Versions Compared

Old Version 3

changes.mady.by.user Sujoy Roy

Saved on

compared with

New Version 4

changes.mady.by.user Sujoy Roy

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

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

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

...

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.


Image Added

Figure Source:  Anderson et al (2012)
Image Removed