**1. Introduction**

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9781412930918.

Association, Chambery (F), August 2013.

38 Dynamic Programming and Bayesian Inference, Concepts and Applications

Remote sensing technologies in the microwave domain have shown the capability to detect and monitor changes related to Earth's surface variables, independently of weather conditions and sunlight.

Among these variables, soil moisture (SM) is one of the most requested ones [1]. This envi‐ ronmental variable is considered important to many ecological processes that occur on Earth's surface, from its relationship to climate events to its importance in terms of water availability for agricultural crops. In fact, it is considered an essential climate variable domain for the Global Earth Observation Climate (GCOS) [2]

At large scale, this biophysical variable is involved in weather and climate, influencing the rates of evaporation and transpiration. At medium-scale it influences hydrological processes such as runoff generation, erosion processes and mass movements and from the agriculture point of view determines the crops growth and irrigation needs. At small or micro-scale it has an impact on soil biogeochemical processes and water quality [3].

The ability to estimate soil moisture from satellites or airborne sensors is very attractive, especially in recent decades where the development of these technologies has taken a signifi‐ cant rise. This has led the possibility to have images with different spatial scales and repetition time. Despite numerous studies of moisture estimation have been developed using optical imaging, the most promising results have been obtained by using images from microwave sensors [1,4,5,6].

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Satellite and aircraft remote sensing allow estimating soil moisture at large-scale, modeling the interactions between land and atmosphere, helping to model weather and climate with high accuracy [7]. In the last years many different approaches have been developed to retrieve surface soil moisture content from SAR sensors [1].

The estimation of soil moisture from SAR sensors is considered as an ill-posed problem, because many factors can contribute to the signal sensor response. The backscattering signal depends greatly on the moisture content, directly related to the dielectric constant of the soil (ε) and other factors such as soil texture, surface roughness and vegetation cover, being the latter the factors that may hinder a correct estimation of soil moisture [1].

Several studies have shown that soil moisture can be estimated from a variety of remote sensing techniques. However, only microwaves have the capability to quantitatively measure soil moisture under a variety of topography and vegetation [8]. The microwave remote sensing has demonstrated the ability to map and monitor relative changes in soil moisture over large areas, as well as the opportunity to measure, through inverse models, absolute values of soil moisture [1].

The sensitivity of SM in the microwave frequency is a well-known phenomenon, although it is still being studied by many research groups. Early researches conducted on the subject [9,10,11], among others, have shown that the sensors which operate at low frequencies of the electromagnetic spectrum, such as P or L band are capable of measuring soil moisture and overcome the influence of vegetation.

Currently most SAR systems on board of satellites (RADARSAT-2, COSMO-SkyMed, and TerraSAR-X) operate at C-and X-band, which are not the most suitable for the estimation of SM content. Some preliminary studies indicate the feasibility to estimate SM using this type of sensor, and specifically the new generation of X-band sensor [12]. However, working at such high frequencies involves dealing with interference effects introduced by the surface rough‐ ness, and especially vegetation coverage as part of the backscatter signal. Therefore, under these operating conditions, an estimate of the SM spatial variations is still a challenge.

April 2012, respectively. For the future nearby, there are expected data from planned L-band missions, such as Argentinian 1A and 1B SAOCOM, whose first launch is expected between 2014-2015; ALOS-2, which is expected to be launched in 2014. Also the SMAP active/passive satellite from the National Aeronautics and Space Administration (NASA), expected for end 2015, is very promising. SMAP will use high-resolution radar observations to disaggregate coarse resolution radiometer observations to produce SM products at 3 km resolution. The SM has been retrieved from radiometer data successfully using various sensors and platforms and

**Figure 1.** Main satellite missions (past, recent and future) designed in the microwave region of the electromagnetic

Integration of Remotely Sensed Images and Electromagnetic Models into a Bayesian Approach for…

http://dx.doi.org/10.5772/57562

41

The most valuable information for the study of the SM has been obtained through the combi‐ nation of different frequencies, polarizations and angles of incidence, as demonstrated in [11, 13,14,15]. The backscatter coefficient is highly sensitive to the micro roughness of the surface and vegetation coverage. These studies have been developed to determine the configuration of "optimal" sensor parameters, in terms of wavelength, frequency, polarization and angle of incidence to reduce interference of these factors when making an accurate estimate of SM.

In reference to specific studies, Holah et al. (2005) [16] found that an accurate estimate of SM can be achieved by using low or moderate (between 20° and 37°) incidence angles. Regarding polarizations, the most sensitive to SM are found to be HH and HV polarization while the less sensitive is VV. Li et al. (2004) [17] and Zhang et al. (2008) [18] found similar results. Further‐ more Autret et al. (1989) [19] and Chen et al. (1995) [20] reported that the influence of surface roughness can be minimized by using co-polarized waves (HH/VV). Therefore, using multiple

these retrieval algorithms have an established heritage [7].

spectrum (based on Richards, 2009).

Figure 1 shows the electromagnetic spectrum in the microwave region ordered according to the variable wavelength (in cm) and frequency (in GHz). In the same figure it is possible to have an overview of the major satellite missions, past, present and future, whose data have been used in numerous studies or can be used in the future to estimate SM.

The possibility of having multiple radar configurations was made possible thanks to the Envisat satellite launched by the European Space Agency (ESA) and its Advanced SAR (ASAR) sensor, operating in C-band [1]. Envisat/ASAR offered, unlike his predecessors, a great capacity in terms of coverage, incidence angles, polarizations and modes of operation, giving a great potential to improve the quality of many applications using SAR data.

Unfortunately, there are no current satellite missions in L band. ALOS, the satellite of Japan Aerospace Exploration Agency (JAXA), with PALSAR microwave sensor does not work since May 2011. At C-band, there is available data only from RADARSAT-2 of the Canadian Space Agency (CSA), because ERS-2 and Envisat from ESA stopped working in September 2011 and

Satellite and aircraft remote sensing allow estimating soil moisture at large-scale, modeling the interactions between land and atmosphere, helping to model weather and climate with high accuracy [7]. In the last years many different approaches have been developed to retrieve

The estimation of soil moisture from SAR sensors is considered as an ill-posed problem, because many factors can contribute to the signal sensor response. The backscattering signal depends greatly on the moisture content, directly related to the dielectric constant of the soil (ε) and other factors such as soil texture, surface roughness and vegetation cover, being the

Several studies have shown that soil moisture can be estimated from a variety of remote sensing techniques. However, only microwaves have the capability to quantitatively measure soil moisture under a variety of topography and vegetation [8]. The microwave remote sensing has demonstrated the ability to map and monitor relative changes in soil moisture over large areas, as well as the opportunity to measure, through inverse models, absolute values of soil

The sensitivity of SM in the microwave frequency is a well-known phenomenon, although it is still being studied by many research groups. Early researches conducted on the subject [9,10,11], among others, have shown that the sensors which operate at low frequencies of the electromagnetic spectrum, such as P or L band are capable of measuring soil moisture and

Currently most SAR systems on board of satellites (RADARSAT-2, COSMO-SkyMed, and TerraSAR-X) operate at C-and X-band, which are not the most suitable for the estimation of SM content. Some preliminary studies indicate the feasibility to estimate SM using this type of sensor, and specifically the new generation of X-band sensor [12]. However, working at such high frequencies involves dealing with interference effects introduced by the surface rough‐ ness, and especially vegetation coverage as part of the backscatter signal. Therefore, under these operating conditions, an estimate of the SM spatial variations is still a challenge.

Figure 1 shows the electromagnetic spectrum in the microwave region ordered according to the variable wavelength (in cm) and frequency (in GHz). In the same figure it is possible to have an overview of the major satellite missions, past, present and future, whose data have

The possibility of having multiple radar configurations was made possible thanks to the Envisat satellite launched by the European Space Agency (ESA) and its Advanced SAR (ASAR) sensor, operating in C-band [1]. Envisat/ASAR offered, unlike his predecessors, a great capacity in terms of coverage, incidence angles, polarizations and modes of operation, giving

Unfortunately, there are no current satellite missions in L band. ALOS, the satellite of Japan Aerospace Exploration Agency (JAXA), with PALSAR microwave sensor does not work since May 2011. At C-band, there is available data only from RADARSAT-2 of the Canadian Space Agency (CSA), because ERS-2 and Envisat from ESA stopped working in September 2011 and

been used in numerous studies or can be used in the future to estimate SM.

a great potential to improve the quality of many applications using SAR data.

latter the factors that may hinder a correct estimation of soil moisture [1].

surface soil moisture content from SAR sensors [1].

40 Dynamic Programming and Bayesian Inference, Concepts and Applications

moisture [1].

overcome the influence of vegetation.

**Figure 1.** Main satellite missions (past, recent and future) designed in the microwave region of the electromagnetic spectrum (based on Richards, 2009).

April 2012, respectively. For the future nearby, there are expected data from planned L-band missions, such as Argentinian 1A and 1B SAOCOM, whose first launch is expected between 2014-2015; ALOS-2, which is expected to be launched in 2014. Also the SMAP active/passive satellite from the National Aeronautics and Space Administration (NASA), expected for end 2015, is very promising. SMAP will use high-resolution radar observations to disaggregate coarse resolution radiometer observations to produce SM products at 3 km resolution. The SM has been retrieved from radiometer data successfully using various sensors and platforms and these retrieval algorithms have an established heritage [7].

The most valuable information for the study of the SM has been obtained through the combi‐ nation of different frequencies, polarizations and angles of incidence, as demonstrated in [11, 13,14,15]. The backscatter coefficient is highly sensitive to the micro roughness of the surface and vegetation coverage. These studies have been developed to determine the configuration of "optimal" sensor parameters, in terms of wavelength, frequency, polarization and angle of incidence to reduce interference of these factors when making an accurate estimate of SM.

In reference to specific studies, Holah et al. (2005) [16] found that an accurate estimate of SM can be achieved by using low or moderate (between 20° and 37°) incidence angles. Regarding polarizations, the most sensitive to SM are found to be HH and HV polarization while the less sensitive is VV. Li et al. (2004) [17] and Zhang et al. (2008) [18] found similar results. Further‐ more Autret et al. (1989) [19] and Chen et al. (1995) [20] reported that the influence of surface roughness can be minimized by using co-polarized waves (HH/VV). Therefore, using multiple polarizations should, in theory, improve the SM estimate. The general consensus of the literature indicates that low incidence angles, long wavelengths (such as L-band) and both HH and HV polarization settings are the most appropriate sensor for an accurate SM estimate [1].

The objective of this research is to examine the capability and accuracy of a Bayesian approach to retrieve surface SM setting different roughness and vegetation conditions in view of an operational use of the algorithm. Several implementations of the main algorithm were designed to evaluate their different capabilities to reproduce the ground reference data. In most cases, these approaches are based on the assumption of predefined behavior of some parameters, such as vegetation and roughness, measured in situ, and then used as conditional

Integration of Remotely Sensed Images and Electromagnetic Models into a Bayesian Approach for…

http://dx.doi.org/10.5772/57562

43

The developed method has been applied to two main test sites, one located in Argentina and the second in Iowa and exploited during the SMEX´02 campaigns. The comparison over two

The SMEX'02 test site was one of the first exploited to test the proposed methodology that was

For this reason larger space is given in this chapter to the Argentinean test site, where SM is being deeply studied because of the near future launch of the first SAOCOM satellite. In fact, there is a particular demand of SM maps from agricultural farmers of the Pampa region for monitoring the crop status, possible evaluation of water demand and yield assessment.

The proposed analysis is applied to two main datasets. The first dataset derives from an experiment carried out in Argentina in view of the SAOCOM mission. The second one is located in the USA and acquired during the SMEX'02 experiments where contemporary to

The procedure adopted here was applied to data from SARAT L Band active sensor. The SARAT SAR is an airborne sensor (figure 2) used to simulate the SAOCOM images to be analyzed in feasibility studies. The SAR Airborne instrument works in L band (λ=23cm) and

The data set consists of field soil moisture content measurements with the corresponding backscattering coefficients at HH, HV, VH and VV polarizations and 25° incidence angle acquired with a L-band SARAT sensor. SARAT project includes an airborne sensor and an experimental agricultural site. It is part of the SAOCOM mission of Argentinean Space Agency (CONAE). The main aim of the SARAT project is to provide full polarimetric SAR images to develop and validate different applications before the launch of the first satellite SAOCOM, the SAOCOM 1A, estimated for the year 2014. The SAR instrument is installed on a Beechcraft Super King Air B-200 from the Fueza Aérea Argentina (FAA) which has a nominal range of flight altitudes between 4000 and 6000 meters above the Earth's surface, resulting in the

test sites is useful to have confirmation on the behavior of the developed algorithms.

probabilities.

later extended to the Argentina test site.

**2. Remote sensing data and study areas**

**2.1. Argentinean study area**

is fully polarimetric.

SAR acquisitions intensively field campaigns were carried out.

formation of images with angles of incidence between 20° and 70°.

Another effective approach to mitigate the ambiguity introduced by the vegetation and roughness is to focus attention on the temporal variations through time series of radar images. In this case the basis is to assume that the average roughness characteristics and vegetation remain almost unaltered while variations in moisture content affect backscatter signal along the time [21, 22]. In this regard, methods have been developed using change detection series, as in the recent study [23] Hornacek et al. (2012) used data from the wide swath Envisat/ASAR acquisition mode as part of an evaluation of the potential of algorithms for estimating SM for Sentinel-1 mission of ESA.

In remote sensing, researchers have to deal with two problems: the direct problem and the inverse problem. The direct problem refers to the development of electromagnetic models that can correctly characterize ground backscatter coefficient by using as input the sensor param‐ eters, such as the angle of incidence (θ<sup>i</sup> ), the wavelength (λ) and a specified polarization configuration, as well as soil parameters, such as dielectric constant and roughness.

These models provide a solid physical description of the interactions between the electromag‐ netic waves and the objects on the Earth's surface (e.g., bare soil or vegetation), allowing to simulate numerous experimental settings in terms of sensor configurations and soil charac‐ teristics. The generality of models is a property essential to avoid dependence on local site conditions and characteristics of the sensor, a situation that often occurs when working with evidence-based algorithms.

Once the models have been validated, it is possible to develop algorithms to invert these models and predict soil surface properties using radar observations as inputs, which is the solution to the inverse problem [24,25,26].

Numerous backscattering models have been developed in recent decades to help determine the relationship between the measured signal at the sensor and biophysical parameters, with particular emphasis on understanding the effects of surface roughness [11, 25, 27]. Considering the inversion of the direct models many approaches have been developed through numerical simulations of forward models which include Look Up Tables, Neural Networks, Bayesian approaches, and minimization techniques.

For example, the potential of some of these approaches to provide accurate maps of SM has been investigated by Pampaloni et al. (2004) [28]. They conducted a performance comparison of the three inversion algorithms using time series of Envisat ASAR cross-and co-polarized images on a farm site in Italy. The algorithms evaluated for accuracy, error rate and compu‐ tational complexity were: multilayer perceptron neural network, a statistical approach based on the Bayesian theorem and iterative optimization algorithm based on the Nelder-Mead method.

Among the different methods, the Bayesian approach has been deeply investigated for its capability to provide an evaluation of the uncertainties on the variable estimates as well as the possibility to create hierarchical models with different sources of information [11, 21, 29, 30].

The objective of this research is to examine the capability and accuracy of a Bayesian approach to retrieve surface SM setting different roughness and vegetation conditions in view of an operational use of the algorithm. Several implementations of the main algorithm were designed to evaluate their different capabilities to reproduce the ground reference data. In most cases, these approaches are based on the assumption of predefined behavior of some parameters, such as vegetation and roughness, measured in situ, and then used as conditional probabilities.

The developed method has been applied to two main test sites, one located in Argentina and the second in Iowa and exploited during the SMEX´02 campaigns. The comparison over two test sites is useful to have confirmation on the behavior of the developed algorithms.

The SMEX'02 test site was one of the first exploited to test the proposed methodology that was later extended to the Argentina test site.

For this reason larger space is given in this chapter to the Argentinean test site, where SM is being deeply studied because of the near future launch of the first SAOCOM satellite. In fact, there is a particular demand of SM maps from agricultural farmers of the Pampa region for monitoring the crop status, possible evaluation of water demand and yield assessment.
