Reply on RC2

The authors propose a methodology to formalize the quantification of a fire behavior variable which, undoubtedly, is frequently estimated in an ad-hoc manner by users of fire detection data, specifically from the fire and natural hazard community, when managing a specific fire event. The authors' approach is potentially suitable to use as the basis for developing a remotely sensed product of interest to the user community. As such, the work is innovative and of undeniable interest. As the article's area of interest is northwestern Europe not a region known for very large or disruptive wildfires and therefore, in the light of climate change induced greater expected future prevalence of the wildfire hazard it also contributes to enhanced understanding of the fire regimes in this part of the world.

The authors propose a methodology to formalize the quantification of a fire behavior variable which, undoubtedly, is frequently estimated in an ad-hoc manner by users of fire detection data, specifically from the fire and natural hazard community, when managing a specific fire event. The authors' approach is potentially suitable to use as the basis for developing a remotely sensed product of interest to the user community. As such, the work is innovative and of undeniable interest. As the article's area of interest is northwestern Europe -not a region known for very large or disruptive wildfires and therefore, in the light of climate change induced greater expected future prevalence of the wildfire hazard -it also contributes to enhanced understanding of the fire regimes in this part of the world.
Notwithstanding these strengths in scientific significance and quality of the presentation, I perceive a certain number of weaknesses that should be addressed before the manuscript is accepted for publication.
Definition and structure of the study area (2.1). To the reader who is not immersed into the study of this area, the choice of study area appears at least somewhat arbitrary. Was the intent here to study an area of Europe somewhat under-represented in the study of wildfire, and therefore to apply boundaries so as to stay clear of the Mediterranean region in the south and the Scandinavian/boreal region in the west and north? The eastern boundary and the choice of the 49th parallel should be better justified. If this is a commonly studied area thus delineated, a citation should be added. This point may appear as a formality, but I believe it is more significant than that, especially when it comes to the statistics presented for the countries outside the British Isles. For example, a quick look into German fire statistics shows that, contrary to the findings presented here, wildfire activity tends to peak in the month of August. It also shows, however, that German wildfires are dominated by fire events in the Land of Brandenburg, which is cut in half by the eastern border of the study area here. Given this kind of limitation, and the extremely small sample size of fires outside the British Isles, I do not think that percountry statistics (3.3 and Fig 4) should be presented for the countries other than the British Isles.
We have further clarified the delineation of the study area. Please, see section 2.1 and Figure 1. "For the purpose of this study, the boundaries of northwest Europe were defined by the northern Atlantic biogeographical region above 49th parallel based on Sundseth et al., (2009), which includes many of the traditionally wet countries such as the United Kingdom, Ireland, the Netherlands, Belgium, and Denmark, northern France and northwestern Deutschland (Fig. 1). We used the 49th parallel to delineate the boundaries of the study area to focus our analysis in the temperate region of northwest Europe, not traditionally considered fire prone, instead of including northern Spain and southern France where fire regimes have been analyzed in previous research (Moreno and Chuvieco, 2013)." We understand the concerns of reviewer 2 regarding the fire activity in Germany since she/he is considering the Land of Brandenburg which is out of the study area (northern Atlantic biogeographical region), probably with a different fire regime. VIIRS data description, limitations, pre-processing and exploratory statistics. Section 2.2 needs to clearly describe which VIIRS product was used (I presume VNP14IMGTDL_NRT), and also confirm that the study is based only on S-NPP VIIRS data (no NOAA-20 data, which would duplicate the data record in the last year or so).

We do think it is worthwhile to keep
We used the VNP14IMGTDL_NRT product based only on S-NPP VIIRS data. We have clarified this in the manuscript and added more details on the VIIRS data description.
Given that the filtering for retained fire detections ("real" fires) ended up rejecting ~90% of fire clusters, it is odd that clustering happened before filtering. The filtering criteria are also not very clear. A cleaner approach would have been to filter by land cover type (or, potentially, by using available GIS data of nature preserves, forested areas etc.) firstand then cluster the remaining events. Regardless, it would be instructive to see some minimal exploratory statistical description of the retained fire events -how many by year? By land cover type? Their final number -256 -is very small compared to the known fires in this area over the 9 years of the study time. This is to be expected as it is known that VIIRS misses many detections. But this fact is a rather relevant limitation of the study, which needs to be discussed. As-is, it seems likely that the results are dominated by particularly large fire years in specific sub-areas, which may very well skew the ROS statistics presented in the results. For example, the 2019 peatland fires in Scotland and Northern England may account for a rather outsized part of the results.
The reviewer is right. After checking the data processing we realized that, in fact, we filtered the VIIRS hotspots before the clustering process. To further clarify this, we have put the old section 2.5 in the current section 2.2 named "Visible Infrared Imaging Radiometer Suite (VIIRS) Data".
We recognize that more fires occurred in the study period in the region of interest. This was discussed in section 4.1 as follows "The lack of fires smaller than 1 km2 can likely be explained by the fact that the VIIRS satellite was unable to capture fires of this magnitude due to limitations of the temporal and spatial resolution.".
While it is likely that these smaller fires are more frequent than indicated, fires of this scale are less likely to be significant contributors to overall burned area (San-Miguel-Ayanz et al., 2021). Moving forward, it is important to consider that the fires included in this study are of mid to large size rather than smaller fires especially when it comes to estimating the ROS as smaller fires are likely to reduce the mean ROS, despite the fact that some small fires may have experienced high ROS over short time periods. Therefore, we can say we studied the ROS of the largest fires that usually lead to the highest ROS.
Algorithm description. In my view, the chief interest of this work is the ROS vector generation algorithm. More effort should be deployed to describe its strengths and limitations. For example, in section 2.4 and Fig. 2, the fire detections are not points, but VIIRS pixels of at least a size 375 x 375 m (or substantially larger if the acquisition is offnadir). The VIIRS data includes complementary information (which may include x and y pixel extent, depending on the product used, and does include a confidence rating) -was this information used in any way and how stable are ROS derivations to this. Also, fire spread has an extremely strong diurnal pattern, so the reporting of spread km/h is a value that has undergone averaging. In Fig 2 you present an example with ~14 h between successive acquisitions, but VIIRS overpasses can re-image the same spot with an interval of 90 min or up to several days, and the ROS values you would obtain would be radically different given the diurnal variation. At the very least you should report the distribution of delta-t values used for ROS calculations, and possibly apply a correction factor based on expected temporal fire activity patterns.
Fire detections were approached as points (hotpots from VIIRS active fire data products). Limitations and assumptions of this approach were mentioned throughout the manuscript. We can only produce average spread rates as we have a 12~14 hours satellite overpass. However, we recognize that temporal variations of ROS may occur during this time period, especially considering diurnal variation as you mentioned.
Some more localized comments: 16/17: Given the substantial statistical limitations of the study, I think that this sentence overstates the amount of insight gained for understanding of fire regimes.
We have removed the word "important" here. We do think we are unraveling unknowns about the state of the wildfire regime characterizing the rate of spread.
31: An anomaly is probably an understatement. There is a long record of fire use for lanscape management by successive human populations.
We agree with the reviewer. We have refined this sentence: "While large and severe fires in these regions were once considered an anomaly, in recent years the occurrence of fires of greater magnitude has been increasing (San-Miguel-Ayanz et al., 2021).".

34-36:
The increasing peatland megafires should probably be mentioned here, especially since my suspicion is that they dominate the dataset this study is based on.

Done.
90: The capitalization of n/Northwest/ern Europe should be unified.

Addressed. "northwest/ern Europe".
120: Extraneous semicolon. Thank you for this comment. The figure caption was wrong. We think it is fine now after adding the data sources.

Addressed.
101/102: The capitalization choice "northern Atlantic Biogeographical region" is odd here and in the following.

We have used "northern Atlantic biogeographical region"
137-143 [re: spatial clustering] There are other algorithms that also do not require cluster centers and number of clusters to be indicated a priori. With about 40,000 detections per year this is not a data volume that would be a problem for example for a variant of DBSCAN. Not that the outcome is going to be very different, but the clustering methodology comes across as somewhat clunky. The 5 km and 20 detections threshold aren't very well justified. (Also, where these distances measured in a projected coordinate system, that is, was the whole dataset reprojected, and if yes to which coordinate system?) Later, in the Results section, there is insufficient reporting on the impact of the parameter choice in clustering on the final dataset of fire events.

The number of annual detections without considering urban/industrial areas was lower than 40,000. Especifically, we had 29,215 detections on wildland areas in 10 years of data. The 5 km grid size and the 20 points threshold for the algorithm clusterization process was heuristically set, as the author's team evaluated several values.The coordination system is Pseudo-Mercator but the calculation of the distance corrects for the inherent distortion of the projection due to the latitude. Thus, distances were measured in meters.
153-159: Missing references for these methodologies. (Also, a diagram would have been helpful.) Thanks for the commentary. We added a new figure with an explanatory diagram.
163: Whenever the word heuristically is used, there should be a justification of the heuristics being applied and ideally an estimate of the uncertainty involved.
The work has been edited as this point you're mentioning was very important. We've done an exploratory analysis of algorithm outputs among different alpha values from 1 km to 10km. We have looked for every polygon generated for each level and concluded that lower alpha values tend to generate smaller and splitted polygons for each fire. From this point we noticed that the previously used alpha value (1 km) underestimated the burned area and so higher values were better for our purpose. Lastly, after an extensive revision, we concluded that the best alpha value was 10 km. We have improved the caption following your suggestions. The hotspots are usually represented by points for improved visualization. We have added the VIIRS spatial resolution in the figure caption.
175/176: There is no description of the final step of the algorithm, that is, the selection of onefinal vector. Is it the one of maximum length, some sort of average, a Gaussian model? What drove the choice of method, and what is the variability of the outcome? It seems to me that each ROS value should come with an uncertainty. As fire can grow in complex ways between successive detections, there is a need to report on what was found -and given the dataset was only 254 fires, case studies should be presented that show typical cases beyond Fig. 2 only.
Our idea is to analyze the ROS in the head of the fires given that it is the more important metric for fire agencies and less impacted by suppression resources. Therefore, we selected the vector with the maximum ROS by time step. We have better shown this in the new Figure 2.
179: These are not false detections. They are true detections of thermal anomalies that are not of interest to the study.

Thank you for making this point. We have clarified our meaning in the text.
193-195: This sentence is unclear to me.
We have added a new Figure 2 with a self explanatory diagram of this process. Some minor changes were done for this paragraph too.
203-208: Section 3.1 should be expanded as a lot of questions remain open. These 254 fire events led to a substantially higher number of "fire spread timesteps". From Fig. 5 my guess is that their number was 758 or thereabouts. Did each of the 254 events contain at least one spread timestep? (If yes, that would be almost surprising -was there anything in the clustering methodology to ensure that each fire had at least two successive acquisitions?) How were the fire spread events distributed -my guess is that a small number of long-running fires dominate the fire spread events. A histogram would be helpful. Also, I miss a discussion of latitude effects on the likelihood of repeat fire detections (because of satellite orbital properties). The entire discussion in 3.2 and 3.3 is tainted if these biases aren't transparently described first.
We have adressed your question and that paragraph was changed in order to explain this process better. We need a minimum of 2 time steps to have ROS vectors in a fire. We haven't assessed latitudinal variations as an error source as there was no substantial difference within our study area (6º degrees approximately) Figure 3: The caption, and the preceding text, should make clear that this burnt area is not the same as that detected in remotely sensed burnt area products, or delineated in the GIS systems maintained by fire managers.
We totally agree with this comment. We have put this information in the caption.
Figure 4: Fires were not detected contrary to the datasets made available by fire mangagement agencies (eg. https://www.ble.de/DE/BZL/Daten-Berichte/Wald/wald_node.html ) . This is understandable but needs to be discussed.

Addressed. Please, see comment about the delineation of the study areas.
251 ff (3.4 and Fig. 5): The values of n vary wildly between the classes. So maybe classes could be grouped to generate similarly sized datasets. What do the error bars represent?
Our objective was to evaluate the same classes generated in the Corine Land Cover product to avoid subjectivity in the decision process of grouping land cover types. To ensure the representative of land cover type classes for further analysis, we created the category "others" to include those land cover type groups with with less than 10 observations ( Figure 5).

Error bars represent the standard error calculated as follows:
269: The authors should agree on one choice of spelling of burnt/burned area.

Addressed. Burned area.
270 ff: The authors discuss some sources of biases (fire size), but should expand on the shortness of their dataset (only 9 years of fires) and how it can skew the results regarding fire activity timing.
We have indicated that there were initial 326,935 fire detections (29,215 filtered for wildland areas) and they were evenly distributed in our study area. Hence we can infer there was not much spatial bias. 9 years of fires could be a limited time range but this work relates to the usage of VIIRS dataset and we used the entire record. In this new manuscript we have discussed about biases that comes with using this dataset : spatiotemporal resolution and minimum threshold of 20 points for ROS vectors generation 286 ff: Not all fire management areas apply the same prescribed burn processes, and permitting is also not homogeneous. This paragraph should be shortened and moved to an earlier location in the manuscript, as it addresses a very minor point. There are some formatting issues with parentheses.
Thank you for this comment. We have removed this paragraph and added a short sentence in the previous paragraph to confirm that the detections are unlikely to include prescribed burns.

300/301: What sensor limitations?
Addressed. Spatio-temporal limitations of data. 341/345: The VIIRS 375 m is unlikely to detect any smoldering peat fires, so this is not surprising at all. You correctly state that what you're seeing is surface fires. The spread of the peat fire is entirely invisible to your methodology.
Agreed. We have removed this mention of smoldering fire.

Addressed.
366ff: How are the low-to-moderate values you're getting impacted by averaging over a diurnal activity variation? Actual instantaneous spread may have been much faster.
We agree with the reviewer but, unfortunately, we can not have a satellite with a shorter time overpass. For instance, we may expect higher ROS if we would have the progression of wildfires hourly. 401 ff: Please remove redundancy in the Conclusions section with what already has been said.

Addressed
To conclude, in my view a quick, accurate and well-understood method to calculate VIIRSbased ROS for fire events would constitute a valuable and welcome contribution to the scientific record and toolset at the disposal of the fire management community. But the authors need to be careful to clearly describe the statistical limitations of their approach when it comes to statements about the NW-European fire regimes, and expand the presentation of the methodology itself.
Thanks for the comments. The work has been edited considering all comments raised by both reviewers that were very useful to improve our manuscript, including the methodology, results and discussion.