Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #381749
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: From standard weather stations to virtual micro-meteorological towers: real-time modeling tool for surface energy fluxes, evapotranspiration, soil temperature and soil moisture estimations

Author
item CELIS, J. - University Of Oklahoma
item MORENO, H. - University Of Texas - El Paso
item BASARA, J. - University Of Oklahoma
item MCPHERSON, R. - University Of Oklahoma
item Cosh, Michael
item OCHSNER, T. - Oklahoma State University
item XIA0, X. - University Of Oklahoma

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/18/2021
Publication Date: 3/26/2021
Citation: Celis, J.A., Moreno, H.A., Basara, J., McPherson, R., Cosh, M.H., Ochsner, T., Xia0, X. 2021. From standard weather stations to virtual micro-meteorological towers: real-time modeling tool for surface energy fluxes, evapotranspiration, soil temperature and soil moisture estimations. Remote Sensing. 13(7):1271. https://doi.org/10.3390/rs13071271.
DOI: https://doi.org/10.3390/rs13071271

Interpretive Summary: Energy flux measurement stations are more costly than simple meteorological stations, but we theorize that calibration equations can be developed to estimate surface fluxes based upon simpler technologies. A study was conducted in northern Oklahoma, where there are numerous surface energy flux systems with which to train the algorithm. Once equations were established, they were transferred to other sites, while updating local parameters as necessary, including vegetation. These new virtual flux towers were a valuable complement to the standard weather station setups. This study is useful for mesonet deployment and siting design.

Technical Abstract: One of the benefits of training a process-based model is the capacity to use it as a complement to standard weather stations for predicting energy fluxes, evapotranspiration, surface and root-zone soil temperature and moisture. In this study, dynamic (i.e. time evolving), vegetation parameters were derived from remotely-sensed MODIS imagery and coupled with a physics-based land surface model (tRIBS) at four eddy covariance (EC) sites in south-central U.S. to test the predictability of micro-meteorological, soil- and energy flux-related variables. The U.S. ARM (cropland) and Marena (grassland) EC sites in northern Oklahoma were used to tune the model respect to energy fluxes, soil temperature and moisture. Calibrated, but static (i.e not time evolving), model parameters mostly related to the soil were then transferred to two other Oklahoma EC sites with matching soil and vegetation type. The paired sites were US-A32 (cropland for US-ARM) and US-A74 (grassland for Marena). While some steady parameters where seamlessly transferred, new dynamic vegetation parameter time series were updated according to MODIS imagery at each site tRIBS captures both seasonal and diurnal cycles of the energy partitioning and soil temperatures across all four stations. Both the transferability of previously calibrated model parameters and the use of evolving MODIS to derive dynamic vegetation parameters are fundamental to provide rapid yet accurate predictions. The model proved to be a strong complement to standard weather stations and could potentially be used as a real-time "virtual EC tower" for accurately predicting surface energy fluxes, surface and root-zone soil temperature and moisture conditions in grassland and cropland areas.