Abstract

Study Region: Southeast Texas, USA. Study Focus: NASA's Soil Moisture Active Passive (SMAP) product, particularly the Level 4 (SMAPL4) data, provides high-resolution and extensive coverage of surface and root zone soil moisture (SM), essential for weather and climate research. However, a latency of 2.5–4.0 days in SMAPL4 data limits its real-time hydrologic and weather prediction applications. to address this, we developed a model integrating deep learning (DL) techniques (Long Short-Term Memory, Fully Connected Neural Network) with Principal Component Analysis (PCA) to nowcast SM data in real-time. the model is trained on multi-source SM observations, including near real-time in-situ and satellite data, and deployed over a 56,000+ km² area in southeast Texas. New Hydrological Insights for the Region: Our DL methodology nowcasts SM accurately in both time and space through real-time assimilation of multi-source data, mitigating SMAP's latency and offering near real-time soil moisture estimates. the nowcasted SM aligns closely with actual SMAPL4 data, capturing spatial and temporal variations. SMAP underestimates the spatio-temporal variability of soil moisture compared to in-situ data, highlighting the necessity for diverse data integration. the proposed framework can improve the real-time flood and drought monitoring and offers insights for various hydrological applications. Nowcasting error mapping identifies regions with higher uncertainties, guiding future model improvements.

Department(s)

Biological Sciences

Publication Status

Open Access

Comments

Jackson School of Geosciences, University of Texas at Austin, Grant PGA #582-21-11425-009

Keywords and Phrases

Nowcasting; SMAP, In-situ Data, Deep Learning; Soil moisture; Spatiotemporal

International Standard Serial Number (ISSN)

2214-5818

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2025 Elsevier, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Dec 2024

Included in

Biology Commons

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