@article{fdi:010084299,
  title = {{A} calibration/disaggregation coupling scheme for retrieving soil moisture at high spatio-temporal resolution : synergy between {SMAP} passive microwave, {MODIS}/{L}andsat optical/thermal and {S}entinel-1 radar data},
  author = {{O}jha, {N}. and {M}erlin, {O}. and {A}mazirh, {A}. and {O}uaadi, {N}. and {R}ivalland, {V}. and {J}arlan, {L}ionel and {E}r-{R}aki, {S}. and {E}scorihuela, {M}. {J}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}oil moisture ({SM}) data are required at high spatio-temporal resolution-typically the crop field scale every 3-6 days-for agricultural and hydrological purposes. {T}o provide such high-resolution {SM} data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. {D}espite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. {I}n this context, the objective of the paper is to develop a new algorithm that combines {SMAP} {L}-band passive microwave, {MODIS}/{L}andsat optical/thermal and {S}entinel-1 {C}-band radar data to provide {SM} data at the field scale at the observation frequency of {S}entinel-1. {I}n practice, it is a three-step procedure in which: (1) the 36 km resolution {SMAP} {SM} data are disaggregated at 100 m resolution using {MODIS}/{L}andsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated {SM} data set is used to calibrate a radar-based {SM} retrieval model and (3) the so-calibrated radar model is run at field scale on each {S}entinel-1 overpass. {T}he calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical ({S}entinel-2) or radar ({S}entinel-1) data. {T}wo radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors ({NDVI}, polarization ratio ({PR}) and radar coherence ({CO})) separately. {B}oth models are applied over three experimental irrigated and rainfed wheat crop sites in central {M}orocco. {T}he field-scale temporal correlation between predicted and in situ {SM} is in the range of 0.66-0.81 depending on the retrieval configuration. {B}ased on this data set, the linear radar model using {PR} as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. {T}he proposed synergistical approach combining multi-resolution/multi-sensor {SM}-relevant data offers the advantage of not requiring in situ measurements for calibration.},
  keywords = {disaggregation ; soil moisture ; synergy ; {S}entinel-1 ; {DISPATCH} ; {SMAP} ; {L}andsat},
  booktitle = {},
  journal = {{S}ensors},
  volume = {21},
  numero = {21},
  pages = {7406 [26 ]},
  year = {2021},
  DOI = {10.3390/s21217406},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084299},
}