Related publications

Here you can find a list of publications on BIOMASS that our project members published lately. We will also add other publications, which are interesting for the community, to the list. Contact us, if you would like us to add your publication.


  • Li, J., Pei, J. , Liu, J. , Wu J. ,Li, B. , Fang C. , Nie, M. (2021). Spatiotemporal variability of fire effects on soil carbon and nitrogen: a global meta-analysis. Wiley Online Library,
  • Martin, A.R., Domke, G.M., Doraisami, M. et al. (2021). Carbon fractions in the world’s dead wood. Nat Commun 12, 889.
  • Harris, N.L., Gibbs, D.A., Baccini, A. et al. (2021). Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234–240.
  • Terrer, C., Phillips, R.P., Hungate, B.A. et al. (2021). A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 .
  • Haaf, D., Six, J. & Doetterl, S. (2021). Global patterns of geo-ecological controls on the response of soil respiration to warming. Nat. Clim. Chang.
  • Qin, Y., Xiao, X., Wigneron, JP. et al. (2021). Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Chang. 11, 442–448.
  • Singh J., Levick S., Guderle M. & C. Schmullius (2020): Moving from plot-based to hillslope-scale assessments of savanna vegetation structure with long-range terrestrial laser scanning (LR-TLS). International Journal of Applied Earth Observation and Geoinformation, Volume 90, August 2020,
  • Qi, W., S. Saarela, J. Armston, G. Ståhl, and R. Dubayah. 2019. Forest Biomass Estimation Over Three Distinct Forest Types Using TanDEM-X InSAR Data and Simulated GEDI LiDAR Data. Remote Sensing of Environment. vol.232 (July), 111283. doi: 10.1016/j.rse.2019.111283
  • Rödig, E., Knapp, N., Fischer, R., Bohn, F.J., Dubayah, R., Tang, H., Huth, A. 2019. From small-scale forest structure to Amazon-wide carbon estimates. Nature Communications 10 , art. 5088.
  • Rodriguez-Veiga, P., S Quegan, J Carreiras, HJ Persson, JES Fransson, A Hoscilo, D Ziolkowski, K Sterenczak, S Lohberger, M Stangel, A Berninger, F Siegert, V Avitabile, M Herold, S Mermoz, A Bouvet, TL Toan, N Carvalhais, M Santoro, O Cartus, Y Rauste, R Mathieu, GP Asner, C Thiel, C Pathe, C Schmullius, FM Seifert, K Tansey & H Balzter (2019): Forest biomass retrieval approaches from earth observation in different biomes., International Journal of Applied Earth Observation and Geoinformation, 77, 53-68.
  • Taubert, F., Fischer, R., Groeneveld, J., Lehmann, S., Müller, M.S., Rödig, E., Wiegand, T., Huth, A., 2018. Global patterns of tropical forest fragmentation. Nature 554, 519–522.Knapp, N., Fischer, R., Huth, A., 2018. Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states. Remote Sensing of Environment 205, 199-209.
  • Rödig, E., Cuntz, M., Rammig, A., Fischer, R., Taubert, F., Huth, A., 2018. The importance of forest structure for carbon fluxes of the Amazon rainforest. Environmental Research Letters 13, 054013.
  • Bohn, F. J., & Huth, A. (2017). The importance of forest structure to biodiversity–productivity relationships. Royal Society open science, 4(1), 1605


  • Wang, J.A., Baccini, A., Farina, M. et al. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nat. Clim. Chang. 11, 435–441 (2021).
  • Burt, A., Vicari, M.B., Da Costa, A.C.L., Coughlin I.,  Meir P.,  Rowland, L.,  Disney M. (2021) New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. The Royal Society Publishing, 8 (2).
  • Duncanson, L. et al.. (2020). Biomass Estimation from Simulated GEDI, ICESat-2 and NISAR Across Environmental Gradients in Sonoma County, California. Remote Sensing of Environment. 242 (March), Art. no. doi: 10.1016/j.rse.2020.11177.
  • Dubois, C., Mueller, M. M., Pathe, C., Jagdhuber, T., Cremer, F., Thiel, C., Schmullius, C. (2020). Characterization of land cover seasonality in Sentinel-1 time series data. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 5(3)
  • Urban, M., Heckel, K., Berger, C., Schratz, P., Smit, I.P.J., Strydom, T., Baade, J. & C. Schmullius  (2020): Woody cover mapping in the savanna ecosystem of the Kruger National Park using Sentinel-1 C-Band time series data, Koedoe 62(1), a1621. doi:10.4102/koedoe.v62i1.1621.
  • Santoro et al. (2020) The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations, ESSD
  • Thiel, C., Müller, M. M., Berger, C., Cremer, F., Dubois, C., Hese, S.,Baade, J., Klan, F., Pathe, C. (2020). Monitoring Selective Logging in a Pine-Dominated Forest in Central Germany with Repeated Drone Flights Utilizing A Low Cost RTK Quadcopter. Drones, 4(2), 11.
  • Ma, X., Migliavacca, M., Wirth, C., Bohn, F. J., Huth, A., Richter, R., & Mahecha, M. D. (2020). Monitoring plant functional diversity using the reflectance and echo from space. Remote Sensing, 12(8), 1248.
  • Fischer, R., Knapp, N., Bohn, F., & Huth, A. (2019). Remote sensing measurements of forest structure types for ecosystem service mapping. In Atlas of Ecosystem Services (pp. 63-67). Springer, Cham.
  • Quegan et al. (2019) The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space, RSE,
  • Vittuci et al. (2019) Vegetation optical depth at L-band and above ground biomass in the tropical range: Evaluating their relationships at continental and regional scales, International Journal of Applied Earth Observation,
  • Fischer, R., Knapp, N., Bohn, F., Shugart, H. H., & Huth, A. (2019). The relevance of forest structure for biomass and productivity in temperate forests: New perspectives for remote sensing. Surveys in Geophysics, 40(4), 709-734.
  • Ma, X., Mahecha, M. D., Migliavacca, M., van der Plas, F., Benavides, R., Ratcliffe, S., Kattge, J., Richter, R., Musavi, T., Baeten, L., Barnoaiea, I., Bohn, F. J., Bouriaud, O., Bussotti, F., Coppi, A., Domisch, T., Huth, A., Jaroszewicz, B., Joswig, J., Pabon-Moreno, D. E., Papale, D., Selvi, F., Laurin, G. V., Valladares, F., Reichstein, M., Wirth, C. (2019). Inferring plant functional diversity from space: the potential of Sentinel-2. Remote Sensing of Environment, 233: 111368. doi:10.1016/j.rse.2019.111368.
  • Stelmaszczuk-Gorska, MA, M Urbazaev, C Schmullius & C Thiel (2018): Estimation of Above-Ground Biomass over Boreal Forests in Siberia Using Updated In Situ, ALOS-2 PALSAR-2, and RADARSAT-2 Data. – Remote Sensing, 10(10), 20.
  • Urbazaev, M, C Thiel, F Cremer, R Dubayah, M Migliavacca, M Reichstein & C Schmullius (2018): Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico. – Carbon Balance and Management, 13, 20.
  • Hoscilo, A., Lewandowska, A., Ziółkowski, D., Stereńczak, K. ,Lisańczuk M., Schmullius, C., Pathe, C. (2018): Forest Aboveground Biomass Estimation using a Combination of Sentinel-1 and Sentinel-2 Data. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing
  • Rödig, E., Cuntz, M., Heinke, J., Rammig, A., Huth, A., (2017). Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory. Global Ecology and Biogeography 26, 1292–1302.
  • Minh, D. H. T. et al.. 2016. “SAR Tomography for the Retrieval of Forest Biomass and Height: Cross-Validation at Two Tropical Forest Sites in French Guiana.” Remote Sensing of Environment. 175: 138-147. doi: 10.1016/j.rse.2015.12.037.
  • Schmullius, C., C. Thiel, C. Pathe, Santoro, M. (2015). Radar Time Series for Land Cover and Forest Mapping. In Remote Sensing Time Series (pp. 323-356). Springer International Publishing.Symposium (pp. 9026-9029). IEEE
  • Santoro, M., C. Schmullius, C., Pathe, C., Schwilk, J. (2012): Pan-boreal mapping of forest growing stock volume using hyper-temporal Envisat ASAR ScanSAR backscatter data,” Proceedings of IGARSS’12, Munich, 23-27 July, pp. 7204-720
  • Le Toan, T. et al.. 2011. The BIOMASS Mission: Mapping Global Forest Biomass to Better Understand the Terrestrial Carbon Cycle. Remote Sensing of Environment. 115 (11): 2850-60. doi: 10.1016/j.rse.2011.03.020.

Biomass product

  • Santoro, M. et al.. (2020). The Global Forest Above-Ground Biomass Pool for 2010 Estimated from High-Resolution Satellite Observations. Earth System Science Data Discussions. July, 1—38. doi: 10.5194/essd-2020-148.
  • Fan, L., et al.. (2019). Satellite-observed pantropical carbon dynamics. Nature Plants, 5(9), 944-951.
  • Bouvet, A. et al.. (2018). “An Above-Ground Biomass Map of African Savannahs and Woodlands at 25 m Resolution Derived from ALOS PALSAR. Remote Sensing of Environment. 206: 156—173. doi: 10.1016/j.rse.2017.12.030.
  • Avitabile, V. et al.. (2016). An Integrated Pan-Tropical Biomass Map Using Multiple Reference Datasets. Global Change Biology. 22 (4): Art. no. 4. doi: 10.1111/gcb.13139.
  • Thurner, M., et al.. (2014). Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, 23(3), 297-310.
  • Liu, Y. Y., Van Dijk, A. I., De Jeu, R. A., Canadell, J. G., McCabe, M. F., Evans, J. P., and Wang, G. 2015. Recent reversal in loss of global terrestrial biomass. Nature Climate Change, 5(5), 470-474.
  • Baccini, A. et al.. (2012). Estimated Carbon Dioxide Emissions from Tropical Deforestation Improved by CarbonDensity Maps. Nature Climate Change. 2 (3): 182—85. doi: 10.1038/nclimate1354.
  • Saatchi, S. S. et al.. (2011). Benchmark Map of Forest Carbon Stocks in Tropical Regions Across Three Continents. Proceedings of the National Academy of Sciences. 108 (24): 9899-9904. doi: 10.1073/pnas.1019576108.

Carbon cycle

  • Yang, H., et al.. (2021). Variations of carbon allocation and turnover time across tropical forests. Global Ecology and Biogeography, 30(6), 1271-1285.
  • Fan, N., Koirala, S., Reichstein, M., Thurner, M., Avitabile, V., Santoro, M., Ahrens, B., Weber, U., and Carvalhais, N. (2020). Apparent ecosystem carbon turnover time: uncertainties and robust features. Earth System Science Data, 12(4), 2517-2536.
  • McNicol, I. M., C. M. Ryan, and E. T. A. Mitchard. (2018). Carbon Losses from Deforestation and Widespread Degradation Offset by Extensive Growth in African Woodlands.” Nature Communications 9: Art. no. 3045, 1-19. doi: 10.1038/s41467-018-05386-z
  • Baccini, A., Walker, W., Carvalho, L., Farina, M., Sulla-Menashe, D., and Houghton, R. A. (2017) Tropical forests are a net carbon source based on aboveground measurements of gain and loss.” Science, 358(6360), 230-234.
  • Thurner, M., Beer, C., Carvalhais, N., Forkel, M., Santoro, M., Tum, M., and Schmullius, C. (2016). “Large‐scale variation in boreal and temperate forest carbon turnover rate related to climate.” Geophysical Research Letters, 43(9), 4576-4585.
  • Bloom, A. A., Exbrayat, J. F., Van Der Velde, I. R., Feng, L., and Williams, M. (2016). The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times. Proceedings of the National Academy of Sciences, 113(5), 1285-1290.
  • Carvalhais, N., et al.. (2014). Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature, 514(7521), 213-217.