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An Air-to-Soil Transition Model for Discrete Scattering-Emission Modelling at L-Band
- Source :
- Journal of Remote Sensing, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- American Association for the Advancement of Science (AAAS), 2021.
-
Abstract
- Topsoil structures and inhomogeneous distribution of moisture in the soil volume will induce dielectric discontinuities from air to bulk soil, which in turn may induce multiple and volume scattering and affect the microwave surface emission. In situ ELBARA-III L-band radiometer observations of brightness temperature TBp (p =H or V polarization) at the Maqu site on the Eastern Tibetan Plateau are exploited to understand the effect of surface roughness on coherent and incoherent emission processes. Assisted with in situ soil moisture (SM) and temperature profile measurements, this study develops an air-to-soil transition (ATS) model that incorporates the dielectric roughness (i.e., resulted from fine-scale topsoil structures and the soil volume) characterized by SM and geometric roughness effects, and demonstrates the necessity of the ATS model for modelling L-band TBp. The Wilheit (1978) coherent and Lv et al. (2014) incoherent models are compared for determining the dielectric constant of bulk soil in the ATS zone and for calculating soil effective temperature Teff. The Tor Vergata discrete scattering model (TVG) integrated with the advanced integral equation model (AIEM) is used as the baseline model configuration for simulating L-band TBp. Whereafter, the ATS model is integrated with the foregoing model for assessing its performance. Results show the ATS-based models reduce the underestimation of TBp (≈20-50 K) by the baseline simulations. Being dynamic in nature, the proposed dielectric roughness parameterization in the ATS model significantly improves the ability in interpreting TBp dynamics, which is important for improving SM retrieval at the global scale.
- Subjects :
- Environmental sciences
GE1-350
Physical geography
GB3-5030
Subjects
Details
- Language :
- English
- ISSN :
- 26941589
- Volume :
- 2021
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.42725bb49059442a8e5dfb4e4dfa67ff
- Document Type :
- article
- Full Text :
- https://doi.org/10.34133/2021/3962350