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In Situ Soil Moisture Sensors in Undisturbed Soils
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[Soil moisture estimation method based on both ground-based remote sensing data and air temperature in a summer maize

Min Zheng Wang1, Guang Sheng Zhou1

  • 1Chinese Academy of Meteorological Sciences, Beijing 100081, China.

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
|May 9, 2018
PubMed
Summary

A new model estimates soil moisture using remote sensing data and air temperature. This method accurately assesses soil relative humidity in summer maize fields, aiding drought monitoring and irrigation management.

Keywords:
remote sensingsoil moisturesummer maizewater deficit index (WDI)

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Area of Science:

  • Agrometeorology
  • Soil Science
  • Remote Sensing

Background:

  • Soil moisture is crucial for the soil-vegetation-atmosphere continuum (SPAC).
  • Accurate soil moisture data is vital for understanding terrestrial ecosystem water status and crop water supply.
  • Estimating soil moisture at various depths is essential for effective water resource management.

Purpose of the Study:

  • To develop and validate a soil moisture estimation model using remote sensing data.
  • To assess the relationship between evapotranspiration deficit ratio and soil relative humidity.
  • To provide a tool for station-scale soil moisture estimation.

Main Methods:

  • Developed a soil moisture estimation model integrating the energy balance equation and water deficit index (WDI).
  • Utilized remote sensing data (Normalized Difference Vegetation Index, surface temperature) and air temperature.
  • Validated the model using data from a summer maize drought experiment with varying irrigation treatments.

Main Results:

  • The model accurately estimated soil relative humidity at different soil depths (up to 50 cm).
  • The hypothesis that evapotranspiration deficit ratio linearly depends on soil relative humidity was supported.
  • Highest accuracy (R²=0.90) was achieved for 0-10 cm surface soil moisture.
  • RMAEs were <15% and RMSEs <20% for deeper soil layers.

Conclusions:

  • The developed model effectively estimates soil moisture in summer maize fields.
  • The model provides a reliable method for drought monitoring and irrigation management.
  • This research offers valuable insights for agricultural water management strategies.