Evapotranspiration (ETa)

Example map of OpenET ensemble mean Evapotranspiration (ETa) for June-July-August 2023

Description

Evapotranspiration (ETa), often referred to as Actual Evapotranspiration, is the amount of water evaporated or transpired from a surface and is estimated in terms of depth in millimeters or inches. All ETa datasets available in Climate Engine are derived using satellite data, including OpenET (Landsat 5/7/8/9), USGS VIIRS ET (VIIRS), and USGS MODIS ET (MODIS) among others. ETa can be estimated using satellite observations of land surface temperature and climate reanalysis datasets by several operational models. ET data is useful for the evaluation of consumptive use estimates in surface water and groundwater-dependent agricultural regions and ecosystems.

ETa models used by datasets in Climate Engine can include:

METRIC

Mapping Evapotranspiration with Internalized Calibration (METRIC) was developed by Allen et al., 2007 and estimates ET by solving the surface energy balance for latent heat. It utilizes Landsat surface reflectance and thermal radiance to estimate net radiation (R), sensible heat (H), and ground heat (G) flux which are used to estimate latent heat (LE) following the equation R+G-H = LE. LE is converted into ET using the latent heat of vaporization and density of water. The ETf is then calculated based on ETo at the time of satellite overpass and linearly interpreted between images and multiplied by daily ETo sourced from GridMET (Abatzoglou 2012) to estimate daily ET. 

SSEBop

Operational Simplified Surface Energy Balance (SSEBop) was developed by Senay et al., 2013, 2018 and is a daily average energy balance model. It estimates ETf using land surface temperature through a thermal index approach. ETf is linear interpolated between images and multiplied by daily ETo sourced from GridMET to estimate daily ETf. 

SIMS

Satellite Irrigation Management Support (SIMS) was developed by Melton et al. 2012 and is a vegetative index approach. It utilizes Landsat imagery and the United States Department of Agriculture Cropland Data Layers as inputs. The normalized difference vegetative index (NDVI) in conjunction with crop type is used estimate (ETf) which is is linearly interpolated between available Landsat images and applied to a reference ET time series sourced from GridMET resulting in a time series of daily and monthly ETa images.

PTJPL

Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) was developed by Fisher et al., 2008 and is based on the Preistly Taylor equation. PT-JPL brakes ET into three components, soil, canopy, and intercepted water which add up to actual ET. Each component is estimated using “ecophysiological constraint functions” which are based on atmospheric moisture and vegetation indices (Fisher 2018).

disALEXI

Disaggregated Atmospheric-Land Exchange Inverse algorithm (disALEXI) was developed by (souce) and is based on a two source energy balance model. Here ET is estimated using land surface temperature acquired at two different times at very coarse spatial scale using the model ALEXI. Landsat LST data is used to desegregate and estimate ET at finer spatial scales using the disALEXI algorithm (Anderson 2018).

References

  • Anderson M.C., & ECOSTRESS algorithm development team (2018).ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Level-3 Evapotranspiration (ET_ALEXI) Algorithm Theoretical Basis Document. Rep., 8 pp, Jet Propulsion Laboratory, Pasadena
  • Fisher, J. B., K. Tu, and D. D. Baldocchi (2008), Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites, Remote Sensing of Environment, 112(3), 901-919.
  • Fisher, J. B., & ECOSTRESS algorithm development team (2018). ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS): Level‐4 Evaporative Stress Index L4(ESI_PT‐JPL) Algorithm Theoretical Basis Document (ATBD) Rep., 8 pp, Jet Propulsion Laboratory, Pasadena.
  • Melton, F. S., Johnson, L. F., Lund, C. P., Pierce, L. L., Michaelis, A. R., Hiatt, S. H., ... & Votava, P. (2012). Satellite irrigation management support with the terrestrial observation and prediction system: a framework for integration of satellite and surface observations to support improvements in agricultural water resource management. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(6), 1709-1721.
  • Allen, R.G., Tasumi, M., Morse, A., Trezza, R., Wright, J.L., Bastiaanssen, W., et al. (2007). Journal of Irrigation and Drainage Engineering Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration ( METRIC )— Applications. Journal of Irrigation and Drainage Engineering, 133(4): 395–406. https://doi.org/10.1061/(ASCE)0733-9437(2007)133
  • Allen, R.G., Tasumi, M., & Trezza, R. (2013). Automated calibration of the METRIC-Landsat evapotranspiration process. Journal of Irrigation and Drainage Engineering, 133(4): 380–394. https://doi.org/10.1111/jawr.12056
  • Senay, G.B., Bohms, S., Singh, R.K., Gowda, P.H., Velpuri, N.M., Alemu, H., & Verdin, J.P. (2013). Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach. Journal of the American Water Resources Association, 49(3): 577–591. https://doi.org/10.1111/jawr.12057
  • Senay, G. B. (2018). Satellite Psychrometric Formulation of the Operational Simplified Surface Energy Balance (Ssebop) Model for Quantifying and Mapping Evapotranspiration. Applied Engineering in Agriculture, 34(3), 555–566. DOI: 10.13031/aea.12614
  • USDA National Agricultural Statistics Service Cropland Data Layer.  Available at https://nassgeodata.gmu.edu/CropScape/
  • Abatzoglou J. T., Development of gridded surface meteorological data for ecological applications and modelling, International Journal of Climatology. (2012) doi: https://doi.org/10.1002/joc.3413

Still need help? Contact Us Contact Us