Introduction to SoilTwin
This tutorial presents an overview of the SoilTwin application’s capabilities. After showcasing how to navigate the map, including the ability to search for and jump to specific locations, this video provides a peek at other functionality. These features include the ability to layer and combine different data sources, and the application’s customization settings, which allow adjustment of units, color schemes, and basemaps.
Among the many datasets supported by SoilTwin, the application provides the ability to drill down into individual soil features as reported within the Soil Survey Geographic Database (SSURGO). For more information on navigating the SSURGO dataset, see the later Viewing Soil Survey Data tutorial.
Visualizing Trends Using Animations
Animated timelapses allow the SoilTwin application to easily visualize trends within soil and weather data. Using maps from the U.S. Drought Monitor as an example, this video showcases how to display an animation of drought levels over time. This visualization in particular puts the explanatory power of animations into context. In addition to allowing selection of specific time ranges of interest, SoilTwin supports adjustment of frame rate and temporal sampling intervals, which allow visualizations across timescales spanning from days to years.
Viewing Soil Survey Data
The USDA Soil Survey Geographic Database (SSURGO) captures detailed soil properties across the United States, including soil texture, salinity, water retention, and agricultural importance. SoilTwin’s interface integrates SSURGO soil texture information in a color-coded fashion, which allows quick visualization of soil types. Drill-downs can inspect SSURGO at multiple levels, including map units demarcating spatial regions, components describing soil variation within map units, and soil horizons capturing composition at specific depths. This tutorial demonstrates how to explore SSURGO data within the SoilTwin application.
Querying for Areas of Interest
SoilTwin can issue queries that cross-cut multiple datasets to locate areas of interest. For example, a user may search for areas that are growing corn, are in a state of drought, and have humidity below a specified threshold. Individual datasets may differ greatly from one another in terms of spatial resolution and temporal sampling intervals, spanning meters to kilometers and days to years. After issuing a query, the application highlights areas of the map and points in time where all conditions are satisfied. Additionally, estimates of total acreage provide context for the scale of the regions matching a query. This tutorial video showcases the construction of a query incorporating multiple variables in this fashion.