Sensitivity Analysis of Bistatic Scattering for Soil Moisture Retrieval

Soil moisture is one of the vital environmental variables in the land–atmosphere cycle. A study of the sensitivity analysis of bistatic scattering coefficients from bare soil at the Ku-band is presented, with the aim of deepening our understanding of the bistatic scattering features and exploring its potential in soil moisture retrieval. First, a well-established advanced integral method was adopted for simulating the bistatic scattering response of bare soil. Secondly, a sensitivity index and a normalized weight quality index were proposed to evaluate the effect of soil moisture on the bistatic scattering coefficient in terms of polarization and angular diversity, and the combinations thereof. The results of single-polarized VV data show that the regions with the maximum sensitivity and high quality index, simultaneously, to soil moisture are in the forward off-specular direction. However, due to the effect of surface roughness and surface autocorrelation function (ACF), the single-polarized data have some limitations for soil moisture inversion. By contrast, the results of two different polarization combinations, as well as a dual-angular simulation of one transmitter and two receivers, show significant estimation benefits. It can be seen that they all provide better ACF suppression capabilities, larger high-sensitivity area, and higher quality indices compared to single-polarized estimation. In addition, dual polarization or dual angular combined measurement provides the possibility of retrieving soil moisture in backward regions. These results are expected to contribute to the design of future bistatic observation systems.

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

Advances in Meteorology ◽

10.1155/2019/3647473 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

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.

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Retrieving Soil Moisture Over Bare Soil from ERS 1 Synthetic Aperture Radar Data: Sensitivity Analysis Based on a Theoretical Surface Scattering Model and Field Data

Water Resources Research ◽

10.1029/95wr03638 ◽

1996 ◽

Vol 32 (3) ◽

pp. 653-661 ◽

Cited By ~ 121

Author(s):

Elio Altese ◽

Orsola Bolognani ◽

Marco Mancini ◽

Peter A. Troch

Keyword(s):

Sensitivity Analysis ◽

Soil Moisture ◽

Synthetic Aperture Radar ◽

Field Data ◽

Bare Soil ◽

Radar Data ◽

Synthetic Aperture ◽

Surface Scattering ◽

Synthetic Aperture Radar Data ◽

Data Sensitivity

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Theoretical Study of Global Sensitivity Analysis of L-Band Radar Bistatic Scattering for Soil Moisture Retrieval

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2018.2858269 ◽

2018 ◽

Vol 15 (11) ◽

pp. 1710-1714 ◽

Cited By ~ 2

Author(s):

Jiangyuan Zeng ◽

Kun-Shan Chen

Keyword(s):

Sensitivity Analysis ◽

Soil Moisture ◽

Global Sensitivity Analysis ◽

Theoretical Study ◽

Global Sensitivity ◽

Bistatic Scattering ◽

L Band

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Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-8841-2012 ◽

2012 ◽

Vol 9 (7) ◽

pp. 8841-8883 ◽

Cited By ~ 1

Author(s):

L. Loosvelt ◽

H. Vernieuwe ◽

V. R. N. Pauwels ◽

B. De Baets ◽

N. E. C. Verhoest

Keyword(s):

Sensitivity Analysis ◽

Soil Moisture ◽

Soil Texture ◽

Compositional Data ◽

High Sensitivity ◽

Hydrologic Model ◽

Model Output ◽

Local Sensitivity ◽

Hydrologic Modelling ◽

Compositional Model

Abstract. Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output w.r.t. a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. We applied this method to a hydrologic model and assessed the sensitivity of the simulated soil moisture content to local changes in soil texture. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moisture retention curve.

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