Yuanhong Song is a master's student in statistics at Washington State University finishing May 2019, where she focused on modeling the spatial-temporal variability of crop and soil using earth observatory data, such as remote sensing image. Song joined the ZZLab since 2017 and has acted our GIS and Remote Sensing specialist since then. Before that, Song obtained her masters degree in soil science at WSU, where she worked on creating digital soil carbon concentration map by integrating in-situ proximally sensed soil hyper-spectrum and remotely sensed crop canopy multi-spectrum data. Song is greatly inspired by the open-science spirit in her previous experiences. Currently, she is working on developing a tool that derives spatial-temporal vegetation Interpretable variation (STVIV) by integrating the publicly free data and open-source platforms such as Google Earth Engine cloud computing platform and R language. STVIV will help crop researchers to assess crop performances without the prerequisite knowledge in GIS or remote sensing.