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Soil Moisture Retrieval Using Multi-Satellite Dual-Frequency GNSS-IR Considering Environmental Factors.

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Title: Soil Moisture Retrieval Using Multi-Satellite Dual-Frequency GNSS-IR Considering Environmental Factors.
Authors: Nie, Shihai1,2,3 (AUTHOR), Jia, Yongjun2,4 (AUTHOR) jiayongjun@mail.nsoas.org.cn, Li, Peng3 (AUTHOR), Wu, Xing4,5 (AUTHOR), Tang, Yuchao2,5 (AUTHOR)
Source: Remote Sensing. Mar2026, Vol. 18 Issue 6, p917. 36p.
Subjects: Soil moisture, Global Positioning System, Entropy (Information theory), Normalized difference vegetation index, Climatology, Random forest algorithms
Abstract: Highlights: What are the main findings? A quantitatively optimized multi-satellite GNSS-IR strategy reveals a clear diminishing-return behavior in soil moisture retrieval, with retrieval performance converging when approximately 5–6 satellites within a single GNSS constellation are jointly used. Dual-frequency entropy-based fusion and nonlinear modeling with environmental factors jointly enhance GNSS-IR soil moisture retrieval accuracy and stability, with site-dependent dominance of temperature, NDVI, and precipitation effects. What are the implication of the main findings? The proposed marginal threshold criterion provides a quantitative basis for configuring multi-satellite GNSS-IR observation schemes, avoiding unnecessary observational redundancy while maintaining stable retrieval performance. Integrating multi-environmental constraints with multi-satellite, dual-frequency GNSS-IR establishes a transferable framework for soil moisture monitoring under heterogeneous climatic and surface conditions. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides a low-cost, all-weather approach for continuous soil moisture content (SMC) retrieval. However, in single-constellation, multi-satellite applications, the optimal satellite number and the combined effects of multiple environmental factors on retrieval accuracy and stability remain insufficiently quantified. To address these issues, this study develops a dual-frequency GNSS-IR SMC retrieval framework that explicitly incorporates multiple environmental factors. Entropy-based fusion (EFM) is used to adaptively weight dual-frequency phase-delay observations, and a marginal-gain criterion is introduced to determine a suitable number of participating satellites. On this basis, univariate linear regression (ULR) and random forest (RF) models are established, and the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation are incorporated into the RF model to improve retrieval robustness and quantify the relative contributions of environmental factors. The results show that multi-satellite combinations significantly improve SMC retrieval performance, while the incremental gain exhibits clearly diminishing returns and converges when the number of participating satellites reaches about 5–6 within a single constellation. Dual-frequency fusion consistently outperforms single-frequency schemes across different GNSS constellations, demonstrating the complementary value of multi-frequency information under multi-satellite conditions. In addition, the environmentally informed nonlinear model achieves higher accuracy and stability than the linear model, and the dominant environmental drivers differ across stations. Overall, this study provides quantitative support for configuring single-constellation multi-satellite GNSS-IR soil moisture monitoring schemes and for improving retrieval robustness under complex environmental conditions. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Soil Moisture Retrieval Using Multi-Satellite Dual-Frequency GNSS-IR Considering Environmental Factors.
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  Data: <searchLink fieldCode="AR" term="%22Nie%2C+Shihai%22">Nie, Shihai</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jia%2C+Yongjun%22">Jia, Yongjun</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<i> jiayongjun@mail.nsoas.org.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Peng%22">Li, Peng</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Xing%22">Wu, Xing</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Yuchao%22">Tang, Yuchao</searchLink><relatesTo>2,5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Mar2026, Vol. 18 Issue 6, p917. 36p.
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  Data: <searchLink fieldCode="DE" term="%22Soil+moisture%22">Soil moisture</searchLink><br /><searchLink fieldCode="DE" term="%22Global+Positioning+System%22">Global Positioning System</searchLink><br /><searchLink fieldCode="DE" term="%22Entropy+%28Information+theory%29%22">Entropy (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Climatology%22">Climatology</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? A quantitatively optimized multi-satellite GNSS-IR strategy reveals a clear diminishing-return behavior in soil moisture retrieval, with retrieval performance converging when approximately 5–6 satellites within a single GNSS constellation are jointly used. Dual-frequency entropy-based fusion and nonlinear modeling with environmental factors jointly enhance GNSS-IR soil moisture retrieval accuracy and stability, with site-dependent dominance of temperature, NDVI, and precipitation effects. What are the implication of the main findings? The proposed marginal threshold criterion provides a quantitative basis for configuring multi-satellite GNSS-IR observation schemes, avoiding unnecessary observational redundancy while maintaining stable retrieval performance. Integrating multi-environmental constraints with multi-satellite, dual-frequency GNSS-IR establishes a transferable framework for soil moisture monitoring under heterogeneous climatic and surface conditions. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides a low-cost, all-weather approach for continuous soil moisture content (SMC) retrieval. However, in single-constellation, multi-satellite applications, the optimal satellite number and the combined effects of multiple environmental factors on retrieval accuracy and stability remain insufficiently quantified. To address these issues, this study develops a dual-frequency GNSS-IR SMC retrieval framework that explicitly incorporates multiple environmental factors. Entropy-based fusion (EFM) is used to adaptively weight dual-frequency phase-delay observations, and a marginal-gain criterion is introduced to determine a suitable number of participating satellites. On this basis, univariate linear regression (ULR) and random forest (RF) models are established, and the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation are incorporated into the RF model to improve retrieval robustness and quantify the relative contributions of environmental factors. The results show that multi-satellite combinations significantly improve SMC retrieval performance, while the incremental gain exhibits clearly diminishing returns and converges when the number of participating satellites reaches about 5–6 within a single constellation. Dual-frequency fusion consistently outperforms single-frequency schemes across different GNSS constellations, demonstrating the complementary value of multi-frequency information under multi-satellite conditions. In addition, the environmentally informed nonlinear model achieves higher accuracy and stability than the linear model, and the dominant environmental drivers differ across stations. Overall, this study provides quantitative support for configuring single-constellation multi-satellite GNSS-IR soil moisture monitoring schemes and for improving retrieval robustness under complex environmental conditions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18060917
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      – Code: eng
        Text: English
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        PageCount: 36
        StartPage: 917
    Subjects:
      – SubjectFull: Soil moisture
        Type: general
      – SubjectFull: Global Positioning System
        Type: general
      – SubjectFull: Entropy (Information theory)
        Type: general
      – SubjectFull: Normalized difference vegetation index
        Type: general
      – SubjectFull: Climatology
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
    Titles:
      – TitleFull: Soil Moisture Retrieval Using Multi-Satellite Dual-Frequency GNSS-IR Considering Environmental Factors.
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            NameFull: Nie, Shihai
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              Text: Mar2026
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              Y: 2026
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