Quantitative Retrieval of Soil Moisture Content and Surface Roughness From Multipolarized Radar Observations of Bare Soil Surfaces

Oh, Yisok

doi:10.1109/tgrs.2003.821065

Cited by 323 publications

(207 citation statements)

References 13 publications

Supporting

Mentioning

203

Contrasting

Unclassified

Order By: Relevance

“…Statistical models based on experimental measurements are also often used in soil moisture estimation. For bare soils, the most popular statistical models are those developed by Oh et al (1992Oh et al ( , 2002 and Oh (2004) which use an inversion diagram based on either the cross-polarized backscattering coefficient σ • HV and the copolarized ratio (σ • HH /σ • VV ) or the copolarized ratio (σ • HH /σ • VV ) and the cross-polarized ratio (σ • VH /σ • VV ). The Dubois model (Dubois et al, 1995) based on the use of multi-polarized radar observations (HH and VV) is also used for estimating soil moisture content.…”

Section: N Baghdadi Et Al: C-band Polarimetric Sar Data Using Neuramentioning

confidence: 99%

Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

Baghdadi

Cresson

Hajj

et al. 2012

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases using or not using a-priori knowledge on soil parameters. The inversion approach was then validated using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates, whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters α 1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm 3 cm −3 ) and surface roughness (root mean square surface height lower or higher than 1.0 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 cm 3 cm −3 without a-priori information on soil parameters and 0.065 cm 3 cm −3 (RMSE) applying a-priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with an RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.

show abstract

Section: N Baghdadi Et Al: C-band Polarimetric Sar Data Using Neuramentioning

confidence: 99%

Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

Baghdadi

Cresson

Hajj

et al. 2012

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Some researchers tried to improve this problem with apply the water-cloud model to consider about the vegetation affect and mixed pixel [24]- [28]. For improving the effect of mixed pixel we applied the water-cloud model, Equation (4). With applying this model the vegetation information was updated according the ground soil samples and plot against the backscattering coefficient (σ˚) of PALSAR image.…”

Section: Comparison Of the Backscattering Coefficientsmentioning

confidence: 99%

“…The backscattering model in literature developed to estimate the soil surface parameter. Those of them most frequently used by other researcher included the two semi-empirical models by Oh et al [1]- [4], and Dubois et al [5] and the physical model the Integral Equation Model (IEM) developed by Fung et al [6]. These models are supposed to regenerate the radar backscattering coefficient (σ˚) by forward model.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Simulated Backscattering Signal and ALOS PALSAR Backscattering over Arid Environment Using Experimental Measurement

Gharechelou¹,

Tateishi²,

Sumantyo³

2015

ARS

View full text Add to dashboard Cite

The purpose of this paper is to simulate the backscattered signal by experimental data and field working then, comparing with the backscattered signal from actual L-band SAR data over arid to semi-arid environments. The experimental data included the laboratory-measured dielectric constant of soil samples and the roughness parameter. A backscattering model used to simulate the backscattering coefficient in sparse vegetation land cover. The backscattering coefficient (σ˚) simulated using the AIEM (advanced integral equation model) based on the experimental data. The roughness data were considered by the field observation, chain method measuring and photogrammetry simulation technique by stereo image of ground real photography. The simulated backscattering coefficients were compared with the real extracted backscattering coefficient (σ˚) from the ALOS PALSAR single and dual polarization mode data. The most problem in backscattering simulation was the vegetation water content. Therefore, the water-cloud model using the water index result of optical data applied on the simulated backscatter model for enhancement the backscattering heterogeneity from vegetation water contents due to the mix pixel of vegetation in spars vegetation. At the results the AIEM model overestimated the backscattering simulation, it might be cause of high sensitivity of this model to roughness. The ALOS PALSAR HV polarization mode is more sensitive than the HH mode to vegetation water content. The water-cloud model could improve the result and the correlation function of the samples was increased but, the difficulties were the input the A and B parameters to model.

show abstract

“…Most field studies and models assume bare soil conditions to measure soil moisture with radar remote sensing and modeling backscatter contributions of the soil surface and subsurface [Dubois et al, 1995, Fung et al, 1992, Merzouki et al, 2011, Oh, 2004, Oh et al, 1992, Oh et al, 2002, Rahman et al, 2008, Verhoest et al, 2007, Zribi and Dechambre, 2003]. …”

Section: Remote Sensing Of Soil Moisturementioning

confidence: 99%

“…By modeling radar backscatter contributions of the soil surface and sub-surface microwave remote sensing can be used to measure soil moisture. Soil water content can be related to backscatter amplitude by inversion of the scattering mechanisms [Oh, 2004,Oh et al, 1992. In order to calculate soil moisture content under vegetation from radar backscatter the water cloud model must be used.…”

Section: Remote Sensing Of Soil Moisturementioning

confidence: 99%

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

Phillips

Newlands

Liang

et al. 2014

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Determining the best way to efficiently use limited water resources, for food and energydedicated crops, has become crucial due to the rise in extreme events (floods/droughts) and higher variability in rainfall attributed to global climate change. Changing climate conditions will require new crops to be adapted to a changing agricultural environment. Reliable information on seasonal trends in crop growth and evapotranspiration with associated uncertainty/confidence ranges is crucial to guide the development of new crops and management strategies to cope with future climate. Given that crop growth is strongly coupled to soil moisture, developing reliable growth curves requires a detailed understanding of soil moisture at the field-scale. Typically, it is impractical to collect soil samples to adequately assess soil moisture that represents both spatial distribution at the field-scale and temporal dynamics on the scale of a growing season (e.g. 110 days for cereals). A novel way to address soil moisture monitoring challenges is through an integrated, agro-ecosystems-level approach using an integrated sensing system that can link data from multiple platforms (wireless sensors, satellites, airborne imagery, near real-time climate stations). Assimilated data can then be fed into predictive models to generate reference crop growth curves and predict regionally-specific yield potentials. However, integrated sensing requires interagency cooperation, common data processing standards and long-term, timely access to data. Large databases need to be reusable by various organizations and accessible in the future, with comprehensive metadata. During the 2012 growing season a feasibility study was conducted which involved measuring field-scale soil moisture with sensor network technology. The experiment utilized radially-distributed sensors for tracking in-season soil moisture data was collect using both automated in-situ sensors and hand-held sensors. Box-plots of soil moisture data was collected with hand-held revealed an early season wet soil moisture regime and late season dry soil moisture regime. The data sampled on July 5, 2012 was selected for ii geostatistical analysis. Bayesian kriging models and Bayesian kriging models with polynomial trends using different combinations prior distributions for the range and nugget were tested. Models with first order polynomial trend, a reciprocal 2 prior distribution for the range and a reciprocal prior distribution for the nugget fit tended to predict the sample distributions the best. Soil moisture was predicted at a set of random point using ordinary kriging, universal kriging, the two Bayesian kriging models and the Bayesian kriging models with first and second order polynomial trends. Overall, the universal kriging and Bayesian trend models predicted similar data distributions. OpenGIS-compliant services and standards were utilized to provide long-term access to sensor data and construct corresponding metadata. Sensor Model Language, an inter-operable metadata format, ...

show abstract

Quantitative Retrieval of Soil Moisture Content and Surface Roughness From Multipolarized Radar Observations of Bare Soil Surfaces

Cited by 323 publications

References 13 publications

Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

Comparison of Simulated Backscattering Signal and ALOS PALSAR Backscattering over Arid Environment Using Experimental Measurement

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

Contact Info

Product

Resources

About