Skip to main content

Soil Moisture Retrieval Using UWB Echoes via ANFIS and ANN

  • Conference paper
  • First Online:
Communications, Signal Processing, and Systems (CSPS 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 463))

Abstract

This paper introduces a new soil moisture (SM) retrieval approach based on ultra-wideband (UWB) echoes. The approach employs two fuzzy logic systems (FLSs) -adaptive network-based fuzzy inference system (ANFIS) and type 1 fuzzy logic system (T1FLS) respectively, to extract features in soil echoes. Artificial neural network (ANN) is applied to classify with different volume water contents (VWCs). 9 types of UWB soil echoes of different texture and VWC are collected and investigated using our approach. Final analysis shows ANFIS with ANN provides a better VWC correct recognition rate (CRR) than T1FLS with ANN at high SNRs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Reichle, R.H., Koster, R.D., Dong, J., Berg, A.A.: Global soil moisture from satellite observations, land surface models, and ground data: implications for data assimilation. J. Hydrometeorol. 5(3), 430–442 (2004)

    Google Scholar 

  2. Lambot, S., Slob, E.C., van den Bosch, I., Stockbroeckx, B., Vanclooster, M.: Modeling of ground-penetrating radar for accurate characterization of subsurface electric properties. IEEE Trans. Geosci. Remote Sens. 42(11), 2555–2568 (2004)

    Google Scholar 

  3. Dawson, M.S., Fung, A.K., Manry, M.T.: A robust statistical-based estimator for soil moisture retrieval from radar measurements. IEEE Trans. Geosci. Remote Sens. 35(1), 57–67 (1997)

    Google Scholar 

  4. Pasolli, L., Notarnicola, C., Bruzzone, L.: Estimating soil moisture with the support vector regression technique. IEEE Geosci. Remote Sens. Lett. 8(6), 1080–1084 (2011)

    Google Scholar 

  5. Zhu, F., Liu, H., Liang, J.: Soil moisture retrieval using fuzzy logic based on UWB signals. In: 2015 International Conference on Wireless Communications & Signal Processing (WCSP), pp. 1–5. IEEE (2015)

    Google Scholar 

  6. Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall PTR, Upper Saddle River (2001)

    Google Scholar 

  7. Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61671138), the Fundamental Research Funds for the Central Universities Project No. ZYGX2015J021, and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaoxu Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, X., Yu, X., Ren, J., Liang, J. (2019). Soil Moisture Retrieval Using UWB Echoes via ANFIS and ANN. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_151

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6571-2_151

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6570-5

  • Online ISBN: 978-981-10-6571-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics