Articles | Volume 13, issue 5
https://doi.org/10.5194/essd-13-1925-2021
https://doi.org/10.5194/essd-13-1925-2021
Data description paper
 | 
08 May 2021
Data description paper |  | 08 May 2021

MOSEV: a global burn severity database from MODIS (2000–2020)

Esteban Alonso-González and Víctor Fernández-García

Related authors

Future permafrost degradation under climate change in a headwater catchment of central Siberia: quantitative assessment with a mechanistic modelling approach
Thibault Xavier, Laurent Orgogozo, Anatoly S. Prokushkin, Esteban Alonso-González, Simon Gascoin, and Oleg S. Pokrovsky
The Cryosphere, 18, 5865–5885, https://doi.org/10.5194/tc-18-5865-2024,https://doi.org/10.5194/tc-18-5865-2024, 2024
Short summary
Time series of alpine snow surface radiative-temperature maps from high-precision thermal-infrared imaging
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024,https://doi.org/10.5194/essd-16-3913-2024, 2024
Short summary
Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter
Marco Mazzolini, Kristoffer Aalstad, Esteban Alonso-González, Sebastian Westermann, and Désirée Treichler
EGUsphere, https://doi.org/10.5194/egusphere-2024-1404,https://doi.org/10.5194/egusphere-2024-1404, 2024
Short summary
Rain-on-snow responses to warmer Pyrenees: a sensitivity analysis using a physically based snow hydrological model
Josep Bonsoms, Juan I. López-Moreno, Esteban Alonso-González, César Deschamps-Berger, and Marc Oliva
Nat. Hazards Earth Syst. Sci., 24, 245–264, https://doi.org/10.5194/nhess-24-245-2024,https://doi.org/10.5194/nhess-24-245-2024, 2024
Short summary
Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation
Esteban Alonso-González, Kristoffer Aalstad, Norbert Pirk, Marco Mazzolini, Désirée Treichler, Paul Leclercq, Sebastian Westermann, Juan Ignacio López-Moreno, and Simon Gascoin
Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023,https://doi.org/10.5194/hess-27-4637-2023, 2023
Short summary

Related subject area

Biogeosciences and biodiversity
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
Kelly S. Aho, Kaelin M. Cawley, Robert T. Hensley, Robert O. Hall Jr., Walter K. Dodds, and Keli J. Goodman
Earth Syst. Sci. Data, 16, 5563–5578, https://doi.org/10.5194/essd-16-5563-2024,https://doi.org/10.5194/essd-16-5563-2024, 2024
Short summary
A spectral–structural characterization of European temperate, hemiboreal, and boreal forests
Miina Rautiainen, Aarne Hovi, Daniel Schraik, Jan Hanuš, Petr Lukeš, Zuzana Lhotáková, and Lucie Homolová
Earth Syst. Sci. Data, 16, 5069–5087, https://doi.org/10.5194/essd-16-5069-2024,https://doi.org/10.5194/essd-16-5069-2024, 2024
Short summary
VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring
Ruxandra-Maria Zotta, Leander Moesinger, Robin van der Schalie, Mariette Vreugdenhil, Wolfgang Preimesberger, Thomas Frederikse, Richard de Jeu, and Wouter Dorigo
Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024,https://doi.org/10.5194/essd-16-4573-2024, 2024
Short summary
Crop-specific management history of phosphorus fertilizer input (CMH-P) in the croplands of the United States: reconciliation of top-down and bottom-up data sources
Peiyu Cao, Bo Yi, Franco Bilotto, Carlos Gonzalez Fischer, Mario Herrero, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 4557–4572, https://doi.org/10.5194/essd-16-4557-2024,https://doi.org/10.5194/essd-16-4557-2024, 2024
Short summary
Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI
Chad A. Burton, Sami W. Rifai, Luigi J. Renzullo, and Albert I. J. M. Van Dijk
Earth Syst. Sci. Data, 16, 4389–4416, https://doi.org/10.5194/essd-16-4389-2024,https://doi.org/10.5194/essd-16-4389-2024, 2024
Short summary

Cited articles

Alonso-González, E. and Fernández-García, V.: MOSEV: a global burn severity database from MODIS (2000–2020), Zenodo [data set], https://doi.org/10.5281/zenodo.4265209, last access: 1 November 2020. 
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, https://doi.org/10.5194/essd-11-529-2019, 2019. 
Boschetti, L., Roy, D. P., Giglio, L., Huang, H., Zubkova, M., and Humber, M. L.: Global validation of the collection 6 MODIS burned area product, Remote Sens. Environ., 235, 111490, https://doi.org/10.1016/j.rse.2019.111490, 2019. 
Botella-Martínez, M. A. and Fernández-Manso, A: Estudio de la severidad post-incendio en la Comunidad Valenciana comparando los índices dNBR, RdNBR y RBR a partir de imágenes Landsat 8, Revista de Teledetección, 49, 33–47, https://doi.org/10.4995/raet.2017.7095, 2017. 
Download
Short summary
We present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area products. The database inludes monthly scenes with the dNBR, RdNBR and post-burn NBR spectral indices at 500 m spatial resolution from November 2000 onwards. Moreover, in this work we show that there is a close relationship between the burn severity metrics included in MOSEV and the same ones obtained from Landsat-8.
Altmetrics
Final-revised paper
Preprint