Geostatistical Methods for Predicting Soil Moisture Continuously in a Subalpine Basin

This study presents spatial statistical methods for examining the distribution of soil moisture in a sub-alpine environment. The high local variability of soil moisture is not well characterized by spatial interpolation from dispersed data points. Interpolation using only field samples from Loch Vale, Rocky Mountain National Park, Colorado produced coarse estimates that followed mean soil moisture trends, but failed to capture local mid-slope variation. A properly specified regression model was identified by using dispersed field samples and ancillary data derived from Ikonos-2 and lidar data. This model predicted soil moisture patterns at a much finer spatial resolution. An intensive field campaign provided independent soil moisture measurements that were used to assess the model’s accuracy. The modeled soil moisture estimates captured local variability associated with topographic terrain differences along mid-slope areas.


  • English

Media Info

  • Media Type: Print
  • Features: Figures; Photos; References; Tables;
  • Pagination: pp 333-341
  • Serial:

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Filing Info

  • Accession Number: 01522440
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 22 2014 4:07PM