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Landsat Collection 1 Provisional Actual Evapotranspiration Product

The Landsat Provisional Actual Evapotranspiration (ETa) science product is generated by calculating the latent heat flux based on surface energy balance principles using a robust model and can be fundamental in the understanding of the spatiotemporal dynamics of water use over land surfaces.

Return to Landsat Actual Evapotranspiration Overview

Access Landsat Collection 2 Provisional Actual Evapotranspiration

Return to Landsat Science Products Overview 


NOTE: The ability to request Landsat Collection 1 Provisional Actual Evapotranspiration processing ended December 16, 2022.  The information on this webpage is for reference purposes only. 

Landsat Collection 2 Provisional Actual Evapotranspiration is available. 


Landsat Provisional Actual Evapotranspiration example
Example of Landsat Provisional Actual Evapotranspiration for agricultural fields near Merced, California in the San Joaquin Basin, using data acquired by Landsat 8 (Path 43 Row 34) on July 31, 2019; Left: Natural Color Surface Reflectance Image, Right: Actual Evapotranspiration Image.

Actual Evapotranspiration is the quantity of water that is removed from a surface due to the processes of evaporation and transpiration and is measured in millimeters (mm).

The Landsat Provisional Actual Evapotranspiration (ETa) science product is generated by calculating the latent heat flux based on surface energy balance principles using a robust model and can be fundamental in the understanding of the spatiotemporal dynamics of water use over land surfaces.

ETa is scene-based and derived from the Landsat Collection 1 Level-2 Provisional Surface Temperature product. Landsat Provisional Surface Temperature is input to a surface energy balance model with external auxiliary data to retrieve the daily total of ETa. The surface energy balance model used in this ETa calculation is the Operational Simplified Surface Energy Balance (SSEBop) model (Senay et al., 2013; Senay, 2018).

The SSEBop model is based on the Simplified Surface Energy Balance (SSEB) approach (Senay et al., 2007, 2011) with a unique parameterization for operational applications. It combines ET fractions, generated from Landsat Collection 1 Provisional Surface Temperature, with reference ET using a thermal index approach based on the principle of satellite psychrometry (Senay, 2018). The unique feature of the SSEBop parameterization is that it uses pre-defined and seasonally dynamic boundary conditions that are specific to each pixel for the “hot/dry” and “cold/wet” reference points.

The original formulation of SSEB is based on the hot and cold pixel principles of the SEBAL (Bastiaanssen et al., 1998) and METRIC (Allen et al., 2007) models. Senay, 2018 provides additional information about the SSEBop algorithm.

The Landsat Collection 1 Provisional Surface Temperature product and auxiliary data are required for successful processing of the ETa product. The ETa auxiliary data include the temperature difference between hot/dry and cold/wet limits (dT), maximum daily air temperature (Ta) obtained from Daymet (, and alfalfa-reference ET (ETr) data retrieved from GridMET ( (Abatzoglou, 2013). The auxiliary data for ETa calculation is already processed, so it is not a dominating factor in the ETa product generation latency.

Note: These data are provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

Product Availability

ETa was available on-demand via USGS EROS Center Science Processing Architecture (ESPA) for Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) scenes acquired over the conterminous United States (CONUS) that were successfully processed to a Collection 1 Provisional Surface Temperature product. ETa products are available to order about 4 days after acquisition (1984 - December 16, 2022). 

Although the ETa algorithm is designed to be applied globally, the current implementation uses auxiliary data that are constrained to the CONUS.

Package Content

ETa are generated at the 30-meter spatial resolution. The default projection system for all Landsat science products is Universal Transverse Mercator (UTM), but other projection systems are available through ESPA. The default file format is Georeferenced Tagged Image File Format (GeoTIFF), but options for other data format delivery are available. More information on Landsat output formats supported by ESPA is available in the ESPA On-Demand Interface User Guide.

Actual Evapotranspiration (ETa): Provides a per-pixel estimate of daily water transfer from the Earth's surface to the atmosphere in units of water depth in millimeters (mm).

ET fraction (ETf): Represents unitless fraction of ETr, nominally varying between 0 and 1 (in SSEBop model the maximum ET fraction is 1.05). This can be used in combination with user provided reference ET (ETr) to create a more accurate ETa which takes into account local weather conditions.

Percent Uncertainty: Provides an estimate of percent uncertainty using the input ST uncertainty and the psychrometric constant.

ET Uncertainty (ETUN): Provides ET product uncertainty in units of water depth (mm) using the Percent Uncertainty value and ETr auxiliary data.

ET Quality Assessment (ETQA): Provides high-level information about the confidence of the ETf calculated.

Pixel Quality Assessment (pixel_qa): The bit combinations that define certain quality conditions. More information about Pixel Quality Assessment can be found in the Landsat 8 Land Surface Reflectance Code (LaSRC) Product Guide and the Landsat 4-7 Surface Reflectance Product Guide 

Metadata: Includes Landsat scene information in XML format.

Caveats and Constraints

  • The current Actual Evapotranspiration science product is considered provisional. The ET algorithm outputs that are generated using Collection 1 Provisional Surface Temperature which has not been completely validated. Most preliminary validation efforts are based on internal surface temperature measurements derived from the Landsat thermal band using modified equations for both atmospheric correction and surface emissivity derived from Normalized Difference Vegetation Index (NDVI). In preliminary validations, the daily and monthly Landsat ETa compared reasonably well with the in-situ data obtained from Eddy-Covariance Flux Towers from the Ameriflux network (Senay et al., 2017; Senay et al., 2019). For additional information about the preliminary validation details, see the References section.
  • The Collection 1 Provisional Surface Temperature product is the primary input to the ETa algorithm. The downwelled radiance used in the Collection 1 Single Channel Provisional Surface Temperature algorithm is erroneously not normalized to provide hemispherical radiance, resulting in ~0.5 Kelvin [K] surface temperature underestimation. This underestimation could result in an overestimation in ETa. For additional information about the Single Channel algorithm used in Collection 1 Surface Temperature please see the Landsat Provisional Surface Temperature Product Guide. This normalization issue in the surface temperature downwelled radiance calculation is fixed in the Landsat Collection 2  Level-2 product.
  • The quantitative uncertainty bands (both percent uncertainty [%] and ET uncertainty [mm]) are derived from the ST uncertainty [K] band present in Collection 1 Provisional Surface Temperature product. Due to a miscalculation in the propagation of uncertainty in the Provisional Surface Temperature product, the uncertainty value in the Surface Temperature Quality Assessment (STQA) band is underestimated by approximately 0.5 [K]. The magnitude of the uncertainty underestimation for any given pixel is dependent on the emissivity standard deviation obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), land cover, and distance to cloud at that pixel. The Surface Temperature uncertainty underestimation could result in an underestimation in the ET uncertainty. For additional information please see Landsat Provisional Surface Temperature Product Guide. Propagation of Surface Temperature uncertainty is fixed in the Collection 2 Level-2 product.
  • ETa is currently available only for the CONUS region. This limitation is imposed by restricted coverage of the boundary condition auxiliary data.
  • Due to the coarse spatial resolution of the ETr auxiliary data in the elevated areas, a blocky artifact may be present in the ETa over mountainous regions. The auxiliary data will likely be refined in the future which may mitigate this artifact.
  • Both reflective and thermal Landsat bands are required for the successful processing of the ETa science product. Therefore OLI-only or TIRS-only Landsat 8 scenes cannot be processed to ETa.
  • If there are insufficient pixels within a Landsat scene with an NDVI greater than 0.7, the SSEBop model parameterization will fail and an ETa product will not be created. This usually occurs in winter months or desert areas where ET is naturally low.
  • The ETa product may contain NoData pixels within the valid areas of a Landsat scene. These NoData pixels are usually the cloud, cloud shadow, or pixels with radiometric saturation that are masked. They could also be due to missing auxiliary data or surface temperature data . These pixels have an ETQA value of 7 as described in Section 4.2.1 of the Landsat Provisional Actual Evapotranspiration Product Guide .
  • The SSEBop model is known to overestimate ET for limited surfaces with very high albedo (bright objects) and high ground heat flux, such as bare soils found in desert white sands or playas (e.g., White Sands National Park, New Mexico). This false positive will likely be remedied in the future in which case this artifact will be resolved.
  • It is important to note that there are two major products: ET fraction (ETf) and actual ET (ETa). The ETa is based on the product of ETf and a climatology (long term normal) reference ET (ETr). Because the ETr is a climatology and does not capture the day-to-day weather variability, comparision against flux tower data may produce relatively larger deviations on a given day, but this effect would be minimized when the ETa data are aggregated to larger time scales such as monthly. Users are encouraged to use the provided ETf with best avaible ETr for the corresponding day for evaluating the performance of the model against daily flux tower or other independent datasets. However, the ETa data are expected to capture the spatial variability adequately.


Landsat Provisional Actual Evapotranspiration Product Guide

Landsat Provisional Actual Evapotranspiration Algorithm Description Document

Citation Information

There are no restrictions on the use of Landsat science products. It is not a requirement of data use, but the following citation may be used in publication or presentation materials to acknowledge the USGS as a data source and to credit the original research.

Landsat Provisional Actual Evapotranspiration Science Product courtesy of the U.S. Geological Survey.

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.

Reprints or citations of papers or oral presentations based on USGS data are welcome to help the USGS stay informed of how data are being used. These can be sent to USGS EROS Customer Services.


Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121-131.

Allen, R.G., Tasumi, M., Trezza, R., 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) – Model. ASCE J. Irrigation and Drainage Engineering 133, 380-394.

Bastiaanssen, W.G.M., M. Menenti, R.A. Feddes, and A. A. M. Holtslag, 1998. The surface energy balance algorithm for land (SEBAL): Part 1 formulation. Journal of Hydrology 212–213: 198–212.

Ji, L., Senay, G., Velpuri, N., & Kagone, S. (2019).  Evaluating the Temperature Difference Parameter in the SSEBop Model with Satellite-Observed Land Surface Temperature Data. Remote Sensing 11 (16): 1947.

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.

Senay, G. B., Budde, M. E., & Verdin, J. P. (2011). Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model. Agricultural Water Management, 98(4), 606-618.

Senay, G. B., Budde, M., Verdin, J. P., & Melesse, A. M. (2007). A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields. Sensors, 7(6), 979-1000.

Senay, G., Bohms, S., Singh R., Gowda, P., Velpuri, N., Alemu H., & Verdin, J. (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.

Senay, G., Friedrichs, M., Singh, R., & Velpuri, N. (2016).  Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin.  Remote Sensing of Environment 185: 171-185.

Senay, G., Schauer, M., Friedrichs, M., Velpuri, N., Singh, R. (2017).  Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States.  Remote Sensing of Environment 202: 98-112.

Singh, R., Senay, G., Velpuri, N., Bohms, S., Scott, R., & Verdin, J. (2014). Actual Evapotranspiration (Water Use) Assessment of the Colorado River Basin at the Landsat Resolution Using the Operational Simplified Surface Energy Balance Model.  Remote Sensing 6 (1): 233-256.