@article{fdi:010093389,
  title = {{M}onitoring individual rice field flooding dynamics over a large scale to improve mosquito surveillance and control},
  author = {{R}andriamihaja, {M}. and {R}andrianjatovo, {T}. {M}. and {E}vans, {M}. {V}. and {I}hantamalala, {F}. {A}. and {H}erbreteau, {V}incent and {R}{\'e}villion, {C}. and {D}ela{\^i}tre, {E}ric and {C}atry, {T}hibault and {G}architorena, {A}ndres},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground {P}rogress in malaria elimination has been hindered by recent changes in mosquito behaviour and increased insecticide resistance in response to traditional vector control measures, such as indoor residual spraying and long-lasting insecticidal nets. {T}here is, therefore, increasing interest in the use of larval source management ({LSM}) to supplement current insecticide-based interventions. {H}owever, {LSM} implementation requires the characterization of larval habitats at fine spatial and temporal scales to ensure interventions are well-placed and well-timed. {R}emotely sensed optical imagery captured via drones or satellites offers one way to monitor larval habitats remotely, but its use at large spatio-temporal scales has important limitations. {M}ethods {A} method using radar imagery is proposed to monitor flooding dynamics in individual rice fields, a primary larval habitat, over very large geographic areas relevant to national malaria control programmes aiming to implement {LSM} at scale. {T}his is demonstrated for a 3971 km2 malaria-endemic district in {M}adagascar with over 17,000 rice fields. {R}ice field mapping on {O}pen{S}treet{M}ap was combined with {S}entinel-1 satellite imagery (radar, 10 m) from 2016 to 2022 to train a classification model of radar backscatter to identify rice fields with vegetated and open water, resulting in a time-series of weekly flooding dynamics for thousands of rice fields.{R}esults{F}rom these time-series, over a dozen indicators useful for {LSM} implementation, such as the timing and frequency of flooding seasons, were obtained for each rice field. {T}hese monitoring tools were integrated into an interactive {GIS} dashboard for operational use by vector control programmes, with results available at multiple scales (district, sub-district, rice field) relevant for different phases of {LSM} intervention (e.g. prioritization of sites, implementation, follow-up).{C}onclusions{S}cale-up of these methods could enable wider implementation of evidence-based {LSM} interventions and reduce malaria burdens in contexts where irrigated agriculture is a major transmission driver.},
  keywords = {{H}ealth surveillance system ; {O}pen{S}treet{M}ap mapping ; {S}ynthetic aperture radar ; {L}arval source management ; {D}ecision-making ; tools ; {M}alaria ; {MADAGASCAR}},
  booktitle = {},
  journal = {{M}alaria {J}ournal},
  volume = {24},
  numero = {1},
  pages = {107 [17 p.]},
  year = {2025},
  DOI = {10.1186/s12936-025-05344-3},
  URL = {https://www.documentation.ird.fr/hor/fdi:010093389},
}