the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Sep 2025
The Potsdam Soil Moisture Observatory: High-coverage reference observations at kilometer scale
Abstract. Cosmic-ray neutron sensing (CRNS) has gained popularity for estimating soil water content (SWC) due to its innovative capability to measure at an intermediate scale – a notable advantage over point-scale sensors, which are often sparsely installed and due to small-scale heterogeneity result in uncertain absolute values. CRNS serves as a crucial link between small and large scales and has been emerging as a reference measurement for remote sensing algorithm validation for its ability to link the small and large scales. Yet, the sparse availability of long-term datasets limits use of this possibility. Within the framework of project SoMMet (21GRD08), multi-scale soil moisture monitoring was implemented to integrate CRNS with complementary in-situ observations. In this paper, we present harmonized soil moisture data from different sensor types including a CRNS cluster, shallow soil moisture measurements and soil moisture profile data, creating a ready-to-use dataset as reference observation for remote sensing products, covering a highly-instrumented agricultural site in the northeast of Germany. The data include 16 stationary CRNS sensors, with co-located point-scale SWC sensors (mostly permanent), two groundwater observation wells, meteorological records, and data from intensive manual sampling campaigns (covering SWC, bulk density, organic matter, etc.). This dataset distinguishes itself from prior studies by the increased area of approx. 1 km² while still having a high sensor density and overlapping footprints of CRNS. This allows a reasonable degree of geostatistical interpolation to obtain complete coverage. The data are available under the (https://doi.org/10.23728/b2share.db88e149f7924919be376909856739f1) (Grosse et al., 2025), providing a new reference data set for remote sensing products, hydrological or land-surface models and for other products linked to soil water balance.
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Status: open (until 29 Oct 2025)
- RC1: 'Comment on essd-2025-546', Anonymous Referee #1, 30 Sep 2025 reply
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RC2: 'Comment on essd-2025-546', Anonymous Referee #2, 09 Oct 2025
reply
This paper presents several extensive datasets collected concurrently from an agricultural area in Germany. The availability of such a diverse range of measurements within a geographically restricted area provides unique opportunities for soil moisture data analysis, as well as for testing data correction and modelling approaches. The authors are to be commended for assembling such a high-quality dataset.
As already noted in the previous review, the manuscript contains many errors and at times gives the impression of a draft. The overall structure requires substantial revision (see specific comments below). While the tables and figures are mostly informative and provide a good overview of the data, the data archive structure and description should be improved (see specific comments below).
Specific comments
The Introduction should conclude with a concise summary of the paper’s structure and the data presented in this data paper.
L59: There are more paper on signal correction that could be mentioned here, e.g. Baatz et al., (2015) introduced a biomass correction for CRNS, Davies et al. (2022) tested optimal temporal filtering methods for CRNS.
62: Brogi et al. (2022) is not about biomass estimation using CRNS. I believe you meant Brogi et al. (2025).
L63: Here you could also cite Bogena et al. (2020).
L65-67: This sentence is unnecessary and could be removed.
L75: “three such data sets“
L123: A separate “Highlights” chapter does not appear necessary. Consider integrating its content into the Introduction as part of the motivation.
L125: This statement is difficult to understand without referring to Figure 2.
L137: Refer to Fig. 2.
L157: Methods and results are mixed in this chapter, which is inappropriate for a scientific publication. Please restructure the manuscript to clearly separate them. You may refer to Heistermann et al. (2022) as a good example, where the methods are presented first, followed by two separate chapters describing the data provided with the paper and exemplary results from the data analysis.
L182-185: Since all CRNS stations are located in close proximity, it would be more appropriate to use meteorological data from the reference station for corrections of all CRNS stations. This approach ensures that corrections are applied consistently, increasing data consistency. Moreover, reference data are generally more accurate than the lower-quality sensor data used at the CRNS stations.
L184-188: In agricultural fields, such as those in this study, biomass changes constantly over the years, which can significantly influence CRNS signals depending on the type of vegetation (e.g., Jakobi et al., 2022). Therefore, the calibration will not implicitly account for this effect. Please discuss this aspect.
L208: The chapter on Bonner sphere measurements feels somewhat isolated. It is also rather long and distracts from the main focus of the paper, i.e., soil moisture data. Therefore, this chapter should be shortened and better integrated into the manuscript.
L399: The section on stable water isotopes in soil and groundwater appears off-topic for a paper focused on soil moisture. Given that only three campaigns may not provide sufficient accuracy to infer groundwater recharge, and the paper already covers a wide range of topics, consider removing this part.
L514-521: Unfortunately, this example demonstrates that the dataset’s value for remote sensing validation is quite limited, as only a few grids of the RS product are actually covered by the CRNS data, with most sensors clustered within a single grid. Therefore, I suggest removing this part.
Data archive
I downloaded some of the data (e.g., CRNS, profile, muon) to check whether the files are well documented and complete. The README file provides a good overview of the data and the units of the values. However, there is no description of the file formats. In addition, the CRNS data are split across two separate files, which is confusing. The same issue applies to the SWC profile data.
Figures
Figure 1 only shows locations of CRNS stations (not shallow SWC and SWC profile stations)
Figure 5: The graph on the right is not easily readable and should be enlarged. The image on the left does not add much value.
References
Baatz, R., Bogena, H. R., Hendricks Franssen, H. J., Huisman, J. A., Montzka, C., & Vereecken, H. (2015). An empirical vegetation correction for soil water content quantification using cosmic ray probes. Water Resources Research, 51(4), 2030-2046.
Bogena, H. R., Herrmann, F., Jakobi, J., Brogi, C., Ilias, A., Huisman, J. A., ... & Pisinaras, V. (2020). Monitoring of snowpack dynamics with cosmic-ray neutron probes: A comparison of four conversion methods. Frontiers in water, 2, 19.
Brogi, C., Jakobi, J., Huisman, J. A., Schmidt, M., Montzka, C., Bates, J. S., ... & Bogena, H. R. (2025). Cosmic-ray neutron sensors provide scale-appropriate soil water content and vegetation observations for eddy covariance stations in agricultural ecosystems. Agricultural and Forest Meteorology, 373, 110731.
Davies, P., Baatz, R., Bogena, H. R., Quansah, E., & Amekudzi, L. K. (2022). Optimal temporal filtering of the cosmic-ray neutron signal to reduce soil moisture uncertainty. Sensors, 22(23), 9143.
Jakobi, J., Huisman, J. A., Fuchs, H., Vereecken, H., & Bogena, H. R. (2022). Potential of thermal neutrons to correct cosmic‐ray neutron soil moisture content measurements for dynamic biomass effects. Water resources research, 58(8), e2022WR031972.
Citation: https://doi.org/10.5194/essd-2025-546-RC2 -
RC3: 'Comment on essd-2025-546', Anonymous Referee #3, 14 Oct 2025
reply
This is a very comprehensive dataset and worth publication. I only have a few suggestions:
(1) The color scheme of Fig. 2 could be updated to improve the contrast among vegetation types. It is very difficult to tell them apart in the map.
(2) Fig. 4 - please note the interpolation method.
(3) A key limitation of cosmic ray neutron sensors is that their penetration depth is approximate, but this site has the advantage of multi-depth conventional soil moisture sensors. Could a comparison graph between CRNS and conventional sensor be made - perhaps similar to Fig. 3, but showing the difference between the CRNS measurements, and the conventionally measured values interpolated to the same nominal depths? This will be very useful in giving the readers an understanding of the measurement uncertainty.
Citation: https://doi.org/10.5194/essd-2025-546-RC3
Data sets
The Potsdam Soil Moisture Observatory: High-coverage reference observations at kilometer scale Peter M. Grosse et al. https://doi.org/10.23728/b2share.db88e149f7924919be376909856739f1
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The Potsdam Soil Moisture Observatory: High-coverage reference observations at kilometer scale
Marret et al 2025
General comments
This is a great dataset and is worthy of publication so that it can be used to the benefit of many studies, particularly those looking to calibrate/validate satellite soil moisture products. The data is of a high quality and is generally well documented and easy to access.
The grammar needed a lot more work than was warranted and is disappointing as a reviewer given the large list of authors. I started making suggestions but gave up after L60 as it was far too distracting and time consuming. I note the quality improved dramatically beyond section 1 but still needs work. Please check everything carefully before resubmission. There are many tools to help with this.
This is worthy of publication after the grammar is fixed
Specific comments
L5- suggest “…for remote sensing algorithm validation due to its ability…”
L15 – suggest “The data are available from: https://doi.org/...”
L19 – rewrite the first sentence it is very hard to get past without rereading many times. Do you mean “Soil water storage varies spatially and temporally and is critical for understanding the water cycle, fluxes between the land surface and atmosphere…”?
L21 – what is an essential climate variable as defined by Bojinski? Sesntence needs explanation.
L25 –Fix grammar “The main challenges in soil moisture observation are…???”
L34 - suggest deleting “at very specific locations”
L52 – suggest changing to “…technology has proven to be a valuable method for intermediate…”
L58 – suggest "Neutron counts are typically accumulated over several hours, corrected for factors such as air pressure, and then converted to volumetric water content using a custom calibration function."
NOTE: after getting so frustrated with grammar and sentence structure I have not made any further grammar suggestions beyond this point as I think this is now beyond the role of a reviewer.
Fig 1 – can you make the site numbers a different colour like white so they can be read
L138 – are the TDT/FDR measurements field calibrated or are factory default calibration used? This is important as factory default values can be very poor
L173 –the instruments sold by Quaesta are made under licence from Hydroinnova – i.e. they are the same thing. It might be a different model but it’s the same technology
L185 – what correction approaches (pressure, vapour, intensity) and calibration equation have been applied to get soil moisture? NOTE – I now see this section 4 (maybe add a note that it is coming later)
L200 - A figure comparing the relative intensity of a couple of adjacent neutron sensors would be nice to see if they respond similarly
L227 – fix reference
L453 – were not removed?
L499 what does 3.6 mean?