the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jul 2024
Soil moisture products consistency for operational drought monitoring in Europe
Abstract. The roadmap to enable operational soil moisture (SM) monitoring for meteorologic and hydrological early warning is challenged by the uncertainty within the available remote sensing and modelling products. This study addressed two relevant uncertainties: the residual trends in the series and the spatial consistency. While the latter has been often revisited to validate remote sensing and modelling products against in-situ data, the former is often disregarded in studies addressing SM changes.
This study evaluated three SM products: (1) the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) active Advanced SCATterometer (ASCAT)-derived dataset, (2) the passive subset of the European Space Agency (ESA) - Climate Change Initiative (CCIp), and (3) the modelled dataset from the European Drought Observatory (EDO). The analysis was carried out over Europe in the period 2007–2022 at 10-day temporal scales.
We obtained that even these popular datasets are subject to patches of spatial inconsistency and residual trends when compared to the in-situ data from the International Soil Moisture Network (ISMN). In view of the great complementarity shown by the active and passive remote sensing and the modelled SM estimates, two merged products are proposed and tested against in-situ data. Results indicate that combining H SAF ASCAT, CCIp and EDO equals or surpasses the spatial and temporal consistency of the individual SM products alone, even when only the near-real-time products of H SAF ASCAT and EDO are combined. Thus, merging remote sensing and modelled SM products is advantageous for enhanced spatial and temporal operational monitoring of SM at the European scale.
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Status: final response (author comments only)
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RC1: 'Comment on hess-2024-182', Matthias Zink, 02 Oct 2024
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AC1: 'Reply on RC1', Jaime Gaona, 10 Nov 2024
Reply to 1st comment on the manuscript of title:
"Soil Moisture consistency for operational drought monitoring"We thank the reviewer Mathias Zink for his thorough revision of the text and his positive impression of the quality of the manuscript and research conducted. His insightful comments on the convenience of emphasizing the novelty and target research gap addressed by the study as well as about the minor corrections have been of great help to improve the manuscript.
In the following lines, we incorporate detailed answers to both the major items requiring attention and the minor corrections demanded. Reviewer demands in bold, author's response without bold and when referring to long chunks of introduced text in italics)
Major items
- The authors should make clearer what is the novelty and the research gap addressed by their study.
There is no single soil moisture (SM) product able to address the monitoring of SM over all environmental conditions given the specific limitations of the different SM data types (i.e. either model-based or from remote sensing active and passive missions experience uncertainties over land covers dominated by vegetation, in climates where snow has a relevant role on the SM regime or over areas of difficult soil characteristics). Furthermore, some combined soil moisture products may overlook the consideration that the products combined should be independent, particularly regarding any participating modelling schemes, in order to maximize the informative value of the combined product or at least to avoid inherited uncertainties. Therefore, there is need for products able to monitor SM evolution over a wider range of conditions which encourages the exploration of combined SM products. All these aspects have been clarified along different sections of the manuscript (abstract (L19-20), last part of introduction where objectives of the study are described (e.g. L111-115), and results (e.g. L395-397, L400-402) and conclusion (e.g. L595-598).
- I suggest to change the title by removing the term drought as the paper is not referring to droughts at all.
While it is true that most of the article refers to a great extent to the SM data of diverse sources instead of to drought characteristics derived from it, there are results using the anomalies of SM (e.g. trends) which indirectly define thresholds, magnitude and duration of the drought events. Nonetheless, we clarify this aspect in multiple sections (e.g. L116) to prevent readers from wondering which is the commented relation of SM to drought.
- How are quality assurance scores (ESA CCI, ISMN) treated within this study?
Quality assurance of ESA CCIp: The main source of quality uncertainty in the case of the application of ESA CCIp over the European continent is derived from the seasonal presence of snow and related processes in the northernmost areas and mountain ranges. Results of the linear and TCA analysis display how snow-capped regions stand out as the most uncertain areas, independently of their vegetation cover (i.e. the other primary factor of uncertainty), primarily due to the altered conditions for signal retrieval under snow cover or frozen soils. Flags of ESA CCIp dataset for snow and frozen soil not only consider snow cover detection based on temperature but also on freeze and thaw conditions based on (Ku-, K- and Ka-band) retrieval (CCIp ATDB guide). In consequence, we apply ESA CCIp snow flags to remove the seasonally affected areas, and as the most restrictive flag of these conditions among the three products, we apply these flag restrictions in all analyses to the rest of the SM products over the same boreal areas.
Quality assurance of ISMN: Flags of ISMN data are present for multiple factors of influence beyond the features of SM series themselves (e.g. temperature and rainfall threshold beyond for instance SM series characteristics such as saturation). In spite of the worth of these multiple flags, all the stations that are included in the ISMN database after harmonization and quality control across Europe with available data in the period were included. This means that despite some flags in certain stations indicating dubious values of SM or about associated variables (such as BIEBRZA network which is known for providing very high SM values) all data available was included, since beyond the reliability of key ISMNs (according to the Quality Assurance for Soil Moisture initiative identifying the most consistent networks) there is no abundance of stations over all the climate types of Europe. Therefore, it was preferred to deal with uncertainty due to intermediate quality scores of some stations’ data than losing the power of valuable yet not optimal SM data from non-key ISMN stations for validating the remote-sensing and model-based products across such a wide range of conditions from boreal to semi-arid conditions.
We have included mention to these relevant aspects at the end of section 3.4.1.:
L226-230: “The different product additionally provides metrics of the error characteristics to identify the areas and periods affected by relevant impactful factors such as snow cover. The flag scheme of ESA CCIp exemplifies the detailed procedures devoted to distinguishing when the data is subject to further filtering. Consequently, considering the notable importance of the snow cover factor in our analysis over the boreal and mountainous areas of Europe, we have applied the flags of ESA CCIp as mask to the data coverage of the other products of the analysis over the snow-prone regions. “
In L232-237: “Conversely to the case of the distributed datasets, the point data from the ISMN database has not been significantly restricted with flags due to the scarcity of this type of data. Multiple areas where snow and icing processes are frequent are barely observed in situ, and consequently, even despite the seasonal uncertainties, RS products provide much more coverage of these areas than the few ISMN stations over these areas. Therefore, including all ISMN networks and data available was the decision adopted to ensure sufficient amount of data for validation over every climatic type and ensure the representativity of a wide range of observed soil moisture conditions.”
- L212 How do the datasets used fulfil these assumptions (of TCA)?:
This study focused on soil moisture data types originating from distinct model-based, active and passive remote sensing missions precisely to avoid overlooking the interdependence existing between multiple available soil moisture products. In numerous cases, remote sensing products of enhanced characteristics such as downscaled or filtered data do introduce further uncertainty by ingesting or using models or equivalent products in their validation. Therefore, our application of only the passive CCIp dataset instead of the combined one aimed to avoid dependence between the active ASCAT H120/H121 dataset and CCI combined also including this MetOp datasets. In this way, we proposed the use of ASCAT H120/H121 as a consistent product known for the quality of its curation as representative of the active RS SM products while CCIp as representative of a well-documented passive product generated without assimilation of models or active remote sensing data. For the same reason we choose the LISFLOOD model inside EDO instead of popular land-surface models participating in reanalysis and the curation of multiple L3 and L4 remote sensing SM products as the model-based product. This choice ensured none of the other selected SM products for the study has utilized LISFLOOD in its generation. Additionally, given the primary operational use of LISFLOOD, it emphasizes our aim of evaluating soil moisture products for monitoring. Nonetheless, clarification on these aspects has been extended in Section 3.4.2. (L242-257):
“ The TCA model assumes linearity of SM retrievals, stationarity of signal and independence of errors from signal or in between product errors (Gruber et al., 2016; Massari et al., 2017; Filippucci et al., 2021). However, since the purpose of the study is not to evaluate the sensitivity of the assumptions of the TCA model we focus on the adequacy of the selected SM products for triplets based on their reported independence and consistency. The LISFLOOD model is not used for the processing or validation of neither the passive and active RS products that complete the product neither on the curation of the ISMN data. The ASCAT active RS SM datasets upgraded from H120 to H121 are supposed to resolve the increasing trend and consequently can be considered only marginally non-stationary. CCIp uncertainties are to a great extent already resolved in the dedicated processing methods developed for the blending of data from different passive SM missions. The same assumption can be applied to the residual trends of EDO and CCIp SM datasets, that are of a magnitude likely more relevant for long-term analysis rather than for its impact on the TCA assumptions.“
Regarding the assumptions of the TCA model, for the assumption of the independence of residuals, overall, the number of triplets available in the period of study is far bigger (up to 536) than the 100-threshold estimated by Scipal et al, (2008) to ensure the independence and no bias of the residual errors. Even the regions prone to be snow-covered present enough coverage (far beyond the 100 values of the threshold) to guarantee that no downgrade in the reliability of the TCA model originates from a lack of data representativity.
Fig. 1: Coverage in number of valid triplets for the analysis of the three products ASCAT H120/H121, CCIp and EDO across Europe in the period 2007-2022.
Regarding the assumed linearity between the signal and the errors, values of the logaritmic signal-to-noise ratio (logSNR) in decibels stay within ranges of linearity of between logSNR and R2 (Mean around 4-7 dB). Furthermore, debates on the linearity matter as reported in (Gruber et al., 2015) indicate that despite the bias that might be introduced by this assumption there is no clear alternative to the consideration of linear models to the relationship between the model and the data because other methods also introduce bias through matching, scaling and application of polynomials. Therefore, the major risk of non-linearity may arise from stationarity that is the following assumption discussed here.
While some of the datasets included here display some non-stationarity (it is the second major goal of the study of evaluating such trends), the assumption refers to both signal and errors also being of constant variance over time. In the first case, since we already indicated in the study the notable correction of the trends from h120 to h121 product, the comparison between the values of error variance, sensitivity and logarithmic signal-to-noise ratio do not vary remarkably in between both cases. These results indicate that despite the importance of the trends for the analysis of changes in soil moisture anomalies, particularly if the analysis focuses on the identification of long-term variations due to climate change, the values of non-stationarity revealed in the Section 4.4. of the manuscript do not seem to compromise the validity of the TCA results in the study.
For the second case of the influence of non-stationarity, we checked the error variance and the other auxiliary metrics between different periods within the study period of known contrasted stationarity (2007-2015 (overall not trendy) and 2015-2022 (overall very trendy)). The results indicate that both the improved versions of the products (e.g. h121) and the slightly trended previous version of products (e.g. ASCAT h120) do not show notable differences in correlation (R_TCA), variance of the errors (VAR), sensitivity (SENS) and signal to noise ratio (logSNR)(metrics now specified at lines 269-274) when considering the different periods. Therefore, despite the inevitable existence of residual non-stationarity, given that the purpose of the study was to highlight the increasing temporal stability of products regarding trends, it has no noticeable consequences in the provided results.
All in all, these considerations referring to the compliance of TCA with its assumptions, favor the independence of the results provided here, but if included in the manuscript might excessively lengthen the text or obscure the interpretation of the main results. They can be included as supplementary information, if necessary.
Fig. 2a: Triplets of ASCAT H121, CCIp and EDO across Europe in the period 2007-2022 (leftmost column), 2007-2015 (central column) and 2015-2022 (right column) showing the results for H120 regarding correlation (R_TCA), variance (VAR), sensitivity (SENS) and logaritmic signal to noise ratio (logSNR).
Fig. 2b: Triplets of ASCAT H121, CCIp and EDO across Europe in the period 2007-2022 (leftmost column), 2007-2015 (central column) and 2015-2022 (right column) showing the results for H121 regarding correlation (R_TCA), variance (VAR), sensitivity (SENS) and logaritmic signal to noise ratio (logSNR).
- L297ff The low scores of EDO in Northern Europe may arise from low performance but good agreement of the other 2 RS products. As you mentioned before they are known to be worse in forested and icy regions. Please elaborate a little in the discussion and add in situ observations as reference to get a better picture.
There are results in Figure 3 that illustrate the case and that can help clarify the origin of the low scores. The detailed description has been added to the section 4.1.1 (L342-349) and included here:
“The negative R-Pearson scores shown in Fig. 3 in between EDO and H120/H121, between CCIp and EDO, and to a lesser extent between CCIp and H120/H121 show even negative R-Pearson correlations in snow-dominated areas and thin soils in mountains and in arid climates. These negative values refer to disagreement between products beyond low performance. The combination express that EDO is the greatest source of such disagreement but differently for northern and southern latitudes: the negative scores induces by the participation of EDO seem to be mostly restricted to snow-dominated regions (compare Fig. 3a and 3b with 3c) while H120/H121 seems to be the primary source of low scores in the arid areas. These results are consistent with the known difficulties of EDO-Lisflood in snow-dominated hydrological regimes and of ASCAT H120/H121 over thin soils of arid areas.”
Figures 4 and 5 also explain differences between the same products in terms of performance over challenging regions, but in this case with triple collocation scores. The additional comments have been added to the section 4.1.2 (L372-378) in between Fig. 4 and 5:
“The low values of triple collocation correlations concurring over the snow-prone areas of Scandinavia and snow-caped mountain ranges (e.g. Alps) affect particularly EDO and CCIp, and experienced a significant improvement over snow-prone lowlands with the update of ASCAT data from H120 and H121. Beyond the evidence that EDO model is limited over snow-dominated regimes, CCIp seems to display a bimodal downgrade of scores with low values over Scandinavia and intermediate ones over the also seasonally snow-dominated but less densely vegetated East Europe suggesting that uncertainties originated from high-latitude and dense canopies may differ from those originated from the snow regime alone (Blyverket et al., 2019)”.
Additional comments regarding this distinct limitation of each data product have been included in the Section 4.3 against in-situ data in the paragraph after Figure 7 (L454-L468):
"“However, the low performance of EDO compared to the other products over boreal areas is in fact better at the specific locations of the ISMN networks of Scandinavia (FMI, GTK, NVE) compared to the results when R Pearson or R_TCA scores are considered. This disagreement indicates more the need of additional networks in such a vast boreal area than actual better performance of model-based estimates over the remote sensing ones.”
Minor items
- L76 please rephrase numerical model into process based or conceptual
Thanks for noticing the need of clarification
- Fig. 1 Labels a and b missing.
Corrected, thanks for noticing the need for clarification.
- L171 Web address of ISMN is outdated. Please update with https://ismn.earth.
Thanks for finding the outdated web address
- Fig. 2 A distinction into further classes like forest, urban etc would be useful.
Fig 2 primarily aims to illustrate the location of the ISMN networks used for the study while providing secondary information about the dominant land use in such location. Our purpose for the land cover colouring was to differentiate if the ISMN locations correspond to areas with (green) or without canopy (yellows) given that vegetation can be the most influential factor over the SM retrievals with remote sensing. Therefore, yellows comprise all land use classes without vegetation effects over the SM retrievals and green those land use classes with vegetation susceptible of effects. Urban areas are also currently colored in their own hue (pink) and in fig. 2 are also visible for great urban areas such as Paris, London, Berlin, Milan... All these aspects will be clarified in the caption of Fig. 2.:
“Yellow/green colours comprise the multiple land cover classes without/with significant vegetation effects on the retrieval of soil moisture using remote sensing. Urban areas are coloured in pink. “
- L190 Please rephrase (The daily values ...)
To clarify the meaning and improve the readiness of the manuscript at L190, the confusing text has been rephrased as "All
“All initial values within each of third of the month at daily time step are aggregated to the reference date of the corresponding interval.”- L197 I guess SMsat shall be ms
Yes, it refers to surface soil moisture saturation, thanks for identifying the clarification needed.
- I suggest to add a table detailing the different spatial and temporal representation/resolution of the data source
While the table can facilitate the understanding of the details of all datasets simultaneously, it barely will include many more details than those included in the data description. Therefore, while the table can still be added, we meanwhile provided clarified description of the spatial and temporal sampling, resolution and their resampling in their sections 3.1 and 3.2
- Please review the use of hyphens instead of a comma when you refer to two panels in a figure, e.g, L260 (Fig. 3b-3e), L261...
Thanks for realizing the misuse of it and the recommendation to revise them.
- L229 R TCAHSAF should be SMHSAF ___ correct the equation 5
Thanks for identifying the mistaken term. Corrected
- L270 caption of Figure 3: Map of temporal R-Pearson correlation instead of spatial.
Yes, corrected, thanks for noticing.
- L318 English grammar, please rephrase (Hence, ...).
Rephrased and clarified in meaning.
- Fig. 7: Please add an axis label to all x axes.
While some of the axis may seem to lack label, as all products a-f) rephrased and clarified in meaning.
- L372 Please rephrase, sentence is hard to understand.
Rephrased and clarified in meaning.
- L399 I am not quite sure about which dashed lines you are talking about.
It was a mistake, referring to previous versions of the figure.
- L400 Dfb instead Db
Yes, corrected, thanks for noticing.
- L414 Please rephrase, sentence not understandable (While the products
Rephrased to clarify to which agreement the sentences refers (the trend in space patterns beyond the agreement in magnitude).
Citation: https://doi.org/10.5194/hess-2024-182-AC1
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AC1: 'Reply on RC1', Jaime Gaona, 10 Nov 2024
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RC2: 'Comment on hess-2024-182', Anonymous Referee #2, 20 Nov 2024
The article primarily describes the comparison among different soil moisture (SM) products and their comparison with in-situ measurements from the ISMN database. This study also evaluates the spatial and temporal consistency of these products and their combined potential for operational soil moisture monitoring. The article is well-written and has scientific clarity but requires addressing some issues before publication. Below are comments that I hope the authors will find helpful:### Major Comments:
1. The abstract seems incomplete. While it mentions details about the data products, it lacks discussion on the methods used. This should be included for completeness.
2. In the introduction section, the authors discuss various topics, such as the advantages and disadvantages of different soil moisture datasets, a brief overview of methods to merge SM products, and the use of SM datasets in time series analysis. However, there is a lack of coherence between the introduction and the objectives. The section does not provide sufficient information on merging soil moisture products. Additionally, the novelty of the study is unclear, and the current introduction fails to identify the existing research gaps.
3. Many sentences in the introduction section are excessively long and difficult to follow. The authors should break these into shorter sentences for better readability.
4. It is unclear whether QA/QC procedures for soil moisture data have been applied in this study. If not, it is strongly recommended to implement quality control (QC) to remove poor-quality data.
5. Throughout the study, the necessity of developing a new merged product is unclear. Why is a new product required when model-based datasets like ERA5-Land already assimilate multiple satellite observations and in-situ measurements? These points need to be clarified before defining the study's objectives.
6. This type of study typically involves hypothesis testing, which is missing in the article. Incorporating a hypothesis would strengthen the study's scientific rigor.### Minor Comments:
- L38: Restructure for clarity.
- L65: Replace "complementary features" with "advantages of active and passive data."
- L75: Rewrite as "...a lot of progress has been made..."
- L78: Correct to "...different modeling schemes have..."
- L85: Revise to "The RS-based modeled products as well as the..."
- L85: The phrase "The RS, model-based means" is confusing and should be clarified.
- L85-86: Validation protocols are widely applied to soil moisture products. The current statement may not be entirely accurate and should be revised.
- L94: Clarify what is meant by "minority among the studies."
- L103: The statement does not align with the title and needs to be rephrased.
- L119: Specify which datasets are limited to high latitudes.
- L144: Clarify whether this refers to "upcoming products" or something else.
- L205: When calculating the mean value, is the number of soil moisture observations consistent across datasets and months? This should be explained.
- L223: Complete the sentence for clarity.
- L226: Use "achieved" instead of "obtained."
- L245-255: The equations may be unnecessary since they are widely used. Citing the original article should suffice.
- L264: Why is soil moisture considered over the Alps? Regions with terrain and other factors prone to SAR-induced errors should be masked.
- L310: The phrase "Sometimes attributed to coverage..." is incomplete and needs clarification.
- L375: Clarify what is meant by "challenging SM."Citation: https://doi.org/10.5194/hess-2024-182-RC2 -
AC2: 'Reply on RC2', Jaime Gaona, 03 Dec 2024
Reply to 2nd comment on the manuscript of title:
"Soil Moisture consistency for operational drought monitoring"We thank the anonymous reviewer for identifying aspects with room for improvement despite noticing the manuscript's clarity. His insightful comments on the convenience of emphasizing the novelty and target research gap addressed by the study as well as about the minor corrections have been of major help in improving the manuscript.
In the following lines, we incorporate detailed answers to the major items requiring attention and minor corrections. (Lines of the modified manuscript refer to the version of the manuscript including all tracked changes but with only the final text visible). [Reviewer demands in bold, author's response without bold and when referring to introduced text in italics)]
### Major Comments:
1. The abstract seems incomplete. While it mentions details about the data products, it lacks discussion on the methods used. This should be included for completeness.Discussion about the methods of TCA, trends and others is now included in the abstract. (All along introduction section, L15-L45)
2. In the introduction section, the authors discuss various topics, such as the advantages and disadvantages of different soil moisture datasets, a brief overview of methods to merge SM products, and the use of SM datasets in time series analysis. However, there is a lack of coherence between the introduction and the objectives. The section does not provide sufficient information on merging soil moisture products. Additionally, the novelty of the study is unclear, and the current introduction fails to identify the existing research gaps.The coherence of intro and goals, and especially in reference to the research gap and novelty of the study has been improved along all sections of the manuscript emphasizing that operational monitoring increasingly demands combined products suitable for anomaly detection across scales and environmental conditions given that no single soil moisture product can accomplish alone due to the specific limitations of each SM data type. L16-25 in the abstract, L107-119, L130/142 in the introduction, in the results L410-414, L440-450, L593-601 and in the conclusion L603-611, 621-626.
Additionally, we also emphasize the relevance of identifying the residual trends in the diverse types of SM products available, because overlooking such trends has a relevant impact on anomaly detection either for monitoring or for specific analysis of tendencies, especially in relation to the ongoing alterations due to climate change.
3. Many sentences in the introduction section are excessively long and difficult to follow. The authors should break these into shorter sentences for better readability.The introduction has been extensively revised for readability. (L52-L148)
4. It is unclear whether QA/QC procedures for soil moisture data have been applied in this study. If not, it is strongly recommended to implement quality control (QC) to remove poor-quality data.
While the steps conducted regarding QA/QC for SM were originally only briefly described, they have been included in the revised manuscript according to reviewer demands.For the products affected by snow and related processes in northern and mountainous regions, we have adopted the flags for snow and frozen soil in the ESA CCIp dataset to ensure the areas most impacted are not seasonally considered. These flag restrictions are then similarly applied to the other SM products in these regions. Specific mention and clarification of this approach is provided in L255-259.
The ISMN data used for validation also includes flags for various factors of uncertainty. However, in this case, given the scarcity of stations across climates and environments even over Europe, it was decided to include all stations with available data at the uppermost depth of interest. However, the series were filtered using the flags that indicate unrealistic data due to discontinuities, out-of-range values, and other artefacts in the data series, regardless of the uncertainties derived from the location, instrument type or other factors. The comment describing this matter has been included in L261-268.
5. Throughout the study, the necessity of developing a new merged product is unclear. Why is a new product required when model-based datasets like ERA5-Land already assimilate multiple satellite observations and in-situ measurements? These points need to be clarified before defining the study's objectives.The merged and ERA5-Land products are not comparable because they are entirely different products even if both use hydrological models. ERA5 reanalysis data is the one that ingests SYNOP meteorological observations and assimilates only active data (the ASCAT datasets) to generate the climate data reanalysis included in ERA5. Then, in an offline simulation, H-TESSEL uses ERA5 climatic data to estimate multiple land variables, including SM. Therefore, the ERA5-Land SM data can be considered entirely modelled nature (by physical models) and therefore produced without the direct use of satellite data. Conversely, the combined RS model-based products are a combination (weighted average) of actual satellite SM active and passive products and also modelled data. The accuracy is also not comparable and the use for cross-scale analysis over regions of a wide range of conditions becomes strongly favourable for the merged products directly derived from the combination of the satellite observations, and even in the case of including modelled data, vastly dominated by them. ERA5-Land tends to provide too smooth estimates that do not capture the heterogeneous nature of SM, something undesirable for operational products applicable across scales. In this study, preliminary comparisons between individual RS products and ERA5-Land show that ERA5-Land may achieve similar compared to RS and merged products over some areas of the well-instrumented European region but underperforms compared to them when less data is available. Multiple revisions of text also led to changes related to point 1 of these major comments and indicated their lines of changes there.
6. This type of study typically involves hypothesis testing, which is missing in the article. Incorporating a hypothesis would strengthen the study's scientific rigour.
The definition of specific hypothesis to evaluate the multiple characteristics of the diverse SM products evaluated in the manuscript—whether comparing different types (RS, model-based, and their combinations), different versions (e.g., ASCAT H120 vs. H121), or against data with completely different statistical properties like ISMN in-situ data—may obscure the already insightful and dependable information obtained from the results about the diverse aspects discussed in this manuscript.
In the study, performance metrics from the TCA analysis (i.e. residual errors, sensitivity, variability and residual to noise ratio) provide sufficiently robust information to discuss the comparative consistencies of the products, as extensive literature applying these metrics in RS SM data has shown. The reader can understand that for the sake of simplicity in a study tackling multiple aspects of SM products the given results condense careful testing of their reliability. For instance, maps providing only R_TCA results aim to ease the interpretation of the spatial patterns that detailed statistics without the description of the spatial dimension may fail to convey. Nonetheless, results based on categorized classes such as the climate types or the validation over ISMN stations were designed aiming to provide the most complete depiction of the statistics of the results, which is in line with the desirable accuracy of hypothesis testing. Fig. 5 serves as an example, because on it either means, medians, quartile ranges and outlier abundance can inform in detail about the significance of the difference between products.### Minor Comments:
- L38: Restructure for clarity.
Thanks for the suggestion, it has been rewritten in a couple of sentences for further clarity. L57-60“...but also, in many critical zone processes (Seneviratne et al., 2010; Green et al., 2019; Bolten and Crow, 2012). Aiming to trackTherefore, the characterization of SM dynamics over time and space such a challenging pace of alteration, the need to characterize SM change was encouraged long ago (Owe et al., 1999) aiming to better understand the implications of SM change and its pace of alteration on related processes. Traditionally such analyses were conducted in field studies but given the heterogeneous nature...”
- L65: Replace "complementary features" with "advantages of active and passive data.Of course both techniques gained attention due to their multiple advantages, but it was their distinct features combined that triggered the interest of the remote sensing community, which eventually materialized in the Soil Moisture Active Passive mission from NASA, which despite malfunctioning, has been the optimal configuration of soil moisture retrieval since thereafter (since 2015). The text has been replaced at L86 to:
“The distinct capabilities..."
- L75: Rewrite as "...a lot of progress has been made..."
Corrected, thanks for noticing. L96
- L78: Correct to "...different modelling schemes have..."
The suggestion has been included. L98
- L85: Revise to "The RS-based modelled products as well as the..." The phrase is confusing and should be clarified.Clarified in L107
- L85-86: Validation protocols are widely applied to soil moisture products. The current statement may not be entirely accurate and should be revised.The sentence did not intend to say that validation is not widely applied. Consequently, it has been reformed and improved to clarify its meaning that only wanted the emphasize the crucial role of ground observations in the process of validation. (L122-125)
- L94: Clarify what is meant by "minority among the studies."The sentence aims to emphasize that the use of SM datasets for analysis like trend evaluation, in which the role of uncertainties can significantly impact the results, is often not supported by an analysis of the uncertainties of the datasets used as input. The sentence has been restated as follows (L130-135):
"Despite the critical role that uncertainties in SM datasets can play in altering the outcomes of a study (e.g. in trend analysis), explicit analyses of the uncertainties of the data inputs (e.g. residual trends) and their propagation into results have always been rare. Only a few studies have incorporated this crucial step despite its relevance, whether from the early days of pioneering SM products (e.g., Künzer et al., 2008; Draper et al., 2009; Liu et al., 2012) or at the present, when new versions of SM products are released with improved processing (e.g. Karthikeyan et al., 2017) or when products incorporate new inputs or ingesting procedures (e.g. Zwieback et al., 2017)."
- L103: The statement does not align with the title and needs to be rephrased.
Partly addresses point 2 in major comments. The title comprises several aspects of data quality under the word “consistency” for the sake of straightforwardness. However, identifying the limitations (i.e, the lack of consistency in some aspects like residual trends) of the input datasets implicitly raises awareness of the suitability of the products for a specific analysis (e.g. trend analysis).Nonetheless, the sentences have been restated as follows for better clarity and alignment with the title and the general objectives of the study (L140-142):
“This study evaluates three types of SM data: passive RS, active RS and model-based nature of near-real-time capabilities, to better explore their individual and combined consistency and suitability for the operational monitoring of SM.”
L119: Specify which datasets are limited to high latitudes.
Thanks for noticing, the sentences inaccurately reflected the intended message, so it has been modified to the following one (L157- L162:
“The geography of Europe comprises a wide range of features across the range of scales covered by the spatial resolution of the RS SM products. However, some RS SM products are somehow limited at high latitudes (latitudes higher than 60ºN) due to physical characteristics of the environment (frozen soil, snow cover, water bodies and vegetation interference) or technical limitations of their sensors (angle of view) results of the boreal belt (60°-72.5°) are included in the study to illustrate the challenges of SM monitoring over such areas. The area of study is displayed using Lambert Azimuthal Equal Area (EPSG: 3035) centred at 50°N, 15°E. (Fig. 1)."
- L144: Clarify whether this refers to "upcoming products" or something else.
It refers to both cases that’s why the reference (Hahn et al., 2017) is provided. Nonetheless to clarify it “both” has been included in the sentence (L188).
- L205: When calculating the mean value, is the number of soil moisture observations consistent across datasets and months? This should be explained.The statistics of temporal coverage (see Fig.1 of Reply to RC1) of the different products at the 10-day period that is used as reference unit for the analysis indicate most areas are covered by acceptable number of valid 10-day values (i.e. for the vast majority of Europe over 200 values, except for a few snow-caped areas, which is far beyond the 100 values minimum indicated as valid threshold for the full-length series analysis performed here to compare the consistency of the datasets. Therefore, there is consistency across datasets because the 10-day period used ensures they are not limited by their revisit time but by the seasonal restriction by snow and ice in areas prone to it in winter (which affects all products simultaneously). We have clarified that no seasonal analysis is considered in the study and there are no risks associated with data abundance in the calculation of the seasonal cycle (L249-253).
“These twelve mean SM values are obtained by averaging all 10-days SM within each calendar month occurring along the full length of the study period (2007-2022) without seasonal focus. In the vast majority of the areas, the number of values involved in obtaining the mean SM of the month surpasses tens of values per month, with only a few alpine or boreal snow-prone areas being calculated with a few values per month. The SM anomaly is finally calculated as the ratio of deviation of SM of any month from the mean SM of that month.”
- L223: Complete the sentence for clarity.
Thanks for noticing. It is partly already clarified in response to RC1 but also expanded here (L295-L300)
"Additionally to the R_TCA score, auxiliary performance metrics such as the error variance, the sensitivity and the signal-to-noise ratio defined in Gruber et al. (2016) are obtained from the TCA analysis and occasionally referred to further clarify or discuss the results. The performance of each product is computed by aggregating the scores over the regions occupied by each Köppen-Geiger climate type across Europe (Fig. 1a, (Beck et al., 2018). Furthermore, the TCA approach and its performance metrics are also suitable for merging RS SM products (Gruber et al., 2017), among many options, as described below.”
- L226: Use "achieved" instead of "obtained."Thanks for the suggestion. It has been changed to “achieved” and revised for further use elsewhere.
- L245-255: The equations may be unnecessary since they are widely used. Citing the original article should suffice.
Either keeping them in or removing them does not cause many inconveniences nor has a lot of impact on the manuscript length. They have been removed from Lines 249-253.
- L264: Why is soil moisture considered over the Alps? Regions with terrain and other factors prone to SAR-induced errors should be masked.We consider that showing results even in areas with effects due to terrain or other physical characteristics of the area (snow, water fraction) displays insightful information about the capabilities of each type of product. Consequently, alpine and boreal areas are not masked to illustrate the differences between products and to precisely show in which areas they concur or differ so that the effect of the combination with the less-impacted model-based estimates can also be evaluated over these challenging areas for RS. In view of Fig. 4, there are substantial differences between ASCAT H120/H121 and CCIp across alpine or boreal areas which are worth further exploration because they do not align with roughness and snow cover fraction distributions alone.
- L310: The phrase "Sometimes attributed to coverage..." is incomplete and needs clarification.
The sentence was wrongly segmented, losing its meaning. It has been reformed and clarified as follows (L406-408):
“Such west-east gradient pattern has been either attributed to coverage according to the number of valid triplets available at a certain location) or subject of discussion about the distinct modes of SM variability in Europe (dominated by seasonal variability in the west but dominated by long-term variability in the east: Fig 11 of Piles et al., 2019)”
- L375: Clarify what is meant by "challenging SM." It referred to “challenging SM and flood conditions ("conditions" was missing in the sentence referring to anomalous conditions) that the area of the UDC_SMOS network experienced during the period 2007-2011. (L493-L495).Citation: https://doi.org/10.5194/hess-2024-182-AC2
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AC2: 'Reply on RC2', Jaime Gaona, 03 Dec 2024
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