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United States Department of Agriculture

Agricultural Research Service

Title: Sensing Soil Moisture Remotely: A Multi-Disciplinary, Geospatial Approach)

item Finn, M
item Bosch, David
item Usery, E
item Giraldo, Mario
item Lewis, David
item Luna, Ronaldo
item Allam, Gopala
item Kincaid, Russell
item Kvien, Craig
item Sullivan, Dana
item Williams, Michael

Submitted to: International Society Remote Sensing of Environment
Publication Type: Abstract Only
Publication Acceptance Date: 1/10/2007
Publication Date: 6/25/2007
Citation: Finn, M.P., Bosch, D.D., Usery, E.L., Giraldo, M., Lewis, D., Luna, R., Allam, G.K., Kincaid, R., Kvien, C., Sullivan, D.G., Williams, M.S. 2007. Sensing Soil Moisture Remotely: A Multi-Disciplinary, Geospatial Approach [abstract]. International Society Remote Sensing of Environment June 25-29, 2007, San Jose, Costa Rica.

Interpretive Summary:

Technical Abstract: It is difficult to obtain spatially-distributed biophysical information using in situ measurement. Existing methods of determining soil water are predominately reliant on direct field instrumentation that supply estimates for lengthy time periods. Engineering hydrology needs have customarily been met through measurement of runoff measured at a basin outlet. Most of the index properties of soil are dependent and can be derived if its moisture content is known. Spaceborne remote sensing of soil moisture can help but because the majority of the data are from systems that were not initially designed to detect soil moisture there is a need for multiple options and for a refinement of methods. We investigated both spaceborne and airborne sensors across the electromagnetic spectrum and at varying spectral and spatial resolutions. Transformations of the spectral reflectance in remotely sensed images could provide significant information on soil water content. We evaluated in situ soil moisture values and compared them to transformation results in order to correlate the transformations with field collected values at specific sampling stations. In addition, we compared an approximation of Tassled Cap (TC) values for the Advance Land Imager to the Landsat TC values and showed a very high correlation to the greenness and wetness layers. We flew an airborne hyperspectral instrument with a Short Wave InfraRed (SWIR) sensor and computed a highly significant (R2 value of 0.79) correlation to a soil moisture probe at 2 inches. Although the model for soil moisture at the 8 inch depth did have a somewhat reasonable R2 of 0.49, the model was barely significant at the 0.05 level. In order to improve confidence in the SWIR sensor to provide relevant data for measuring soil moisture at the 2 inch depth, we need to be more certain that the electromagnetic waves have an unobstructed interaction with the soil and then a clear path to the sensor.

Last Modified: 05/22/2017
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