@article{fdi:010081078,
  title = {{M}apping and characterization of phenological changes over various farming systems in an arid and semi-arid region using multitemporal moderate spatial resolution data},
  author = {{L}ebrini, {Y}. and {B}oudhar, {A}. and {L}aamrani, {A}. and {H}titiou, {A}. and {L}ionboui, {H}. and {S}alhi, {A}. and {C}hehbouni, {A}bdelghani and {B}enabdelouahab, {T}.},
  editor = {},
  language = {{ENG}},
  abstract = {{C}hanging land use patterns is of great importance in environmental studies and critical for land use management decision making over farming systems in arid and semi-arid regions. {U}nfortunately, ground data scarcity or inadequacy in many regions can cause large uncertainties in the characterization of phenological changes in arid and semi-arid regions, which can hamper tailored decision making towards best agricultural management practices. {A}lternatively, state-of-the-art methods for phenological metrics' extraction and long time-series analysis techniques of multispectral remote sensing imagery provide a viable solution. {I}n this context, this study aims to characterize the changes over farming systems through trend analysis. {T}o this end, four farming systems (fallow, rainfed, irrigated annual, and irrigated perennial) in arid areas of {M}orocco were studied based on four phenological metrics ({P}h{M}) (i.e., great integral, start, end, and length of the season). {T}hese were derived from large {M}oderate resolution {I}maging {S}pectroradiometer ({MODIS}) normalized difference vegetation index ({NDVI}) time-series using both a machine learning algorithm and a pixel-based change analysis method. {R}esults showed that during the last twenty-year period (i.e., 2000-2019), a significant dynamism of the plant cover was linked to the behavior of farmers who tend to cultivate intensively and to invest in high-income crops. {M}ore specifically, a relevant variability in fallow and rainfed areas, closely linked to the weather conditions, was found. {I}n addition, significant lag trends of the start (-6 days) and end (+3 days) were found, which indicate that the length of the season was related to the spatiotemporal variability of rainfall. {T}his study has also highlighted the potential of multitemporal moderate spatial resolution data to accurately monitor agriculture and better manage land resources. {I}n the meantime, for operationally implementing the use of such work in the field, we believe that it is essential consider the perceptions, opinions, and mutual benefits of farmers and stakeholders to improve strategies and synergies whilst ensuring food, welfare, and sustainability.},
  keywords = {{MODIS} ; trend ; machine learning ; change detection ; {M}ann {K}endall ; {NDVI} ; phenology ; {MAROC}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {13},
  numero = {4},
  pages = {578 [21 p.]},
  year = {2021},
  DOI = {10.3390/rs13040578},
  URL = {https://www.documentation.ird.fr/hor/fdi:010081078},
}