On the Retrieval of Soil Moisture in Wheat Fields From L-Band SAR Based on Water Cloud Modeling, the IEM, and Effective Roughness Parameters

2011 ◽  
Vol 8 (4) ◽  
pp. 740-744 ◽  
Author(s):  
Hans Lievens ◽  
Niko E. C. Verhoest
Keyword(s):  
Soil Moisture ◽  
Roughness Parameters ◽  
Effective Roughness ◽  
Water Cloud ◽  
L Band ◽  
Cloud Modeling

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%.

Estimation of soil moisture and surface roughness parameters using L-band SAR measurements

1995 ◽  
Author(s):  
Jiancheng Shi ◽  
Peggy E. O'Neill ◽  
Ann Hsu ◽  
Jakob J. Vanzyl ◽  
Mark S. Seyfried
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Roughness Parameters ◽  
Surface Roughness Parameters ◽  
L Band

scholarly journals Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model

2022 ◽  
Vol 14 (2) ◽  
pp. 404
Author(s):  
Yaqing Gou ◽  
Casey M. Ryan ◽  
Johannes Reiche
Keyword(s):  
Soil Moisture ◽  
Aboveground Biomass ◽  
Cloud Model ◽  
Large Area ◽  
Moisture Effects ◽  
Radar Imagery ◽  
Water Cloud ◽  
L Band ◽  
The Difference ◽  
Moisture Conditions

Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil moisture in AGBC retrieval from L-band radar imagery in central Mozambique, where woodland ecosystems dominate. Cross-validation results suggest that (1) the standard WCM effectively accounts for soil moisture effects, especially for areas with AGBC ≤ 20 tC/ha, and (2) the standard WCM significantly improved the quality of regional AGBC mosaics by reducing the stripes between adjacent paths caused by the difference in soil moisture conditions between different acquisition dates. By applying the standard WCM, the difference in mean predicted AGBC for the tested path with the largest soil moisture difference was reduced by 18.6%. The WCM is a valuable tool for AGBC mapping by reducing prediction uncertainties and striping effects in regional mosaics, especially in low-biomass areas including African woodlands and other woodland and savanna regions. It is repeatable for recent L-band data including ALOS-2 PALSAR-2, and upcoming SAOCOM and NISAR data.

scholarly journals Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 135
Author(s):  
Min Zhang ◽  
Fengkai Lang ◽  
Nanshan Zheng
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Satellite Data ◽  
Cloud Model ◽  
Growth Cycle ◽  
Wheat Growth ◽  
Vegetation Fraction ◽  
Effective Roughness ◽  
Water Cloud ◽  
Optical Satellite Data

The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction (fveg) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (R2) about 0.7390.

scholarly journals Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands

2020 ◽  
pp. 1-20
Author(s):  
Zhen Wang ◽  
Tianjie Zhao ◽  
Jianxiu Qiu ◽  
Xuesheng Zhao ◽  
Rui Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Cloud Model ◽  
Water Cloud ◽  
L Band

Estimating Effective Roughness Parameters of the L-MEB Model for Soil Moisture Retrieval Using Passive Microwave Observations From SMAPVEX12

2015 ◽  
Vol 53 (7) ◽  
pp. 4091-4103 ◽  
Author(s):  
Brecht Martens ◽  
Hans Lievens ◽  
Andreas Colliander ◽  
Thomas J. Jackson ◽  
Niko E. C. Verhoest
Keyword(s):  
Soil Moisture ◽  
Passive Microwave ◽  
Roughness Parameters ◽  
Effective Roughness

scholarly journals Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 35
Author(s):  
Xiaodong Huang ◽  
Beth Ziniti ◽  
Michael H. Cosh ◽  
Michele Reba ◽  
Jinfei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Canopy Cover ◽  
The United States ◽  
Optical Data ◽  
Study Region ◽  
Model Based ◽  
Key Indicator ◽  
Ground Measurement ◽  
L Band

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

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