@article{fdi:010076479,
  title = {{U}pscaling forest biomass from field to satellite measurements : sources of errors and ways to reduce them},
  author = {{R}{\'e}jou-{M}{\'e}chain, {M}axime and {B}arbier, {N}icolas and {C}outeron, {P}ierre and {P}loton, {P}ierre and {V}incent, {G}r{\'e}goire and {H}erold, {M}. and {M}ermoz, {S}. and {S}aatchi, {S}. and {C}have, {J}. and de {B}oissieu, {F}. and {F}eret, {J}. {B}. and {T}akoudjou, {S}. {M}. and {P}{\'e}lissier, {R}apha{\¨e}l},
  editor = {},
  language = {{ENG}},
  abstract = {{F}orest biomass monitoring is at the core of the research agenda due to the critical importance of forest dynamics in the carbon cycle. {H}owever, forest biomass is never directly measured; thus, upscaling it from trees to stand or larger scales (e.g., countries, regions) relies on a series of statistical models that may propagate large errors. {H}ere, we review the main steps usually adopted in forest aboveground biomass mapping, highlighting the major challenges and perspectives. {W}e show that there is room for improvement along the scaling-up chain from field data collection to satellite-based large-scale mapping, which should lead to the adoption of effective practices to better control the propagation of errors. {W}e specifically illustrate how the increasing use of emerging technologies to collect massive amounts of high-quality data may significantly improve the accuracy of forest carbon maps. {F}urthermore, we discuss how sources of spatially structured biases that directly propagate into remote sensing models need to be better identified and accounted for when extrapolating forest carbon estimates, e.g., through a stratification design. {W}e finally discuss the increasing realism of 3{D} simulated stands, which, through radiative transfer modelling, may contribute to a better understanding of remote sensing signals and open avenues for the direct calibration of large-scale products, thereby circumventing several current difficulties.},
  keywords = {{B}iomass ; {C}alibration ; {C}arbon ; {E}rror propagation ; {F}ield data ; {M}odelling},
  booktitle = {},
  journal = {{S}urveys in {G}eophysics},
  volume = {40},
  numero = {4},
  pages = {881--911},
  ISSN = {0169-3298},
  year = {2019},
  DOI = {10.1007/s10712-019-09532-0},
  URL = {https://www.documentation.ird.fr/hor/fdi:010076479},
}