Evaluation of ASCAT soil moisture retrievals and their potential to detect intraday variability
Abstract
Accurate sub-daily soil moisture (SM) retrievals from satellite observations remain a major challenge due to sparse temporal sampling and retrieval uncertainties. This study introduces a localized convolutional neural network (CNN-l) framework designed to enhance SM estimates from Advanced SCATterometer (ASCAT) observations by exploiting spatial features and adapting to local conditions. The proposed approach achieves strong agreement with ERA5 reference SM, with total correlation coefficients exceeding 0.9, even at a sub-daily scale. Validation against in situ measurements from 568 monitoring sites across the contiguous United States (CONUS) shows a median temporal correlation of 0.65, compared to 0.59 for the operational ASCAT H120 product. Our CNN-based retrievals also reveal meaningful intraday variability when SM signals exceed retrieval uncertainty, particularly during heavy precipitation events (>10 mm d −1 ), offering new insight into short-term hydrological responses. Future efforts should prioritize the integration of complementary satellite observations from multiple instruments to enhance retrieval accuracy, robustness, and temporal resolution. Additionally, strategies to improve retrieval of extremes (such as localization strategies or variable augmentation) should be further developed.
Related
- Assessing the potential of Individual Foraminifera Analyses ( δ ¹⁸ O) to reconstruct variability, seasonality and extremes in the tropical Indian Ocean
- Evaluation of sea ice variability in three ocean reanalyses and two regional models in the Barents and Kara Seas in 1991–2020
- Generative and deterministic deep learning models comparison for fine-scale precipitation retrievals from infrared brightness temperature
- Adaptive observation weighting in TCKF1D-Var for ground-based multi-sensor thermodynamic retrievals prior to nocturnal heavy precipitation over China
- Collaborative Neural Networks significantly Improve Global Aerosol Retrievals From Multi-Angle Polarimeters
BibTeX
@article{dinh2026evaluation,
title = {{Evaluation of ASCAT soil moisture retrievals and their potential to detect intraday variability}},
author = {Lan Anh Dinh and Filipe Aires and Victor Pellet},
journal = {Earth Observation},
year = {2026},
month = sep,
doi = {10.5194/eo-1-105-2026},
url = {https://hal.science/hal-05760526v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Evaluation of ASCAT soil moisture retrievals and their potential to detect intraday variability
AU - Lan Anh Dinh
AU - Filipe Aires
AU - Victor Pellet
PY - 2026
DA - 2026/09/17
JO - Earth Observation
DO - 10.5194/eo-1-105-2026
UR - https://hal.science/hal-05760526v1
AB - Accurate sub-daily soil moisture (SM) retrievals from satellite observations remain a major challenge due to sparse temporal sampling and retrieval uncertainties. This study introduces a localized convolutional neural network (CNN-l) framework designed to enhance SM estimates from Advanced SCATterometer (ASCAT) observations by exploiting spatial features and adapting to local conditions. The proposed approach achieves strong agreement with ERA5 reference SM, with total correlation coefficients exceeding 0.9, even at a sub-daily scale. Validation against in situ measurements from 568 monitoring sites across the contiguous United States (CONUS) shows a median temporal correlation of 0.65, compared to 0.59 for the operational ASCAT H120 product. Our CNN-based retrievals also reveal meaningful intraday variability when SM signals exceed retrieval uncertainty, particularly during heavy precipitation events (>10 mm d −1 ), offering new insight into short-term hydrological responses. Future efforts should prioritize the integration of complementary satellite observations from multiple instruments to enhance retrieval accuracy, robustness, and temporal resolution. Additionally, strategies to improve retrieval of extremes (such as localization strategies or variable augmentation) should be further developed.
SN - 3054-1786
ER -