# An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. The Traditional Single-Frequency Retrieval Method of GNSS-IR

_{d}and A

_{m}are the amplitude of the direct and reflected signal, respectively; φ is the phase difference between the direct and reflected signals.

_{d}that does not contain the information about the surface environment. Therefore, direct signal components are first removed through detrending the SNR data using two-order polynomial fitting and mainly focusing on analyzing the reflected signal components [11].

#### 2.2. An Improved Method

#### 2.2.1. Multi-Frequency Fusion Retrieval Procedure

#### 2.2.2. Use PCA to Extract Main Feature Components of Single-Frequency

**P**.

#### 2.2.3. Use Entropy Method and Priori Information to Fuse Multi-Frequency Features

^{2})

^{k}are calculated according to the fused characteristic parameters from each frequency:

#### 2.2.4. Use LightGBM to Establish a Retrieval Model

_{i}(x) is a sub model and the composite model is defined as:

## 3. Experiments

#### 3.1. PBO H2O Network Experiments

#### 3.2. Henan Experiment

## 4. Discussions

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Hall, C.D.; Cordey, R.A. Multistatic Scatterometry. In Proceedings of the International Geoscience & Remote Sensing Symposium, Edinburgh, UK, 12–16 September 1988. [Google Scholar]
- Rodriguez-Alvarez, N.; Bosch-Lluis, X.; Camps, A.; Ramos-Perez, I.; Valencia, E.; Park, H.; Vall-Llossera, M. Vegetation Water Content Estimation Using GNSS Measurements. IEEE Geosci. Remote Sens. Lett.
**2012**, 9, 282–286. [Google Scholar] [CrossRef] - Jia, Y.; Jin, S.; Savi, P.; Gao, Y.; Tang, J.; Chen, Y.; Li, W. GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation. Remote Sens.
**2019**, 11, 1655. [Google Scholar] [CrossRef] [Green Version] - Alonso-Arroyo, A.; Camps, A.; Park, H.; Pascual, D.; Onrubia, R.; Martín, F. Retrieval of Significant Wave Height and Mean Sea Surface Level Using the GNSS-R Interference Pattern Technique: Results From a Three-Month Field Campaign. IEEE Trans. Geosci. Remote Sens.
**2015**, 53, 3198–3209. [Google Scholar] [CrossRef] [Green Version] - Rodriguez-Alvarez, N.; Aguasca, A.; Valencia, E.; Bosch-Lluis, X.; Ramos-Pérez, I.; Park, H.; Vall-Llossera, M. Snow monitoring using GNSS-R techniques. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada, 24–29 July 2011; pp. 4375–4378. [Google Scholar]
- Jin, S.; Najibi, N. Sensing snow height and surface temperature variations in Greenland from GPS reflected signals. Adv. Space Res.
**2014**, 53, 1623–1633. [Google Scholar] [CrossRef] - Santi, E.; Paloscia, S.; Pettinato, S.; Fontanelli, G.; Clarizia, M.P.; Comite, D.; Floury, N. Remote Sensing of Forest Biomass Using GNSS Reflectometry. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2020**, 13, 2351–2368. [Google Scholar] [CrossRef] - Ferrazzoli, P.; Guerriero, L.; Pierdicca, N.; Rahmoune, R. Forest biomass monitoring with GNSS-R: Theoretical simulations. Adv. Space Res.
**2011**, 47, 1823–1832. [Google Scholar] [CrossRef] - Gao, H.; Yang, D.; Zhang, B.; Wang, Q.; Wang, F. Remote Sensing of Sea Ice Thickness with GNSS Reflected Signal. J. Electron. Inf. Technol.
**2017**, 39, 1096–1100. [Google Scholar] - Strandberg, J.; Hobiger, T.; Haas, R. Coastal Sea Ice Detection Using Ground-Based GNSS-R. IEEE Geosci. Remote Sens. Lett.
**2017**, 14, 1552–1556. [Google Scholar] [CrossRef] - Larson, K.M.; Small, E.E.; Gutmann, E.; Bilich, A.; Axelrad, P.; Braun, J. Using GPS Multipath to Measure Soil Moisture Fluctuations: Initial Results. GPS Solut.
**2008**, 12, 173–177. [Google Scholar] [CrossRef] - Larson, K.M.; Small, E.E.; Gutmann, E.D.; Bilich, A.L. Use of gps receivers as a soil moisture network for water cycle studies. Geophys. Res. Lett.
**2008**, 35, 851–854. [Google Scholar] [CrossRef] [Green Version] - Ban, W.; Yu, K.; Zhang, X. GEO-Satellite-Based Reflectometry for Soil Moisture Estimation: Signal Modeling and Algorithm Development. IEEE Trans. Geosci. Remote Sens.
**2017**, 56, 1829–1838. [Google Scholar] [CrossRef] - Yan, S.; Li, Z.; Yu, K.; Zhang, K. GPS-R L1 interference signal processing for soil moisture estimation: An experimental study. Eurasip J. Adv. Signal Process.
**2014**, 2014, 107. [Google Scholar] [CrossRef] [Green Version] - Wan, W.; Larson, K.M.; Small, E.E.; Chew, C.C.; Braun, J.J. Using geodetic GPS receivers to measure vegetation water content. GPS Solut.
**2015**, 19, 237–248. [Google Scholar] [CrossRef] - Larson, K.M.; Small, E.E. Normalized Microwave Reflection Index: A Vegetation Measurement Derived From GPS Networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2014**, 7, 1501–1511. [Google Scholar] [CrossRef] - Yuan, Q.; Li, S.; Yue, L.; Li, T.; Shen, H.; Zhang, L. Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point–Surface Fusion of MODIS Products and GNSS-IR Observations. Remote Sens.
**2019**, 11, 1440. [Google Scholar] [CrossRef] [Green Version] - Larson, K.M.; Gutmann, E.D.; Zavorotny, V.U.; Braun, J.J.; Williams, M.W.; Nievinski, F.G. Can we measure snow depth with GPS receivers? Geophys. Res. Lett.
**2009**, 36, L17502. [Google Scholar] [CrossRef] [Green Version] - Gutmann, E.D.; Larson, K.M.; Williams, M.W.; Nievinski, F.G.; Zavorotny, V.U. Snow measurement by GPS interferometric reflectometry: An evaluation at Niwot Ridge, Colorado. Hydrol. Process.
**2012**, 26, 2951–2961. [Google Scholar] [CrossRef] - Tabibi, S.; Geremia-Nievinski, F.; Dam, T.V. Statistical Comparison and Combination of GPS, GLONASS, and Multi-GNSS Multipath Reflectometry Applied to Snow Depth Retrieval. IEEE Trans. Geosci. Remote Sens.
**2017**, 55, 3773–3785. [Google Scholar] [CrossRef] - Larson, K.M.; Braun, J.J.; Small, E.E.; Zavorotny, V.U.; Gutmann, E.D.; Bilich, A.L. GPS Multipath and Its Relation to Near-Surface Soil Moisture Content. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2010**, 3, 91–99. [Google Scholar] [CrossRef] - Chew, C.C.; Small, E.E.; Larson, K.M. Effects of Near-Surface Soil Moisture on GPS SNR Data: Development of a Retrieval Algorithm for Soil Moisture. IEEE Trans. Geosci. Remote Sens.
**2013**, 52, 537–543. [Google Scholar] [CrossRef] - Vey, S.; Güntner, A.; Wickert, J.; Blume, T.; Ramatschi, M. Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: A case study for Sutherland, South Africa. GPS Solut.
**2016**, 20, 641–654. [Google Scholar] [CrossRef] - Zavorotny, V.U.; Larson, K.M.; Braun, J.J.; Small, E.E.; Gutmann, E.D.; Bilich, A.L. A Physical Model for GPS Multipath Caused by Land Reflections: Toward Bare Soil MoistureRetrievals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2010**, 3, 100–110. [Google Scholar] [CrossRef] - Yang, T.; Wan, W.; Chen, X.; Chu, T.; Hong, Y. Using BDS SNR Observations to Measure Near-Surface Soil Moisture Fluctuations: Results From Low Vegetated Surface. IEEE Geosci. Remote Sens. Lett.
**2017**, 14, 1308–1312. [Google Scholar] [CrossRef] - Feng, Q.; Zheng, N. Retrieving Soil Moisture Using Signal-to-noise Ratio of GPS Signal by Assisted Machine Learning Algorithm. Bull. Surv. Mapp.
**2018**, 7, 106–111. [Google Scholar] - Jin, L.; Yang, L.; Han, M.; Hong, X.; Sun, B.; Liang, Y. Soil moisture inversion method based on GNSS-IR dual frequency data fusion. J. Beijing Univ. Aeronaut. Astronaut.
**2019**, 45, 1248. [Google Scholar] - Sun, B.; Liang, Y.; Han, M.; Yang, L.; Jin, L.; Hong, X. A method for GNSS-IR soil moisture inversion based on GPS multi-satellite and triple-frequency data fusion. J. Beijing Univ. Aeronaut. Astronaut.
**2020**, 46, 1089–1096. [Google Scholar] - Chew, C.C. Soil Moisture Remote Sensing using GPS-Interferometric Reflectometry. Dissertations & Theses—Gradworks. In 2017 Forum on Cooperative Positioning and Service (CPGPS); IEEE: Piscataway, NJ, USA, 2015. [Google Scholar]
- Small, E.E.; Larson, K.M.; Chew, C.C.; Dong, J.; Ochsner, T.E. Validation of GPS-IR Soil Moisture Retrievals: Comparison of Different Algorithms to Remove Vegetation Effects. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2016**, 9, 4759–4770. [Google Scholar] [CrossRef] - Zribi, M.; Gorrab, A.; Baghdadi, N.; Lili-Chabaane, Z.; Mougenot, B. Influence of Radar Frequency on the Relationship Between Bare Surface Soil Moisture Vertical Profile and Radar Backscatter. IEEE Geosci. Remote Sens. Lett.
**2013**, 11, 848–852. [Google Scholar] [CrossRef] [Green Version] - Qi, X. Technology Research of Face Recognition Based on PCA; Wuhan University of Technology: Wuhan, China, 2007. [Google Scholar]
- Asante-Okyere, S.; Shen, C.; Ziggah, Y.Y.; Rulegeya, M.M.; Zhu, X. Principal Component Analysis (PCA) Based Hybrid Models for the Accurate Estimation of Reservoir Water Saturation. Comput. Geosci.
**2020**, 145, 104555. [Google Scholar] [CrossRef] - Qiao, J. Application of Improved Entropy Method in Henan Sustainable Development Evaluation. Resour. Sci.
**2004**, 26, 113–119. [Google Scholar] - Wang, F.; Mao, A.; Li, H.; Jia, M. Quality Measurement and Regional Difference of Urbanization in Shandong Province Based on the Entropy Method. Sci. Geogr. Sin.
**2013**, 33, 1323–1329. [Google Scholar] - Ke, G.; Meng, Q.; Finley, T.; Wang, T.; Chen, W.; Ma, W.; Ye, Q.; Liu, T. LightGBM: A highly efficient gradient boosting decision tree. In Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 4–9 December 2017; pp. 3147–3155. [Google Scholar]
- Zhang, Y.; Zhu, C.; Wang, Q. LightGBM-based model for metro passenger volume forecasting. IET Intelligent Transport Systems
**2020**, 14, 1815–1823. [Google Scholar] [CrossRef] - Wang, F.; Cheng, H.; Dai, H.; Han, H. Freeway Short-Term Travel Time Prediction Based on LightGBM Algorithm. In IOP Conference Series: Earth Environmental Science; IOP Publishing: Bristol, UK, 2021; Volume 638, pp. 12–29. [Google Scholar]
- Gurtner, W.; Estey, L. Rinex: The Receiver Independent Exchange Format Version 3.04; Astronomical Institute, University of Bern and UNAVCO: Bolulder, CO, USA, 2007. [Google Scholar]

**Figure 4.**(

**a**) The distribution and surrounding environment of P037, P041, P043 stations; (

**b**) the soil moisture-rainfall diagram during the experimental period.

**Figure 8.**(

**a**) Instrument and equipment (

**mid**), surrounding environment (

**left**), and soil moisture sensor (

**right**). The red box represents the antenna and receiver; the blue box represents power supply equipment. (

**b**) The soil moisture-rainfall diagram during the experimental period.

Station | Latitude and Longitude | Location | Year | Time Span/Days of Year |
---|---|---|---|---|

P037 | 38.42°N,105.10°W | Canon, Colorado | 2014 | 145–294 |

P041 | 39.95°N,105.19°W | Boulder, Colorado | 2012 | 87–236 |

P043 | 43.88°N,104.49°W | Newcastle, Wyoming | 2016 | 184–333 |

Item | Revisit Period (Days) |
---|---|

GPS | 1 |

BDS(GEO and IGSO satellites) | 1 |

BDS(MEO satellites) | 7 |

Station | Method | Correlation Coefficient | Root-Mean-Square-Error (cm ^{3}/cm^{3}) | Mean-Absolute-Error (cm ^{3}/cm^{3}) |
---|---|---|---|---|

P037 | Proposed | 0.9007 | 0.0217 | 0.0190 |

L2-LightGBM | 0.8493 | 0.0270 | 0.0237 | |

Linear | 0.7403 | 0.0364 | 0.0319 | |

P041 | Proposed | 0.9045 | 0.0172 | 0.0142 |

L2-LightGBM | 0.8596 | 0.0327 | 0.0286 | |

Linear | 0.7677 | 0.0568 | 0.0525 | |

P043 | Proposed | 0.9524 | 0.0120 | 0.0100 |

L2-LightGBM | 0.8896 | 0.0149 | 0.0116 | |

Linear | 0.8033 | 0.0209 | 0.0168 |

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**MDPI and ACS Style**

Chen, K.; Cao, X.; Shen, F.; Ge, Y.
An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data. *Remote Sens.* **2021**, *13*, 3725.
https://doi.org/10.3390/rs13183725

**AMA Style**

Chen K, Cao X, Shen F, Ge Y.
An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data. *Remote Sensing*. 2021; 13(18):3725.
https://doi.org/10.3390/rs13183725

**Chicago/Turabian Style**

Chen, Kun, Xinyun Cao, Fei Shen, and Yulong Ge.
2021. "An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data" *Remote Sensing* 13, no. 18: 3725.
https://doi.org/10.3390/rs13183725