• Chen, N., Tsendbazar, N. E., Requena Suarez, D., Silva-Junior, C. H. L., Verbesselt, J., Herold, M.. Revealing the spatial variation in biomass uptake rates of Brazil’s secondary forests.. ISPRS Journal of Photogrammetry and Remote Sensing. 2024. https://doi.org/10.1016/j.isprsjprs.2023.12.013
  • Hunka, N., Santoro, M., Armston. J. et al.. On the NASA GEDI and ESA CCI biomass maps: aligning for uptake in the UNFCCC global stocktake.. Environmental Research Letters. 203. https://doi.org/10.1088/1748-9326/ad0b60
  • Dalagnol, R., Wagner, F., H., Galvão, L., S. et al.. Mapping tropical forest degradation with deep learning and Planet NICFI data.. Remote Sensing of Environment.. 2023. https://doi.org/10.1016/j.rse.2023.113798
  • Mo, L., Zohner, C.M., Reich, P.B. et al.. Integrated global assessment of the natural forest carbon potential.. Nature. 2023. https://doi.org/10.1038/s41586-023-06723-z
  • Araza, A., de Bruin, S., Hein, L. and Herold, M.. Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting.. Scientific Reports. 2023. https://doi.org/10.1038/s41598-023-38935-8
  • Yang, H., Ciais, P., Frappart, F., et al.. Global increase in biomass carbon stock dominated by growth of northern young forests over past decade.. Nature Geoscience. 2023. https://doi.org/10.1038/s41561-023-01274-4
  • Araza, A., Herold, M., de Bruin, S., et al.. Past decade above-ground biomass change comparisons from four multi-temporal global maps. International Journal of Applied Earth Observation and Geoinformation. 2023. https://doi.org/10.1016/j.jag.2023.103274
  • Chen, N., Tsendbazar, N.E., Requena Suarez, D., Verbesselt, J. and Herold, M.. Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi-source remote sensing data. Remote Sensing in Ecology and Conservation. 2023. https://doi.org/10.1002/rse2.328
  • Ochiai, O., Poulter, B., Seifert, F.M. et al.. Towards a roadmap for space-based observations of the land sector for the UNFCCC Global Stocktake.. iScience. 2023. http://dx.doi.org/10.1016/j.isci.2023.106489
  • Labrière, N., Davies, S. J., Disney, M. I., Duncanson, L. I., Herold, M., Lewis, S. L., Phillips, O. L., Quegan, S., Saatchi, S. S., Schepaschenko, D. G., Scipal, K., Sist, P., & Chave, J.. Toward a forest biomass reference measurement system for remote sensing applications. Global Change Biology. 2023. https://doi.org/10.1111/gcb.16497
  • Bennett, A. C., Rodrigues de Sousa, T., Monteagudo-Mendoza, A., Esquivel-Muelbert, A., Morandi, P. S., Coelho de Souza, F., ... & Phillips, O. L.. Sensitivity of South American tropical forests to an extreme climate anomaly. Nature Climate Change. 2023.
  • Viana Santos, H. K., Borges De Lima, R., Figueiredo De Souza, R. L., Cardoso, D., Moonlight, P. W., Teixeira Silva, T., ... & Phillips, O. L.. Spatial distribution of aboveground biomass stock in tropical dry forest in Brazil. iForest-Biogeosciences and Forestry. 2023.
  • Málaga, N., De Bruin, S., McRoberts, R.E., Arana Olivos, A., de la Cruz Paiva, R., Durán Montesinos, P., Requena Suarez, D. and Herold, M.. Precision of subnational forest AGB estimates within the Peruvian Amazonia using a global biomass map.. International Journal of Applied Earth Observation and Geoinformation.. 2022. http://dx.doi.org/10.1016/j.jag.2022.103102
  • Fan, L., Wigneron, J.P., Ciais, P. et al.. Siberian carbon sink reduced by forest disturbances.. Nature Geoscience. 2022. http://dx.doi.org/10.1038/s41561-022-01087-x
  • Tao, S., Chave, J., Frison, P.L. and Saatchi, S.. Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts.. PNAS. 2022. http://dx.doi.org/10.1073/pnas.2116626119
  • Liang, J., Gamarra, J.G.P., Picard, N. et al.. Co-limitation towards lower latitudes shapes global forest diversity gradients.. Nature Ecology and Evolution.. 2022. http://dx.doi.org/10.1038/s41559-022-01831-x
  • Santoro, M., Cartus, O., Wegmüller, U., Besnard, S., Carvalhais, N., Araza, A., Herold, M., Liang, J., Cavlovic, J., Engdahl, M.E.. Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure.. Remote Sensing of Environment. 2022. https://doi.org/10.1016/j.rse.2022.113114
  • Yang, H., Ciais, P., Wigneron, J.P., Chave, J., Cartus, O., Chen, X., Fan, L., Green, J.K., Huang, Y., Joetzjer, E. and Kay, H.. Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño.. PNAS. 2022. https://doi.org/10.1073/pnas.2101388119
  • Araza, A., de Bruin, S., Herold, M., Quegan, S., Labriere, N., Rodriguez-Veiga, P., Avitabile, V., Santoro, M., Mitchard, E.T.A., Ryan, C.M., Phillips, O.L., Willcock, S., Verbeeck, H., Carreiras, J., Hein, L. et al.. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps. Remote Sensing of Environment. 2022. https://doi.org/10.1016/j.rse.2022.112917.
  • Adzhar, R., Kelley, D. I., Dong, N., George, C., Torello Raventos, M., Veenendaal, E., ... & Gerard, F.. MODIS Vegetation Continuous Fields tree cover needs calibrating in tropical savannas.. Biogeosciences. 2022.
  • de Lima, R. A., Phillips, O. L., Duque, A., Tello, J. S., Davies, S. J., de Oliveira, A. A., ... & Vásquez, R.. Making forest data fair and open.. Nature Ecology & Evolution. 2022.
  • Sousa, T. R., Schietti, J., Ribeiro, I. O., Emílio, T., Fernández, R. H., Ter Steege, H., ... & Costa, F. R.. Water table depth modulates productivity and biomass across Amazonian forests.. Global Ecology and Biogeography. 2022.
  • Kay, H., Santoro, M., Cartus, O., Bunting, P. and Lucas, R.. Exploring the relationship between forest canopy height and canopy density from Spaceborne LiDAR observations. Remote Sensing. 2021. https://doi.org/10.3390/rs13244961
  • Duncanson, L., Armston, J., Disney, M. et al.. Good Practices for Satellite-Derived and Product Validation: Land Product Validation Subgroup (WGCV/CEOS).. 2021. https://doi.org/10.5067/doc/ceoswgcv/lpv/agb.001
  • Santoro, M., Cartus, O. and Fransson, J.E.. Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden.. Remote Sensing of Environment. 2021. https://doi.org/10.1016/j.rse.2020.112235
  • ForestPlots.net, Blundo, C., Carilla, J., Grau, R., Malizia, A., Malizia, L., Osinaga-Acosta, O., Bird, M., et al.. Taking the pulse of Earth’s tropical forests using networks of highly distributed plots.. Biological Conservation. 2021. https://doi.org/10.1016/j.biocon.2020.108849
  • Rodríguez-Veiga, P., Carreiras, J., Smallman, T.L., Exbrayat, J.-F., Ndambiri, J., Mutwiri, F., Nyasaka, D., Quegan, S., Williams, M. and Balzter, H. Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion.. Remote Sensing. 2020. https://doi.org/10.3390/rs12152380
  • Schepaschenko, D., Chave, J., Phillips, O.L., Lewis, S.L., Davies, S.J., Réjou-Méchain, M., Sist, P., Scipal, K., Perger, C., Herault, B. and Labrière, N.. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass.. Scientific Data. 2019. https://doi.org/10.1038/s41597-019-0196-1
  • Herold, M., Carter, S., Avitabile, V., Espejo, A.B., Jonckheere, I., Lucas, R., McRoberts, R.E., Næsset, E., Nightingale, J., Petersen, R. and Reiche, J.. The role and need for space-based forest biomass-related measurements in environmental management and policy.. Surveys in Geophysics.. 2019. https://doi.org/10.1007/s10712-019-09510-6
  • Berzaghi, F., Longo, M., Ciais, P., Blake, S., Bretagnolle, F., Vieira, S., Scaranello, M., Scarascia-Mugnozza, G. and Doughty, C.E.. Carbon stocks in central African forests enhanced by elephant disturbance.. Nature Geoscience. 2019. https://www.nature.com/articles/s41561-019-0395-6
  • Fischer, F. J., Maréchaux, I., & Chave, J.. Improving plant allometry by fusing forest models and remote sensing. New Phytologist.. 2019. https://doi.org/10.1111/nph.15810
  • Chave, J., Davies, S. J., Phillips, O. L., Lewis, S. L., Sist, P., Schepaschenko, D., ... & Duncanson, L.. Ground data are essential for biomass remote sensing missions. Surveys in Geophysics. 2019. https://doi.org/10.1007/s10712-019-09528-w
  • Quegan, S., Le Toan, T., Chave, J. et al.. The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space. Remote Sensing of Environment. 2019. https://doi.org/10.1016/j.rse.2019.03.032
  • Tebaldini, S., Minh, D. H. T., d’Alessandro, M. M., Villard, L., Le Toan, T., & Chave, J.. The Status of Technologies to Measure Forest Biomass and Structural Properties: State of the Art in SAR Tomography of Tropical Forests. Surveys in Geophysics. 2019. https://doi.org/10.1007/s10712-019-09532-0
  • Duncanson, L., Armston, J., Disney, M. et al.. The importance of consistent global forest aboveground biomass product validation. Surveys in Geophysics. 2019. https://doi.org/10.1007/s10712-019-09538-8
  • Schepaschenko, D., See, L., Lesiv, M., Bastin, J.F., Mollicone, D., Tsendbazar, N.E., Bastin, L., McCallum, I., Bayas, J.C.L., Baklanov, A. and Perger, C. Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery. Surveys in Geophysics. 2019. https://doi.org/10.1007/s10712-019-09533-z
  • Phillips, O.L., Sullivan, M.J.P., Baker, T.R., Monteagudo Mendoza, A., Nunez Vargas, P., Vasquez, R.. Species Matter: Wood density Influences Tropical Forest Biomass at Multiple Scales.. Surveys in Geophysics.. 2019. https://doi.org/10.1007/s10712-019-09540-0
  • Schepaschenko, D., Moltchanova, E., Shvidenko, A., Blyshchyk, V., Dmitriev, E., Martynenko, O., See, L. and Kraxner, F.. Improved estimates of biomass expansion factors for Russian forests. Forests. 2018. https://doi.org/10.3390/f9060312