The dataset contains daily grass-reference evapotranspiration (ETo) maps stored as ASCII files. ETo at a 2 km spatial resolution are calculated statewide using the American Society of Civil Engineers version of the Penman-Monteith equation (ASCE-PM). Required input parameters for the ASCE-PM ETo equation are solar radiation, air temperature, relative humidity, and wind speed at two meters height. These parameters are estimated for each 2 km pixel using various methods. Daily solar radiation is generated from the visible band of the National Oceanic and Atmospheric Administration's (NOAA) Geostationary Operational Environmental Satellite (GOES) using the Heliosat-II model. This model is designed to convert images acquired by the Meteosat satellite into maps of global (direct plus diffused) irradiation received at ground level.

Global alfalfa-reference potential evapotranspiration (ETr) is a key model parameter in actual evapotranspiration (ETa) modeling for worldwide applications. This dataset was constructed for use with the Operational Simplified Surface Energy Balance (SSEBop) model as a key driver of the final ETa magnitude. SSEBop is a parametric energy balance-based model that determines actual ET as the product of two independent estimates: 1) the SSEBop modeled ET fraction (ETf), an index nominally varying between 0 and 1 and derived from observed Landsat surface temperature using satellite psychrometry, and 2) the potential ET (maximum) under environmental conditions for an alfalfa crop (in millimeters). As SSEBop ETf can now be modeled for any Landsat scene across the globe, a suitable global ETr climatology dataset needed to be created. This global ETr data is a fusion of several different remote sensing and modeling products: 1981-2010 climatological normal (daily mean) ETr from Gridmet over the continental United States and 1981-2010 climatological normal MERRA-2 Fine Resolution ETr for all areas outside of the continental United States that has been scaled and corrected via terrestrial ecoregions from OneEarth and scaled using Worldclim Version 3 ETo (Abatzoglou 2013; Dinerstein et al., 2017; Hobbins et al., 2022; Zomer et al., 2022). The final mosaic has been smoothed and resampled to 1-km spatial resolution. The final dataset is a daily dataset of 366 GeoTIFF raster files for each day of the year including the leap day and representing a climatological normal (1981-2010) alfalfa-reference potential ET (ETr) for the entire global extent.

These datasets are continuous parameter grids (CPG) of monthly mean evapotranspiration data for March through September, years 2000 through 2015, in the Pacific Northwest. Source evapotranspiration data was produced using the operational Simplified Surface Energy Balance (SSEBop) model.

These datasets are continuous parameter grids (CPG) of monthly mean evapotranspiration data for March through September, years 2000 through 2015, in the Pacific Northwest. Source evapotranspiration data was produced using the operational Simplified Surface Energy Balance (SSEBop) model.

These datasets are continuous parameter grids (CPG) of monthly mean evapotranspiration data for March through September, years 2000 through 2015, in the Pacific Northwest. Source evapotranspiration data was produced using the operational Simplified Surface Energy Balance (SSEBop) model.

These datasets are continuous parameter grids (CPG) of monthly mean evapotranspiration data for March through September, years 2000 through 2015, in the Pacific Northwest. Source evapotranspiration data was produced using the operational Simplified Surface Energy Balance (SSEBop) model.

These datasets are continuous parameter grids (CPG) of monthly mean evapotranspiration data for March through September, years 2000 through 2015, in the Pacific Northwest. Source evapotranspiration data was produced using the operational Simplified Surface Energy Balance (SSEBop) model.

These datasets are continuous parameter grids (CPG) of monthly mean evapotranspiration data for March through September, years 2000 through 2015, in the Pacific Northwest. Source evapotranspiration data was produced using the operational Simplified Surface Energy Balance (SSEBop) model.

Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack a ground-based monitoring network, remote sensing allows for accurate and consistent estimates of ETa across a large spatial extent – though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an Ensemble Mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (i.e., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. Four input ETa products (i.e., Nagler-ET(EVI2), SSEBop-LS, SSEBop-MOD, MODIS-ET) were used to develop the EMET product (see Processing Steps 1 and 2). This data release consists of a series of five zipped folders, of which each include a series of months of the EMET image product for the Los Planes basin in Baja California Sur, Mexico. We use the following naming convention to recognize the respective month for each image: "YYYY_MM" (i.e., 2006_01 for January 2006). The first zipped folder (i.e., EnsembleMeanETa_EMET_Monthly_2006_2021) contains the full suite of images for all months between January 2006 and December 2021 (n = 192 bands). The remaining four zipped folders contain monthly images for four-year periods within the data release to allow for the user to more easily download images for single years. The four periods follow: (1) 2006 through 2009, (2) 2010 through 2013, (3) 2014 through 2017, and (4) 2018 through 2021 (n = 48 bands each).

Three ET datasets were generated to evaluate the potential integration of Landsat and Sentinel-2 data for improved ET mapping. The first ET dataset was generated by linear interpolation (Lint) of Landsat-based ET fraction (ETf) images of before and after the selected image dates. The second ET dataset was generated using the regular SSEBop approach using the Landsat image only (Lonly). The third ET dataset was generated from the proposed Landsat-Sentinel data fusion (L-S) approach by applying ETf images from Landsat and Sentinel. The scripts (two) used to generate these three ET datasets are included – one script for processing SSEBop model to generate ET maps from Lonly and another script for generating ET maps from Lint and L-S approach.