Ocean-Surface Heterogeneity Mapping (OHMA) to Identify Regions of Change

Round 1

Reviewer 1 Report

The authors addressed my concerns and provided more explanation about the STH parameter.  

Although, the added detail is helpful, I was disappointed that they did not take the opportunity to present a jargon-free explanation that can convey a physical / mental picture of how the STH parameter is obtained --  that would satisfy biological oceanographers that it makes physical sense.  Perhaps the best test is to explain how the STH parameter is calculated to an audience of Physicists.  I suggest Physicists, because they will not be intimidated by the mathematics and statistics.

Author Response

Please see the attachment. Please note that the responses to all three reviewers are collated into a single document as some reviewer comments were inter-related.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

The submitted for publication manuscript provides an example of objective analysis oriented on recognition of ocean surface heterogeneity using a kind of cluster analysis, named by authors as OHMA by similarity with used as a base the LaHMa (Land Heterogeneity Mapping) algorithm.

As a main analyzed parameter of OHMA is considered Surface spatio-Temporal Heterogeneity (STH) which seems to be a number of clasters or classes needed for describing an observed sea surface temperature (SST) variability in each location with the "best" precision.

In addition to used in LaHMA criterias, in OHMA the  Jeffries-Matusita metric is used for STH estimation. Authors give definition of this metric as equations (1,2) but for the manuscript dealing with an application of statistical methods I surely lack the mathematical description of used objects and methods.

Following analysis of SST variability using calculated STH indexes and other statistics shows scientifically interesting results and after some revisions could be published in the Remote Sensing - Oceans journal.

Some questions and recommendations.

1. Page 3, reference to not published yet paper of Scarrott et al. Reference miss the page numbers, paper is referred by an author name.

2. Page 4, "... unstable meandering of the Gulf Stream near surface current ...". Recommend shorten to "...meandering of the Gulf Stream current...". Otherwise it needs to explain why only unstable meandering, why Gulf Stream is surface current.

3. Page 5, please delete the sentence "The SST data measurements were recorded in Kelvin (K)." because it is not important at all. If not so - please explain why it is important point.

4. Page 6 and through all manuscript - the Matusita name in Figures is given as Mathusita (figure 3 and 4).

5. Page 6, formulas 1 and 2. Guess that B needs indexes i,j

6. Pages 7, "x and y = two temporal signatures ...", but there are no x and y in given formulas. "i and j"?  

7. Page 8, paragraph "For each cluster dataset image..." is very technical and, if left - it needs a lot more explanation of used objects or better - moved to Appendix.

8. Page 11, topography data description, is it GEBCO or SRTM15?

9. Page 11, GLM abbreviation appear , but is not defined.

10. Page 17.What is "EO-derived front"?

11. Page 27 and 29, appendix A and B, figure number 1, change to A1, B1.

12. Appendix B, is it necessary and mentioned some where in manuscript?

 

Author Response

Please see the attachment. Please note that the responses to all three reviewers are collated into a single document as some reviewer comments were inter-related.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Good job! See PDF for review.

Comments for author File: Comments.pdf

Author Response

Please see the attachment. Please note that the responses to all three reviewers are collated into a single document as some reviewer comments were inter-related.

 

Author Response File: Author Response.pdf

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

This study presents a development of the Ocean-Surface Heterogeneity Mapping (OHMA) procedure, which applies an adapted Land Heterogeneity Mapping algorithm to hyper-temporal satellite-derived datasets to map the spatio-temporal heterogeneity (STH) of North Atlantic ocean surface waters. It consists in performing multiple Iterative Self-Organising Data Analysis clustering runs. An important aspect of the procedure is the coupling of the divergence peak-seeking approach with an analysis of the minimum Jeffries-Mathusita measure of separability. The process of optimizing the number of clusters and producing the cluster ensemble is thus fully automated, without need for expert knowledge of the region.

Outputs include: (i) a single STH dataset, (ii) an optimal cluster image dataset which best represents the variability in the data, and (iii) the temporal signatures associated with that cluster image.

Using Generalised Linear Modelling, the 2011 STH dataset was compared to a number of modelled fronts, currents and bathymetric products generated using data from different satellite sensors, and in-situ measurements. It is shown that the methodology captures information on a range of oceanographic features, which are difficult to measure at large spatial scales using in-situ measurements alone. The algorithm proved effective at highlighting areas of increased SST front frequency, indicators of boundaries between water masses.

This study represents a valuable contribution to extend the use of satellite-derived datasets in order to provide information on oceanographic features which are difficult to measure at large spatial scales using in-situ measurements alone. I recommend publication with minor revisions, related to the questions included below.

1. To my understanding, the algorithm reflects spatial heterogeneities, although the spatial signatures of some clusters could be distinct (e.g., Figs. 8, 9 and 12). If this is correct, then what is mapped by the algorithm is only the spatial heterogeneity, but not the “temporal” one, in contrast with what is stated in text, that the spatio-temporal heterogeneity is mapped.
2. What are the arguments supporting the idea that the ocean surface spatio-temporal heterogeneity is related to biological processes which affect the distributions of marine species.
3. Would be very interesting to test the robustness of the identified clusters when the analyzed period is changed. Have you performed any preliminary analysis in which you applied the algorithm to SST data extending over a different period?

Reviewer 2 Report

See attached file for comments.

Comments for author File: Comments.pdf

Reviewer 3 Report

The manuscript describes an Ocean-surface Heterogeneity MApping (OHMA) algorithm that uses satellite datasets to map the spatio-temporal heterogeneity of ocean surface waters. The algorithm produces a surface spatio-temporal heterogeneity dataset and an optimised spatio-temporal classification of the ocean surface.  Validation was carried out using Underway-derived temperature data.  Four case studies were described to demonstrate that spatio-temporal heterogeneity (STH) values were related to a range of region-specific surface and sub-surface characteristics (fronts, currents, bathymetry).

Browsing through the paper and figures, the paper suggests a good deal of valuable information can be extracted by the OHMA algorithm. The key product is the STH. However, I could find no intelligible description of how a value of STH was obtained anywhere in the paper!  There should be a simple way to explain what STH actually measures -- not just that it is a key output of a complicated algorithm.  STH must be explained in transparent terms in Section 2.  Similarly, how the classification of the ocean surface (fronts, currents, etc) should be explained also in section 2.

The extraction of fronts and currents from satellite SST has also been carried out using singularity exponents -- a methodology developed by Turiel and co-workers at the Institute of Marine Science in Barcelona.  I recommend that the authors consider the following paper:  

A. Turiel et al, "Tracking oceanic currents by singularity analysis of Microwave Sea Surface Temperature images", Remote Sensing of Environment, 112 (2008) 2246–2260. doi: 10.1016/j.rse.2007.10.007

See also: http://bec.icm.csic.es/data/singularity-analysis-service/