@article{fdi:010084518,
  title = {{I}rrigation mapping on two contrasted climatic contexts using {S}entinel-1 and {S}entinel-2 data},
  author = {{E}lwan, {E}. and {L}e {P}age, {M}ichel and {J}arlan, {L}ionel and {B}aghdadi, {N}. and {B}rocca, {L}. and {M}odanesi, {S}. and {D}ari, {J}. and {S}egui, {P}. {Q}. and {Z}ribi, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his study aims to propose an operational approach to map irrigated areas based on the synergy of {S}entinel-1 ({S}1) and {S}entinel-2 ({S}2) data. {A}n application is proposed at two study sites in {E}urope-in {S}pain and in {I}taly-with two climatic contexts (semiarid and humid, respectively), with the objective of proving the essential role of multi-site training for a robust application of the proposed methodologies. {S}everal classifiers are proposed to separate irrigated and rainfed areas. {T}hey are based on statistical variables from {S}entinel-1 and {S}entinel-2 time series data at the agricultural field scale, as well as on the contrasted behavior between the field scale and the 5 km surroundings. {T}he support vector machine ({SVM}) classification approach was tested with different options to evaluate the robustness of the proposed methodologies. {T}he optimal number of metrics found is five. {T}hese metrics illustrate the importance of optical/radar synergy and the consideration of multi-scale spatial information. {T}he highest accuracy of the classifications, approximately equal to 85%, is based on training dataset with mixed reference fields from the two study sites. {I}n addition, the accuracy is consistent at the two study sites. {T}hese results confirm the potential of the proposed approaches towards the most general use on sites with different climatic and agricultural contexts.},
  keywords = {{S}entinel-1 ; {S}entinel-2 ; irrigation map ; support vector machine ; {ESPAGNE} ; {ITALIE} ; {CATALUNA} ; {EMILIA} {ROMAGNA}},
  booktitle = {},
  journal = {{W}ater},
  volume = {14},
  numero = {5},
  pages = {804 [13 ]},
  year = {2022},
  DOI = {10.3390/w14050804},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084518},
}