Several key trends in the geospatial industry over the last few years have created the conditions needed to truly leverage remote sensing as a technology that has the potential to provide tremendous benefit to civil society. These include changes in data policy, most notably with USGS's decision in 2008 to make all Landsat archives free and open, a policy which led the European Space Agency to also implement free and open access to the Copernicus constellation. The volume of data that is increasingly collected by more satellites changes the traditional paradigm in remote sensing from "pixel torture" to operational monitoring using large multi-sensor hyper temporal datasets. Cloud computing infrastructure, automation and machine learning also increasingly play a key role in enabling organizations to make use of large archives on a systematic basis. Hyper-temporal analysis is certainly possible due to all these trends, however two key obstacles remain: ensuring perfect geometric alignment is attained, as well as ensuring surface reflectance or SAR backscatter is normalized to remove atmospheric effects and consistently measure surface reflectance. Once processed, these datasets can be loaded into data cubes which are ideally suited to optimize data access and the deployment of sophisticated and validated hyper-temporal algorithms that can reveal incredible insights about our planet. A few examples will be highlighted including measuring the time and locations of land conversion to urban areas, measuring the frequency and location of flooding in our river basins, or the rates of deforestation over time.