Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation
A robust physics-based combined active-passive (C-AP), or active-passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function, which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in C-AP estimation, surface roughness can be considered a free parameter. Extensive Monte Carlo numerical simulations and assessment using field data have been performed both to evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors range from 0.18 to 0.03 cm 3 /cm 3 for two different land-cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.
Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation
A robust physics-based combined active-passive (C-AP), or active-passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function, which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in C-AP estimation, surface roughness can be considered a free parameter. Extensive Monte Carlo numerical simulations and assessment using field data have been performed both to evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors range from 0.18 to 0.03 cm 3 /cm 3 for two different land-cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.
Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation
Akbar, Ruzbeh (Autor:in) / Cosh, Michael H / O'Neill, Peggy E / Entekhabi, Dara / Moghaddam, Mahta
2017
Aufsatz (Zeitschrift)
Englisch
Lokalklassifikation TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
The Use of Radar Backscattering Signals for Measuring Soil Moisture and Surface Roughness
| Online Contents | 1995
Estimation of Soil Moisture and Surface Roughness Parameters from Backscattering Coefficient
| Online Contents | 1993