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Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage.

Shuai Huang1,2, Jianli Ding3,4, Jie Zou5,6

  • 1College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China. 107556516063@stu.xju.edu.cn.

Sensors (Basel, Switzerland)
|February 2, 2019
PubMed
Summary

This study developed a soil moisture retrieval model for arid regions using microwave scattering data. The model accurately estimates soil moisture, offering a method for large-scale, real-time detection in dry environments.

Keywords:
AIEM modelSentinel-1microwave remote sensingsoil moisture

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Area of Science:

  • Earth Science
  • Remote Sensing
  • Hydrology

Background:

  • Soil moisture is crucial for land-atmosphere energy exchange, water circulation, and carbon cycles.
  • Accurate soil moisture data is vital for understanding arid ecosystem dynamics.

Purpose of the Study:

  • To develop and validate a soil moisture retrieval model for arid regions with sparse vegetation.
  • To establish a database of microwave scattering characteristics for arid soils.

Main Methods:

  • Utilized an advanced integral equation model for numerical simulations.
  • Established a database of surface microwave scattering characteristics.
  • Developed a soil moisture retrieval model using Sentinel-1 radar parameters.

Main Results:

  • Found significant logarithmic correlations between backscattering coefficient, soil moisture, and surface roughness.
  • Successfully retrieved spatial soil moisture distribution consistent with research area characteristics.
  • Achieved a high correlation coefficient (0.8488) between simulated and measured 0-10 cm soil moisture.

Conclusions:

  • The developed retrieval model is applicable for deriving surface soil moisture in arid oasis regions.
  • The study provides a viable method for real-time, large-scale soil moisture detection in arid areas.