A robust statistical-based estimator for soil moisture retrieval from radar measurements

Dawson, M.S.; Fung, A.K.; Manry, M.T.

doi:10.1109/36.551934

Cited by 38 publications

(23 citation statements)

References 22 publications

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“…While extensive work has been done on the use of neural networks for processing remotely sensed data, only few studies had investigated the potential of NN for soil parameters estimation (e.g. Baghdadi et al, 2002a;Dawson et al, 1997;Notarnicola et al, 2008;Paloscia et al, 2002Paloscia et al, , 2008Paloscia et al, , 2010Santi et al, 2004;Satalino et al, 2002).…”

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.

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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.

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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.

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“…Two methodologies are reviewed in this subsection. One is the multi-dimensional regression technique developed by Dawson et al [1] and the other is the inversion using artificial neural networks (ANNs) [4]. …”

Section: Methodologies For Inversionmentioning

confidence: 99%

“…For example, Oh et al [7] have developed empirical relations between backscatter coefficients and surface soil moisture content. And Dawson et al [1] have examined a multidimensional statistical estimation method based on theoretical scattering model and applied to experimental data. Furthermore, Hoeben et al [5] used active microwave observations of the surface soil moisture content to estimate the root zone soil moisture profile by data assimilation.…”

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confidence: 99%

Estimation of soil moisture content from L- and P-band AirSAR data: A case study in Jeju, Korea

et al. 2002

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Abstract-During the PacRim AirSAR campaign in Korea, the ground truth data about soil moisture content and surface roughness characteristics were collected. We intend to retrieve the surface parameters over the bare soil from multipolarization and multi-frequency AirSAR data. In this study, the theoretical scattering model, the IEM model is inverted by two existing algorithms -the multi -dimensional regression technique by Dawson et al.[1] and the inversion using 3-layer artificial neural networks (ANNs) [4]. As the first step, backscatter coefficients are calculated based on the ground truth information, and then training patterns are generated from within the valid ranges of surface parameters using the IEM model. The trained inversion models are tested to a set of AirSAR data a s well as synthetic data. Root mean square (RMS) errors of estimated soil moisture from the AirSAR data are average 3.1% in the regression and 4.2% in the inversion using the ANNs. The methods to improve the inversion accuracy are investigated. First, the normalization of signal parameters reduced the number of pixels that fail to reasonable results in the regression model. Second, the use of copolarization ratio as input units in the ANNs inversion scheme improve the soil moisture estimation, which results in an average RMS error of 2.9%.

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“…In the following years, other works combined electromagnetic models with NN approaches. In 1997, Dawson et al [119] considered the ANN for the retrieval a multilayer perceptron basis function (MLPBF), that is a fully-connected network, an improved version of the simple feed-forward MLP network. In detail, MLPBF has more free parameters (weights) and, thus, a higher pattern storage capacity.…”

Section: Machine Learning Methodologies For Soil Moisture Retrievalmentioning

confidence: 99%

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

et al. 2015

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Abstract:The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods.

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A robust statistical-based estimator for soil moisture retrieval from radar measurements

Cited by 38 publications

References 22 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

Estimation of soil moisture content from L- and P-band AirSAR data: A case study in Jeju, Korea

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

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