Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA using multi-satellite data fusion

As a primary flux in the global water cycle, evapotranspiration (ET) connects hydrologic and 15 biological processes and is directly affected by water and land management, land use change and climate variability. Satellite remote sensing provides an effective means for diagnosing ET patterns over heterogeneous landscapes; however, limitations on the spatial and temporal resolution of satellite data, combined with the effects of cloud contamination, constrain the amount of detail that a single satellite can provide. In this study, we describe an application of a multi-sensor ET data fusion system over a mixed forested/agricultural landscape in 20 North Carolina, USA during the growing season of 2013. The fusion system ingests ET estimates from a Two- Source Energy Balance (TSEB) model applied to thermal infrared remote sensing retrievals of land surface temperature from multiple satellite platforms: hourly geostationary satellite data at 4-km resolution, daily 1-km imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), and bi-weekly Landsat thermal data sharpened to 30-m. These multiple datastreams are combined using the Spatial-Temporal Adaptive 25