Managing Global Resources for a Secure Future

2017 Annual Meeting | Oct. 22-25 | Tampa, FL

106010 Integration of Remote Sensing and in-Situ Data to Estimate Soil Moisture across Mixed Land Cover Types.

Poster Number 1035

See more from this Division: SSSA Division: Soil Physics and Hydrology
See more from this Session: Soil Physics and Hydrology General Poster Session 2

Wednesday, October 25, 2017
Tampa Convention Center, East Exhibit Hall

Briana M. Wyatt, Oklahoma State University, Stillwater, OK, Tyson E. Ochsner, Plant and Soil Sciences, Oklahoma State University, Stillwater, OK and Chris B. Zou, Natural Resources Ecology and Management, Oklahoma State University, Stillwater, OK
Poster Presentation
  • Wyatt SSSA 2017.pdf (537.3 kB)
  • Abstract:
    Soil moisture is an essential earth variable which has been proven to affect near-surface temperature and climate, hydrological processes, agricultural production, and health of ecological systems. However, the majority of soil moisture data currently available from in-situ monitoring networks reflect conditions only under grasslands, and soil water conditions under other land cover types often differ from those at grassland sites. Remotely-sensed vegetation index data, such as those from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, have shown strong potential to improve high-resolution soil moisture estimates across various land cover types by integration into hydrological models for enhanced large-scale soil moisture estimation. The objective of this ongoing research is to integrate remotely sensed vegetation index data and in-situ meteorological data to accurately estimate high-resolution soil moisture across areas of intermixed land cover types.

    See more from this Division: SSSA Division: Soil Physics and Hydrology
    See more from this Session: Soil Physics and Hydrology General Poster Session 2