Evaluation of ASCAT soil moisture retrievals and their potential to detect intraday variability

Dinh, Lan Anh; Aires, Filipe; Pellet, Victor

doi:10.5194/egusphere-2026-2360

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https://doi.org/10.5194/egusphere-2026-2360

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28 Apr 2026

| 28 Apr 2026

Status: this preprint is open for discussion and under review for Earth Observation (EO).

Evaluation of ASCAT soil moisture retrievals and their potential to detect intraday variability

Lan Anh Dinh, Filipe Aires, and Victor Pellet

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 day⁻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.

Received: 24 Apr 2026 – Discussion started: 28 Apr 2026

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Lan Anh Dinh, Filipe Aires, and Victor Pellet

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RC1:
'Comment on egusphere-2026-2360', Anonymous Referee #1, 15 May 2026 reply
The study presents a novel approach for retrieving ASCAT soil moisture via a convolutional neural network (CNN) that explicitly models spatial dependencies. The validation is robust, using both the ERA5 reanalysis and in situ soil moisture from the ISMN. Furthermore, the performance of the CNN-based ASCAT SM is compared with the H SAF ASCAT SM data record (derived using the change detection approach). The results are convincing, with improvements to the correlation coefficient for the CNN ASCAT SM relative to the H SAF ASCAT SM. However, the study would benefit from a clearer description of the machine learning architecture for reproducibility. Also, stratifying the ISMN validation results according to land cover and vegetation types would be informative.

Major comments:
The description of the NN in Sections 3.1 and 3.2 should give more detail on hyperparameter tuning and overfitting prevention. Furthermore, a diagram of the steps would be beneficial for the reader.

The validation against in situ data in Section 4.2 does not give any information about the land cover and vegetation types for the different networks, and how this affects the relative performance of the SM datasets. From Figure 2 (b) it seems that the CNN soil moisture is particularly advantageous in the mountainous regions in the western US. On the other hand, H120 seems to perform slightly better in the central Great Planes, which is more agricultural.

Minor comments:
There are many minor grammatical mistakes and the paper would benefit from a thorough proof read

The authors should discuss the degree of dependence on ERA5 (training target vs validation independence)

In Section 1 the authors should define what the surface soil moisture represents (i.e. top few centimetres of soil)

Line 139: It seems this should sentence should start with a bullet point like the previous two bullet points

Line 168: The second y_predshould be y_ref

Line 233: The red point is shown in Figure 4 (a), not Figure 4 (b)

Line 252: section 55.1 typo

Line 269: ERA5-24h should be defined before the abbreviation is first used

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Citation: https://doi.org/10.5194/egusphere-2026-2360-RC1
RC2: 'Comment on egusphere-2026-2360', Anonymous Referee #2, 07 Jun 2026 reply

Review of
Evaluation of ASCAT soil moisture retrievals and their potential to detect intraday variability
by Dinh et al.
General comments:
This is an interesting paper demonstrating the potential of machine learning to enhance existing soil moisture products derived from EUMETSAT's ASCAT instruments on MetOp satellites. A neural network (NN) is trained using ERA5 simulations as a reference. The resulting soil moisture retrievals are less noisy than the original ASCAT soil moisture product. In addition, the authors provide some evidence suggesting that sub-daily soil moisture variability could be observed through NN retrievals. While the paper is well written, the addition of a diagram presenting the NN training and retrievals with inputs and outputs would be useful. Also missing is a table summarising mean score values per experiment with respect to in situ observations. In addition to scores covering the entire four-year period, the table should also indicate seasonal scores (e.g. DJF, MAM, JJA and SON across years). Finally, the approach is somewhat circular since ERA5-derived variables (ST and LAI) are used to predict soil moisture, as well as ASCAT backscatters. This shortcoming should be clearly acknowledged, and the reasons for it should be made clear.
Recommendation: major revisions.
Particular comment:
- L. 41: "SM estimates at sub-daily resolution" is not clear. Do you mean that several ASCAT observations are available for a given location on the same day? Or does the time of observation vary from one day to the next?
- L. 44: ASCAT observations are global. Why focusing on CONUS? Any reason for that?
- L. 102: Does “the ERA5 LAI product” exist? As far as I am aware, ERA5 neither simulates dynamic LAI nor integrates LAI observations. This sentence needs to be clarified.
- L. 225: Replace "extracting a reliable diurnal signal is a true challenge!" by "extracting a reliable signal is challenging".
- L. 232 (Fig. 4): ERA5 precipitation data can be subject to significant biases. Could you include in situ precipitation observations in Fig. 4b?
- L. 240: This study does not actually demonstrate the intraday capability of the method. In situ observations of precipitation would provide a more convincing demonstration.
- L. 252: "section 55.1"?

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Citation: https://doi.org/10.5194/egusphere-2026-2360-RC2

Lan Anh Dinh, Filipe Aires, and Victor Pellet

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Short summary

Soil moisture (SM) plays a key role in weather, agriculture, and water management. While satellites can measure SM from space, obtaining accurate, frequent measurements throughout the day remains challenging. Here, we explore how deep learning models can improve sub-daily SM estimates. Our approach focuses on capturing spatial patterns and adapting to local conditions. Using data from the ASCAT satellite instrument, we show that this model can produce reliable SM estimates multiple times a day.


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