A Deep Learning Approach for Short-Term Soil Moisture Retrieval Using CYGNSS

Muhammed Rasit Cevikalp*, Mustafa Serkan Isik, Mehmet Furkan Celik, Nebiye Musaoglu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this study, short-term soil moisture values were estimated in the CONUS region using Cyclone Global Navigation Satellite System (CYGNSS) observations between August 2018 and October 2022 along with ancillary data including precipitation, temperature, normalized difference vegetation index (NDVI), land cover classification (LCC), elevation, and soil type. The study employs Long Short-Term Memory (LSTM) neural networks to demonstrate the effectiveness of this method in accurately estimating daily soil moisture at International Soil Moisture Network (ISMN) stations. Furthermore, the study analyzes SHapley Additive exPlanations (SHAP) values to understand how each feature contributes to the model's predictions.

Original languageEnglish
Title of host publicationIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4472-4476
Number of pages5
ISBN (Electronic)9798350360325
DOIs
Publication statusPublished - 2024
Event2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece
Duration: 7 Jul 202412 Jul 2024

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Country/TerritoryGreece
CityAthens
Period7/07/2412/07/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Cyclone Global Navigation Satellite System
  • eXplainable artificial intelligence
  • GNSS-Reflectometry
  • Long short-term memory

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