Soil Moisture Retrieval From Remotely Sensed Data: Neural Network Approach Versus Bayesian Method

Dipt. Interateneo di Fis., Politec. di Bari, Bari
IEEE Transactions on Geoscience and Remote Sensing (Impact Factor: 3.51). 02/2008; 46(2):547 - 557. DOI: 10.1109/TGRS.2007.909951
Source: IEEE Xplore


Neural network (NN) approaches and statistical methods, based on a Bayesian procedure, are applied and compared in soil moisture (SM) retrieval from remotely sensed data. The principles and the practical implementations of Bayesian procedures and NNs are briefly discussed in terms of the advantages and disadvantages of each. Experimental tests are carried out by using the same set of training and test data for each method. The methodologies have been applied to two sets of data to retrieve SM from bare soils and to verify their accuracy. One data set contains scatterometer and radiometer data acquired on a variety of agricultural fields in different polarizations, frequencies, and incidence angles. The other is made up of five experiments carried out with a C-band scatterometer on rough and smooth soils at different polarizations and incidence angles. There are significant similarities in the performance of each method; they both retrieve the same features and trends in the analyzed data sets. Algorithm performances change according to SM level and data configuration. The main difficulties are found in retrieving low SM values, and in this case, the error on estimates is reduced when the data with two polarizations or two incidence angles are inserted in the inversion procedure. One major difference between the methodologies is that the NN performance improves, with respect to the Bayesian method, when more inputs are presented as two polarizations or two incidence angles in the training phase.

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    • "When using only one radar channel (one incidence angle and one polarization), a better estimate of soil moisture is obtained for a SAR configuration that minimizes the effects of surface roughness (low incidence angle) (Ulaby et al., 1978; Le Toan, 1982; Baghdadi et al., 2006a; Zribi 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., 2002, 2008, 2010; Santi et al., 2004; Satalino et al., 2002). Inversion approaches using a priori information on soil parameters were developed to improve soil moisture retrieval from SAR data. "
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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 α1 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 cm3 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 cm3 cm-3 without a-priori information on soil parameters and 0.065 cm3 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.
    Hydrology and Earth System Sciences 06/2012; 16(6):1607-1621. DOI:10.5194/hess-16-1607-2012 · 3.54 Impact Factor
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    • "From the methodological viewpoint, the retrieval of soil moisture content can be considered as a mapping problem from the space of the measured signal (i.e., the backscattering signal) to the space of the desired biophysical parameter (i.e., the soil moisture content). This task is commonly addressed by means of the inference of the desired mapping from theoretical forward models, such as the Integral Equation Model (IEM), with the use of iterative methods or nonlinear machine learning techniques [12] [14]. Theoretical models can describe a great variety of experimental conditions in terms of acquisition parameters and target properties. "
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    Applied and Environmental Soil Science 01/2011; 2011(1). DOI:10.1155/2011/175473
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    • "Soil moisture is one of the important parameters for ecosystem modeling, simulation, and predictions [5], and recent applications of remote sensing to soil moisture assessment have suggested that good measurement approaches provide more efficient and effective results. Many models have been developed to retrieve soil moisture conditions (e.g., [6]–[11]). "
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    ABSTRACT: Soil moisture is an important indicator of the land surface environment. The combination of land surface temperature (LST) and normalized difference vegetation index (NDVI) could enhance the ability of extracting information on soil moisture conditions. In this study, we employed multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data products of LST, NDVI, and land cover types to obtain the information about soil moisture for the greater Changbai Mountains. We selected nine time periods in 2007 for inversion of the soil moisture conditions and focused the analysis on four critical time periods. According to the spatial pattern of the LST and NDVI, we established the ??wet-edge?? and ??dry-edge?? equations and determined the relative parameters. We obtained the temperature-vegetation dryness index (TVDI) using the wet-edge and dry-edge relationships to reveal temporal changes of the land surface soil moisture conditions of the study area. We also analyzed the relationship between different land cover types in five TVDI classes. This paper demonstrates that TVDI is an effective indicator to detect soil moisture status in the greater Changbai Mountains region.
    IEEE Transactions on Geoscience and Remote Sensing 07/2010; 48(6-48):2509 - 2515. DOI:10.1109/TGRS.2010.2040830 · 3.51 Impact Factor
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