A genetic programming (GP)-based, nonlinear modeling structure relates soil moisture with synthetic-apertureradar (SAR) images to present representative soil moisture estimates at the watershed scale. Surface soil moisture measurement is difficult to obtain over a large area due to a variety of soil permeability values and soil textures. Point measurements can be used on a smallscale area, but it is impossible to acquire such information effectively in largescale watersheds. This model exhibits the capacity to assimilate SAR images and relevant geoenvironmental parameters to measure soil moisture. In the past, spaceborne radar imaging satellites used all-weather observation, but estimation methods of soil moisture based on active or passive satellite images remains uncertain. Estimation of soil moisture based on SAR measurement was made possible by developing linear regression models and nonlinear regression models in a single land use/land cover from several hundred square meters to several square kilometers, based on traditional statistical regression theory. This GP-based artificial intelligence mode uses an evolutionary computational approach to estimate soil moisture with a variety of land use/land cover patterns.

The function derived in the evolutionary computation links a series of crucial topographical and geographical features including slope, aspect, vegetation cover, and soil permeability with well-calibrated SAR data. Research findings indicate that this development and application of the GP model has proved useful for generating a highly nonlinear structure in regression regimes, which exhibit strong statistical correlations between the model estimates and the ground truth measurements (volumetric water content), based on unseen datasets.

Using this model, science missions would be capable of handling large-scale moisture estimation using spaceborne satellite images, and could generate multi-temporal soil moisture maps over seasons. The GP-model is ultimately extensible and interoperable for any river basin of interest, though the impact of landscape complexity needs to be studied further.

This work was done by Ni-Bin Chang of Texas A&M University for Stennis Space Center.

Inquiries concerning rights for its commercial use should be addressed to:

Texas A&M University 332 Wisenbacker Eng. Research Center

College Station, TX 77843-3000

Phone No.: (407) 823-1375

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Refer to SSC-00256, volume and number of this NASA Tech Briefs issue, and the page number.