These operational land imager (OLI) value added data sets, maps, and associated ancillary data were compiled as part of an 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 Vegetation Index (NDVI) time series imagery from Landsat OLI were acquired and processed over time and space along seven predefined reaches that capture different natural states and management conditions. We used Landsat OLI 30m data as an improvement upon past studies that were based on coarser remote sensing data from the NASA MODIS sensor (250 m). The OLI 30m images provide better characterization and performance over these rather narrow riparian corridors. To capture the change over time we used a simple differencing technique that compares two annual average growing season VI cycles (limited to May-October). These 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 immediately 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 the riparian corridor is responding 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 processing (see below) which involved reprojection to a common projection, masking to only retain the area of interest, quality analysis to discard poor data, and then value addition to generate the NDVI and the difference maps. 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.