Soil Moisture Retrieval Based on Satellite-Borne GNSS-R Technology

Jiangyang Li, Yongchao Zhu, Tingye Tao, Juntao Wang

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

2 Citations (Scopus)

Abstract

The global water cycle is affected by Soil moisture which plays an important role in the fields of hydrology and agriculture. Therefore, it is of great significance to study the theory and method of soil moisture detection. Currently, soil moisture detection methods are very rich. As an emerging remote sensing technology, satellite-borne GNSS-R has the advantages of abundant signal sources, low cost and Global coverage, etc. It has attracted people's attention to use satellite-borne GNSS-R technology to retrieve soil moisture. In this paper, a method of soil moisture retrieval based on satellite-borne GNSS-R is proposed. Soil moisture was constructed as a function of surface reflectivity, vegetation optical depth, roughness coefficient and temperature which are extracted from CYGNSS data and SMAP data. Then we use neural network model training data to determine the mathematical model of soil moisture retrieval. African soil moisture status was obtained using CYGNSS data from July to December 2018. Finally, the soil moisture retrieval method proposed in this paper was evaluated by comparing with the soil moisture data provided by SMAP. The results show that the soil moisture retrieval method proposed in this paper has a good consistency with SMAP soil moisture. This shows that GNSS reflection signals collected by CYGNSS project can be used to retrieve soil moisture and satellite-borne GNSS-R technology has great potential to obtain soil moisture with high precision and high spatial and temporal resolution. And it also provides a new method for soil moisture retrieval.

Original languageEnglish
Title of host publicationChina Satellite Navigation Conference, CSNC 2021, Proceedings
EditorsChangfeng Yang, Jun Xie
PublisherSpringer Science and Business Media Deutschland GmbH
Pages54-59
Number of pages6
ISBN (Print)9789811631375
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event12th China Satellite Navigation Conference, CSNC 2021 - Nanchang, China
Duration: 22 May 202125 May 2021

Publication series

NameLecture Notes in Electrical Engineering
Volume772 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference12th China Satellite Navigation Conference, CSNC 2021
Country/TerritoryChina
CityNanchang
Period22/05/2125/05/21

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

  • CYGNSS
  • GNSS-R
  • Neural network
  • SMAP
  • Soil moisture

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