Mapping Soil Moisture through Remote Sensing
Hydrology, Soil Science, and GIS
Project Description: Moisture availability is a fundamental control on geomorphic and ecosystem processes, especially in arid environments such as the Southwest. Water connects the features within the landscape: the type of vegetation and its growth rates, the degree of soil development, the availability and movement of nutrients in the soil, the topography of the area, and more.
A common method for determining soil moisture is through on-site, field measurements. This method has distinct limitations. It is time consuming, expensive and only produces information from within the footprint of the instrument. However, recent work utilizing remote sensing to evaluate soil moisture has produced promising results.
Remote sensing acquires and interprets geospatial data on the properties of a feature or object without being in contact with that object. Remote sensing includes aerial photography, satellite imagery (Landsat) and laser altimetry (LIDAR). Remote sensing is a cost-effect technique for sampling many types of data over large areas with increased frequency. Analysis of satellite images has shown that changes in soil moisture fluxes can be identified through remote sensing, allowing for repeated analyses of an area over time and on much greater scales than can be achieved by in situ instrumentation.
This project seeks to further develop the technique of remote sensing soil moisture. The study area is Socorro County, New Mexico and includes the Sevilleta National Wildlife Refuge and the Hilton Ranch. Analysis of a number of Landsat images taken over the last ten years has identified consistent patterns in soil moisture distribution that may also reflect changes in soil properties. Participants will familiarize themselves with the Landsat data and then conduct field studies of the soil properties to verify the remote sensing technique.
Students engaged in this project will:
- Use ERDAS Imagine to review Landsat images
- Conduct field mapping of the study area, focused on soil descriptions
- Develop a soil map using ArcGIS for comparison with the ERDAS data