@article{fdi:010044356,
  title = {{A}lbedo and {LAI} estimates from {FORMOSAT}-2 data for crop monitoring},
  author = {{B}saibes, {A}. and {C}ourault, {D}. and {B}aret, {F}. and {W}eiss, {M}. and {O}lioso, {A}. and {J}acob, {F}r{\'e}d{\'e}ric and {H}agolle, {O}. and {M}arloie, {O}. and {B}ertrand, {N}. and {D}esfond, {V}. and {K}zemipour, {F}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his paper aimed at estimating albedo and {L}eaf {A}rea {I}ndex ({LAI}) from {FORMOSAT}-2 satellite that offers a unique source of high spatial resolution (eight meters) images with a high revisit frequency (one to three days). {I}t mainly consisted of assessing the {FORMOSAT}-2 spectral and directional configurations that are unusual, with a single off nadir viewing angle over four visible-near infra red wavebands. {I}mages were collected over an agricultural region located in {S}outh {E}astern {F}rance, with a three day frequency from the growing season to post-harvest. {S}imultaneously, numerous ground based measurements were performed over various crops such as wheat, meadow, rice and maize. {A}lbedo and {LAI} were estimated using empirical approaches that have been widely used for usual directional and spectral configurations (i.e. multidirectional or single nadir viewing angle over visible-near infrared wavebands). {T}wo methods devoted to albedo estimation were assessed. based on stepwise multiple regression and neural network ({NNT}). {A}lthough both methods gave satisfactory results, the {NNT} performed better (relative {RMSE}=3.5% versus 7.3%), especially for low vegetation covers over dark or wet soils that corresponded to albedo values lower than 0.20. {F}our approaches for {LAI} estimation were assessed. {T}he first approach based on a stepwise multiple regression over reflectances had the worst performance (relative {RMSE}=65%), when compared to the equally performing {NDVI} based heuristic relationship and reflectance based {NNT} approach (relative {RMSE}=34%).{T}he {NDVI} based neural network approach had the best performance (relative {RMSE}=27.5%), due to the combination of {NDVI} efficient normalization properties and {NNT} flexibility. {T}he high {FORMOSAT}-2 revisit frequency allowed next replicating the dynamics of albedo and {LAI}, and detecting to some extents cultural practices like vegetation cuts. {I}t also allowed investigating possible relationships between albedo and {LAI}. {T}he latter depicted specific trends according to vegetation types, and were very similar when derived from ground based data, remotely sensed observations or radiative transfer simulations. {T}hese relationships also depicted large albedo variabilities for low {LAI} values, which confirmed that estimating one variable from the other would yield poor performances for low vegetation cover with varying soil backgrounds. {F}inally, this empirical study demonstrated, in the context of exhaustively describing the spatiotemporal variability of surface properties, the potential synergy between 1) ground based web-sensors that continuously monitor specific biophysical variables over few locations, and 2) high spatial resolution satellite with high revisit frequencies.},
  keywords = {{A}lbedo ; {L}eaf {A}rea {I}ndex ; {FORMOSAT}-2 data ; {O}ff-nadir single viewing ; {S}tepwise multiple regression ; {N}eural networks ; {W}heat ; {M}eadow ; {M}aize ; {R}ice},
  booktitle = {},
  journal = {{R}emote {S}ensing of {E}nvironment},
  volume = {113},
  numero = {4},
  pages = {716--729},
  ISSN = {0034-4257},
  year = {2009},
  DOI = {10.1016/j.rse.2008.11.014},
  URL = {https://www.documentation.ird.fr/hor/fdi:010044356},
}