay, G.B., Halverson, G., Knipper, K.R., Minor, B., Pearson, C., Wang, T., Yang, Y., Evett, S.R., French, A.N., Jasoni, R., Kustas, W.P. 2023. Development of a benchmark eddy flux ET dataset for evaluation of remote sensing ET models over the CONUS. Agricultural and Forest Meteorology. 331. Article 109307. https://doi.org/10.1016/j.agrformet.2023.109307.
DOI: https://doi.org/10.1016/j.agrformet.2023.109307

Interpretive Summary: OpenET is a collaborative and user-driven data system aimed at providing open access to satellite-based water use data at field scale.These data will support a wide range of decision making in water resource management, from water accounting to irrigation scheduling. OpenET employs six well-established remote sensing methods for estimating evapotranspiration (ET) at 30-m spatial resolution using imagery from the Landsat satellites. To establish credibility of these ET data sources, the models have been compared to a large suite of ground-based measurements that serve as a benchmark dataset. These measurements have been collected across the United States and sample a range in land cover, land management, and climate conditions. This paper describes the construction of this dataset, including procedures for quality control and gap-filling. A follow-on paper will present the results of the model intercomparison and evaluation study based on this benchmark dataset.

Technical Abstract: A large sample of ground-based evapotranspiration (ET) datasets in the U.S., primarily from eddy covariance systems, were post-processed to produce a daily and monthly ET benchmark dataset for intercomparison and evaluation of OpenET remote sensing ET (RSET) models. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. Visual-based data quality checks and filtering were also performed. The resulting dataset includes 243,048 daily and 5,284 monthly ET values that were corrected for energy imbalance from 195 stations, with all data falling between 1995-2021. Flux footprint predictions were developed for each station for sampling OpenET RSET model pixels for comparison with tower observations. Static flux footprints were developed based on average wind direction and speed, while dynamic hourly footprints were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to one another to evaluate their relative spatial coverage. We assessed average daily energy imbalance using 172 sites, suggesting overall turbulent fluxes were understated by about 12 percent relative to available energy. Multiple linear regression analyses suggest that daily average latent energy flux may be typically understated slightly more than is sensible heat flux. We will continue to improve this ET dataset, as it has many potential applications, and we hope that its development is useful to the wider scientific community.