@article{PAR00026894,
  title = {{R}etrieval of micro-phytoplankton density using {S}entinel-3 and {MODIS} satellite sensors on the {E}astern {A}lgerian coast},
  author = {{A}bdallah, {K}. {W}. and {H}arid, {R}. and {D}emarcq, {H}erv{\'e} and {S}amar, {F}. and {D}jabourabi, {A}. and {I}zeboudjen, {H}. and {B}achari, {N}. {E}. and {H}ouma-{B}achari, {F}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he spatiotemporal variability of the major phytoplankton groups, such as dinoflagellates and diatoms, provides crucial information about the ecosystem's status, especially when it comes to coastal regions that are influenced by permanent anthropogenic pressure. {T}he purpose of this study was to develop specific models for the retrieval of diatoms and dinoflagellates in {A}nnaba {B}ay and {E}l {K}ala's coast (the eastern part of the {A}lgerian coast). {W}e established a data set that included quantified micro-phytoplankton densities from seawater samples obtained at distinct stations along the study area during different seasons and their corresponding {S}entinel-3 and {MODIS} reflectance ({R}rs) values. {S}everal band ratios based on the blue-green and near infrared-red ({NIR}-red) parts of the spectrum have been tested, as well as the {G}eneralized {L}inear {M}odel ({GLM}) with 12 bands using the machine learning approach, in order to validate the most accurate model for quantifying micro-phytoplankton density. {T}he results revealed the efficiency of the 6{B}.{S}3 (r = 0.90, {RMSE} = 3364.2 {C}ells l-1) based on the band ratio, which employs six bands in the blue-green and red parts of the spectrum, and the 12{B}.{S}3 (r = 0.84, {RMSE} = 410.49 {C}ells l-1) based on the machine learning approach, which employs 12 bands of the spectrum for the estimation of diatom and dinoflagellate densities, respectively. {O}n the other hand, both groups exhibit strong correlation with the 5{B}.{M}, which involves 5 bands of {MODIS} {R}rs (r = 0.90 and 0.76 for diatoms and dinoflagellates, respectively). {M}apping phytoplankton densities revealed that {S}entinel-3 data and models outperformed those of {MODIS} and were more suitable for monitoring diatoms and dinoflagellates along {A}lgeria's eastern coast.},
  keywords = {{A}lgorithm ; {D}iatoms ; {D}inoflagellates ; {M}editerranean ; {M}achine learning ; {M}odel ; {R}eflectance ; {R}emote sensing ; {MEDITERRANEE}},
  booktitle = {},
  journal = {{T}halassas},
  volume = {[{E}arly access]},
  numero = {},
  pages = {21},
  ISSN = {0212-5919},
  year = {2023},
  DOI = {10.1007/s41208-023-00624-8},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00026894},
}