@article{fdi:010060789,
  title = {{I}mproving operational land surface model canopy evapotranspiration in {A}frica using a direct remote sensing approach},
  author = {{M}arshall, {M}. and {T}u, {K}. and {F}unk, {C}. and {M}ichaelsen, {J}. and {W}illiams, {P}. and {W}illiams, {C}. and {A}rdo, {J}. and {B}oucher, {M}arie and {C}appelaere, {B}ernard and de {G}randcourt, {A}. and {N}ickless, {A}. and {N}ouvellon, {Y}. and {S}choles, {R}. and {K}utsch, {W}.},
  editor = {},
  language = {{ENG}},
  abstract = {{C}limate change is expected to have the greatest impact on the world's economically poor. {I}n the {S}ahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. {S}tudies on climate change and the intensification of the water cycle in sub-{S}aharan {A}frica are few. {T}his is due in part to poor calibration of modeled evapotranspiration ({ET}), a key input in continental-scale hydrologic models. {I}n this study, a remote sensing model of transpiration (the primary component of {ET}), driven by a time series of vegetation indices, was used to substitute transpiration from the {G}lobal {L}and {D}ata {A}ssimilation {S}ystem realization of the {N}ational {C}enters for {E}nvironmental {P}rediction, {O}regon {S}tate {U}niversity, {A}ir {F}orce, and {H}ydrology {R}esearch {L}aboratory at {N}ational {W}eather {S}ervice {L}and {S}urface {M}odel ({GNOAH}) to improve total {ET} model estimates for monitoring purposes in sub-{S}aharan {A}frica. {T}he performance of the hybrid model was compared against {GNOAH} {ET} and the remote sensing method using eight eddy flux towers representing major biomes of sub-{S}aharan {A}frica. {T}he greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. {T}he reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.},
  keywords = {{AFRIQUE} {SUBSAHARIENNE}},
  booktitle = {},
  journal = {{H}ydrology and {E}arth {S}ystem {S}ciences},
  volume = {17},
  numero = {3},
  pages = {1079--1091},
  ISSN = {1027-5606},
  year = {2013},
  DOI = {10.5194/hess-17-1079-2013},
  URL = {https://www.documentation.ird.fr/hor/fdi:010060789},
}