We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. For this study, the vegetation conditions are characterized using a series of remote sensing vegetation indices developing using satellite imagery, including the Normalized Difference Vegetation Index (NDVI) and the Tasseled Cap (TC) Transformation. The NDVI is a commonly used vegetation index that quantifies relative greenness of the vegetation based on the plant’s photosynthetic activity, measured as a ratio between the Near Infrared (NIR) and Red bands (Tucker, 1979). The NDVI equation follows: NDVI = (NIR band - Red band) / (NIR band + Red band). NDVI has a range of -1 to 1, though green vegetation theoretically ranges from 0 to 1. Dense green vegetation is represented with values closer to 1 while barren soil, rock, and less-dense surface vegetation has values closer to 0. Values below 0 often represent water due to its unique reflective characteristics. The TC transformation is an approached used to transform satellite imagery into a collection of spectral metrics that can quantify various aspects of the vegetation and soil surfaces (Kauth and Thomas, 1976). Specifically, the TC transformation develops 6 separate metrics, though we only assess the three primary metrics: (i) brightness (transformation 1), (ii) greenness (transformation 2), and (iii) wetness (transformation 3). The TC transformation metrics are calculated using a series of coefficients multiplied across reflectance values for the suite of Landsat bands, then summed across each metric. No specific range is identified for the TC transformation metrics, though for both brightness and greenness, positive values represent brighter and greener conditions, respectively, while negative values represent wetter conditions for wetness. Because bandwidths differ slightly between Landsat 4, 5, 7 and Landsat 8, we use two sets of coefficients and complete the calculation separately before combining the collections into a single series of images (DeVries et al., 2016; Zhai et al., 2022). All raster products were developed using the Google Earth Engine (GEE) cloud computing software program for the Upper Gila River watershed. This is a Child Item for the Parent data release, Mapping Riparian Vegetation Response to Climate Change on the San Carlos Apache Reservation and Upper Gila River Watershed to Inform Restoration Priorities: 1935 to Present - Database of Trends in Vegetation Properties and Climate Adaptation Variables. This Child Item consists of a two multi-band raster stacks. The raster stacks are identified by the vegetation index they represent (i.e., NDVI, TC greenness). Each band within the separate raster stacks represents a month from January 2014 through December 2020 (i.e., band 1 is January 2014 and band 84 is December 2020). Though the full study extends until December 2021, we chose to remove data from 2021 from the phenological analysis due to increased fire activity during 2021. Additionally, we chose not to include the TC transformation metrics of brightness and wetness to focus on metrics to simply measure vegetation greenness.