@article{fdi:010075597,
  title = {{I}ncluding {S}entinel-1 radar data to improve the disaggregation of {MODIS} land surface temperature data},
  author = {{A}mazirh, {A}. and {M}erlin, {O}livier and {E}r-{R}aki, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he use of land surface temperature ({LST}) for monitoring the consumption and water status of crops requires data at fine spatial and temporal resolutions. {U}nfortunately, the current spaceborne thermal sensors provide data at either high temporal (e.g. {MODIS}: {M}oderate {R}esolution {I}maging {S}pectro-radiometer) or high spatial (e.g. {L}andsat) resolution separately. {D}isaggregating low spatial resolution ({LR}) {LST} data using ancillary data available at high spatio-temporal resolution could compensate for the lack of high spatial resolution ({HR}) {LST} observations. {E}xisting {LST} downscaling approaches generally rely on the fractional green vegetation cover (f(gv)) derived from {HR} reflectances but they do not take into account the soil water availability to explain the spatial variability in {LST} at {HR}. {I}n this context, a new method is developed to disaggregate kilometric {MODIS} {LST} at 100 m resolution by including the {S}entinel-1 ({S}-1) backscatter, which is indirectly linked to surface soil moisture, in addition to the {L}andsat-7 and {L}andsat-8 ({L}-7 & {L}-8) reflectances. {T}he approach is tested over two different sites-an 8 km by 8 km irrigated crop area named "{R}3" and a 12 km by 12 km rainfed area named "{S}idi {R}ahal" in central {M}orocco ({M}arrakech) on the seven dates when {S}-1, and {L}-7 or {L}-8 acquisitions coincide with a one-day precision during the 2015-2016 growing season. {T}he downscaling methods are applied to the 1 km resolution {MODIS}-{T}erra {LST} data, and their performance is assessed by comparing the 100 m disaggregated {LST} to {L}andsat {LST} in three cases: no disaggregation, disaggregation using {L}andsat f(gv) only, disaggregation using both {L}andsat f(gv) and {S}-1 backscatter. {W}hen including f(gv), only in the disaggregation procedure, the mean root mean square error in {LST} decreases from 4.20 to 3.60 degrees {C} and the mean correlation coefficient ({R}) increases from 0.45 to 0.69 compared to the non-disaggregated case within {R}3. {T}he new methodology including the {S}-1 backscatter as input to the disaggregation is found to be systematically more accurate on the available dates with a disaggregation mean error decreasing to 3.35 degrees {C} and a mean {R} increasing to 0.75.},
  keywords = {{LST} ; {D}isaggregation ; {S}oil moisture ; {S}entinel-1 ; {MODIS}/{T}erra {L}andsat ; {MAROC} ; {MARRAKECH} ; {HAOUZ} {PLAINE}},
  booktitle = {},
  journal = {{ISPRS} {J}ournal of {P}hotogrammetry and {R}emote {S}ensing},
  volume = {150},
  numero = {},
  pages = {11--26},
  ISSN = {0924-2716},
  year = {2019},
  DOI = {10.1016/j.isprsjprs.2019.02.004},
  URL = {https://www.documentation.ird.fr/hor/fdi:010075597},
}