A Method of Determining Soil Water Content from Remotely Sensed Data

The availability of soil moisture affects plant production potential, rainfall runoff volume, and many other parameters that are of interest to agricultural production, forest management, soil conservation, and watershed management and modeling. Transformations of the spectral reflectance in remotely sensed images may be able to provide significant information on soil water content and, if augmented with existing soil and other geographic information, such as terrain elevation and slope, may provide accurate data on soil water content.

The overarching objectives are to determine the ability to process datasets to generate soil moisture values that match field collected data in a watershed of the Suwannee River in northern Florida and southern Georgia from spaceborne and airborne sensors. We intend, initially, to interrogate the Landsat Enhanced Thematic Mapper Plus (ETM+), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Aircraft-based hyperspectral sensors such as AVIRIS, and The Advanced Microwave Scanning Radiometer (AMSER-E) of the Earth Observing System for this purpose.

We propose to examine the ability to generate accurate soil water content from ETM+, ASTER, AVIRIS, and AMSER-E images in combination with soil, terrain, and other geographic data. The primary objective is to develop a methodology to use remotely sensed data to predict soil moisture accurately. Spectral and spatial transformations can be used to extract soil moisture from remotely sensed images when combined with appropriate geographic data such as terrain and soil types.

For a small watershed in South Georgia, the USDA has 30 instrumented stations continuously collecting soil water content. To these 30 stations, USDA will add 50 more. USDA will collaborate with the USGS to provide accurate soil water content and its distribution among the sampling stations for this watershed for any time. We will then apply standard transformations, such as the Kauth-Thomas or Tassel-Capped transformation, to the image data to generate measures of greenness, brightness, and wetness. The results of these image transformations will be combined with elevation, slope, soil and other geographic data to determine soil water content as a distributed parameter for the watershed.

• Proposal
  • A Method of Determining Soil Water Content from Remotely Sensed Data (9k pdf file)
• Publications and Reports
  • Measuring Soil Moisture in Remotely Sensed Images - Abstract from the Proceedings of the 31st International Symposium on Remote Sensing of Environment - Global Monitoring for Sustainability and Security, Saint Petersburg, Russia (13k pdf file)
• Presentations
  • Determining Soil Water Content from Remotely Sensed Data - Poster presented at the USGS Land Remote Sensing Science Fair, Reston, VA. (3,610k pdf file)