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 language | English |
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Title of host publication | IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4472-4476 |
Number of pages | 5 |
ISBN (Electronic) | 9798350360325 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece Duration: 7 Jul 2024 → 12 Jul 2024 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Conference
Conference | 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 |
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Country/Territory | Greece |
City | Athens |
Period | 7/07/24 → 12/07/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Cyclone Global Navigation Satellite System
- eXplainable artificial intelligence
- GNSS-Reflectometry
- Long short-term memory