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). |
Input data requirements |
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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 | |
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)