@article{fdi:010095210,
  title = {{S}patially remotely sensed evapotranspiration estimates in {S}ahel region using an ensemble contextual model with automated heterogeneity assessment},
  author = {{F}ahrani, {N}. and {E}tchanchu, {J}ordi and {B}oulet, {G}illes and {G}amet, {P}. and {O}lioso, {A}. and {D}ezetter, {A}lain and {B}odian, {A}. and {C}hahinian, {N}an{\'e}e and {M}allick, {K}. and {O}llivier, {C}hlo{\'e} and {R}oupsard, {O}. and {A}llies, {A}. and {D}emarty, {J}{\'e}rome},
  editor = {},
  language = {{ENG}},
  abstract = {{W}ater scarcity and the inter-annual variability of water resources in semi-arid areas are limiting factors for agricultural production. {T}he characterization of plant water use, together with water stress, can help us to monitor the impact of drought on agrosystems and ecosystems, especially in the {S}ahel region. {I}ndeed, this region is identified as a 'hot spot' for climate change. {I}n-situ measurements often are insufficient for accounting for spatial variability at large scales (sup. 100 km) due to the scarcity of gauge networks. {T}o tackle this issue, remotely sensed evaporation is often used. {I}n this study, estimates using thermal infrared and visible data from {MODIS}/{TERRA} and {AQUA} are used. {S}patially distributed estimates of the daily actual evapotranspiration ({ET}d) are simulated using the {EVASPA} {S}-{SEBI} {S}ahel ({E}3{S}) ensemble contextual method over a mesoscale area (145x145 km) in central {S}enegal. {E}3{S} uses a set of different methods in order to identify the dry and wet edges of the surface temperature/albedo scatterplot and therefore estimate the evaporative fraction ({EF}). {H}owever, contextual approaches assume the simultaneous presence of sufficient fully wet and fully dry pixels within the same satellite image. {T}his assumption of heterogeneity does not always hold, especially in the {S}ahel, which is characterized by the alternation of dry and wet seasons due to the monsoon-influenced climate. {T}o tackle this issue, {E}3{S} uses different sets of methods depending on the season, based on local knowledge. {T}he present study thus aims at generalizing the approach by proposing a new version of {E}3{S} called ?{E}3{S}-{V}2?. {T}his latter allows an automatic detection of different heterogeneity conditions. {T}herefore, a sensitivity analysis examining the effect of using different {EF} estimation methods over different spatial coverages was performed. {I}t made it possible to identify relevant normalized indicators to determine the heterogeneity level, as well as to discriminate among the most adapted {EF} determination methods for each situation. {F}rom this analysis, an automated procedure of method selection according to the heterogeneity conditions is proposed. {A} local-scale evaluation was performed using eddy-covariance measurements in the {S}enegal {G}roundnut {B}asin. {A} spatialized evaluation was also performed using {GLEAM} and {ERA}5-{L}and, which are proven reference {ET}d products over the area. ?{E}3{S}-{V}2? simulations yield comparable performances with in-situ and reference products in our study area.},
  keywords = {{SAHEL} ; {SENEGAL} ; {ZONE} {SEMIARIDE}},
  booktitle = {},
  journal = {{S}cience of {R}emote {S}ensing},
  volume = {11},
  numero = {},
  pages = {100229 [17 ]},
  ISSN = {2666-0172},
  year = {2025},
  DOI = {10.1016/j.srs.2025.100229},
  URL = {https://www.documentation.ird.fr/hor/fdi:010095210},
}