the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Feb 2026
Next-generation Metop ASCAT Surface Soil Moisture datasets
Abstract. This article presents the next-generation of the Advanced Scatterometer (ASCAT) surface soil moisture (SSM) dataset, bringing the operational near real-time (NRT) product up to date with the historical offline data record. For years, the offline data record has benefited from successive algorithmic improvements while the NRT product has only received minor updates. This release now applies the latest soil moisture retrieval algorithm and consistent fixed-Earth grid to both data streams, creating a unified dataset and representing a major advancement for the ASCAT SSM NRT product. Furthermore, the standard 12.5 km sampling ASCAT SSM dataset is now complemented by a new high-resolution 6.25 km sampling ASCAT SSM product. This is achieved by customising the spatial resampling process of the ASCAT Level 1B full-resolution backscatter data. A new key development in the change detection algorithm for ASCAT SSM concerns the estimation of the dry and wet backscatter references. Specifically, a moving-window approach is now applied instead of the full time series to mitigate artificial trends caused by long-term land cover changes. Additionally, a new monthly subsurface scattering flag has been added to filter out unreliable SSM measurements where backscatter and soil moisture indicate an inverted relationship.
Quality control of the ASCAT SSM datasets is performed by using soil moisture estimates from Noah GLDAS-2.1 and the ESA CCI Passive Soil Moisture (SM) v09.1 product, as well as in-situ observations provided by the International Soil Moisture Network (ISMN). The validation results show that both ASCAT SSM datasets have a comparable performance in terms of the Pearson correlation coefficient (ASCAT SSM 6.25 km vs ESA CCI Passive SM: 17.9 % > 0.75 and 57.8 % > 0.5; ASCAT SSM 12.5 km vs ESA CCI Passiv SM: 19.6 % > 0.75 and 59.2 % > 0.5) and signal-to-noise ratio (SNR) derived using triple collocation analysis (ASCAT SSM 6.25 km SNR: 56.0 % > 0 dB, 35.6 % > 3 dB, ASCAT SSM 12.5 km SNR: 58.1 % > 0 dB, 38.6 % > 3 dB,). The best performance can be found in regions with strong seasonal variability, including monsoonal, savanna, Mediterranean, and tropical wet-and-dry zones. A lower performance can be found in areas characterised by limited soil moisture variability (such as deserts), dense vegetation, pronounced topographic complexity, wetland areas, or higher latitudes (> 60° N) experiencing longer periods of frozen soil and snow cover.
The ASCAT SSM datasets are publicly available online https://doi.org/10.15770/EUM_SAF_H_0011 and https://doi.org/10.15770/EUM_SAF_H_0012 (2025a, c), while ASCAT SSM NRT products are additionally distributed via the broadcasting system EUMETCast.
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Status: final response (author comments only)
- RC1: 'Comment on essd-2025-746', Anonymous Referee #1, 25 Feb 2026
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RC2: 'Comment on essd-2025-746', Anonymous Referee #2, 05 Mar 2026
General comments：
The manuscript presents a new ASCAT surface soil moisture (SSM) dataset with a nominal sampling of 6.25 km alongside the standard 12.5 km product. The development of higher-resolution scatterometer-based soil moisture datasets is valuable for a wide range of hydrological, agricultural, and climate applications, and the effort to provide publicly accessible datasets through the H SAF framework is highly appreciated. The manuscript is generally well written and provides a detailed description of the processing chain and validation procedures.
However, in its current form the manuscript does not clearly communicate some key aspects of the dataset development and positioning relative to previous ASCAT SSM products. Therefore, I recommend major revisions before the manuscript can be considered for publication.
First, the title refers to the dataset as a “Next-generation” ASCAT soil moisture product. While the abstract briefly mentions several methodological updates to the ASCAT SSM retrieval algorithm, these improvements are not clearly highlighted in the methodology section. The methods are described in great detail, but it remains difficult for readers to identify the key algorithmic advancements compared to previous ASCAT SSM releases. It would therefore be helpful if the authors could more clearly summarize the main updates and distinguish them from earlier ASCAT SSM datasets in the methodology section.
Second, I suggest that the validation section include a comparison with previous versions of the ASCAT SSM datasets. This would help to more clearly quantify the improvements in performance or data quality introduced by the updated processing chain, and would make the characterization of this dataset as a “Next-generation” product more convincing.
Specific comments：
- The abstract should provide clearer information about the dataset. In particular, the temporal coverage of the dataset is not specified, which is essential information for a data description paper. In addition, although a DOI is provided, it is not clear which DOI corresponds to the 6.25 km dataset and which corresponds to the 12.5 km dataset. The version identifier of the dataset should also be specified.
- The introduction does not clearly highlight the limitations of the previous ASCAT SSM products and the motivation for the new dataset release.
- The title of Table 1 should include the term “microwave”.
- Table 1 currently provides only limited information (spatial resolution, temporal coverage and references). It would be useful to include additional characteristics of the different soil moisture products, such as sensor type (active/passive), observation frequency band, and whether the product provides absolute or relative soil moisture estimates.
- Table 2 could be removed and the information integrated into the text.
- The datasets used in this study have different spatial resolutions, but the manuscript does not clearly describe how they were spatially matched with the ASCAT grid. Please clarify the resampling or spatial matching method used. In addition, it would be helpful to provide download links or access information for the datasets introduced in this section.
- Figure 1 provides only a very high-level overview of the workflow. Given the extensive description in the methodology section, the figure could be expanded to better illustrate the key processing steps and algorithm components, which would help readers more easily understand the overall processing chain.
- The methodology section is very detailed and, in some places, somewhat verbose. The authors may consider condensing less essential descriptions so that the key processing steps and methodological aspects are easier to follow.
- L196: The manuscript states that the theoretical spatial resolution of the 6.25 km product is about 15 km, while the actual spatial resolution cannot be determined precisely. It would be helpful to clarify the typical effective resolution or its expected range.
- The caption of Figure 4 is too brief.
- L465: The ASCAT SSM product is expressed in degree of saturation, while the reference datasets (e.g., GLDAS, ESA CCI, and ISMN) are typically provided in volumetric soil moisture (m³ m⁻³). The manuscript does not clearly describe how this unit mismatch is handled in the validation procedure.
Citation: https://doi.org/10.5194/essd-2025-746-RC2 -
RC3: 'Comment on essd-2025-746', Anonymous Referee #3, 09 Mar 2026
Review
Title: Next-generation Metop ASCAT Surface Soil Moisture datasets
Authors: Sebastian Hahn et al.
MS No.: essd-2025-746The paper describes the new version of the ASCAT surface soil moisture data. The changes in the algorithm w.r.t. the current version are substantial, making a full-length data description article necessary. The article topic is definitely of interest to ESSD readers.
The paper is generally well written. The ASCAT soil moisture data have been used very widely for many years, and the new version will be an important dataset for the community. Error estimates are included with the dataset, and the error estimates as well as the data quality are adequately described in the article. The length of the article is appropriate, and the article is well-structured and clear.
Having said that, I was missing a one-to-one comparison of the current (old) and new data versions.
The paper is suitable for publication after the authors address the comments below.
Major comment:
The one big missing piece is an apples-to-apples comparison and validation of the current and new versions of the ASCAT data. This includes two aspects:
- a) The implicit assumption is that the new product is better, or at least no worse, than the current product version. The article only touches on this in Lines 608-611. I recognize that the current data come in two flavors (time-series product and NRT product), which are generated with different algorithms and might thus have different quality. I also recognize that the current data and the new data are on different grids. Finally, I recognize that the processing changed significantly from the current version to the new version. But from a user’s perspective, the most important piece of information is the quality of the new data compared to the current data. None of the above gridding and processing differences is truly an obstacle to a comparison of the current (old) and new versions. The authors should add at least one of the current products to at least one of the validation results. Since the ISMN validation used the QA4SM tool, it should be very straightforward to add the current (i.e., old) time-series product to the ISMN validation in Fig 15.
- b) Another burning question for this reader is whether the new version of the ASCAT data is suitable as an extension of the current (old) version. ASCAT data are routinely used in the land analysis of NWP and reanalysis products, where rescaling is baked into the system. Do the current (old) and new data have sufficiently similar climatologies to allow for simply replacing the input ASCAT obs? Or do the assimilation systems need to be recalibrated for the new ASCAT version? At a minimum, a comparison of the climatological mean and std-dev of the current (old) and new products would be needed to start answering this question.
Minor comments:
1) The mathematical notation needs minor improvement and clarification:
- a) Equation (3): Please clarify subscript “i”. I assume the meaning of “i" here is is different from that of i=1,12[13] used in section 3.3.1, and it is also different from that of “i” in (5). Perhaps best to use a different index variable in (3).
- b) Equation (8): I understand that \hat{sigma} here indicates the backscatter after normalization of the azimuth angle phi. But I did not see \hat{sigma} again in the remainder of the paper. (Perhaps I missed it.) I expected to see \hat{sigma} used in section 3.3.3 (“Incidence angle dependency”). It remains unclear to me if there is an order to the azimuth and incidence angle correction or normalization. Please clarify.
- c) Equation (12): Subscript “m” has not been introduced. I understand it stands for mid-beam. Line 290 introduces subscripts “a” and “f”, although somewhat implicitly, and Line 274 introduces the triplet “Fore, Mid, or Aft” (but not the lower-case subscripts “f”, “m”, and “a”). Please clarify.
- d) Equation (15) introduces the symbol “d” for day-of-year. This is easily confused with the subscript “d” for “dry (reference)”. I recommend changing the symbol for day-of-year to “DOY”, or at least an uppercase “D”, to more clearly distinguish the “day-of-year” and “dry” notation.
- e) Equations (17-18): The (percentile) symbols “P_2” and “P_98” have not been defined explicitly. The meaning can be inferred from the context, but the reader should not be asked to sort this out.
- f) Equations (17-18): I’m unsure what exactly is included in the set of data months in the argument of P_2 and P_98. For example, if time “t” falls into August 2015, the set of months here could include all months within the period June 2012 through November 2018 (that is, Aug 2015 plus/minus 42 months, and assuming my math is correct). Or it could include on the August months, that is {Aug 2012, Aug 2013, Aug 2014, Aug 2015, Aug 2016, Aug 207, Aug 2018}. The text in Line 355 (“account for seasonal vegetation effects”) suggests that only August data are included. But the notation suggests to me that all months in the 85-month period are included. This requires a bit more clarification in the text and/or equations.
- g) Line 389: Replace “(i.e., a=5, b=95)” with “(i.e., a=5%, b=95%)”. (Missing units.)
2) Lines 55-56: “… this gives users the advantage…” The “relative” soil moisture units of ASCAT soil moisture are not an “advantage” over the “absolute” soil moisture units of SMAP and SMOS soil moisture. Users can just as readily rescale SMAP and SMOS data, if that is what they want or need to do. Please revise the sentence in question accordingly.
3) Table 2: The “observation periods” of Metop-B and Metop-C do not end on 2024-12-31. Both satellites remain operational as of this writing. Perhaps this end date refers to the Metop-B/C data used in the derivation of the algorithm constants? Please clarify. Also, Line 543 notes an end date of 2023-12-31 for data used in the SNR validation, but I could not find an end date for the ISMN validation.
4) Line 104: “generate a frozen soil and snow cover probability flag”. Based on later text, I understand this flag to be “climatological”. I suggest changing this to “generate a climatological frozen soil and snow cover probability flag” (or similar clarification)
5) Line 396: Add explicit reference to section 3.3.2, where “ESD” is defined.
6) Line 445: replace “While in these regions observation noise is typically higher, …” with “While observation noise is typically higher in topographically complex regions, …”. (The preceding sentence is about all land regions, not just topographically complex regions.)
7) Table 3: I recommend adding additional information to this table. This might require formatting the table as a “sideways” table or transposing the table. But I think the additional clarity is worth the formatting complications.
- a) Provide quantitative information about the latency of each product.
- b) Provide explicity information about the swath “duration” (PDU). g., H130 should have 60-minute PDUs, and H122 should have 3-minute PDUs. (It’s possible that by adding this info the “Category” information becomes redundant; but there’s nothing wrong with some level of redundancy if it helps the reader’s understanding.)
- c) The data format column could be dropped by adding the following to the caption: “All products are available in netCDF format. H121 and H29 are also available in BUFR format.”
Editorial comments:
Line 49 (typo): delete “with” (or otherwise fix grammar)
Line 81: delete “currently” (redundant)
Line 166 (typo): replace “scatters” with “scatter” or “scattering” or “scatterers”
Line 213 (typo): replace “observation” with “observations”
Line 499 (typo): Replace “application” with “applications”
Citation: https://doi.org/10.5194/essd-2025-746-RC3
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- 1
Review of
Next-generation Metop ASCAT Surface Soil Moisture datasets
by Hahn et al.
General comments:
This study provides a detailed account of the methods used to create a soil moisture dataset with a C-band spaceborne scatterometer. This dataset is publicly available. The paper is well written, and I only have a few minor comments.
Recommendation: minor revisions.
Particular comments:
- Overuse of the word “generation”: “next-generation Metop”, “ERA5 … fifth generation”, “generation of the backscatter”, “time series generation”, “Metop-Second Generation”, “generation of ASCAT SSM”. Where possible, I would suggest finding alternatives to the word 'generation'. In particular, I don’t understand why the dataset associated with this paper should be described as 'next-generation'. In the title and abstract, I would replace 'next-generation' with 'novel'.
- L. 204 (Eq. 2): “w” is undefined.
- L. 223: The Fibonacci grid appears to be a new concept, at least in the context of geophysical files. The authors should justify this choice more clearly and explain how the ASCAT data users can read such data using standard GIS software or Python libraries.
- L. 251-252 : "However, this format compromises the ease of future data extension due its strict sorting structure" is not very clear. Could you clarify and explain why?
- L. 294 (Eq. 9): ":=" do you mean "="?
- L. 394 (Eq. 21): Is there any justification for using a linear relationship to convert ASCAT sigma0 values into soil moisture values? Using a sigmaoid function would easily solve the outlier issue.
- L. 433: What is the procedure for dealing with temporary standing water, for example during flood events?
- L. 453: Is the likelihood of soil freezing indicated by a flag? Soil freezing and snow cover could be derived in real time from numerical weather forecast models.
- L. 534-535: Replace by: "SWI is computed using an exponential filter of SSM. This mimics water infiltration into deeper soil layers. It does so in a purely statistical way."
- L. 551: I would replace 'best performance' with 'best agreement', given that passive microwave SM is not perfect.
Editorial comments:
- The Pearson correlation coefficient (R) is defined multiple times. The first definition is sufficient. Then, use 'R'. Same for ‘SNR’.
- Is it "H SAF" or "HSAF"? 'H SAF' looks strange.