PlotToSat: A tool for generating time-series signatures from Sentinel-1 and Sentinel-2 at field-based plots for machine learning applications

PlotToSat offers a practical and time efficient way to the challenge of extracting time-series from multiple Earth Observation (EO) datasets at numerous plots spread across a landscape. This opens up new opportunities to understand and model various ecosystems. Regarding forest ecology, plot networks play a vital role in monitoring and understanding the dynamics of forest ecosystems. These networks often contain thousands of plots arranged systematically to represent an ecosystem. Combining field data collected at plots with EO time-series will allow us to better understand phenology and ecosystem composition, structure and distribution. Linking plot networks with EO data without PlotToSat is time consuming and computational expensive because plots are small and spread out, requiring data from multiple satellite tiles. PlotToSat processed a full year of multi-tile Sentinel-1 and Sentinel-2 data (estimated 18.3TB) at 15,962 plots from the fourth Spanish Forest Inventory in less than 24 h. PlotToSat, implemented using the Python API of Google Earth Engine, offers a new and unique workflow that is innovative due to its efficient, scalable and adaptable implementation. It supports Sentinel-1 and Sentinel-2 data, but its flexible design eases integration of additional EO datasets. New environmental modelling is expected to emerge facilitating EO time-series analyses and investigating interactive effects of environmental drivers. • PlotToSat enables practical fusion of plot networks with time-series satellite data efficiently. • Integrates remotely sensed and field data, creating novel research possibilities. • Enhances plot networks with multi-spectral and SAR time-series data. • Processed around 18.3TB of Sentinel data at 15,962 plots in under 24 h. • Scales to larger regions and more plots, aiding diverse scientific disciplines.