Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model-A case-study over the Po-Valley

被引:18
作者
Quast, Raphael [1 ]
Wagner, Wolfgang [1 ]
Bauer-Marschallinger, Bernhard [1 ]
Vreugdenhil, Mariette [1 ]
机构
[1] TU Wien, Dept Geodesy & Geoinformat, Vienna, Austria
关键词
Sentinel-1; Radiative transfer; RT1; Soil moisture; Vegetation; Microwave; SAR; SCATTERING; VALIDATION; EXTENSION;
D O I
10.1016/j.rse.2023.113651
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Soil moisture is an important variable controlling many land surface processes and is used to quantify precipitation, drought, flooding, irrigation and other factors that influence decision making and risk-assessment. This paper presents the retrieval of high resolution (-1 km) soil moisture data from Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter measurements using a new bistatic radiative transfer modeling framework (RT1) previously only tested for scatterometer data. The model is applied over a diverse set of landcover types across the entire Po-Valley in Italy over a 4-year time-period from 2016 to 2019. The performance of the soil moisture retrievals is analyzed with respect to the ERA5-Land reanalysis dataset. The model parameterisation and retrieval method are chosen such as to constitute a trade-off between a physically plausible and a computationally feasible modeling approach. The results demonstrate the potential of RT1 for the retrieval of high-resolution soil moisture data from SAR time series.
引用
收藏
页数:15
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共 67 条
  • [41] Matplotlib: A 2D graphics environment
    Hunter, John D.
    [J]. COMPUTING IN SCIENCE & ENGINEERING, 2007, 9 (03) : 90 - 95
  • [42] Jordahl Kelsey, 2021, Zenodo, DOI [10.5281/ZENODO.2585848, 10.5281/ZENODO.4464949]
  • [43] Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission
    Kerr, YH
    Waldteufel, P
    Wigneron, JP
    Martinuzzi, JM
    Font, J
    Berger, M
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2001, 39 (08): : 1729 - 1735
  • [44] Koike M., 2002, J Indian Soc Remote Sens, V30, P229
  • [45] World map of the Koppen-Geiger climate classification updated
    Kottek, Markus
    Grieser, Jorgen
    Beck, Christoph
    Rudolf, Bruno
    Rubel, Franz
    [J]. METEOROLOGISCHE ZEITSCHRIFT, 2006, 15 (03) : 259 - 263
  • [46] McKinney W., 2010, P 9 PYTH SCI C, P56, DOI DOI 10.25080/MAJORA-92BF1922-00A
  • [47] High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series
    Mengen, David
    Jagdhuber, Thomas
    Balenzano, Anna
    Mattia, Francesco
    Vereecken, Harry
    Montzka, Carsten
    [J]. REMOTE SENSING, 2023, 15 (09)
  • [48] SymPy: symbolic computing in Python']Python
    Meurer, Aaron
    Smith, Christopher P.
    Paprocki, Mateusz
    Certik, Ondrej
    Kirpichev, Sergey B.
    Rocklin, Matthew
    Kumar, AMiT
    Ivanov, Sergiu
    Moore, Jason K.
    Singh, Sartaj
    Rathnayake, Thilina
    Vig, Sean
    Granger, Brian E.
    Muller, Richard P.
    Bonazzi, Francesco
    Gupta, Harsh
    Vats, Shivam
    Johansson, Fredrik
    Pedregosa, Fabian
    Curry, Matthew J.
    Terrel, Andy R.
    Roucka, Stepan
    Saboo, Ashutosh
    Fernando, Isuru
    Kulal, Sumith
    Cimrman, Robert
    Scopatz, Anthony
    [J]. PEERJ COMPUTER SCIENCE, 2017,
  • [49] More J. J., 1978, Proceedings of the Biennial Conference on numerical analysis, P105
  • [50] Mosello B., 2015, DEAL CLIMATE CHANGE, P81, DOI [10.1007/978-3-319-15389-6_4, DOI 10.1007/978-3-319-15389-6, DOI 10.1007/978-3-319-15389-6_4]