Modelling of Microwave Multi-Frequency Emission and Backscatter by a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP)

2021 ◽  
Author(s):  
Hong Zhao ◽  
Yijian Zeng ◽  
Bob Su ◽  
Jan Hofste
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Microwave Radiometer ◽  
Soil Surface ◽  
Equation Model ◽  
Passive Microwave ◽  
The Tibetan Plateau ◽  
Microwave Sensor ◽  
L Band ◽  
Transfer Modelling

<p>Emission and backscattering at different frequencies have varied responses to soil physical processes (e.g., moisture redistribution, freeze-thaw) and vegetation growing/senescencing. Combing the use of active and passive microwave multi-frequency signals may provide complementary information, which can be used to better retrieve soil moisture, and vegetation biomass and water content for ecological applications. To this purpose, a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP) was adopted in this study to simulate both emission (T<sub>B</sub>) and backscatter (σ<sup>0</sup>), in which the CLAP is backboned by the TorVergata model for modelling vegetation scattering, and an air-to-soil transition model (ATS) (accounting for surface dielectric roughness) integrated with the Advanced Integral Equation Model (AIEM) for modelling soil surface scattering. The accuracy of CLAP was assessed by both ground-based and spaceborne measurements, and the former was from the deployed microwave radiometer/scatterometer observatory at Maqu site on an alpine meadow over the Tibetan plateau. Specifically, for the passive case, simulated T<sub>B</sub> (emissivity multiplied by effective temperature) were compared to the ground-based ELBARA-III L-band observations, as well as C-band Advanced Microwave Scanning Radiometer 2 (AMSR2) and L-band Soil Moisture Active Passive (SMAP) observations. For the active case, simulated σ<sup>0 </sup>were compared to the ground-based scatterometer C- and L-bands observations, and C-band Sentinel and L-band Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) observations. This study is expected to contribute to improving the soil moisture retrieval accuracy for dedicated microwave sensor configurations.</p>

scholarly journals Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

2010 ◽  
Vol 7 (4) ◽  
pp. 4995-5031 ◽  
Author(s):  
H. Lievens ◽  
N. E. C. Verhoest ◽  
E. De Keyser ◽  
H. Vernieuwe ◽  
P. Matgen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
State Of The Art ◽  
Incidence Angle ◽  
Soil Surface ◽  
Equation Model ◽  
Roughness Parameters ◽  
Effective Roughness ◽  
Soil Surface Roughness ◽  
L Band ◽  
Microwave Backscatter

Abstract. Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art backscatter models is not yet fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of field-measured roughness. This paper analyses effective roughness parameters derived from C- and L-band SAR observations over a large number of agricultural seedbed sites in Europe and furthermore shows that parameters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows the estimation of effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at C- and L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.

scholarly journals Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

2011 ◽  
Vol 15 (1) ◽  
pp. 151-162 ◽  
Author(s):  
H. Lievens ◽  
N. E. C. Verhoest ◽  
E. De Keyser ◽  
H. Vernieuwe ◽  
P. Matgen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Incidence Angle ◽  
Soil Surface ◽  
Equation Model ◽  
Roughness Parameters ◽  
Effective Roughness ◽  
Soil Surface Roughness ◽  
L Band ◽  
Microwave Backscatter ◽  
Param Eters

Abstract. Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art back\\-scatter models is not fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of field-measured roughness. This paper analyses effective roughness parameters derived from C- and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that param\\-eters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at C- and L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.

Large area mapping of soil moisture using the ESTAR passive microwave radiometer in Washita'92

1995 ◽  
Vol 54 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Thomas J. Jackson ◽  
David M. Le Vine ◽  
Calvin T. Swift ◽  
Thomas J. Schmugge ◽  
Frank R. Schiebe
Keyword(s):  
Soil Moisture ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Large Area

Sampling depth of L-band radiometer measurements of soil moisture and freeze-thaw dynamics on the Tibetan Plateau

2019 ◽  
Vol 226 ◽  
pp. 16-25 ◽  
Author(s):  
Donghai Zheng ◽  
Xin Li ◽  
Xin Wang ◽  
Zuoliang Wang ◽  
Jun Wen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
The Tibetan Plateau ◽  
Sampling Depth ◽  
Freeze Thaw ◽  
L Band ◽  
Band Radiometer

scholarly journals A Simulation Study of GNSS-R Polarimetric Scattering from the Bare Soil Surface Based on the AIEM

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xuerui Wu ◽  
Shuanggen Jin
Keyword(s):  
Soil Moisture ◽  
Circular Polarization ◽  
Soil Surface ◽  
Bare Soil ◽  
Satellite System ◽  
Equation Model ◽  
The Other ◽  
Scattering Coefficients ◽  
Remote Sensing Technique ◽  
Transmitted Signal

In the past two decades, global navigation satellite system-reflectometry (GNSS-R) has emerged as a new remote sensing technique for soil moisture monitoring. Some experiments showed that the antenna of V polarization is more favorable to receive the reflected signals, and the interference pattern technique (IPT) was used for soil moisture and retrieval of other geophysical parameters. Meanwhile, the lower satellite elevation angles are most impacted by the multipath. However, electromagnetic theoretical properties are not clear for GNSS-R soil moisture retrieval. In this paper, the advanced integral equation model (AIEM) is employed using the wave-synthesis technique to simulate different polarimetric scatterings in the specular directions. Results show when the incident angles are larger than 70°, scattering at RR polarization (the transmitted signal is right-hand circular polarization (RHCP), while the received one is also RHCP) is larger than that at LR polarization (the transmitted signal is RHCP, while the received one is left-hand circular polarization (LHCP)), while scattering at LR polarization is larger than that at RR polarization for the other incident angles (1°∼70°). There is an apparent dip for VV and VR scatterings due to the Brewster angle, which will result in the notch in the final receiving power, and this phenomenon can be used for soil moisture retrieval or vegetation corrections. The volumetric soil moisture (vms) effects on their scattering are also presented. The larger soil moisture will result in lower scattering at RR polarization, and this is very different from the scattering of the other polarizations. It is interesting to note that the surface correlation function only affects the amplitudes of the scattering coefficients at much less level, but it has no effects on the angular trends of RR and LR polarizations.

Soil Moisture Retrieval Using Ground-Based L-Band Passive Microwave Observations in Northeastern USA

2014 ◽  
Vol 13 (3) ◽  
pp. vzj2013.06.0101 ◽  
Author(s):  
Marouane Temimi ◽  
Tarendra Lakhankar ◽  
Xiwu Zhan ◽  
Michael H. Cosh ◽  
Nir Krakauer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Passive Microwave ◽  
L Band ◽  
Northeastern Usa

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

2009 ◽  
Vol 10 (1) ◽  
pp. 213-226 ◽  
Author(s):  
Matthias Drusch ◽  
Thomas Holmes ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo
Keyword(s):  
Soil Moisture ◽  
Dynamic Range ◽  
Microwave Radiometer ◽  
Space Station ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Database ◽  
Brightness Temperatures ◽  
L Band ◽  
Rms Errors

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.

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