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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 ' 08 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)