@article{fdi:010089619,
  title = {{C}hlorophyll-a detection algorithms at different depths using in situ, meteorological, and remote sensing data in a {C}hilean {L}ake},
  author = {{R}odríguez-{L}ópez, {L}. and {A}lvarez, {D}. and {U}sta, {D}. {B}. and {D}uran-{L}lacer, {I}. and {A}lvarez, {L}. {B}. and {F}agel, {N}. and {B}ourrel, {L}uc and {F}rappart, {F}. and {U}rrutia, {R}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n this study, we employ in situ, meteorological, and remote sensing data to estimate chlorophyll-a concentration at different depths in a {S}outh {A}merican freshwater ecosystem, focusing specifically on a lake in southern {C}hile known as {L}ake {M}aihue. {F}or our analysis, we explored four different scenarios using three deep learning and traditional statistical models. {T}hese scenarios involved using field data ({S}cenario 1), meteorological variables ({S}cenario 2), and satellite data ({S}cenarios 3.1 and 3.2) to predict chlorophyll-a levels in {L}ake {M}aihue at three different depths (0, 15, and 30 m). {O}ur choice of models included {SARIMAX}, {DGLM}, and {LSTM}, all of which showed promising statistical performance in predicting chlorophyll-a concentrations in this lake. {V}alidation metrics for these models indicated their effectiveness in predicting chlorophyll levels, which serve as valuable indicators of the presence of algae in the water body. {T}he coefficient of determination values ranged from 0.30 to 0.98, with the {DGLM} model showing the most favorable statistics in all scenarios tested. {I}t is worth noting that the {LSTM} model yielded comparatively lower metrics, mainly due to the limitations of the available training data. {T}he models employed, which use traditional statistical and machine learning models and meteorological and remote sensing data, have great potential for application in lakes in {C}hile and the rest of the world with similar characteristics. {I}n addition, these results constitute a fundamental resource for decision-makers involved in the protection and conservation of water resource quality.},
  keywords = {remote sensing ; machine learning ; lake ; chlorophyll-a at depth ; {CHILI}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {16},
  numero = {4},
  pages = {647 [21 p.]},
  year = {2024},
  DOI = {10.3390/rs16040647},
  URL = {https://www.documentation.ird.fr/hor/fdi:010089619},
}