Development of High Performance Computing Tools for Estimation of High-Resolution Surface Energy Balance Products Using sUAS Information.

(small-Unmanned Aircraft System) and advanced surface energy balance models allow detailed assessment and monitoring (at plant scale) of different (agricultural, urban, and natural) environments. Significant progress has been made in the understanding and modeling of atmosphere-plant-soil interactions and numerical quantification of the internal processes at plant scale. Similarly, progress has been made in ground truth information comparison and validation models. An example of this progress is the application of information using the Two-Source Surface Energy Balance () model in commercial vineyards by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment - Project in California. With advances in frequent data collection for larger areas, information processing becomes computationally expensive on local computers. Additionally, fragmentation of different models and tools necessary to process the data and validate the results is a limiting factor. For example, in the referred project, commercial software ( and Excel) and Python and Matlab code are needed to complete the analysis. There is a need to assess and integrate research conducted with and surface energy balance models in a sharing platform to be easily migrated to high performance computing () resources. This research, sponsored by the National Science Foundation Cyber Training Fellowships, is integrating disparate software and code under a unified language (Python). The Python code for estimating the surface energy fluxes using TSEB2T model as well as the EC footprint analysis code for ground truth information comparison were hosted in myGeoHub site https://mygeohub.org/ to be reproducible and replicable.