@article{fdi:010079957,
  title = {{C}ombining multi-sensor satellite imagery to improve long-term monitoring of temporary surface water bodies in the {S}enegal {R}iver floodplain},
  author = {{O}gilvie, {A}ndrew and {P}oussin, {J}ean-{C}hristophe and {B}ader, {J}ean-{C}laude and {B}ayo, {F}. and {B}odian, {A}. and {D}acosta, {H}. and {D}ia, {D}. and {D}iop, {L}. and {M}artin, {D}idier and {S}ambou, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{A}ccurate monitoring of surface water bodies is essential in numerous hydrological and agricultural applications. {C}ombining imagery from multiple sensors can improve long-term monitoring; however, the benefits derived from each sensor and the methods to automate long-term water mapping must be better understood across varying periods and in heterogeneous water environments. {A}ll available observations from {L}andsat 7, {L}andsat 8, {S}entinel-2 and {MODIS} over 1999-2019 are processed in {G}oogle {E}arth {E}ngines to evaluate and compare the benefits of single and multi-sensor approaches in long-term water monitoring of temporary water bodies, against extensive ground truth data from the {S}enegal {R}iver floodplain. {O}tsu automatic thresholding is compared with default thresholds and site-specific calibrated thresholds to improve {M}odified {N}ormalized {D}ifference {W}ater {I}ndex ({MNDWI}) classification accuracy. {O}tsu thresholding leads to the lowest {R}oot {M}ean {S}quared {E}rror ({RMSE}) and high overall accuracies on selected {S}entinel-2 and {L}andsat 8 images, but performance declines when applied to long-term monitoring compared to default or site-specific thresholds. {O}n {MODIS} imagery, calibrated thresholds are crucial to improve classification in heterogeneous water environments, and results highlight excellent accuracies even in small (19 km2) water bodies despite the 500 m spatial resolution. {O}ver 1999-2019, {MODIS} observations reduce average daily {RMSE} by 48% compared to the full {L}andsat 7 and 8 archive and by 51% compared to the published {G}lobal {S}urface {W}ater datasets. {R}esults reveal the need to integrate coarser {MODIS} observations in regional and global long-term surface water datasets, to accurately capture flood dynamics, overlooked by the full {L}andsat time series before 2013. {F}rom 2013, the {L}andsat 7 and {L}andsat 8 constellation becomes sufficient, and integrating {MODIS} observations degrades performance marginally. {C}ombining {L}andsat and {S}entinel-2 yields modest improvements after 2015. {T}hese results have important implications to guide the development of multi-sensor products and for applications across large wetlands and floodplains.},
  keywords = {wetlands ; optical remote sensing ; spatial accuracy ; water bodies ; {S}enegal {R}iver floodplain ; {L}andsat ; {S}entinel-2 ; {MODIS} ; {SENEGAL} ; {FLEUVE} {SENEGAL} {VALLEE} ; {SENEGAL} {COURS} {D}'{EAU} ; {PODOR} {BASSIN}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {12},
  numero = {19},
  pages = {3157 [30 ]},
  year = {2020},
  DOI = {10.3390/rs12193157},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079957},
}