@article{fdi:010083401,
  title = {{N}ational-scale cropland mapping based on phenological metrics, environmental covariates, and machine learning on {G}oogle {E}arth {E}ngine},
  author = {{H}titiou, {A}. and {B}oudhar, {A}. and {C}hehbouni, {A}bdelghani and {B}enabdelouahab, {T}.},
  editor = {},
  language = {{ENG}},
  abstract = {{M}any challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. {A}ccordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology, and the upsurge of cloud computing solutions such as {G}oogle {E}arth {E}ngine ({GEE}). {T}herefore, the present work is an attempt to automate the extraction of multi-year (2016-2020) cropland phenological metrics on {GEE} and use them as inputs with environmental covariates in a trained machine-learning model to generate high-resolution cropland and crop field-probabilities maps in {M}orocco. {T}he comparison of our phenological retrievals against the {MODIS} phenology product shows very close agreement, implying that the suggested approach accurately captures crop phenology dynamics, which allows better cropland classification. {T}he entire country is mapped using a large volume of reference samples collected and labelled with a visual interpretation of high-resolution imagery on {C}ollect-{E}arth-{O}nline, an online platform for systematically collecting geospatial data. {T}he cropland classification product for the nominal year 2019-2020 showed an overall accuracy of 97.86% with a {K}appa of 0.95. {W}hen compared to {M}orocco's utilized agricultural land ({SAU}) areas, the cropland probabilities maps demonstrated the ability to accurately estimate sub-national {SAU} areas with an {R}-value of 0.9. {F}urthermore, analyzing cropland dynamics reveals a dramatic decrease in the 2019-2020 season by 2% since the 2018-2019 season and by 5% between 2016 and 2020, which is partly driven by climate conditions, but even more so by the novel coronavirus disease 2019 ({COVID}-19) that impacted the planting and managing of crops due to government measures taken at the national level, like complete lockdown. {S}uch a result proves how much these methods and associated maps are critical for scientific studies and decision-making related to food security and agriculture.},
  keywords = {{G}oogle {E}arth {E}ngine ; cropland mapping ; cloud computing ; {S}entinel-2 ; phenology ; random forest ; {MAROC}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {13},
  numero = {21},
  pages = {4378 [26 p.]},
  year = {2021},
  DOI = {10.3390/rs13214378},
  URL = {https://www.documentation.ird.fr/hor/fdi:010083401},
}