The Landsat archive can be used to track long-term (e.g. >50 years) changes in land surface variables. Some of the sensors used in the Landsat mission are considered broadly equivalent, such as the TM and Enhanced Thematic Mapper Plus (ETM+) sensors, and the Operational Land Imager (OLI) and OLI-2 sensors. However, substantial differences in atmospheric correction algorithms, radiometric resolution, and spectral response functions, suggest that cross-sensor harmonization should be implemented when using data from OLI/ OLI-2 sensors with TM/ETM+ data. To facilitate effective land use land cover (LULC) change detection mapping using the majority of the Landsat archive, we created the Landsat ETM+ OLI Harmonization Script (LEOHS) using Google Earth Engine (GEE) to create ETM+/OLI harmonization functions over user-defined regions. LEOHS uses Collection-2 Landsat Tier 1 imagery in GEE and Theil-Sen regressions to create harmonization functions with ETM+ and OLI pixels that were observed within +/- one day. To illustrate the benefits of LEOHS, we conducted a LULC case study in Menorca, Spain. We trained a Random Forest (RF) classifier on a 2020 OLI image and 10,000 samples of three landcover classes (high vegetation, low vegetation, and urban). The trained RF model was used to predict LULC in a 1990 TM image that was harmonized using LEOHS, and a non-harmonized image. The maps generated from the harmonized and non-harmonized images had overall accuracies of 84% and 72% respectively, illustrating the substantial gains in regional LULC studies from using harmonized imagery produced by LEOHS. Landsat ETM+/OLI harmonization is essential for unbiased time series analysis using most of the Landsat collection.