@article{fdi:010086494,
  title = {{D}etecting irrigation events over semi-arid and temperate climatic areas using {S}entinel-1 data : case of several summer crops},
  author = {{B}azzi, {H}. and {B}aghdadi, {N}. and {N}ajem, {S}. and {J}aafar, {H}. and {L}e {P}age, {M}ichel and {Z}ribi, {M}. and {F}araslis, {I}. and {S}piliotopoulos, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}rrigation monitoring is of great importance in agricultural water management to guarantee better water use efficiency, especially under changing climatic conditions and water scarcity. {T}his study presents a detailed assessment of the potential of the {S}entinel-1 ({S}1) {S}ynthetic {A}perture {R}adar ({SAR}) data to detect irrigation events at the plot scale. {T}he potential of the {S}1 data to detect the irrigation events was carried out using the {I}rrigation {E}vent {D}etection {M}odel ({IEDM}) over semi-arid and temperate oceanic climates in five study sites in south {E}urope and the {M}iddle {E}ast. {T}he {IEDM} is a decision tree model initially developed to detect irrigation events using the change detection algorithm applied to the {S}1 time series data. {F}or each study site and at each agricultural plot, all available {S}1 images during the period of irrigation were used to construct an {S}1 time series and apply the {IEDM}. {D}ifferent types of major summer irrigated crops were analyzed in this study, including {M}aize, {S}oybean, {S}orghum and {P}otato, mainly with the sprinkler irrigation technique. {T}he irrigation detection accuracy was evaluated using {S}1 images and the {IEDM} against the climatic condition of the studied area, the vegetation development (by means of the normalized difference vegetation index, {NDVI}) and the revisit time of the {S}1 sensor. {T}he main results showed generally good overall accuracy for irrigation detection using the {S}1 data, reaching 67% for all studied sites together. {T}his accuracy varied according to the climatic conditions of the studied area, with the highest accuracy for semi-arid areas and lowest for temperate areas. {T}he analysis of the irrigation detection as a function of the crop type showed that the accuracy of irrigation detection decreases as the vegetation becomes well developed. {T}he main findings demonstrated that the density of the available {S}1 images in the {S}1 time series over a given area affects the irrigation detection accuracy, especially for temperate areas. {I}n temperate areas the irrigation detection accuracy decreased from 70% when 15 to 20 {S}1 images were available per month to reach less than 56% when less than 10 {S}1 images per month were available over the study sites.},
  keywords = {remote sensing ; precision agriculture ; sustainability ; climate change ; {EUROPE} ; {MOYEN} {ORIENT} ; {FRANCE} ; {LIBAN} ; {GRECE} ; {ZONE} {SEMIARIDE} ; {ZONE} {TEMPEREE}},
  booktitle = {},
  journal = {{A}gronomy},
  volume = {12},
  numero = {11},
  pages = {2725 [19 p.]},
  year = {2022},
  DOI = {10.3390/agronomy12112725},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086494},
}