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.

This dataset contains Landsat-derived images of Evaporative Fraction (ETf), Reference Evapotranspiration (ETo), and Actual Evapotranspiration (ETa) over a portion of California’s Central Valley for 15 dates in 2016. Each of the 15 images used in this study had three corresponding Tif files representing ETf, ETo, and ETa. Data used in this project was sourced from Landsat 8 Surface Reflectance Tier 1 images processed in Google Earth Engine (GEE). These images contain five visible and near-infrared (VNIR) bands and two short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature. To determine thermal properties of images to aid in ET calculation, the TIR Band 10 (B10) containing brightness temperature was chosen to determine Land Surface Temperature (LST).

This dataset contains Landsat-derived images of Evaporative Fraction (ETf), Reference Evapotranspiration (ETo), and Actual Evapotranspiration (ETa) over a portion of California’s Central Valley for 15 dates in 2016. Each of the 15 images used in this study had three corresponding Tif files representing ETf, ETo, and ETa. Data used in this project was sourced from Landsat 8 Surface Reflectance Tier 1 images processed in Google Earth Engine (GEE). These images contain five visible and near-infrared (VNIR) bands and two short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature. To determine thermal properties of images to aid in ET calculation, the TIR Band 10 (B10) containing brightness temperature was chosen to determine Land Surface Temperature (LST).

Remote sensing-based evapotranspiration (ET) can be derived using various methods, from soil moisture accounting to vegetation-index based approaches to simple and complex surface energy balance techniques. Due to the complexity of fully representing and parameterizing ET sub-processes, different models tend to diverge in their estimations. However, most models appear to provide reasonable estimations that can meet user requirements for seasonal water use estimation and drought monitoring. One such model is the Operational Simplified Surface Energy Balance (SSEBop). This study presents a formulation of the SSEBop model using the psychrometric principle for vapor pressure/relative humidity measurements where the “dry-bulb” and “wet-bulb” equivalent readings can be obtained from satellite-based land surface temperature estimates.

Historical (1984-2014) Landsat-based ET maps were generated for Palo Verde Irrigation District (PVID) and eight other sub-basins in parts of Middle and Lower Central Valley, California. A total of 3,396 Landsat images were processed using the Operational Simplified Surface Energy balance (SSEBop) model that integrates weather and remotely sensed images to estimate monthly and annual ET within the study areas over the 31 years. Model output evaluation and validation using gridded-flux data and water balance ET approaches indicated relatively strong association between SSEBop ET and validation datasets. Historical trend analysis of seven agro-hydrologic variables were done using the Seasonal Mann-Kendall test.

The associated geotiff rasters represents the total actual evapotranspiration (ETa) from June through September for the years 2004, 2006, 2008-2010, and 2013-2016 for the entire Klamath Basin in southern Oregon. The ETa was created using Landsat imagery and the Operational Simplified Surface Energy Balance (SSEBop) model to estimate actual ET and the Python scripts to complete that process is also provided. Additionally, the June-September average (mean) ETa for the "base years" of 2004, 2006, 2008-2010 and ETa Anomaly (deviation from the base years average mean) for each year between 2013-2016 is provided. Text files of SSEBop daily actual ET along with actual ET from Ameriflux eddy co-variance flux tower sites is also provided including an R code to generate comparison charts.

The associated geotiff rasters represents the total actual evapotranspiration (ETa) from June through September for the years 2004, 2006, 2008-2010, and 2013-2016 for the entire Klamath Basin in southern Oregon. The ETa was created using Landsat imagery and the Operational Simplified Surface Energy Balance (SSEBop) model to estimate actual ET and the Python scripts to complete that process is also provided. Additionally, the June-September average (mean) ETa for the "base years" of 2004, 2006, 2008-2010 and ETa Anomaly (deviation from the base years average mean) for each year between 2013-2016 is provided. Text files of SSEBop daily actual ET along with actual ET from Ameriflux eddy co-variance flux tower sites is also provided including an R code to generate comparison charts.

These spatially explicit Enhanced Vegetation Index (EVI) and evapotranspiration (ET) map derived from time series Landsat images, maps, and associated ancillary data were compiled as part of ongoing research aimed at quantifying the riparian vegetation greenness and water use in the lower Colorado River Delta in Mexico. In order to create trend and anomaly maps that characterize these ecosystems, both EVI and ET from-Landsat-OLI were processed over time and space along seven pre-defined reaches that capture different natural states and management conditions. We used EVI from the Landsat operational land imager (OLI) sensor (30 m) as an input to our ET algorithm that was previously based on coarser resolution EVI from the NASA MODIS sensor (250 m). The work explored how to improve the spatial resolution of the ET estimates of riparian plant water use. OLI 30 m images provide better characterization and performance over these rather narrow riparian corridors and thus provide better estimation of riparian-area greenness and plant water use at this scale. To capture the trends and changes over time needed for estimating ET, which is dependent upon the EVI input data, we used a simple differencing technique that compares two annual average growing season EVI and ET cycles (limited to May-October). The EVI and ET anomaly maps capture how the corridor vegetation health responds to both natural and anthropogenic changes. We limited this study to the full OLI record (2013-2019) since we were interested in understanding the response to Minute 319 pulse flow of 2014. The difference maps are an ideal tool for capturing how the released water impacted vegetation and its water use immediately after the release and over long time. The Minute 319 pulse flow science team in collaboration with the University of Arizona have developed a data processing system to support this effort with focus on understanding how remote sensing data analysis techniques can aid in assessing the riparian corridor response to these natural and anthropogenic stressors. All data associated with this project were acquired from the LP-DAAC and pre-processed to remove and capture issues prior to further analyses (see below). Preprocessing involves reprojection to a common map system, masking to only retain the area of interest, quality analysis to discard poor data, and then value addition to generate the EVI and difference maps as well as water use difference maps produced with ET-from-Landsat-EVI. The data acquisition and analysis were performed at the University of Arizona VIP lab (vip.arizona.edu) using their large Linux cluster of computing and storage resources. A mix of off the shelf software and specialized in-house tools were used to carry the different steps and analyses.

These spatially explicit Enhanced Vegetation Index (EVI) and evapotranspiration (ET) map derived from time series Landsat images, maps, and associated ancillary data were compiled as part of ongoing research aimed at quantifying the riparian vegetation greenness and water use in the lower Colorado River Delta in Mexico. In order to create trend and anomaly maps that characterize these ecosystems, both EVI and ET from-Landsat-OLI were processed over time and space along seven pre-defined reaches that capture different natural states and management conditions. We used EVI from the Landsat operational land imager (OLI) sensor (30 m) as an input to our ET algorithm that was previously based on coarser resolution EVI from the NASA MODIS sensor (250 m). The work explored how to improve the spatial resolution of the ET estimates of riparian plant water use. OLI 30 m images provide better characterization and performance over these rather narrow riparian corridors and thus provide better estimation of riparian-area greenness and plant water use at this scale. To capture the trends and changes over time needed for estimating ET, which is dependent upon the EVI input data, we used a simple differencing technique that compares two annual average growing season EVI and ET cycles (limited to May-October). The EVI and ET anomaly maps capture how the corridor vegetation health responds to both natural and anthropogenic changes. We limited this study to the full OLI record (2013-2019) since we were interested in understanding the response to Minute 319 pulse flow of 2014. The difference maps are an ideal tool for capturing how the released water impacted vegetation and its water use immediately after the release and over long time. The Minute 319 pulse flow science team in collaboration with the University of Arizona have developed a data processing system to support this effort with focus on understanding how remote sensing data analysis techniques can aid in assessing the riparian corridor response to these natural and anthropogenic stressors. All data associated with this project were acquired from the LP-DAAC and pre-processed to remove and capture issues prior to further analyses (see below). Preprocessing involves reprojection to a common map system, masking to only retain the area of interest, quality analysis to discard poor data, and then value addition to generate the EVI and difference maps as well as water use difference maps produced with ET-from-Landsat-EVI. The data acquisition and analysis were performed at the University of Arizona VIP lab (vip.arizona.edu) using their large Linux cluster of computing and storage resources. A mix of off the shelf software and specialized in-house tools were used to carry the different steps and analyses.

The spreadsheet includes a tab for each figure and table in the publication titled "Mapping Actual Evapotranspiration using Landsat for the Conterminous United States: Google Earth Engine Implementation and Assessment of the SSEBop Model" by Senay et al. 2021. Each tab includes the graphic and the data used to create it.