This volume teaches readers how to sort through the huge mountain climate and agricultural science data to figure out what is actually actionable. With the development of sensing technology, petabytes of data about climate and agriculture have been observed. While the volume of data is impressive, collecting big data for data's sake is really not that useful. We need actionable data that can be acted upon. However, digging actionable insights from the accumulated big data and delivering to worldwide stakeholders is a field that still far from being mature. While traditional data mining technology struggles to follow the pace of data accumulation, scientific evolution has called out new technologies such as numeric modelling and machine learning to tackle those grand climate/agriculture challenges, such as forecasting extreme climate events, increasing farm productivity and sustainability, monitoring emissions from space, boosting smart agriculture, watching peatland, understanding aerosols, modelling agriculture-human interaction, informing policy, and channeling markets to a healthier and environment-friendly direction. There is no universal solution to accomplish any of these grand tasks, but through three sections filled with chapters from leading scientists, this book aims to get us closer. Part I of the book defines what actionable science is and what makes data actionable. Part II offers case studies and use scenarios of these principles in action. Part III looks ahead to the future of actionable science to address climate and agricultural issues.

Dr. Ziheng Sun is a Research Assistant Professor at George Mason University in Fairfax Virginia.