DisALEXI (Atmosphere-Land Exchange Inverse (ALEXI) flux disaggregation approach)

General Description	DisALEXI is a multiscale flux modeling system based on the principal of surface energy balance and uses remotely sensed maps of land surface temperature (LST) as primary input. The Two-Source Energy Balance (TSEB) land surface representation can be used to downscale the GOES-based ALEXI flux estimates to finer spatial scale using LST data from moderate to high resolution TIR imaging systems. No central website (see source code)
Model Domain	Water fluxes for soil and vegetation.
Developer	Supported by NASA, USDA funding (2003) and NOAA funding
Hardware computing requirements	Recommendation for parallelization in computing resources. Run through any python modules.
Code language	Python (open source version)
Original application	Academic research model.
Public/proprietary and cost	Open source python but with BSD 3-Clause License (copyright Continuum Analytics, Inc.) as per GitHub repository
Physically or empirically based	Empirical
Mathematical methods used	Two-source energy balance (TSEB). LST are generated from geostationary satellites. Thermal infrared (TIR) is used by ALEXI model to generate maps of daily actual ET at 4-8km resolution. DisALEXI downscales data to a finer resolution using data fusion from MODIS and Landsat 7/8.
Input data requirements	Landsat 7/8 (30m) MODIS (1km) ALEXI (GOES 4km) Meteorological data (eddy covariance – vapor pressure, wind speed, air temp, insolation)
Outputs	Raster values for hourly soil fluxes, daily ET, and hourly canopy fluxes
Pre-processing and post-processing tools	GIS or other raster data analytical tools recommended.
Representation of uncertainty	None
Prevalence	Predominantly academic, recently converted to Python format
Ease of use for public entities	Requires advanced Python and remote sensing capabilities as well as appropriate computing resources depending on the scale of analysis
Ease of obtaining information and availability of technical support	Documentation available in GitHub but still in development. Scientific basis well established, but model code still in development.
Source code availability	https://github.com/bucricket/projectMAS
Status of model development	PyDisALEXI is currently in development and first release is on GitHub available for download. Current research is being coordinated by Michael Shull (NOAA) from the University of Maryland and others.
Challenges for integration	Code is still in development. Raster outputs would need to be matched with other model output scales. Hourly outputs may need to be compiled to other time scales.

References

Anderson, M. C., Kustas, W. P., Alfieri, J. G., Gao, F., Hain, C., Prueger, J. H., Chávez, J. L. (2012). Advances in Water Resources Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX 2008 field campaign. Advances in Water Resources, 50, 162–177. https://doi.org/10.1016/j.advwatres.2012.06.005

Anderson, M. (2018). A Comparative Study for Estimating Crop Evapotranspiration in the Sacramento-San Joaquin Delta - Appendix E. Atmosphere-Land Exchange Inverse Flux Disaggregation Approach (DisALEXI). Davis, CA.

Norman, J. M., Anderson, M. C., Kustas, W. P., French, A. N., Mecikalski, J., Torn, R., Tanner, B. C. W. (2003). Remote sensing of surface energy fluxes at 101-m pixel resolutions. Water Resources Research, 39(8). https://doi.org/10.1029/2002WR001775

Semmens, K. A., Anderson, M. C., Kustas, W. P., Gao, F., Alfieri, J. G., McKee, L., Vélez, M. (2016). Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sensing of Environment, 185, 155–170. https://doi.org/10.1016/j.rse.2015.10.025

Schull, M. A., Hain, C., Anderson, M. C., Zhan, X., & Neale, C. (2017). An open source tool to estimate regional and field- scale evapotranspiration. 2017 CICS Science Meeting, Nov 6-8 2017.

Figure Source: Anderson et al (2012)

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