Review Reports - Monitoring Water Diversity and Water Quality with Remote Sensing and Traits

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This review focused on the application of RS techniques in monitoring of water diversity and quality, and defined five RS-based water diversity characteristics. This work is very interesting and valuable, but the overall organization of this manuscript was terrible. The article was long-winded, and the key points were not highlighted. The authors had listed a series of related literatures in this manuscript, while failed to carry out a systematic summary. The authors should reformulate this manuscript.

The title showed that, Linking Remote Sensing and Geodiversity and their Traits Relevant to Biodiversity. How did you define Geodiversity and Biodiversity? Geodiversity and Biodiversity were unclear in this manuscript. Moreover, five RS-based characteristics for describing water diversity were proposed. What is the relationship between the water diversity with Geodiversity and Biodiversity? Especially, the detailed definition of the proposed characteristics about water diversity should be provided, and the difference between the five characteristics should be highlighted. Moreover, the classification of these related works in the five water diversity is unreasonable. For instance, the studies about phytoplankton were mentioned in water structural diversity and taxonomic diversity.

The appendix is redundant. The information is unclear, and I cannot understand why the authors listed these literatures in this table.

Water genesis diversity or Water genese diversity? Check it.

In Figure 2, Hyperspectral (Andras)? What did you mean? All of the sensors in this figure were hyperspectral imaging?

Part of Figure 4 was missing.

Comments on the Quality of English Language

Extensive editing of English language required

Author Response

Reviewer 1

1.)This review focused on the application of RS techniques in monitoring of water diversity and quality, and defined five RS-based water diversity characteristics. This work is very interesting and valuable, but the overall organisation of this manuscript was terrible.

My Comment:

Thank you for your overall assessment that the paper is very interestingly written.

I regret that you do not like the overall organisation of the article. We have changed the title of the paper (see comment 4), which also clarifies the overall organisation of the article.

Over the years, I have been able to gain a lot of experience in the overall organisation of such an article (see the following papers by me). In the end, the present organisation of the article has proven itself, which is simple but more accessible due to the enormous complexity and especially for non-experts of remote sensing.

Lausch, A., Selsam, P., Pause, M., Bumberger, J., 2024. monitoring vegetation- and geodiversity with remote sensing and traits. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 382. https://doi.org/10.1098/rsta.2023.0058

Lausch, A., Schaepman, M.E., Skidmore, A.K., Catana, E., Bannehr, L., Bastian, O., Borg, E., Bumberger, J., Dietrich, P., Glässer, C., Hacker, J.M., Höfer, R., Jagdhuber, T., Jany, S., Jung, A., Karnieli, A., Klenke, R., Kirsten, T., Ködel, U., Kresse, W., Mallast, U., Montzka, C., Möller, M., Mollenhauer, H., Pause, M., Rahman, M., Schrodt, F., Schmullius, C., Schütze, C., Selsam, P., Syrbe, R., Truckenbrodt, S., Vohland, M., Volk, M., Wellmann, T., Zacharias, S., Baatz, R., 2022. Remote Sensing of Geomorphodiversity Linked to Biodiversity-Part III: Traits, Processes and Remote Sensing Characteristics. Remote Sens. 14, 2279. https://doi.org/10.3390/rs14092279

Lausch, A., Bastian, O., Klotz, S., Leitão, P.J., Jung, A., Rocchini, D., Schaepman, M.E., Skidmore, A.K., Tischendorf, L., Knapp, S., 2018. Understanding and assessing vegetation health by in situ species and remote-sensing approaches. Methods Ecol. Evol. 9, 1799-1809. https://doi.org/10.1111/2041-210X.13025

2.)The article was long-winded, and the key points were not highlighted.

My Comment:

I have streamlined the article to avoid lengthiness.

Your second statement "the key points were not highlighted". I have revised and emphasised the key points in the conclusion.

3.)The authors had listed a series of related literatures in this manuscript, while failed to carry out a systematic summary. The authors should reformulate this manuscript.

My Comment:

The manuscript is clearly structured. Each of the chapters 4.2.1 -4.2.5 can only describe examples of how RS captures one of the 5 characteristics of water diversity. It is not possible to describe all possibilities, as this would correspond to a complete book.

A summary is shown in Figure 11 and Table 1 (Appendix).

Figure 11. which of the 5 properties (water trait div., water genesis div., water taxonomic div. and water functional diversity diversity) can be recorded by RS has been moved to the Appendix.

4.)The title showed that, Linking Remote Sensing and Geodiversity and their Traits Relevant to Biodiversity. How did you define Geodiversity and Biodiversity? Geodiversity and Biodiversity were unclear in this manuscript.

My Comment:

Thank you for your very good advice. We have now changed the title, as the new title corresponds to the aims of the paper. The new title is: "Monitoring water diversity with remote sensing and traits.

The topic of the interaction and influence of biodiversity and geodiversity on water diversity and its characteristics is an enormously important component, but is not the subject of this paper. Therefore, geodiversity and biodiversity were not defined in the paper. However, I have included a reference to the paper Lausch et al. 2024, where vegetation diversity and geodiversity are defined, as the 5 characteristics of water quality are of course influenced by biodiversity and geodiversity.

5.)Furthermore, five RS-based characteristics for describing water diversity were proposed. What is the relationship between the water diversity with Geodiversity and Biodiversity?

My Comment:

The focus in this paper is on the 5 RS based characteristics of water diversity. The title has been changed accordingly. The topic "relationship between the water diversity with Geodiversity and Biodiversity" is not the subject of the paper. This relationship requires one or more papers and, as you rightly emphasise, a completely different paper structure.

6.)Especially, the detailed definition of the proposed characteristics about water diversity should be provided, and the difference between the five characteristics should be highlighted.

My Comment:

A definition of the proposed 5 characteristics of water diversity was given.

Figure 11 also refers to the 5 characteristics and thus the differences that are recorded using RS. If I were to integrate these again in a separate table for each chapter, another 2 pages would be required for each chapter, making a total of 10 pages, which would go beyond the scope of the paper.

7.)Moreover, the classification of these related works in the five water diversity is unreasonable. For instance, the studies about phytoplankton were mentioned in water structural diversity and taxonomic diversity.

My Comment:

This categorisation is very important and useful in the context of recording the characteristics. This categorisation is therefore useful in order to be able to classify and better understand the indicators of water diversity quantified from RS. A very important figure is Figure 1 and its description.

This paper provides a new way of thinking and categorising in-situ methods in particular, showing that traits are the common interface between RS and in-situ methods.

This enables non-RS experts in particular to better understand the complexity and possibilities of recording water diversity using RS.

"the studies about phytoplankton were mentioned in water structural diversity and taxonomic diversity"

My Comment:

This is correct. The detection of phytoplankton by RS can be derived by indicators that reflect both structural diversity (distribution, composition and configuration) and can also be assigned to taxonomic and functional diversity. It is therefore dependent on the RS indicator

E.g. phytoplankton populations, community structures, phytoplankton functional types (PFTs),

8.)The appendix is redundant. The information is unclear, and I cannot understand why the authors listed these literatures in this table.

My Comment:

The redundant information has been deleted and revised. The purpose of this table is to provide an overview of which properties can be captured by RS. I will therefore leave the table in the appendix so that the reader can refer to the literature.

9.)Water genesis diversity or Water genese diversity? Check it.

My Comment:

Thank you for pointing this out. I have standardised the term.

10)In Figure 2, Hyperspectral (Andras)? What did you mean? All of the sensors in this figure were hyperspectral imaging? Part of Figure 4 was missing.

My Comment:

I fixed the error in Figure 2 and Figure 4 (moving it to the right place so that the right part is not cut off).

11)Comments on the Quality of English Language. Extensive editing of English language required

My Comment:

Thank you for pointing this out. The article has been revised by a native speaker with regard to the English language.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Suggestion:

pg.9:

However, the monitoring of water bodies and their quality using remote sensing

poses a challenge compared to terrestrial remote sensing for several reasons: Light

absorption and scattering in water

...for several reasons: (I) Light... ( add first reason ? )

-----------------

Questions:

Q1: Each "pixel" in hyperspectral recorded space may correspond actually an squared area with some tents of meters...

You have to relay in very accurate GPS systems!

When performing "evolutionary studies"...

Are you sure that, by using different surveillance sources, at different time intervals, you are

actually recording "the same pixel" as before?

How accurate is this?

Can you please include some information in this subject?

Q2: In order to avoid "adverse effects and reading errors" I believe that you could use some "reference areas" as

"standards" in order to "correct / check" sensors readings?

Maybe you should also insert and discuss this aspect?

Author Response

Reviewer 2

Many thanks for your constructive comments on the paper.

1.)pg.9:

However, the monitoring of water bodies and their quality using remote sensing poses a challenge compared to terrestrial remote sensing for several reasons: Light absorption and scattering in water ...for several reasons: (I) Light... ( add first reason ? )

My Comment:

Many thanks for your comment. I have implemented this in the text.

2.)Q1: Each "pixel" in hyperspectral recorded space may actually correspond to a squared area with some tents of metres...You have to relay in very accurate GPS systems!

My Comment:

Well, drones and airborne hyperspectral systems can generate very high spatial resolution images. Yes, very good GPS/INS units are required here. However, very good GPS/INS units can be extremely expensive (as installed in the spaceborne RS satellites, for example). We had a GPS/INS unit in the airborne area for around €50,000. For drones, the use of very good GPS/INS units is also very difficult, as the GPS/INS units have to be very light. The performance and weight of drones are getting better and better, but ...

This discrepancy is particularly noticeable with LiDAR drones, where the LiDAR point accuracy must be very accurate.

3.)When performing "evolutionary studies"...Are you sure that, by using different surveillance sources, at different time intervals, you are actually recording "the same pixel" as before? How accurate is this? Can you please include some information in this subject?

My Comment:

Thank you for your enquiry. The aim of different monitoring sources or multisensory and multitemporal techniques is to capture as many different traits of terrestrial (vegetation and geodiversity) and aquatic diversity as possible in order to understand the categorisation, classification or ongoing processes induced or controlled by genesis.

There are various approaches to the different geometric resolutions of different sensors ....

Kabisch, N., Selsam, P., Kirsten, T., Lausch, A., Bumberger, J., 2019. A multi-sensor and multi-temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes. Ecol. Indic. 99. https://doi.org/10.1016/j.ecolind.2018.12.033

4.)Q2: In order to avoid "adverse effects and reading errors" I believe that you could use some "reference areas" as "standards" in order to "correct / check" sensors readings? Maybe you should also insert and discuss this aspect?

My Comment:

Yes, this is a very good idea and important. In-situ measurements for the correction (radiometric/geometric) of the RS data are always recorded during every aerial survey. Wireless sensor networks are also interesting. There are also WSNs for aquatic applications.

I have also included the lake laboratory in the in-situ part for calibration and validation. Unfortunately, the in-situ measurements are very time-consuming in order to measure numerous traits in the body of water, as bodies of water are extremely dynamic and only the upper part of the body of water traits can be recorded with optical sensors.

These technical difficulties and current methods of pre-processing water RS data are, in my opinion, the most difficult RS discipline. However, I will not go into these methodological approaches in the paper, as this is not the subject of this paper. Enclosed, however, is a paper on WSN for the terrestrial area only.

Mollenhauer, H., Borg, E., Pflug, B., Fichtelmann, B., Dahms, T., Lorenz, S., Mollenhauer, O., Lausch, A., Bumberger, J., Dietrich, P., 2023. Ground Truth Validation of Sentinel-2 Data Using Mobile Wireless Ad Hoc Sensor Networks (MWSN) in Vegetation Stands. Remote Sens. 15, 4663. https://doi.org/10.3390/rs15194663

Question (3): I think he is referring to stationary (point) measurements compared to images. Only mineral deposits in the soil are truly localised. Air pollutants are transported over large areas and water samples can map an entire catchment area. The image shows the effect on large areas. The "measured value" in the image consists of repeated recordings of numerous pixels (time series). Small spatial differences do not significantly change the statistics. For causal relationships, the pattern of temporal changes is therefore more important than the exact location of the measurement. There is plenty of theoretical work on the comparability of satellite sensors and changes in the sensors during operation (4 examples are included).

I understand it as always being the same area at a geogr. coordinate/ or in a pixel grid. The spatial positional inaccuracies are small and when it comes to validation studies, we often use so-called macropixels, i.e. the central pixel at a coordinate and the 3x3 or 5x5 (e.g.) pixel area or XxX metres around it; so that in studies with a large data basis, minimal spatial differences in the grids of the individual satellite sensors do not play a major role. That would be an example of the diff. approaches in your answer.

For classical measurements, reference areas are common for technical and financial reasons (Hydrology Jena: Upper Gera, UFZ: Bode etc.). In practice, stationary measurements are checked for instrument errors and individual measurements are compared in space and time. Nevertheless, reference areas are always random samples. They may have unusual characteristics. In the ideal case, random measurements distributed over a large area in space and time are compared with continuous and large-scale image data from remote sensing. If the same patterns are registered in space and time, the results can be extrapolated.

Wulder, M.A., Roy, D.P., Radeloff, V.C., Loveland, T.R., Anderson, M.C., Johnson, D.M., Healey, S., Zhu, Z., Scambos, T.A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C.J., Masek, J.G., Hermosilla, T., White, J.C., Belward, A.S., Schaaf, C., Woodcock, C.E., Huntington, J.L., Lymburner, L., Hostert, P., Gao, F., Lyapustin, A., Pekel, J.F., Strobl, P., Cook, B.D., 2022. Fifty years of Landsat science and impacts. Remote Sens. Environ. 280, 113195. https://doi.org/10.1016/j.rse.2022.113195

Claverie, M., Ju, J., Masek, J.G., Dungan, J.L., Vermote, E.F., Roger, J.C., Skakun, S. V., Justice, C., 2018. The Harmonised Landsat and Sentinel-2 surface reflectance data set. Remote Sens. Environ. 219, 145-161. https://doi.org/10.1016/j.rse.2018.09.002

Roy, D.P., Li, J., Zhang, H.K., Yan, L., Huang, H., Li, Z., 2017. Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalise MSI reflectance to nadir BRDF adjusted reflectance. Remote Sens. Environ. 199, 25-38. https://doi.org/10.1016/j.rse.2017.06.019

Roy, D.P., Kovalskyy, V., Zhang, H.K., Vermote, E.F., Yan, L., Kumar, S.S., Egorov, A., 2016. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalised difference vegetation index continuity. Remote Sens. Environ. 185, 57-70. https://doi.org/10.1016/j.rse.2015.12.024

See also ACIX-Aqua data sets/network from Nima Pahlevan and GLORIA. They have collected all possible measurement data globally to create a kind of reference measurement data collection. The data is now also available on Pangaea. But the uncertainties already mentioned (different measuring devices, samples, etc.) are also present here.

2021, Pahlevan, N., Mangin, A., Balasubramanian, S.V., Smith, B., Alikas, K., Arai, K., Barbosa, C., Bélanger, S., Binding, C., Bresciani, M., Giardino, C., Gurlin, D., Fan, Y., Harmel, T., Hunter, P., Ishikaza, J., Kratzer, S., Lehmann, M.K., Ligi, M., Ma, R., Martin-Lauzer, F.-R., Olmanson, L., Oppelt, N., Pan, Y., Peters, S., Reynaud, N., Sander de Carvalho, L.A., Simis, S., Spyrakos, E., Steinmetz, F., Stelzer, K., Sterckx, S., Tormos, T., Tyler, A., Vanhellemont, Q., & Warren, M. ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters. Remote Sensing of Environment, 258, 112366, https://doi.pangaea.de/10.1594/PANGAEA.948492 and the paper: https://www.nature.com/articles/s41597-023-01973-y