@article{fdi:010084520,
  title = {{M}onitoring of inland water levels by satellite altimetry and deep learning},
  author = {{N}ino, {F}ernando and {C}oggiola, {C}. and {B}lumstein, {D}. and {L}asson, {L}. and {C}almant, {S}t{\'e}phane},
  editor = {},
  language = {{ENG}},
  abstract = {{D}eep convolutional neural networks ({NN}s) have proven their efficiency for image processing and are routinely used for image classification. {I}n this article, we use them to convert radar measurements into water distance and ultimately into water levels of inland waterbodies. {T}he measurements used are the successive echoes of the spaceborne radar altimeter signal on a waterbody, the radargram. {W}e show that by using forward modeling with an accurate altimetry simulator, we can generate a sufficient amount of radargrams and train a deep {NN} accurately enough to obtain water level series from radargrams in a hydrology context. {T}he method is validated at selected waterbodies by comparing these water level time series with in situ measurements on rivers whose width varied between 50 m and 4 km. {T}he correlation of these time series with in situ data was over 0.95 with root mean square error ({RMSE}) between 26 and 43 cm. {T}he results were also more robust than the {O}ffset {C}enter of {G}ravity ({OCOG})/{I}ce-1 retracker time series of the same data. {T}he validation shows that this automatic method performs generally as well as a carefully tuned manual method for removing outliers from the ranges provided by the state of the art classical retrackers used by the spatial hydrology community. {T}his new tool is a big step toward a generic, global, and automated method to retrieve inland water levels from altimetry measurements. {T}his goal is especially important in the context of continuously declining number of in situ measurements and of utmost importance for adequate water resources management at the global scale.},
  keywords = {{R}adar ; {A}ltimetry ; {S}paceborne radar ; {S}atellites ; {H}ydrology ; {S}ea measurements ; {R}adar tracking ; {A}rtificial intelligence ({AI}) ; deep learning ; hydrology ; {J}ason-3 ; neural network ({NN}) ; radar ; satellite ; altimetry},
  booktitle = {},
  journal = {{IEEE} {T}ransactions on {G}eoscience and {R}emote {S}ensing},
  volume = {60},
  numero = {},
  pages = {4205814 [14 p.]},
  ISSN = {0196-2892},
  year = {2022},
  DOI = {10.1109/tgrs.2021.3138329},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084520},
}