The globe is currently undergoing a range of alarming changes related to social and environmental systems, and the links between the two. Our ability as researchers to study the dynamics of these ongoing processes is essential for real-world understanding and application of management strategies that can mitigate potentially negative outcomes. The scale of change and its associated impact generated by natural and anthropogenic drivers varies across the landscape, such as local degradation of ecosystem services, regional deforestation, large scale urbanization, and widespread yet geographically specific changes yielded by vagaries in climate. Understanding such critical changes is of paramount importance for the future wellbeing of the coupled human-natural systems that we are all a part of and on which we all depend. Historically, one of the greatest limitations in our ability to study these systems with remote sensing technology has been inadequate availability of time series datasets that provide fine enough spatial and temporal resolution capable of identifying processes of global environmental change (GEC). However, with the advances in sensors used for environmental remote sensing, as well as the improvements in data storage and distribution, we now have the capacity to employ time series techniques for detecting GEC and addressing the multitude of questions surrounding its impacts. Currently, many of the time series methodologies being applied to examine this suite of issues are still in development, and as such, there is significant space for growth, innovation, and exploration in the field of time series remote sensing analysis (TSRSA). Only a few decades ago, what was considered a detailed TSRSA may have involved three or more Landsat images and corresponding land cover classifications with a set time interval, such as a decadal study (Southworth et al., 2004; Mondal and Southworth 2009; Cassidy et al., 2010; Gibbes et al., 2010; Adhikari and Southworth 2012). This has given way to analyses with much finer temporal resolution, including remotely sensed information with daily global coverage dating as far back as 30 years (Zhu and Southworth, 2012; Campo-Bescós et al., 2013; Haro-Carrión et al., 2020; Herrero et al., 2020). The tremendous amount of data now available for remote sensing research cannot be efficiently utilized with traditional methodologies of analysis, creating a need for new approaches and techniques. Use of artificial intelligence (AI), specifically related to issues of big data and machine learning, including deep learning, are all possible innovations within this field. As such, we are now on the cusp of being able to effectively investigate some of the most pressing environmental concerns of our time at a temporal scale relevant to climatological, ecological, and social systems. The coming decade will surely present landmark innovations, introduce novel approaches, and yield breakthroughs in understanding our world. Such advances will undoubtedly be facilitated by the enhanced accessibility of remotely sensed datasets with greater temporal range, which will enable more effective monitoring and detection of GEC. Traditionally, the most common approach used in remote sensing studies has been a selection of two to five dates, for which individual landcover classifications were produced and change was determined by comparing classifications over time (Southworth et al., 2004; Mondal and Southworth 2009; Cassidy et al., 2010; Gibbes et al., 2010; Adhikari and Southworth 2012). While these studies were very useful and often linked directly with ancillary information from surveys for greater insight