Data used for parameter optimization.
\r\n\t
",isbn:"978-1-83968-930-7",printIsbn:"978-1-83968-929-1",pdfIsbn:"978-1-83968-931-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"f159c09dab49a9bc6239b42660d8e8ec",bookSignature:"Dr. Yongxia Zhou",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10310.jpg",keywords:"Brain Science, Brain-Computer Interface, Imaging of Neural Networks, Brain Networks, Brain Function, Molecular Imaging, Brain and Mind, Functional Imaging, Multimodal Imaging, Neuroplasticity Enhancement, Learning, Memory",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 28th 2020",dateEndSecondStepPublish:"October 26th 2020",dateEndThirdStepPublish:"December 25th 2020",dateEndFourthStepPublish:"March 15th 2021",dateEndFifthStepPublish:"May 14th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Yongxia Zhou had completed her Ph.D. from the University of Southern California in Biomedical imaging (2004) and had been trained and worked as a neuroimaging scientist in several prestigious institutes including Columbia University, New York University, University of Pennsylvania. Her research interest is focused on neuroimaging and neuroscience applications.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"259308",title:"Dr.",name:"Yongxia",middleName:null,surname:"Zhou",slug:"yongxia-zhou",fullName:"Yongxia Zhou",profilePictureURL:"https://mts.intechopen.com/storage/users/259308/images/system/259308.jpeg",biography:"Yongxia Zhou obtained a PhD from the University of Southern California in Biomedical Imaging in 2004. Her main research interest is in radiology and neuroscience applications. She had been trained and worked as a medical imaging scientist at several prestigious institutes including Columbia University, University of Pennsylvania, and the National Institutes of Health (NIH). Her research focuses on multimodal neuroimaging integration including MRI/PET and EEG/MEG instrumentation that makes the best use of multiple modalities to help interpret underlying disease mechanisms. She has authored six monograph books, and edited several books for well-known publishers including IntechOpen and Nova Science. 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From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Soil moisture controls the ratio of runoff and infiltration (Delworth & Manabe, 1988; Wagner et al., 2003), decides the energy fluxes (Entekabi et al., 1996; Prigent et al., 2005) and influents vegetation development and then carbon cycle. A long term soil moisture data set on a region scale therefore could provide valuable information for researches such as climate change and global warming (Seneviratne et al., 2006), and then improve the weather forecasting (Beljaars et al., 1996; Schar et al., 1999) and water resources management.
\n\t\t\tSoil moisture profile can be observed at point scale by using gravimetric sampling or some automatic probes, such as Time Domain Reflectometry (TDR), Neutron Probe (NP), etc. These methods are commonly used to provide accurate and continuous soil moisture information and adopted by the meteorology, hydrology and agriculture stations. But these point information are not enough for the regional research and application, and are also not available in the remote areas where difficult to access and to maintain such stations. On the other hand, satellite remote sensing offers a possibility to measure surface soil moisture at regional, continental and even global scales.
\n\t\t\tAlthough surface soil moisture can be estimated indirectly from visible/infrared remote sensing data (Verstraeten et al., 2006), it failed to produce routinely soil moisture map mainly due to factors inherent in optical remote sensing, such as atmosphere effects, cloud masking effects and vegetation cover masking effects. Fortunately, microwave remote sensing offers a possibility to observe area-averaged surface soil moisture regularly in the global scale, by directly measuring to the soil dielectric properties which are strongly related to the liquid moisture content (Hipp, 1974). Moreover, extra advantages of microwave remote sensing include: (1) long wavelength in microwave region which enable the low frequency microwave signals to penetrate clouds and to provide physical information of the land surface; and (2) independent of illumination source which enables the spaceborne sensors to observe earth all-day with all-weather coverage.
\n\t\t\tThere are two approaches through which microwave remote sensing estimating surface soil moisture: active ways by Radar and/or Synthetic Aperture Radar (SAR) with high spatial resolution (in the order of ten to hundred meters) and long revisiting period (about 1 month), passive ways by radiometers with coarse resolution (~ order of 10 km) and frequent temporal coverage (daily or bi-daily). Considering the temporal resolution requirement of the meteorological and hydrological modeling, passive ways are more suitable for the application in these fields and have been wildly used in recent 30 years.
\n\t\t\tAlthough it was recognized early that microwave sensors operated at L-band (1-2GHz) provide the best surface soil moisture observation (Schmugge et al. 1988), L-band radiometers are not equipped on any satellites. This situation is mainly due to the limitation of our current technical capability, which is a significant challenge to built L-band antenna big enough to provide reasonable resolution. So all current available passive microwave remote sensing data are observed by radiometers operating with higher frequencies, such as the Scanning Multichannel Microwave radiometer (SMMR; 6.6, 10.7, 18.0, 21 and 37 GHz) on board Nimbus-7 Pathfinder (Gloersen & Barath, 1977), the Special Sensor Microwave Imager (SSM/I; 19.35, 22.2, 37.0 and 85.5 GHz) on board Defense Meteorological Satellite Program (DMSP) (Hollinger et al., 1990), and the Advanced Microwave Scanning Radiometer (AMSR-E; 6.925, 10.65, 18.7, 23.8, 36.5 and 89 GHz) of the Earth Observing System (EOS) on board Aqua (Kawanishi et al., 2003). Among them, AMSR-E is the only passive systems which including surface soil moisture as a target product. In terms of continuous observation, SSM/I series, starting from 1987, is highly expected to provide long-term global soil moisture estimation.
\n\t\t\tA number of techniques have been used to estimate surface soil moisture information from microwave remote sensed data, such as statistical inversion (Njoku & Kong, 1977), artificial neural networks (Said et al., 2008), and genetic algorithm (Singh & Kathpalia, 2007). However, only the radiative transfer-based methods are considered as the true retrieval, for both passive and active techniques. The radiative transfer models adopted in those methods generally are consists of three parts: a dielectric model (Wang & Schmugge, 1980; Dobson et al., 1985; Mironov et al., 2004) which relating surface soil moisture content to the dielectric constants; a surface roughness model (Choudhury et al., 1979; Fung et al., 1992; Wegmuller & Matzler, 1999; Chen, et al. 2003; Shi, et al. 2005) which accounting for surface scattering effects; and a vegetation layer model (Ulaby et al., 1983; Paloscia & Pampaloni, 1988; Jackson & Schmugge, 1991) which accounting for the vegetation masking effects.
\n\t\t\tAlgorithms considering various other factors have also been proposed to retrieve soil moisture content from passive microwave remote sensed data. T. Jackson (Jackson, 1993) developed a so-called single channel algorithm (SCA), in which the brightness temperature of the 6.9 GHz horizontal polarization channel was used. In this algorithm, ancillary data such as air temperature, land cover, Normalized Difference Vegetation Index (NDVI), surface roughness, and soil texture and porosity are needed. The algorithm of Njoku et al (Njoku & Entekhabi, 1996; Njoku et al., 2003) is a multiple channel iterative retrieval algorithm. It uses the brightness temperature observed by the lowest six channels of AMSR-E. Using their algorithm, the surface temperature, the vegetation opacity and the soil moisture are estimated simultaneously. The algorithm proposed by Paloscia (Paloscia et al., 2001; 2006) is an experiment-based linear regression retrieval, in which soil moisture is estimated by using both the Polarization Index (PI) at 10.7GHz and the brightness temperature at 6.9GHz.
\n\t\t\tAfter more than 20 years effort, good results were obtained and several global and continental scale soil moisture datasets (e.g. Njoku et al. 2003; Owe et al. 2008) were generated. But both the quality and application region of these algorithms can be further improved. For example, Shibata et al. (2003) pointed out that the soil moisture in desert regions retrieved from AMSR-E soil moisture algorithms indicate very wet areas. To solve such problem, the forward model, viz. Radiative Transfer Model (RTM) should be improved firstly.
\n\t\t\tIn this study, we present a new soil moisture retrieval algorithm developed at the University of Tokyo. This algorithm is based on a modified radiative transfer model (Lu et al., 2006), in which the volume scattering inside soil layers is calculated through dense media radiative transfer theory (DMRT) (Wen et al., 1990; Tsang & Kong, 2001) and the surface roughness effect is simulated by Advanced Integration Equation Model (AIEM) (Chen et al., 2003). The optimal values of forward model parameters are estimated using in situ observation data and lower frequency brightness temperature data. And with those optimized parameters, we run the forward model to generate a lookup table, which relates the variables of interest, such as soil moisture content, soil physical temperature, vegetation water content and atmosphere optical thickness, to the brightness temperature or some indexes calculated from brightness temperature data. Finally, soil moisture content is estimated by linearly interpolating the brightness temperature or index into the inversed lookup table. The algorithm was validated by using the AMSR-E match up data set at Mongolia region. Moreover, the capability of our algorithm to retrieve soil moisture from SSM/I was also checked at the same region.
\n\t\t\tThe paper is organized as follows. In Section 2, we present our physically-based radiative transfer model, emphasizing the soil RTM, so-called DMRT-AIEM model. In Section 3 we describe the structure of our algorithm. Section 4 and 5 discusses the application of our new algorithm on AMSR-E and SSM/I data, respectively. Section 6 contains some concluding remarks.
\n\t\tOur algorithm is based on a look up table, which is a database of brightness temperature simulated by a radiative transfer model for various possible conditions. The quality of retrieved soil moisture, therefore, is heavily dependent on the performance of the radiative transfer model. So, the main task of our algorithm development was to develop a physically-based soil moisture retrieval algorithm, which is able to estimate soil moisture content from low frequency passive microwave remote sensing data and to overcome the misrepresent problems occurred in dry areas.
\n\t\t\tFor the land surface remote sensing by spaceborne microwave radiometers, the radiative transfer process from land to space can be divided into as four stages as follows:
\n\t\t\t\t(1) Radiative transfer inside soil media.
\n\t\t\t\tThe initial incident energy is treated as the one starting from the deep soil layer, which propagates through many soil layers, attenuating by the soil absorption effects (dominative at wet cases) and volume scattering effects (dominative at dry cases), experiencing multi-reflection effects between the interfaces of soil layers, finally reaching the soil/air interface.
\n\t\t\t\t(2) Surface scattering process at soil/air interface.
\n\t\t\t\tAt the soil/air interface, the surface scattering influences this upward initial radiation by changing its direction, magnitude and polarization status. At the same time, the downward radiation from the cosmic background, atmosphere, precipitation and canopy are reflected by the air/soil interface, and parts of the reflected radiation propagate along the same direction as that emitted from the soil layers.
\n\t\t\t\tThe upward radiation just above the soil/air interface, therefore, is not only the product of soil medium but also the product of downward radiation.
\n\t\t\t\t(3) Radiative transfer inside vegetation layers.
\n\t\t\t\tAfter leaving the soil/air interface, the upward radiation propagates through the canopy layer (if there are vegetations), experiences the volume scattering effects from the leaves and stems of vegetations and the multi-reflection effects between canopy/air and soil/air interfaces. At the same time, parts of the upward radiation from vegetations join our target radiation.
\n\t\t\t\t(4) Radiative transfer inside atmosphere layers.
\n\t\t\t\tAfter transmitting from vegetation layer, the radiation continues its way, traversing the cloud and precipitation layers, affected by the absorptive atmosphere gases, scattered by precipitation drops, incorporating the emission from surroundings, finally detected by the sensors boarded on satellites.
\n\t\t\t\tThe story of radiative transfer is so complicate that make it necessary to simplify the process to make it computable. In microwave region, the reflectivity of the air/soil interface is generally small. The downward radiation from vegetation and rainfall, which is reflected by the soil surface, therefore, is neglected. Moreover, for the lower frequencies region of microwave, the atmosphere is transparent. Finally, after neglecting all the downward radiation and parts of upward radiation from surroundings, the radiative transfer model is written as:
\n\t\t\t\twhere T\n\t\t\t\t\t\n\t\t\t\t\t\tbs\n\t\t\t\t\t is the emission of the soil layer, T\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t is the vegetation temperature, T\n\t\t\t\t\t\n\t\t\t\t\t\tr\n\t\t\t\t\t is the temperature of precipitation droplets, τ\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t and ω\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t are the vegetation opacity and single scattering albedo, and τ\n\t\t\t\t\t\n\t\t\t\t\t\tr\n\t\t\t\t\t and ω\n\t\t\t\t\t\n\t\t\t\t\t\tr\n\t\t\t\t\t are the opacity and single scattering albedo of precipitation.
\n\t\t\t\tFor the frequencies less than 18GHz, equation (1) can be even simplified by omitting the precipitation layer, as:
\n\t\t\t\tMicrowave can penetrate into soil media, especially for dry cases, in which the penetration depth of C-band is about several centimeters. The soil moisture observed by microwave remote sensing, therefore, is inside a soil media with a volume of several centimeters depth. The radiative transfer process inside a soil media includes various effects, such as moisture and temperature profile effects and the volume scattering effects of dry soil particles. To simulate these effects, the dielectric constant model should be addressed at first.
\n\t\t\t\t(1) Dielectric constant model of soil
\n\t\t\t\tIn the view of microwave, soil is a multi-phase mixture, with a dielectric constant decided by moisture content, bulk density, soil textural composition, soil temperature and salinity. In our algorithm, the dielectric constant of soil is calculated using Dobson model (Dobson et al., 1985):
\n\t\t\t\twhere ρ\n\t\t\t\t\t\n\t\t\t\t\t\tb\n\t\t\t\t\t is the bulk density of soil, ρ\n\t\t\t\t\t\n\t\t\t\t\t\tss\n\t\t\t\t\t\n\t\t\t\t\t= 2.65 is the density of solid soil particle; ε\n\t\t\t\t\t\n\t\t\t\t\t\tss\n\t\t\t\t\t\n\t\t\t\t\t= (4.7, 0.0) is the dielectric constant of soil particle; m\n\t\t\t\t\t\n\t\t\t\t\t\tv\n\t\t\t\t\t is the volumetric water content; ε\n\t\t\t\t\t\n\t\t\t\t\t\tfw\n\t\t\t\t\t is the dielectric constant of free water, can be calculated by the model proposed by Ray (Ray, 1972); α=0.65 is an empirical parameter; and β is a soil texture dependent parameter as follows:
\n\t\t\t\twhere S and C are the sand and clay fraction of the soil, respectively.
\n\t\t\t\t(2) Profile effects of soil media
\n\t\t\t\tThe heterogeneity inside soil media causes the so-called profile effects. The profile effects can be accounted for by using the simple zero-order noncoherent model proposed by Schmugge and Choudhury (1981) or by more complicate first-order noncoherent model given by Burke et al. (1979). The volume scattering effects inside soil media are not included in both models.
\n\t\t\t\tIn order to include the volume scattering effects, a more complicate model was adopted in our algorithm. We assumed that the soil has a multi-layer structure and is composed of many plane-parallel and azimuthally symmetric soil slabs with spherical scattering particles. The radiative transfer process in a plane-parallel and azimuthally symmetric soil slab with spherical scattering particles can be expressed as (Tsang & Kong, 1977):
\n\t\t\t\twhere I\n\t\t\t\t\t\n\t\t\t\t\t\tP\n\t\t\t\t\t\n\t\t\t\t\t(τ,μ) is the radiance at optical depth τ (dτ=K\n\t\t\t\t\t\n\t\t\t\t\t\te\n\t\t\t\t\t\n\t\t\t\t\tdz, with extinction coefficient K\n\t\t\t\t\t\n\t\t\t\t\t\te\n\t\t\t\t\t and layer depth dz) in direction μ for polarization status P (horizontal or vertical), ω\n\t\t\t\t\t\n\t\t\t\t\t\t0\n\t\t\t\t\t is the single scattering albedo of a soil particle, B(τ) is the Planck function and P\n\t\t\t\t\t\n\t\t\t\t\t\tij\n\t\t\t\t\t (i,j=H or V) is the scattering phase function. The 4-stream fast model proposed by Liu (Liu, 1998) solves (5) by using the discrete ordinate method and assuming that no cross-polarization exist. The Henyey-Greenstein formula (Henyey & Greenstein, 1941) is used to express the scattering phase function.
\n\t\t\t\t(3) Volume scattering effects of dry soil particles
\n\t\t\t\tWith considering the facts that the soil particles are densely compacted, the multi-scattering effects of soil particles should be accounted for. In our algorithm, this volume scattering effect were calculated by the so-called dense media radiative transfer theory (DMRT) under Quasi Crystalline Approximation with Coherent Potential (QCA-CP) (Wen et al., 1990; Tsang & Kong, 2001). Dense Media radiative transfer theory was derived from Dyson’s equation under the quasi-crystalline approximation with coherent potential (QCA-CP) and the Bethe-Salpeter equation under the ladder approximation of correlated scatterers.
\n\t\t\t\tBy using the DMRT, the extinction coefficient K\n\t\t\t\t\t\n\t\t\t\t\t\te\n\t\t\t\t\t and albedo ω used in equation (5) were calculated. And then the radiance of each soil slab was calculated by the 4-stream fast model. The radiance just below the soil/air interface was obtained by integrating the radiance from bottom layer to the top layer. Finally, the apparent emission of soil media, T\n\t\t\t\t\t\n\t\t\t\t\t\tbs\n\t\t\t\t\t in equation (1) and (2), was obtained.
\n\t\t\tWhen an electromagnetic wave reaches the air/soil interface, it suffers the reflection and refraction due to the dielectric constant changing in the two sides of the interface. The roughness of the interface divides the reflected wave into two parts, one is reflected in the specular direction and another is scattered in all directions. Generally, the specular component is often referred to as the coherent scattering component. And the scattered component is known as the diffuse or noncoherent component, which consists of power scattered in all directions but with a smaller magnitude than that of the coherent component. Qualitatively, surface roughness increases the apparent emissivity of natural surfaces, which is caused by increased scattering due to the increase in surface area of the emitting surfaces. And it was demonstrated by many researches that the surface roughness has a nonnegligible effects on the accuracy of soil moisture retrieval by spaceborne microwave sensors (Oh & Key, 1998; Singh et al., 2003). In general, the surface roughness effects are simulated by two ways: semi-empirical models and fully physical-based models.
\n\t\t\t\t(1) Semi-empirical models
\n\t\t\t\tThe semi-empirical models are simply and do not cost too much computation efforts. The parameters used in semi-empirical models are often derived from field observations. Depending on the parameters involved, there are three different semi-empirical models: Q-H model (Choudhury et al., 1979; Wang & Choudhury, 1981), Hp model (Mo & Schmugge, 1987; Wegmuller & Matzler, 1999; Wigneron et al., 2001) and Qp model (Shi et al., 2005).
\n\t\t\t\t(2) Fully physical-based model
\n\t\t\t\tIn our algorithm, we simulated the land surface roughness effect using the Advanced Integral Equation Model (AIEM) (\n\t\t\t\t\t\tChen et al., 2003\n\t\t\t\t\t). AIEM is a physically-based model with only two parameters: standard deviation of the height variations s (or rms height) and surface correlation length
By coupling AIEM with DMRT (DMRT-AIEM), this radiative transfer model for soil media is fully physically-based. As a result, the parameters of DMRT-AIEM, such as the rms height, correlation length and soil particle size, have clear physical meanings and their values can be obtained either from field measurement or theoretical calculation.
\n\t\t\tThe existence of canopy layers complicates the electromagnetic radiation which is originally emitted solely by soil layers. The vegetation may absorb or scatter the radiation, but it will also emit its own radiation. The effects of a vegetation layer depend on the vegetation opacity τ\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t and the single scattering albedo of vegetation ω\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t (Schmugge & Jackson, 1992). The vegetation opacity in turn is strongly affected by the vegetation columnar water content W\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t. The relationship can be expressed as (Jackson & Schmugge, 1991):
\n\t\t\t\twhere λ is the wavelength, θ the incident angle, W\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t the vegetation water content. χ and b\' are parameters determined by vegetation type.
\n\t\t\t\tThe single scattering albedo, ω\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t, describes the scattering of the emitted radiation by the vegetation. It is a function of plant geometry, and consequently varies according to plant species and associations. The value of it is small in the low frequency microwave region (Palosica & Pampaloni, 1988; Jackson & Oneill, 1990). In our algorithm, ω\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t is calculated by
\n\t\t\t\tThe value of albedo parameter ω\n\t\t\t\t\t\n\t\t\t\t\t\t0\n\t\t\t\t\t is decided empirically in current researches.Experimental data for this parameter are limited, and values for selected crops have been found to vary from 0.04 to about 0.12.
\n\t\t\t\tBy combing the T\n\t\t\t\t\t\n\t\t\t\t\t\tbs\n\t\t\t\t\t solved by equation (5), the surface reflectivity calculated by AIEM, the vegetation opacity τ\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t calculated by equation (6), and the vegetation single scattering albedo ω\n\t\t\t\t\t\n\t\t\t\t\t\tc\n\t\t\t\t\t estimated by equation (7), a physical-based radiative transfer model was developed.
\n\t\t\tThe basis of our algorithm is a data base of brightness temperature and/or some indexes calculated from brightness temperature. By searching the data base (or look up table) with the satellite observation as the input, soil moisture and other related variables of interest can be estimated quickly. Such high searching speed is the main reason why we adopt the look up table method for soil moisture retrieval. The implementation of our algorithm consists of three steps: (1) fixing the parameters used in the forward model; (2) generating a look up table by running forward model; and (3) retrieving soil moisture by searching the look up table.
\n\t\t\tAs in other physically-based algorithms, such as that developed by Njoku et al. (2003) and the SCA developed by Jackson (1993), the parameters used in our algorithm have clear physical meanings. This advantage derives from the strength of the forward radiative transfer model. Before running the forward RTM to generate look up table, the parameters should be confirmed at first. The parameters to be confirmed include rms height (s), correlation length (l), soil particle sizes (r) and vegetation parameters such as χ and b′. Currently, we can obtain these parameters through two methods: best-fitting method and a parameter optimization method supported by a Land Data Assimilation System developed in the University of Tokyo (LDAS-UT).
\n\t\t\t\t(1) Best-fitting method
\n\t\t\t\tFor the region where in-situ soil moisture and temperature observation are available and when such observation are also representative, we can use a best-fitting way to optimize parameters. In order to simplify the calculation, low frequencies simulation and observation were used. These parameters are optimized by minimizing the cost function:
\n\t\t\t\twhere the subscript sim denotes the model simulated value and obs is the observed value. n is the number of samples used in the optimization. p denotes the polarization status: H for horizontal polarization and V for vertical. f is some frequency in the long wavelength region where the atmospheric effect may be ignored, such as 6.9, 10.7 and 18.7 GHz of AMSR-E, 1.4 GHz of SMOS and 19GHz of SSM/I.
\n\t\t\t\t(2) Parameter optimization by LDAS-UT
\n\t\t\t\tFor most remote regions, in-situ representative observation is not available. A more general parameter optimization method is proposed by Yang et al. (2007). In this method, long term (around 2 months) meteorological field was used to drive a land surface model (Simple Biosphere model, SiB2) to generate time series of soil moisture and temperature data set. And then corresponding TB was simulated with our RTM. Since the land surface parameter set (soil texture, porosity, particle size, roughness, etc.) was also used in SiB2, simulated soil moisture and temperature and corresponding TB were varying as the parameter set changes. By minimizing the difference between simulated TB and that of satellite observation, the best parameter set can be obtained. The optimized parameters by LDAS-UT, therefore, are depended on models and also influenced by the quality of forcing data. The detail of this method can be found from Yang et al. (2009).
\n\t\t\tAfter Step 1, the optimal parameter values are then stored in the forward RTM. We then run the forward model by inputting all possible values of variables used in Equation (1), such as soil moisture content, soil temperature, vegetation water content and atmosphere optical thickness. A family of brightness temperatures is then generated. Based on this brightness temperature database, we select brightness temperatures of special frequencies and polarization to compile a lookup table or to calculate some indices to compile a lookup table. For example, in order to partly remove the influences of physical temperature, the ratio of TB at different frequencies and polarizations can be used. For instant, we can compile a look up table by using the index of soil wetness (ISW) (Koike et al., 1996; Lu et al., 2009), and Polarization Index (PI) (Paloscia & Pampaloni, 1988).
\n\t\t\tThe lookup table generated in Step 2 is reversed to give a relationship which maps the brightness temperature or indices obtained from satellite remote sensing data to the variables of interest (such as soil moisture, soil temperature and vegetation water content). Finally, we estimate soil moisture content by linear interpolation of the brightness temperature or indices into the inverted lookup table.
\n\t\t\tWe tested our algorithm by retrieving soil moisture and temperature from AMSR-E TB data at a Coordinate Enhanced Observing Period (CEOP) (Koike, 2004) reference site in the Mongolian Gobi. The results were validated by comparing with in situ measurements byAutomatic Stations for Soil Hydrology (ASSH) and Automatic Weather Stations (AWS).
\n\t\t\tThe application region of this research is the AMPEX (ADEOS II Mongolian Plateau EXperiment for Ground Truth) area. AMPEX has joined the CEOP as the Mongolia reference site. AMPEX is designed to validate the AMSR and AMSR-E soil moisture algorithm(s). In this area, meteorological and land hydrological factors are measured with very densely installed instruments. AMPEX is located in the Mongolian Plateau, 235 km south of Ulan Bator. The area stretches 160km in the longitudinal direction (106 E~108 30′E) and 120 km in the latitudinal direction (45 30′N~47 N) on the Mandalgobi, where 6 AWSs and 12 ASSHs were installed. Figure 1 illustrates the distribution of observation sites in this area. For more details of AMPEX, please visit the following website: http://home.hiroshima-u.ac.jp/~ampex/hm/index-e.htm.
\n\t\t\t\tDistribution of ASSH and AWS in AMPEX study area.
The ASSHs provide soil moisture and temperature profile measured at two depths, 3 cm and 10 cm below the surface. The AWSs provide soil moisture and temperature profiles measured at four depths: 3 cm, 10 cm, 40 cm and 100 cm below the surface. The soil moisture measurements used TDRs, and the soil temperature was measured by platinum resistance thermometers.
\n\t\t\t\tThrough matching the AMSR-E footprints to in situ stations, we generated a match-up data set consisting of brightness temperature data observed by AMSR-E and in situ data measured by SMTMS and AWS. The coverage of this data set is 2.5 by 2.5 degrees, with a resolution 0.05 degrees for all frequencies. The in situ data consists of soil moisture and soil temperature data. It is in the form of an image type and a text type. The text files record AMSR-E brightness temperature and in situ data at each ground station. The in situ data include observations made within 12 hours of the AMSR-E observation. In this research, the mach-up in situ data at each AMSR-E satellite over passing is calculated by interpolating the in situ data on the hour.
\n\t\t\t\tBased on the AMSR 2002 field experiment results, the soil bulk density in this region is 1.258 g•cm-3. The soil texture is obtained from the Net Primary Productivity (NPP) Database (Chuluun & Ojima, 1996): a sand fraction of 0.6, a silt fraction of 0.2 and a clay fraction of 0.2.
\n\t\t\t\tThere is sparse vegetation in study area. The vegetation water content was measured in June and August, 2003. Based on this in situ observation, we found that the maximum vegetation water content in our study area was 0.11 kg•m-2. It is a small value reflecting the sparse vegetation coverage.
\n\t\t\tWith the AMSR-E match-up data, the land surface parameters can be obtained easily by using the best-fitting methods. AMSR-E TB data obtained from low frequency channels (6.925, 10.65 and 18.7 GHz) were used to optimize model parameters. Since the wavelength of those channels is generally much larger than the diameter of atmospheric particles, the atmospheric effect is negligible for the data measured with those channels.
\n\t\t\t\tAs reported in the literature, it is reasonable to assume that there is little or no volume scattering for soil moisture levels over 10% (Ulaby et al., 1986). So, we first used the data observed on wet days to estimate the roughness parameters, rms height and correlation length, in a best-fitting way. The particle size parameter could then be obtained by running a coupled DMRT-AIEM model to best fit the data observed on dry days.
\n\t\t\t\tIn order to run DMRT, we used uniform soil moisture and temperature vertical profiles with the value observed at 3 cm depth. The bottom of the soil medium was set to be 1.0m (layer thickness is 1cm) and the brightness temperature at the bottom was assumed to be the soil physical temperature at that level, that is, the emissivity was equal to one. The downward radiation from each soil layer, reflected at the bottom boundary, was not considered in this study. The interactions at the boundaries between neighboring soil layers were also neglected because of the vertically uniform soil moisture and temperature profiles.
\n\t\t\t\tFirst, we used the AIEM model to best fit several wet day observations by changing rms height (s) and correlation length (l). Second, employing this set of s and l, we could obtain the surface emissivity for all observations. Third, with some dry day observations, we could best fit the particle size parameter using the DMRT-AIEM model. Finally, we calculated brightness temperature from April 10th 2003 to April 30th 2004 with best-fitting parameters.
\n\t\t\t\tHere, we use the A3 station as an example, to introduce the whole procedure and the result. Information about the data we used to calibrate the model is listed in following table.
\n\t\t\t\tcases | \n\t\t\t\t\t\t\tNumber of days | \n\t\t\t\t\t\t\tMv range (%) | \n\t\t\t\t\t\t\tT(3cm) range | \n\t\t\t\t\t\t\tPeriod | \n\t\t\t\t\t\t
Wet | \n\t\t\t\t\t\t\t32 | \n\t\t\t\t\t\t\t10~20 | \n\t\t\t\t\t\t\t275.26~291.25 | \n\t\t\t\t\t\t\tMay. 12 th ~ Aug. 20 th ,03 | \n\t\t\t\t\t\t
Dry | \n\t\t\t\t\t\t\t40 | \n\t\t\t\t\t\t\t1~7 | \n\t\t\t\t\t\t\t273.1~293.8 | \n\t\t\t\t\t\t\tApr.14 th ~Jul.13 th ,03 | \n\t\t\t\t\t\t
ALL | \n\t\t\t\t\t\t\t254 | \n\t\t\t\t\t\t\t1~20 | \n\t\t\t\t\t\t\t270~293.8 | \n\t\t\t\t\t\t\tApr. 12 th ,03 ~Apr, 30 th , 04 | \n\t\t\t\t\t\t
Data used for parameter optimization.
The calibrated parameter values of AIEM with consideration of shadowing effects are: s = 0.46 cm; l = 1.03 cm. Then, with this set of s and l, using data for 40 dry days, we best fit the particle size parameter as in table 2:
\n\t\t\t\tF(GHz) | \n\t\t\t\t\t\t\t6.925 | \n\t\t\t\t\t\t\t10.65 | \n\t\t\t\t\t\t\t18.7 | \n\t\t\t\t\t\t\t23.8 | \n\t\t\t\t\t\t\t36.5 | \n\t\t\t\t\t\t
Wave Length λ (cm) | \n\t\t\t\t\t\t\t1.997 | \n\t\t\t\t\t\t\t1.298 | \n\t\t\t\t\t\t\t0.739 | \n\t\t\t\t\t\t\t0.581 | \n\t\t\t\t\t\t\t0.379 | \n\t\t\t\t\t\t
R(cm) | \n\t\t\t\t\t\t\t0.45 | \n\t\t\t\t\t\t\t0.307 | \n\t\t\t\t\t\t\t0.165 | \n\t\t\t\t\t\t\t0.126 | \n\t\t\t\t\t\t\t0.084 | \n\t\t\t\t\t\t
R/λ | \n\t\t\t\t\t\t\t0.104 | \n\t\t\t\t\t\t\t0.109 | \n\t\t\t\t\t\t\t0.103 | \n\t\t\t\t\t\t\t0.100 | \n\t\t\t\t\t\t\t0.102 | \n\t\t\t\t\t\t
Best-fitting particle size parameters in Mongolia.
As in Table 2, the best-fit particle sizes change at different frequencies: longer wavelengths are matched with larger particle sizes. However, the ratio between the best-fit radius and the wavelength in the sand is nearly constant. Therefore, we call the best-fit radius the effective radius. The effective radii are generally larger than the physical values, consistent with similar results reported by Kendra and Sarabandi (1999).
\n\t\t\tBased on the best-fitted parameter sets, we build a lookup table composed of the soil physical temperature, soil moisture content, brightness temperature at 10.65GHz vertical polarization and an index dTB calculated as follows:
\n\t\t\t\tLookup table for the AMSR-E soil moisture retrieval algorithm.
The lookup table of our AMSR-E algorithm is shown in Figure 2. It covers a region in which soil moisture content varies from 2% to 40%, and soil physical temperature varies from 270K to 303K. Compared with in situ observation values, this range is large enough to include all of the actual soil moisture and temperature states in Mongolia.
\n\t\t\t\tSince the one-to-one relationship in our lookup table is very clear, it becomes simple to reverse the lookup table, so that the soil moisture can easily be estimated from the AMSR-E data set.
\n\t\t\tIn this study, we retrieved soil moisture data for the period from July to August, 2003. The estimation is shown in Figure 3 for (a) time variation and (b) accuracy comparison.
\n\t\t\t\ta) Time series of retrieved soil moisture, observed soil moisture and precipitation. (b) Comparison of retrieval results with in situ observation.
It is clear from figure 3 that the algorithm gives a reliable soil moisture content estimate in both tendency and amplitude. The value of R-square is 0.3953, and the Standard Error of the Estimate (SEE) is 3.8%. From figure 3(a), we find some overestimation around Aug. 4, 14 and 20, when moderate rainfall (5~10 mm) occurred. Such errors can be attributed partly to the difference between the TDR sensor depth and the penetration depth of the X band and Ku band. Moderate rainfall makes the soil surface much wetter than the soil 3cm below the surface where the TDR sensors were located. Such vertical heterogeneity of soil moisture in the first 3cm of soil was not considered in our algorithm. On the other hand, the wet surface situation decreases the penetration depth dramatically. The combination of these reasons makes our algorithm estimate higher soil moisture content than the in situ observations for moderate rainfall periods.
\n\t\t\t\tOne advantage of our proposed algorithm is that it estimates soil physical temperature and soil moisture simultaneously. This is important for studies involving energy and water budget, such as studies of land surface processes and of weather forecasting.
\n\t\t\t\tThe average retrieved physical temperature for ASSH stations is shown in figure 4(a) and figure 4(b). As in soil moisture comparisons, the algorithm effectively retrieved physical temperature on average for 10 stations. The value of R-square is 0.5458, and the value of SEE is 4.4K. As with our soil moisture analysis, the overestimation of daily temperature variation can also be explained partly as the results of different observation depths.
\n\t\t\t\ta) Time series of retrieved and in situ observations of soil physical temperature. (b) Comparison of algorithm-estimated soil physical temperature with in situ observation.
Since the parameters used in our RTM have clear physical meaning, they are independent on the configuration of radiometers. The parameters used in AMSR-E soil moisture retrieval therefore can be directly used to the SSM/I data set, in the same region. In this test, we first checked the accuracy of TB simulation of our DMRT-AIEM model. And then a look up table for SSM/I data was generated and soil moisture was retrieved.
\n\t\t\tUsing the parameters optimized by AMSR-E math-up data set, with the in-situ observed soil moisture and temperature as input, we run the DMRT-AIEM model to generate TB at 19.35 and 37.0 GHz, two frequencies operated by SSM/I. The SSM/I TB validation results were shown in figure 5, for the A3 station, during the period from Jul. 1st to Jul. 30th, 2003.
\n\t\t\t\tComparison of simulated brightness temperature with the one observed by SSM/I.
From figure 5, it is clear that, for the vertical polarization, the TB simulated by DMRT-AIEM is in a good correlation with the SSM/I observation, with slight underestimation for 19 GHz and overestimation for 37 GHz. For the horizontal polarization, the performance of our RTM is not so good. Statistically, the Average Absolute Error (AAE, see equation (10)) and the square of correlation coefficient between observed brightness temperature and simulated one are listed in table 3.
\n\t\t\t\tChannel | \n\t\t\t\t\t\t\t19V | \n\t\t\t\t\t\t\t19H | \n\t\t\t\t\t\t\t37V | \n\t\t\t\t\t\t\t37H | \n\t\t\t\t\t\t
AAE (K) | \n\t\t\t\t\t\t\t4.34 | \n\t\t\t\t\t\t\t4.41 | \n\t\t\t\t\t\t\t3.45 | \n\t\t\t\t\t\t\t3.85 | \n\t\t\t\t\t\t
R 2 | \n\t\t\t\t\t\t\t0.66 | \n\t\t\t\t\t\t\t0.38 | \n\t\t\t\t\t\t\t0.63 | \n\t\t\t\t\t\t\t0.33 | \n\t\t\t\t\t\t
AAE and correlation coefficient of DMRT-AIEM model for SSM/I data.
where, TBS is simulated brightness temperature, TBO is observed brightness temperature by spaceborne sensor; n is number of samples.
\n\t\t\tThrough the TB validation, it was confirmed that our DMRT-AIEM model was able to produce reasonable TB at vertical polarization channels of SSM/I. But there was some gaps between the simulated TB with the one observed by SSM/I, especially for horizontal polarization channels. Moreover, as we know, the atmosphere effects should be considered for 37 GHz. All of these make it difficult to build a look up table with the same way used for AMSR-E. Authors proposed a simple solution by nudging the SSM/I TB data to fit the simulation and by using PI and ISW indexes to generate a look up table. Detail of the TB adjustments and look up table generation can be found from (Ohta et al., 2007). Figure 6 shows the look up table for SSM/I, in which the PI calculated from 19 GHz and the ISW calculated from the horizontal polarization of 37 and 19 GHz were used. The black points represent the PI and ISW values calculated from corresponding TB data observed by SSM/I.
\n\t\t\t\tDiagram of the Look Up Table for SSM/I soil moisture algorithm.
Comparison of soil moisture retrieved from SSM/I with in-situ observation.
By using the PI-ISW look up table, soil moisture was estimated from SSM/I TB data. The results are shown in figure 7, for the period from July to August, 2003. The line represents the in-situ soil moisture observation; the open cycles are SSM/I soil moisture estimate. From this figure, it is clear that the performance of SSM/I soil moisture retrieval algorithm is very good. So, it is feasible to get reasonable soil moisture estimation form SSM/I with the helps from AMSR-E. But we must keep it in mind that the TB adjustment was applied to the SSM/I data. Such good performance of SSM/I algorithm is therefore just for the special cases where the in-situ observation are available and the appropriate TB adjustment are possible.
\n\t\t\tSpatial distributed soil moisture information is an essential parameter for hydrological, meteorological and ecological studies. This paper presents the structure and contents of a soil moisture retrieval algorithm for the spaceborne passive microwave remote sensing. This algorithm was validated by using the AMSR-E match-up data set at CEOP Mongolia reference site. Comparing to the in-situ observation, reliable surface soil moisture was retrieved by the algorithm.
\n\t\t\tThe transferability of our algorithm was also tested by using SSM/I TB data. At first, it was demonstrated that the forward RTM of our algorithm was capable to represent the SSM/I TB data only after the parameters were calibrated by AMSR-E data set. And then, with some adjustments to the SSM/I TB data, reasonable surface soil moisture was also retrieved from SSM/I data by our algorithm.
\n\t\t\tThe results presented in this paper clearly show that we had built a bridge between the parameters retrieved from AMSR-E and those for SSM/I. With some further consideration about the difference between AMSR-E and SSM/I, e.g. the footprint size and the observation patterns, it is believed that our algorithm could provide a possibility to use the long historical global data observed by SSM/I. Moreover, it is possible to extend our algorithm to other available radiometers. And then, we can merge multi-sensor or/and multi-satellite observations to generate a long term global historical soil moisture product. Such a long term historic data set should be much useful for large scale hydrological and climatologic studies.
\n\t\t\tAs mentioned in section 1, the retrieval of surface soil moisture is physically limited by the current satellite instruments which are operating at high frequencies. The low frequency, i.e. L-band, passive microwave soil moisture observation will firstly be available through the launch of Soil Moisture Ocean Salinity (SMOS) mission of ESA (Kerr, et al., 2001). NASA will provide a combined L-band radiometer and L-band radar observation through the Soil Moisture Active and Passive (SMAP) mission (Entekhabi, et al., 2008). Since the configuration of our algorithm was not specified to any sensors, it is also possible to apply our algorithm to the incoming L-band radiometers. We hope this algorithm will be helpful for these future soil moisture missions and for connecting current available C-band and X-band observations to the L-band observations.
\n\t\tThis study was carried out as part of the Coordinated Enhanced Observing Period (CEOP) and Verification Experiment for AMSR/AMSR-E funded by the Japanese Science and Technology Corporation for Promoting Science and Technology Japan and by the Japan Aerospace Exploration Agency (JAXA). The authors express their great gratitude to them.
\n\t\tEpileptic seizure detection deals with the process of detecting a seizure when it occurs. The need of the day is to take forward this work to eventually predict a seizure much before it is detected as it the very nature of the seizure that it is random. This chapter discusses various methods to do the same.
\nThe cause of disorder will remain unexplained unless a complete cure is possible and available. Two practical engineering approaches are used to research in epilepsy. The first approach involves monitoring the brain activity on multiple scales which gives us a base to understand the generation of seizures. The second approach is to model the natural properties of the brain network and manipulate these for the modulation of seizure generation.
\nThis work mainly concentrates on amalgamation of the above approaches towards developing a closed loop device which has a feedback of brain signals to the device so that it can control interventions that stop seizures.
\nThe main objective in this chapter is a search for a precursor for seizure prediction mainly in the preictal phase as shown in the Figure 1. This may have form of an identifiable, significant pattern, feature or a pattern to extract the feature.
\nSeizure prediction methodology.
Five techniques are used to achieve this objective. They are:
\nUsing Lyapunov exponents.
\nUsing Cross wavelets [1].
\nFourier Bessel function [2].
\nWavelets [3].
\nEMD [4].
\nA seizure prediction method to predict the transitions between Inter ictal and pre ictal states using cross wavelet and Lyapunov exponent features and neural network for binary classification had been proposed [1]. The CHB-MIT database was used.
\nThe cross wavelet transform (XWT) of two time series xn and yn is defined as WXY = WXWY∗, where * denotes complex conjugation. We further define the cross wavelet power as \n
A mathematical function which detects chaos is the Lyapunov exponents. Lyapunov exponents are the average exponential rates of divergence or convergence of nearby orbits in phase space.
\nWhere \n
The data is divided into Preictal and interictal as per the information of expert. Three types of preictal data is considered for experimentation. The methods adopted for prediction system are as shown in the block diagram below (Figures 2 and 3):
\nBlock diagram of epilepsy prediction system using cross wavelets, Lyapunov exponents and neural networks.
Block diagram showing flow of seizure prediction using wavelet.
The data is having 23 channels. The channels are selected as per standard bipolar montage, electrode placement and channel information is provided in Table 1 in which channels are divided as 11 pairs to calculate cross wavelet coefficients.
\nPair Number | \nLeft side Electrodes | \nChannel Number | \nRight Side electrodes | \nChannel Number | \n
---|---|---|---|---|
1 | \nFp1 -F7 | \n1 | \nFp2-F8 | \n13 | \n
2 | \nFp1-F3 | \n5 | \nFp2-F4 | \n9 | \n
3 | \nT7-P7 | \n3 | \nT8-P8 | \n15 | \n
4 | \nC3-P3 | \n7 | \nC4-P4 | \n11 | \n
5 | \nP3-O1 | \n8 | \nP4-O2 | \n12 | \n
6 | \nP7-O1 | \n4 | \nT8-O2 | \n16 | \n
Division of channels into 11 pairs to calculate cross wavelet coefficients.
where F:Frontal P:Posterior T:Temporal C:Central O:Occipital.
Cross wavelet features are extracted from 11 channel pairs which are applied to Feed forward Back propagation neural network having two layers with 11 input neurons as input layer and one output neuron as one output layer. +1 is assigned as target for pre ictal features and − 1 for inter ictal features. The network trained and tested for various feature vectors and the results are tabulated in Table 2.
\nData | \nTrue positive (TP) | \nFalse positive(FP) | \nSensitivity (%) | \nSpecificity (%) | \n
---|---|---|---|---|
Preictal (1 min) | \n152 | \n28 | \n8x.4 | \n— | \n
Preictal (2 min) | \n295 | \n65 | \n81.9 | \n— | \n
Preicta (5 min) | \n634 | \n86 | \n88.05 | \n— | \n
\n | TN | \nFN | \n\n | \n |
Inter Ictal | \n902 | \n34 | \n\n | 96.36 | \n
\n | Over all accuracy (%) | \n90.3 | \n
Prediction performance of neural network with cross wavelet features.
The above table can be interpreted as follows:
\nFor the consideration of interictal period, it is the TN and FN values which are taken into consideration as we need to minimize false alerts. It can be seen that the TN and FN values were 902 and 34 respectively with 96.36% specificity. The preictal data on the other hand had 88.05 sensitivity for 5 minutes data.
\nThe lyapunov exponent is calculated from 23 channels, the extracted features are given to Feed forward back propagation neural network. 23 input nodes and one output node. The network is trained with preictal and interictal features the training performance is evaluated and results are tabulated in Table 3.
\nData | \nTrue positive (TP) | \nFalse positive(FP) | \nSensitivity (%) | \nSpecificity (%) | \n
---|---|---|---|---|
Preictal | \n180 | \n0 | \n100 | \n— | \n
Inter Ictal | \n297-TN | \n3-FN | \n— | \n99 | \n
\n | Overall accuracy (%) | \n99.37 | \n
Prediction performance of neural network with lyapunov features.
From the above Table 3, we can notice that the number of TP values for preictal period is 180 whereas there were no FP and 100% sensitivity when prediction was done with lyapunov features. In comparison, the inter ictal period had shown 287 TN and 3 FN with 99% specificity. The overall accuracy was 99.37%.
\nFeature extraction is done using DWT. EEG signals contain all the useful information below 30 Hz and for this reason 4 decomposition levels D1-D4 and one final approximation, A4 are chosen [3].
\nBased on EEG Ictal period marking of experts selected preictal and interictal periods. These data is decomposed using discrete wavelet transform [3]. Out of 7 sub bands selected three sub bands D2, D3, Dx. These decomposition details are mentioned in Table 4.
\nDECOMPOSED SIGNAL | \nFREQUENCY BANDS(HZ) | \nDECOMPOSITION LEVEL | \n
---|---|---|
D1 | \n128—256 | \n1(NOISES) | \n
D2 | \n64–128 | \n2(HIGHGAMA) | \n
D3 | \n32----64 | \n3(GAMA) | \n
D4 | \n16---32 | \n4(BETA) | \n
D5 | \n8-----16 | \n5ALPHA) | \n
D6 | \n4 -----8 | \n6(THEETA) | \n
A6 | \n0---4 | \n6(DELTA) | \n
Frequency bands and corresponding decomposition levels.
From these sub bands 4 features power, covariance, inter Quartile Range (IQR) and median absolute deviation (MAD) are extracted from 23 channels of pre ictal and interictal EEG data. Three channels are selected and the feature vector size is Equal to 36 = 3 (channels) x 3 (sub bands D2, D3, D4) x4 (features-power, covariance, IQR, and MAD) from each epochs of preictal and Interictal EEG data. These features are applied to feed forward back propagation neural network as shown in Figure 4. Two layers are used hidden layer 36 neurons and output layer having 36 neurons. It is binary classification target +1 is assigned for preictal (Epiliptic) data and − 1 is assigned to Inter Ictal (normal). Total 1588 epochs (1 second) are used for classification 800 for training and 788 used for testing. The performance is evaluated in terms of sensitivity, Specificity and Overall accuracy.
\nTwo types of data is chosen. First data has a time horizon of around 5 minutes for the pre-ictal period while the second has the time horizon for 10 minutes. The inter-ictal period is considered to be around 2 hours in order to nullify the post-ictal or seizure effects.
For comparison of performance, Elman Back propagation neural network is used. The performance of Elman Network is tabulated in Table 5. Sensitivity in Elman network is high, specificity and overall accuracy are low. By comparisons of two types of neural networks feed forward network having better overall performance as the overall accuracy is about 88.71% compared to 85.9% of Elman back propagation.
\n(TP) | \n(FP) | \nSensitivity(%) | \n(TN) | \n(FN) | \nSpecificity (%) | \nOverall accuracy (%) | \n
---|---|---|---|---|---|---|
296 | \n4 | \n98.6 | \n381 | \n107 | \n78.1 | \n85.9 | \n
Elman back propagation neural network performance.
(TP) | \n(FP) | \nSensitivity (%) | \n(TN) | \n(FN) | \nSpecificity (%) | \nOverall accuracy(%) | \n
---|---|---|---|---|---|---|
273 | \n27 | \n91 | \n462 | \n62 | \n87 | \n88.71 | \n
Feed forward neural network performance.
Any signal can be represented in terms of Fourier Bessel series due to its decaying nature. An EEG signal is expanded into a Fourier Bessel series [2]. In this way, an EEG signal can be segmented and periods interictal and ictal are classified to predict the occurrence of seizure.
\nA 1–1 mapping exists between the frequencies and the coefficients. \n
All the above Figures 5–7 show the segmented bands of a seizure signal.
\nFirst plot shows original signal followed by segmented EEG seizure signal of ictal period.
First plot shows original signal followed by segmented EEG seizure signal of inter ictal period.
First plot shows original signal followed by segmented EEG seizure signal of pre-ictal period.
The five features energy in each sub band, fmean, IQR and MAD are extracted from each sub band.
\nThe Figure 8 shows the sum of all Bessel coefficients the preictal and interictal features are discriminating.
\nAbsolute sum of Bessel coefficients with red being Preictal and blue being Interictal EEG signals.
From the Figure 9 it can be observed that the feature, Median absolute deviation of Fourier Bessel coefficients for the Interictal and preictal are discriminating.
\nMAD of coefficients with red being Preictal and blue being Interictal EEG signals.
The inter ictal and pre ictal data is prepared as per the information in Table 7. The calculated Fourier-Bessel Coefficients from inter ictal and pre ictal data is given to Neural Network with 64 input neurons, one output neuron and one hidden layer. The Feed Forward Back propagation algorithm was used as shown in Figure 10. The network is trained −1 as target for inter -ictal and + 1 for pre-ictal.
\nEEG Sub Band | \nFrequency Range (Hz) | \nFourier-Bessel Coefficient(m) | \n
---|---|---|
DELTA | \n0–4 | \n0–4 | \n
THEETA | \n4–7 | \n4–7 | \n
ALPHA | \n7–13 | \n7–13 | \n
LOW BETA | \n13–15 | \n13–15 | \n
HIGH BETA | \n15–30 | \n15–30 | \n
LOW GAMA | \n30–65 | \n30–65 | \n
HIGH GAMA | \n65–120 | \n65–120 | \n
Mapping of frequencies to the Fourier-Bessel coefficients.
The neural network architecture used above contains three layers: 64 neuron input layer, 1 neuron output layer and a hidden layer in the middle which also has 64 neurons.
The trained network is simulated with Inter-ictal and Pre-ictal data. There was one epoch as false negative and zero epochs as false positives. The simulation results had garnered 150 epochs of inter -ictal and 150 epochs of pre-ictal data. Inter ictal period is used to study sensitivity where as the pre ictal data is used for specificity.
\nThe number of false negative values should be low so that it should have high sensitivity. The specificity must be high with lower false positive values. From Table 8, it is observed that sensitivity, specificity and accuracy of the proposed method is superior and the seizure is predicted before 5 minutes for subject 1.
\nFile Name | \nFile Start Time | \nFile End Time | \nNumber of Seizures | \nSeizure start seconds | \nSeizure End seconds | \n
---|---|---|---|---|---|
chb01_01 | \n11:42:54 | \n12:42:54 | \n0 | \n— | \n— | \n
chb01_03 | \n13:43:04 | \n14:43:04 | \n1 | \n2996 | \n3036 | \n
chb01_15 | \n01:44:44 | \n2:44:44 | \n1 | \n1732 | \n1772 | \n
Seizure information of Subject-1 with timing in seconds.
TP | \nFN | \nSensitivity | \nTN | \nFP | \nSpecificity | \nTCA | \n
---|---|---|---|---|---|---|
149 | \n01 | \n99.33 | \n150 | \n0 | \n100% | \n99.6% | \n
Sensitivity, specificity and classification accuracy.
The inter-ictal and pre ictal data is prepared as per the information in Table 10. The trained network is simulated with inter-ictal and pre-ictal data. There were zero epochs as false negative and zero epochs as false positives.
\nFile Name | \nNumber of Seizures | \nSeizure Start(seconds) | \nSeizure End(seconds) | \n
---|---|---|---|
chb24_13 | \n1 | \n3288 | \n3304 | \n
chb24_14 | \n1 | \n1939 | \n1966 | \n
chb24_15 | \n1 | \n3552 | \n3569 | \n
Seizure information of Subject-2 with timing in seconds.
The simulation results of 150 epochs of inter-ictal and 150 epochs of pre-ictal data have been tabulated as below in Table 11.
\nTP | \nFN | \nSE | \nTN | \nFP | \nSP | \nTCA | \n
---|---|---|---|---|---|---|
150 | \n0 | \n100% | \n150 | \n0 | \n100% | \n100% | \n
Sensitivity, specificity and classification accuracy.
The number of false negative and false positive values was minimum due to the fact that the testing was done for shorter periods.
\nFrom Table 11 it is observed that for shorter periods under consideration seizure is predicted before 5 minutes for subject 2 with 100% accuracy.
\nThe selection of data was done a bit different from the previous works. Care has been taken to reduce the effects of post seizure by taking a minimum gap of 2 hours in the inter-ictal period.
\nUsing the EEG data as compiled from above, IMF’s are extracted using the EMD technique. Using these IMF’s, features such as Kurtosis, Inter-quartile range and Median Absolute Deviation are extracted. The following Figure 11 shows the steps involved in the study for prediction. The extracted features are used for training the Neural network and the results are tabulated.
\nSteps involved in epileptic seizure prediction using epileptic zone. It is divided into three parts. 1) the first part extracted the IMF’s while in the second part 2) features are extracted from these IMF’s. These features are given as 3) input to the neural network in the third part.
For patient 8, source has been localized as discussed in the topic of source localization. It has been observed that 4 channels 6,8,20 and 21 have been the most significant channels. These channels are decomposed into 4 IMF’s out of which 3 significant features are extracted thus a total of 4x4x3 = 48 features are extracted.
\n600 preictal and interictal epochs of 2 second duration are considered respectively, which means 1200 epochs (600 + 600 = 1200) with 48 features add up to a total input vector of 1200x48 to the neural network. This is tabulated as shown below in Table 12.
\nFEATURE | \nVECTOR LENGTH | \n
---|---|
CHANNELS | \n4 (6,8,20 and 21) | \n
INTRINSIC MODE FUNCTIONS | \n4 levels | \n
FEATURES | \n3 (MAD, IQR, Kurtosis) | \n
TOTAL FEATURE VECTOR | \n4 X 4 X 3 = 48 | \n
PRE-ICTAL EPOCHS [2 SECOND] | \n600 | \n
INTER-ICTAL EPOCHS [2 SECOND] | \n600 | \n
TOTAL INPUT VECTOR TO NN | \n(1200) X 48 | \n
An overview of the input vector to neural network.
The following results were obtained in this method:
\nThe concept is extended to all the patients whose source has been localized as shown in below Table 14.
\nThe prediction method is run on the entire channels localized from the source as derived from Table 14. The results are as shown in the Table 13. The above results are obtained for data of short intervals. A testing has been run for continues data whose results are as shown in the figures below:
\n\n | True Positive(TN) | \nFalse Negative(FP) | \nSensitivity (%) | \nTrue Negative(TN) | \nFalse Positive(FP) | \nSpecificity (%) | \nOver all accuracy | \n
---|---|---|---|---|---|---|---|
[5 Min] | \n289 | \n11 | \n96.33 | \n290 | \n10 | \n96.67 | \n96.5 | \n
[10 Min] | \n300 | \n— | \n100 | \n295 | \n5 | \n98.33 | \n99.16 | \n
Sensitivity, specificity and classification accuracy using epileptic zone for prediction.
When a seizure free data is considered, there is a chance for false alarm. Consider the Figure 12 where the result of testing of continuous seizure free data is shown.
\nThe testing for continuous seizure data where seizure is predicted 30.4 min before onset.
This false positive problem in seizure free data cannot be taken as a chance for seizure. Thus a false alarm avoidance methodology should be used (Figures 13 and 14).
\nA continuous seizure free data is used for testing. Since it is seizure free no transition should occur. There can be some spikes observed from the above zoomed in figure.
Continuous seizure data with false positive values.
A continuous occurrence of around 10 can be ignored so that no false alarm is triggered. In the above Figures 9 and 10 continuous occurrences happen. Thus, it can be ignored.
\nA new method is proposed for generalization of prediction. There are a few limitations using generalization of epileptic seizure prediction. One of the limitations is the variation issue. Focal seizures are particular to the part of the brain.
\nGeneralization of seizure prediction is possible with the help of epileptic source localized perfectly with clinical support using PET, FMRI, etc. For this work, the results of source localization are used. Table 14 shows the results obtained from source localization. The data of these six patients are considered and a generalization is applied by averaging of the each level. The results obtained are as tabulated below in Table 16.
\nPatient | \nChannels | \n
---|---|
1 | \n1, 5, 9, 13, 14, 15 and 21 | \n
2 | \n1,12,15 and 9 | \n
3 | \n1,4,6,8,14,20 and 21 | \n
5 | \n2,3,9,15,19 and 23 | \n
8 | \n6,8,20 and 21 | \n
24 | \n5,6,20 and 21 | \n
Source localization results.
\n | True Positive (TP) | \nFalse Negative (FN) | \nSensitivity (%) | \nTrue Negative (TN) | \nFalse Positive (FP) | \nSpecificity (%) | \nOver all accuracy | \n
---|---|---|---|---|---|---|---|
[chb01] | \n290 | \n10 | \n96.66 | \n277 | \n33 | \n89 | \n92.8 | \n
Chb02 | \n282 | \n18 | \n94 | \n290 | \n10 | \n96.66 | \n95.3 | \n
Chb03 | \n284 | \n16 | \n9x.66 | \n288 | \n12 | \n96 | \n95.3 | \n
Chb05 | \n270 | \n30 | \n90 | \n264 | \n36 | \n88 | \n89 | \n
Chb24 | \n288 | \n12 | \n96 | \n286 | \n14 | \n95.33 | \n95.6 | \n
Sensitivity, specificity and classification accuracy using epileptic zone for prediction for all patients from source localization in Table 14.
Subjects | \nSensitivity (%) | \nSpecificity (%) | \nOver all accuracy (%) | \n
---|---|---|---|
Multiple(6) | \n81.7 | \n76.2 | \n79.75 | \n
Sensitivity, specificity and over all accuracy obtained for generalization of prediction.
S No | \nAuthor | \nyear | \nData Base | \nAlgorithm | \nPrediction Time | \nSpecificity | \nSensitivity | \nAccuracy | \n
---|---|---|---|---|---|---|---|---|
1 | \nHaddad, T [5] | \n2014 | \nEEG | \ngraph theory | \n30 min | \n— | \n— | \n72% | \n
2 | \nNai-Fu Chang [6] | \n2012 | \nCHB-MIT | \nwavelet coherence | \n— | \n\n | \n | 70% | \n
3 | \nChristopher J. James [7] | \n2009 | \n— | \nICA, Phase Synchronization | \n35 min | \n65–80% | \n65–100% | \n— | \n
4 | \nMaryann D’Alessandro [8] | \n2003 | \nEEG | \nintelligent genetic search process | \n\n | 90.47% | \n62.5% | \n\n |
5 | \nLeon D. Iasemidis [9] | \n2003 | \nEEG | \nLyapunov exponents | \n71.7 min | \n\n | \n | \n |
6 | \nPiotr Mirowski [10] | \n2009 | \nEEG | \ncross correlation | \n\n | \n | 71% | \n\n |
7 | \nChisci [11] | \n2010 | \nFreiburg ECOG | \nSVM classifier based on the Kalman filter, | \n— | \n100% | \n100% | \n— | \n
8 | \nDorai, Arvind [12] | \n2010 | \nEEG | \nLyapunov exponents | \n65 seconds | \n\n | \n | 8x.17% | \n
9 | \nYang Zheng[13] | \n2014 | \nEEG | \nbivariate empirical mode decomposition | \n— | \n— | \n\n | \n |
10 | \nPeyvand Ghaderyan [14] | \n2014 | \nFreiburg EEG | \nKNN-SVM | \n— | \n86.1% | \n91.11% | \n— | \n
11 | \npresent work | \n2013 | \nCHB-MIT | \nLyapunov exponents | \n2 min | \n99% | \n100% | \n99.37% | \n
12 | \npresent work | \n2013 | \nCHB-MIT | \nWavelets | \n5 min | \n100% | \n91% | \n88.71% | \n
13 | \npresent work | \n2014 | \nCHB-MIT | \nFourier Bessel | \n5 min | \n100% | \n99.33% | \n99.6% | \n
14 | \npresent work | \n2014 | \nCHB-MIT | \nLocalization-EMD-ANN | \n5 min | \n96.67% | \n96.33% | \n96.5% | \n
15 | \npresent work | \n2014 | \nCHB-MIT | \nLocalization-EMD-ANN | \n10 min/30 min | \n98.33% | \n100% | \n99.16% | \n
Comparison of prediction results.
From the above table it can be noticed that the sensitivity obtained by generalization is 81.7%, while the specificity is 76.2%. The overall prediction accuracy stands at 79.75%.
\nEMD proves to be a good technique for seizure prediction. The main distinguishing attribute of this work is that it has been able to forecast the seizure about 30 minutes in advance. This might be a result obtained due to the preictal period being much longer and the effects being nullified. The other existing prediction works were capable of only a few minutes. This gives the work much weight in the field of medicine as an alarm can be raised much well in advance and the life of a patient can be saved by alerting either the doctors or the patient himself to take necessary precautions. The concept of generalization can be improved with the help of other existing source localization techniques which make use of PET, FMRI, etc.
\nThe existing works for prediction using Lyapunov exponents as seen in S.no “5” had a prediction time of 71.7 minutes. The present work done using Lyapunov exponents was able to achieve a staggering result of 2 minutes prediction time with 99% specificity, 100% sensitivity and an overall classification accuracy of 99.97%.
\nS.no “2” had got a classification accuracy of 70% using wavelet coherence. The present work achieved a classification accuracy of 88.71% with 100% specificity and 91% sensitivity. The present works using Fourier Bessel as well as the EMD techniques have got good results.
The above table is an indicator that progressive improvement has taken place in both the prediction time and prediction accuracy after the employment of localization and selecting only certain electrodes of interest. Most of the previous literature is incomplete and this work aimed to bridge the gap. There has been significant success achieved in this segment.
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