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In Situ Soil Moisture Sensors in Undisturbed Soils
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Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods.

Ting Yang1,2, Jundong Wang3,4, Zhigang Sun1,2,3,4

  • 1CAS Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances soil moisture retrieval accuracy using Cyclone Global Navigation Satellite System (CYGNSS) data by fusing it with optical/microwave remote sensing variables. A Gradient Boost Regression Tree model improves large-scale soil moisture mapping, even with environmental interference.

Keywords:
CYGNSSGBRTdata fusionland coversoil moisture

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Area of Science:

  • Earth Science
  • Remote Sensing
  • Environmental Monitoring

Background:

  • Cyclone Global Navigation Satellite System (CYGNSS) offers spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) for soil moisture retrieval.
  • Complex environmental factors like vegetation and ground roughness interfere with accurate soil moisture (SM) measurements.

Purpose of the Study:

  • Develop a high-accuracy model for CYGNSS-based soil moisture retrieval.
  • Mitigate the impact of environmental interference on SM estimation.

Main Methods:

  • Fused CYGNSS normalized surface reflectivity with optical/microwave remote sensing variables.
  • Employed a Gradient Boost Regression Tree (GBRT) model with land-type data for multi-variable SM retrieval.
  • Developed multiple models tailored for six distinct land types.

Main Results:

  • Validated the methodology in southeastern China, showing strong correlations with existing satellite products and in situ data.
  • Achieved high accuracy metrics: R = 0.765, ubRMSE = 0.054 m³m⁻³ vs. SMAP; R = 0.653, ubRMSE = 0.057 m³m⁻³ vs. ERA5 SM; R = 0.691, ubRMSE = 0.057 m³m⁻³ vs. in situ SM.
  • Demonstrated improved CYGNSS SM retrieval accuracy through data fusion and a multi-model approach.

Conclusions:

  • The study successfully improved CYGNSS soil moisture retrieval accuracy by integrating auxiliary remote sensing data.
  • The developed multi-layer, multi-model approach effectively addresses varying land properties for robust SM estimation.