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@ARTICLE{SchnbrodtStitt:897477,
      author       = {Schönbrodt-Stitt, Sarah and Ahmadian, Nima and Conrad,
                      Christopher and Kurtenbach, Markus and Romano, Nunzio and
                      Bogena, Heye and Vereecken, Harry and Nasta, Paolo},
      title        = {{S}tatistical {E}xploration of {SENTINEL}-1 {D}ata,
                      {T}errain {P}arameters, and in-situ {D}ata for {E}stimating
                      the {N}ear-{S}urface {S}oil {M}oisture in a {M}editerranean
                      {A}groecosystem},
      journal      = {Frontiers in water},
      volume       = {3},
      issn         = {2624-9375},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2021-03810},
      pages        = {655837},
      year         = {2021},
      abstract     = {Reliable near-surface soil moisture (θ) information is
                      crucial for supporting risk assessment of future water
                      usage, particularly considering the vulnerability of
                      agroforestry systems of Mediterranean environments to
                      climate change. We propose a simple empirical model by
                      integrating dual-polarimetric Sentinel-1 (S1) Synthetic
                      Aperture Radar (SAR) C-band single-look complex data and
                      topographic information together with in-situ measurements
                      of θ into a random forest (RF) regression approach (10-fold
                      cross-validation). Firstly, we compare two RF models'
                      estimation performances using either 43 SAR parameters
                      (θNovSAR) or the combination of 43 SAR and 10 terrain
                      parameters (θNovSAR+Terrain). Secondly, we analyze the
                      essential parameters in estimating and mapping θ for S1
                      overpasses twice a day (at 5 a.m. and 5 p.m.) in a high
                      spatiotemporal (17 × 17 m; 6 days) resolution. The
                      developed site-specific calibration-dependent model was
                      tested for a short period in November 2018 in a field-scale
                      agroforestry environment belonging to the “Alento”
                      hydrological observatory in southern Italy. Our results show
                      that the combined SAR + terrain model slightly outperforms
                      the SAR-based model (θNovSAR+Terrain with 0.025 and 0.020
                      m3 m−3, and $89\%$ compared to θNovSAR with 0.028 and
                      0.022 m3 m−3, and $86\%$ in terms of RMSE, MAE, and R2).
                      The higher explanatory power for θNovSAR+Terrain is
                      assessed with time-variant SAR phase information-dependent
                      elements of the C2 covariance and Kennaugh matrix (i.e., K1,
                      K6, and K1S) and with local (e.g., altitude above channel
                      network) and compound topographic attributes (e.g., wetness
                      index). Our proposed methodological approach constitutes a
                      simple empirical model aiming at estimating θ for rapid
                      surveys with high accuracy. It emphasizes potentials for
                      further improvement (e.g., higher spatiotemporal coverage of
                      ground-truthing) by identifying differences of SAR
                      measurements between S1 overpasses in the morning and
                      afternoon.},
      cin          = {IBG-3},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000678440700001},
      doi          = {10.3389/frwa.2021.655837},
      url          = {https://juser.fz-juelich.de/record/897477},
}