@article{fdi:010090532,
  title = {{M}ulti-scale datasets for monitoring {M}editerranean oak forests from optical remote sensing during the {SENTHYMED}/{MEDOAK} experiment in the north of {M}ontpellier ({F}rance)},
  author = {{A}deline, {K}. and {F}{\'e}ret, {J}. {B}. and {C}lenet, {H}. and {L}imousin, {J}. {M}. and {O}urcival, {J}. {M}. and {M}ouillot, {F}lorent and {A}lleaume, {S}. and {J}olivot, {A}. and {B}riottet, {X}. and {B}idel, {L}. and {A}ria, {E}. and {D}efossez, {A}. {T}. {M}. and {G}aubert, {T}. and {G}iffard-{C}arlet, {J}. and {K}empf, {J}. and {L}ongepierre, {D}. and {L}opez, {F}. and {M}iraglio, {T}. and {V}igouroux, {J}. and {D}ebue, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{M}editerranean forests represent critical areas that are increasingly affected by the frequency of droughts and fires, anthropic activities and land use changes. {O}ptical remote sensing data give access to several essential biodiversity variables, such as species traits (related to vegetation biophysical and biochemical composition), which can help to better understand the structure and functioning of these forests. {H}owever, their reliability highly depends on the scale of observation and the spectral configuration of the sensor. {T}hus, the objective of the {SENTHYMED}/{MEDOAK} experiment is to provide datasets from leaf to canopy scale in synchronization with remote sensing acquisitions obtained from multi platform sensors having different spectral characteristics , spatial resolutions. {S}even monthly data collections were performed between {A}pril and {O}ctober 2021 (with a comple- mentary one in {J}une 2023) over two forests in the north of {M}ontpellier, {F}rance, comprised of two oak endemic species with different phenological dynamics (evergreen: {Q}uercus ilex and deciduous: {Q}uercus pubescens) and a variability of canopy cover fractions (from dense to open canopy). {T}hese collections were coincident with satellite multispectral {S}entinel -2 data and one with airborne hyperspectral {AVIRIS}- {N}ext {G}eneration data. {I}n addition, satellite hyperspectral {PRISMA} and {DESIS} were also available for some dates. {A}ll these airborne and satellite data are provided from free online download websites. {E}ight datasets are presented in this paper from thirteen studied forest plots: special{I}ntscript overstory and understory inventory, special{I}ntscript 687 canopy plant area in- dex from {L}i-{COR} plant canopy analyzers, special{I}ntscript 1475 in situ spectral reflectances (oak canopy, trunk, grass, limestone, etc.) from {ASD} spectroradiometers, special{I}ntscript 92 soil moistures and temperatures from {IMKO} and {C}ampbell probes, special{I}ntscript 747 leaf -clip optical data from {SPAD} and {DUALEX} sensors, special{I}ntscript 2594 in -lab leaf directional -hemispherical reflectances and transmittances from {ASD} spectroradiometer coupled with an integrating sphere, special{I}ntscript 747 in -lab measured leaf water and dry matter content , additional leaf traits by inversion of the {PROSPECT} model and special{I}ntscript {UAV}-borne {L}i{DAR} 3-{D} point clouds. {T}hese datasets can be useful for multi -scale and multi -temporal calibration/validation of high level satellite vegetation products such as species traits, for current and future imaging spectroscopic missions, and by fusing or comparing both multispectral and hyperspectral data. {O}ther targeted applications can be forest 3-{D} modelling, biodiver- sity assessment, fire risk prevention and globally vegetation monitoring.},
  keywords = {{O}ak forests ; {S}pecies inventory ; {C}anopy plant area index ; {L}eaf traits ; {O}ptical properties ; {UAV}-borne {L}i{DAR} data ; {A}irborne hyperspectral ; imagery ; {M}ultispectral and hyperspectral satellite data ; {FRANCE} ; {ZONE} {MEDITERRANEENNE}},
  booktitle = {},
  journal = {{D}ata in {B}rief},
  volume = {53},
  numero = {},
  pages = {110185 [29 p.]},
  ISSN = {2352-3409},
  year = {2024},
  DOI = {10.1016/j.dib.2024.110185},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090532},
}