The chapter discusses the creation and validation of a Landsat Surface Reflectance Dataset for North America, covering the periods 1990 and 2000. The dataset, processed by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) at NASA's Goddard Space Flight Center, provides 30-meter resolution surface reflectance data, which can be used for assessing environmental and land-cover changes, producing biophysical products, and merging reflectance data from multiple sensors. The raw imagery was obtained from the Landsat GeoCover dataset, which was calibrated, converted to top-of-atmosphere reflectance, and atmospherically corrected using the MODIS/6S methodology. Initial comparisons with ground-based optical thickness measurements and MODIS imagery indicate comparable uncertainty in Landsat surface reflectance. The processing approach, including ingestion, calibration, and atmospheric correction, is detailed, and the dataset's characteristics and validation methods are described. The dataset supports various applications, such as mapping land-cover changes, surface water resources, and vegetation biophysics, and future work will address issues like cloud masking and aerosol overestimation. The chapter concludes by discussing the potential for fully operational atmospheric correction of Landsat imagery and the long-term benefits of such a system.The chapter discusses the creation and validation of a Landsat Surface Reflectance Dataset for North America, covering the periods 1990 and 2000. The dataset, processed by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) at NASA's Goddard Space Flight Center, provides 30-meter resolution surface reflectance data, which can be used for assessing environmental and land-cover changes, producing biophysical products, and merging reflectance data from multiple sensors. The raw imagery was obtained from the Landsat GeoCover dataset, which was calibrated, converted to top-of-atmosphere reflectance, and atmospherically corrected using the MODIS/6S methodology. Initial comparisons with ground-based optical thickness measurements and MODIS imagery indicate comparable uncertainty in Landsat surface reflectance. The processing approach, including ingestion, calibration, and atmospheric correction, is detailed, and the dataset's characteristics and validation methods are described. The dataset supports various applications, such as mapping land-cover changes, surface water resources, and vegetation biophysics, and future work will address issues like cloud masking and aerosol overestimation. The chapter concludes by discussing the potential for fully operational atmospheric correction of Landsat imagery and the long-term benefits of such a system.