the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
SMOS L-VOD shows that post-fire recovery of dense forests is slower than what is depicted with X- and C-VOD and optical indices
- 1Centre d’Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse (CNES/CNRS/INRAE/IRD/UPS), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France
- 2GlobEO, 31400 Toulouse, France
- 1Centre d’Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse (CNES/CNRS/INRAE/IRD/UPS), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France
- 2GlobEO, 31400 Toulouse, France
Abstract. Anthropogenic climate change is now considered to be one of the main factors causing an increase in both frequency and severity of wildfires. These fires are prone to release substantial quantities of CO2 in the atmosphere and to destroy natural ecosystems while reducing biodiversity. Depending on the ecosystem and climate regime, fires have distinct triggering factors and impacts. To better analyse and describe fire impact on different biomes, we investigated pre and post fire vegetation anomalies at global scale. The study was performed using several remotely sensed quantities ranging from optical vegetation indices (the enhanced vegetation index (EVI)) to vegetation opacities obtained at several microwave wavelengths (X-band, C-band, and L-band vegetation optical depth (X-VOD, C-VOD, and L-VOD)), ranging from 2 to 20 cm. It was found that C- and X-VOD are mostly sensitive to fire over low vegetation areas (grass and small bushes) or over tree leaves; while L-VOD depicts better the fire impact on tree trunks and branches. As a consequence, L-VOD is probably a better way of assessing fire impact on biomass. The study shows that L-VOD can be used to monitor fire affected areas as well as post-fire recovery, especially over densely vegetated areas.
Emma Bousquet et al.
Status: final response (author comments only)
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RC1: 'Comment on bg-2021-289', David Chaparro, 09 Dec 2021
- AC1: 'Reply on RC1', Emma Bousquet, 12 Jan 2022
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RC2: 'Comment on bg-2021-289', Matthias Forkel, 09 Jan 2022
Review of the manuscript „SMOS L-VOD shows that post-fire recovery of dense forests is slower than what is depicted with X- and C-VOD and optical indices“ by Bousquet et al. 2021
The study investigates the temporal anomalies of different remotely-sensed climate variables and vegetation properties before and after fire. The main finding is that L-VOD recovery slower after fire than X-VOD, C-VOD or optical vegetation indices. The study provides an important contribution to the research on the understanding of VOD data in different wavelengths and on the use of VOD to study fire. The text is written in an easy to understand language, however some sentences are written too simple and are unspecific and by this make partly wrong statements (see specific comments).
Fire is both driven by fuel loads and fuel moisture (Chuvieco et al., 2014) and VOD is sensitive both to biomass and fuel moisture content (FMC), or vegetation water content (VWC) (Konings et al., 2019). Especially, X-VOD is more sensitive to FMC than other microwave-derived surface properties (Fan et al., 2018). Hence VOD was already successfully used in data-driven fire models (Forkel et al., 2017; Kuhn-Régnier et al., 2021). However, it is not yet clear how the different VOD bands are sensitive to either biomass or FMC and hence of the biomass- or moisture-“component” of VOD is more important for fire prediction. The same question is true for post-fire recovery: Does VOD measure really a recovery of biomass or a recovery of the fuel moisture? Note that different changes in VOD and optical indices and their relation with land cover trends have been also investigated in Andela et al. (2013). Those sensitivities of VOD to biomass and moisture are important to consider in the context of this study, however, those lines of thought were not included in the introduction and/or discussion. Hence, the objective of the study is also not entirely clear. Are you aiming to use VOD as a proxy for post-fire biomass recovery? If yes, how could you disentangle the moisture effect? The role of biomass and moisture on VOD should be introduced in chapter 1 and discussed in the discussion.
Like the second referee, I am puzzled about the selection of fires for the analysis (Fig. 4a). Globally, fire activity is strongest in the African Savannahs (mostly in the Sahel), in northern Australia and South America (Giglio et al., 2013; Andela et al., 2019). However, the Sahel and Australia (and the Mediterranean) are almost completely missing in the selection of fires. What are the reasons? Does it make sense to not consider some of the most fire-prone regions in the analysis? You need to revise this (or clarify it).
Specific comments
L 34: „Authors found“ – unclear which authors are meant
L 94-95: The sentence is not clear and wrong. L-band was already used in conjunction with other VOD data. See for example (Fan et al., 2018)
L 96: Using the abbreviation “VV” for vegetation variables in a study that makes use of microwave remote sensing data is not ideal because VV stands for vertically polarized sensed and received radiation. In order to avoid confusion, I recommend using another abbreviation.
L 208-209: What is the size of those fires relative to the spatial resolution of the SMOS data? How many pixels are included in those fires? Did you apply any threshold for minimum fire sizes in order to select the case studies?
L 256: Here a caption for a sub-chapter is missing because the chapter is still about the Santarem case study and not about the data analysis.
L 274: I don’t understand the point iii – You excluded entire Australia from the global analysis? Why? Can you improve this description.
L 280-285: How was the anomaly computation done if a pixel experienced multiple fires?
L 413: Those time lags between fire and fuel availability were also recently reported here (Kuhn-Régnier et al., 2021).
L 419: What does L-VOD “measure” in grasslands and savannahs? L-VOD should be close to zero in grasslands as the L-band microwave penetrate the grass layer. Can you provide some more explanation and references about the sensitivity of L-VOD in grasslands?
L 425-426: Note that there are much more differences in fire regimes between North American and Siberian boreal forests (Rogers et al., 2015). In your analysis, different signals might be mixed and hence it would be better to investigate the two regions separately.
L 439: “It can be explained” – It is not clear to what “it” refers. I assume “it” refers to “L-VOD” from the previous sentence but then the sentence does not make sense. Please revise.
L 502-503: I doubt this statement as it is written. You come to this statement by averaging over many fire events from several continents but for most individual fires, it will be likely very hard to see an effect in L-VOD because most fires are much smaller than the spatial resolution of L-VOD. You need to revise the sentence in a way that it is specific to the results of your study.
Fig. 3: Some of the colours are almost impossible to see, especially the pale red and pale green of fires and L-VOD, respectively. What means “Fire nb” in the axis label?
Fig. 4: The time series plots are too small and the labels are not sharp.
References
Andela, N., Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M., and McVicar, T. R.: Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data, Biogeosciences, 10, 6657–6676, https://doi.org/10.5194/bg-10-6657-2013, 2013.
Andela, N., Morton, D. C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., Van Der Werf, G. R., and Randerson, J. T.: The Global Fire Atlas of individual fire size, duration, speed and direction, Earth Syst. Sci. Data, 11, 529–552, 2019.
Chuvieco, E., Aguado, I., Jurdao, S., Pettinari, M. L., Yebra, M., Salas, J., Hantson, S., de la Riva, J., Ibarra, P., Rodrigues, M., Echeverría, M., Azqueta, D., Román, M. V., Bastarrika, A., Martínez, S., Recondo, C., Zapico, E., and Vega, J. M.: Integrating geospatial information into fire risk assessment, Int. J. Wildland Fire, 23, 606, https://doi.org/10.1071/WF12052, 2014.
Fan, L., Wigneron, J.-P., Xiao, Q., Al-Yaari, A., Wen, J., Martin-StPaul, N., Dupuy, J.-L., Pimont, F., Al Bitar, A., Fernandez-Moran, R., and Kerr, Y. H.: Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region, Remote Sens. Environ., 205, 210–223, https://doi.org/10.1016/j.rse.2017.11.020, 2018.
Forkel, M., Dorigo, W., Lasslop, G., Teubner, I., Chuvieco, E., and Thonicke, K.: A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1), Geosci. Model Dev., 10, 4443–4476, https://doi.org/10.5194/gmd-10-4443-2017, 2017.
Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4), J. Geophys. Res. Biogeosciences, 118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013.
Konings, A. G., Rao, K., and SteeleâDunne, S. C.: Macro to micro: microwave remote sensing of plant water content for physiology and ecology, New Phytol., 223, 1166–1172, https://doi.org/10.1111/nph.15808, 2019.
Kuhn-Régnier, A., Voulgarakis, A., Nowack, P., Forkel, M., Prentice, I. C., and Harrison, S. P.: The importance of antecedent vegetation and drought conditions as global drivers of burnt area, Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, 2021.
Rogers, B. M., Soja, A. J., Goulden, M. L., and Randerson, J. T.: Influence of tree species on continental differences in boreal fires and climate feedbacks, Nat. Geosci., 8, 228–234, https://doi.org/10.1038/ngeo2352, 2015.
- AC2: 'Reply on RC2', Emma Bousquet, 29 Jan 2022
Emma Bousquet et al.
Emma Bousquet et al.
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