Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data

Shi, Jiancheng; Wang, J.; Hsu, A.Y.; O’Neill, Peggy; Engman, Edwin T.

doi:10.1109/36.628792

Cited by 424 publications

(63 citation statements)

References 15 publications

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“…The relation between soil surface parameters and SAR signals is usually defined using backscattering models [Sahebi et al, 2002]. These models are classified into three groups [Walker et al, 1999]: 1) theoretical, such as Integral Equation Model (IEM) [Fung et al, 1992]; 2) empirical, such as Attema and Ulaby [1978], Shi et al [1997] and Sahebi et al [2003]; 3) semi-empirical, such as Dubois et al [1995] and Oh et al [1992]. They are used to model the behavior of signals in reaction to soil surface; however, the inverse of these models can be applied to estimating soil surface parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The sensor parameters are represented by variations in signal backscatter as a function of wavelength (λ), incidence angle (θ), and polarization (p); target parameters are related to surface parameters, such as roughness and dielectric constant. To estimate soil surface parameters, different backscattering models have been introduced by Ulaby et al [1981], Oh et al [1992], Fung et al [1992], Shi et al [1997], Sahebi et al [2002], and other researchers. Semi-empirical models provide relatively simple relationships between surface properties and SAR signals; however, they rely on parameters which are often site specific and therefore valid only for specific soil conditions.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Different Backscattering Models for Bare Soil Surface Parameters Estimation from SAR Data in band C, L and P

Mirmazloumi

Sahebi

2016

European Journal of Remote Sensing

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Synthetic Aperture Radar (SAR) is the most widely used sensor for retrieving soil surface parameters. This study was performed in two steps. In the first step, estimated backscattering coefficients using three models, Oh, Dubois, and IEM, in three bands of P, L, and C and two polarizations of HH and VV were compared with those extracted from SAR data acquire from AIRSAR over LWREW experimental site located in southwestern Oklahoma with a sub-humid climate. The results showed that the Oh model in band C had the best accuracy in both polarizations (RMSE_HH=1.48 and RMSE_VV=1.1). Dubois and IEM models were appropriately accurate in band L; however, both were less accurate compared with the Oh model in band C. In the second step, ground truth measurements of soil roughness, dielectric constant, and correlation length were compared with the corresponding results of inversion backscattering models. Based on the findings, it was concluded that IEM performed better at estimating soil roughness with RMSE=0.37, while Oh more accurately assessed dielectric constant in all three bands and at depths of 0-3 cm and 3-6 cm. All results confirmed that band P was not appropriate for retrieving soil surface parameters using backscattering models compared with the bands of C and L.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Different Backscattering Models for Bare Soil Surface Parameters Estimation from SAR Data in band C, L and P

Mirmazloumi

Sahebi

2016

European Journal of Remote Sensing

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“…As for the site AG002, the (average) retrieved results from AIRSAR images acquired on different days for PTSM and other existing, well established, theoretical [Hajnsek et al, 2003] or empirical [Oh et al, 1992;Shi et al, 1997] methods are reported in Table 1, together with "in situ" measured values. Notice that at L-band SPM based PTSM always provides better results than other methods, with the exception, in some days, of the Oh empirical method.…”

Section: L-band Retrieval Resultsmentioning

confidence: 99%

“…Besides, concerning this issue, during the last years several papers were addressed to develop effective techniques able to estimate ground parameters and founded on models able to describe low frequency (L, P bands) polarimetric scattering from natural areas, e.g. [Mo et al, 1988;van Zyl et al, 1991;Oh et al, 1992;Dubois et al, 1995;Altese et al, 1996;Shi et al, 1997;Le Hégarat-Mascle et al, 2002;Hajnsek et al, 2003]. To this aim, recently we proposed a Polarimetric Two-Scale Model (PTSM) [Iodice et al, , 2011, used to retrieve the surface parameters of bare soils from polarimetric SAR data.…”

Section: Introductionmentioning

confidence: 99%

An SPM/PO-based Polarimetrie Two-Scale Model

Iodice

Natale

Riccio

2012

European Journal of Remote Sensing

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A polarimetric two-scale scattering model employed to retrieve the surface parameters of bare soils from polarimetric SAR data is presented. The scattering surface is here considered as composed of randomly tilted rough facets, for which the SPM or the PO hold. The facet random tilt causes a random variation of the local incidence angle, and a random rotation of the local incidence plane around the line-of-sight, which in turn causes a random rotation of the facet scattering matrix. Unlike other similar already existing approaches, our method considers both these effects. The proposed scattering model is then used to retrieve bare soil moisture and large-scale roughness from the co-polarized and cross-polarized ratios.

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“…Los datos obtenidos con sensores SAR han sido utilizados para determinar HS con modelos de diferente naturaleza (Shi et al, 1997;Dubois et al, 1995). Estos se pueden clasificar en modelos semiempíricos y empíricos, teóricos, numéricos y analíticos de la retrodispersión tal como se detalla en Kornelsen y Coulibaly (2013).…”

Section: Introductionunclassified

Determinación de la humedad de suelo mediante regresión lineal múltiple con datos TerraSAR-X

et al. 2016

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Resumen: Los primeros cinco centímetros del suelo forman una interfaz donde se producen los principales intercambios de flujos de calor entre la superficie terrestre y la atmósfera. La teledetección ha demostrado ser una excelente herramienta para el seguimiento de datos espacial y temporalmente distribuidos de las características sobresalientes de la superficie terrestre, incluidos los parámetros del suelo. Los sensores de microondas activos (Synthetic Aperture Radar -SAR) ofrecen la posibilidad de monitorizar la humedad de suelo (HS) a escala global, regional y local, mediante la modelación de los procesos involucrados. Diversos algoritmos de inversión han sido desarrollados para derivar información geofísica, como HS, a partir de información SAR. Muchos de ellos utilizan modelos electromagnéticos para simular el coeficiente de retrodispersión y se basan en técnicas estadísticas tales como redes neuronales, mé-todos de inversión y modelos de regresión. Estudios recientes han demostrado que las técnicas simples de regresión múltiple arrojan resultados aceptables. Las variables geofísicas implicadas en estas metodologías son descriptivas de la estructura del suelo, las características de las microondas y la cobertura del suelo. Por esto, en este trabajo se propone desarrollar un modelo de regresión lineal múltiple para estimar HS en zonas de llanura combinando datos de la misión satelital TerraSAR-X y datos de una estación meteorológica. La modelación propuesta involucra las variables hidrológicas que caracterizan las zonas de llanura, donde los movimientos verticales de agua en el suelo predominan sobre el escurrimiento horizontal. Los resultados obtenidos muestran que la retrodispersión, la precipitación y la humedad relativa del aire son las variables explicativas de HS. El modelo obtenido arrojó un RMSE de 5,4 y un R 2 de 0,6.Palabras clave: humedad de suelo, regresión múltiple, TerraSAR-X. Soil moisture estimation using multi linear regression with terraSAR-X dataAbstract: The first five centimeters of soil form an interface where the main heat fluxes exchanges between the land surface and the atmosphere occur. Besides ground measurements, remote sensing has proven to be an excellent tool for the monitoring of spatial and temporal distributed data of the most relevant Earth surface parameters including soil's parameters. Indeed, active microwave sensors (Synthetic Aperture Radar -SAR) offer the opportunity to monitor soil moisture (HS) at global, regional and local scales by monitoring involved processes. Several inversion algorithms, that derive geophysical information as HS from SAR data, were developed. Many of them use electromagnetic models for simulating the backscattering coefficient and are based on statistical techniques, such as neural networks, inversion methods and regression models. Recent studies have shown that simple multiple regression techniques yield satisfactory results. The involved geophysical variables in these methodologies are descriptive of the soil structure,

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Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data

Cited by 424 publications

References 15 publications

Assessment of Different Backscattering Models for Bare Soil Surface Parameters Estimation from SAR Data in band C, L and P

Assessment of Different Backscattering Models for Bare Soil Surface Parameters Estimation from SAR Data in band C, L and P

An SPM/PO-based Polarimetrie Two-Scale Model

Determinación de la humedad de suelo mediante regresión lineal múltiple con datos TerraSAR-X

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