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.