I would also like to extend thanks to those whose help contributed to this thesis: Dr. Guoqing Sun at the University of Maryland, USA for providing his code for the 3D radarbackscatter model and Dr. Julia McMorrow at the University of Manchester for valuable consultation on oil-palm literature and contacts. I would also like to thank PT. Salim Plantation for providing support and logistics during the two weeks of field work in Indonesia; and especially Dr. Sugih Wanasuria and Ir. Fathoni for making all the arrangements. I would also like to mention Mr. Wage and the plantation workers whose help on a daily basis made the task of taking measurements in the field so much easier. Dr. Marianne Edward at Leicester University gave valuable assistance during the two weeks of field work. Dr. Geoff Cookmartin at Sheffield University provided assistance in the measurement of the dielectric constant of the oil-palm samples. Special thanks to LAP AN, Indonesia for providing ERS SAR and Landsat TM images, especially to Ir. Arisdiyo, Drs. Yansen, Ir. Arum Tjahyaningsih and Drs. Ahmad Maryanto for providing help with data processing and acquisition. Also thanks to NASDA, Japan for providing JERS SAR images and John Owens at University College London for allowing me to use some results from his M.Sc thesis work for my model simulation.
backscatter during 3005 substorms identified from IMAGE FUV observations in the period May 2000 to December 2005. We find that the global level of backscattered signal rises during the 90 min preceding substorm onset by 20%, peaking a few minutes prior to the expansion phase and then gradually declining to approximately the pre-substorm level over the following 90 min. In the nightside ionosphere, the level of backscatter begins to fall a few minutes prior to substorm onset, with an overall reduction of 25% in the hour following onset. This modest ‘‘loss’’ of backscatter is concentrated in the region poleward of 70° magnetic latitude, with significant levels of backscatter actually shifting to lower magnetic latitudes. Although radar oper- ations in the 8 – 14 MHz frequency range in the nightside ionosphere generally result in a significant fraction of backscatter data, there is evidence that operations at fre- quencies outside this range might prove advantageous. For example, the 8 – 10 MHz band, which yields excellent radarbackscatter in the 60° – 70° magnetic latitude region of the nightside ionosphere does not perform as well in the 70°– 80° region within ±30 min of substorm onset.
By comparing multi-instrument observations, we have iden- tified perturbations in the location and intensity of artificial HF radarbackscatter as being caused by the passage of TIDs through the pumped volume of plasma. The presence of at- mospheric gravity wave-induced TIDs was inferred from the skip distance variation in the HF radar measurements and the electron density perturbations measured by incoherent scat- ter radar. A raytracing model of the HF pump wave prop- agation allows us to understand some of the HF radar phe- nomena in terms of the modification of the region of the ionosphere in which the pump wave can reach the upper- hybrid resonance level where the irregularities are excited. In particular we can understand the extensions of the artificial backscatter region as being related to the increased altitude of UHR when the disturbance travels over the pump transmitter and an erosion of the poleward edge of the region as being due to the pump being unable to access the UHR altitude in that region. According to both data and modelling, these ef- fects become more pronounced as the amplitude of the TID (in terms of relative electron density perturbation) increases. We can see that it is possible to determine from the artifi- cial backscatter observations alone, the period and the line- of-sight phase speed component of the TID and therefore the horizontal wavelength. Furthermore, with the aid of a ray- tracing model, it would be possible to estimate the TID am- plitude and vertical wavelength by fitting the model to the observations. In this way, observations of TIDs in artifi- cial backscatter can be used to support observations based on ground scatter skip distance variations. The artificial ir- regularities are in-situ tracers of the ionospheric distortions caused by a TID and thus represent a more direct means of accessing TID parameters than inverting ground scatter mea- surements.
The measured spectral widths associated with HF radar ob- servations are known to exhibit characteristics related to the ionospheric irregularities being diagnosed and to the mech- anism responsible for creating them. The spectral width of a backscattered signal can, for example, permit the distinc- tion between various types of irregularities (e.g. Haldoupis, 1989; Milan and Lester, 2001). Pinnock et al. (1995) has shown that HF radar spectral widths provide a useful way of identifying the location of the ionospheric signature of the magnetospheric cusp. The spectral widths associated with cusp scatter are generally high and variable, and are accompanied by variable line of sight velocities. Baker et al. (1995) also reported that these high spectral widths are in reality multicomponent spectra which are not resolved by the fitacf routine. They suggested that the multicomponent spectra were caused by highly variable and turbulent electric fields. Such fields have been identified in the cusp in satellite data in association with strong particle precipitation (Baker et al., 1990). It has also been suggested that the observed cusp spectra may relate to strong variations that have a discrete nature (Hanuise et al., 1991). That implies that the observed spectra could be due to spatial structures, such as velocity shears, with scale sizes less than the size of the radar range cell. Small-scale vortical structures generated by filamentary field-aligned currents (FACs) have also been hypothesised as a potential mechanism for producing multiple velocity com- ponents inside a radar range cell (Andr´e et al., 2000a). In this case the vortex would be maintained by a current, the result of a diverging electric field at the bottom of the field-aligned components of the FAC. Under these conditions, the irreg- ularities required for radarbackscatter may be generated by the current convective instability (Ossakow and Chaturvedi, 1979).
The prerequisite for extracting buried utility’s geometry and radiometry physical properties is through accurate interpretation of backscatter image, so call radargram. Nowadays, GPR data processing and interpretation work are performed totally by commercial software that is associated with the GPR system. This commercial software belongs to the Commercial Off-The-Shelf (COTS) product, where end-users are unable to configure any processing flow of the software for the necessity of their works. In this context, most of the existing GPR software is aimed for commercial use and not for research (Vera et al., 2008). The theory and source code that are used in the software is not disclosed to the end-users, due to trade secret, thereby, the processing and interpretation work can only be done in “black-box” manner. For every individual processing and interpretation work done in the majority of the mapping projects, there is no statistical assessment. This is because the results are depending mainly on the operator’s interpretation experience and prior knowledge regarding the structure of the subsurface features. The purpose of good interpretation for retrieving information from the radargram which enables characterisation of subsurface physical or natural properties rather than just to “see something” in the radargram are never being practised in the industry. Numerical modelling analysis which able to simulate subsurface properties and realistically represent the geometry and structure for subsurface feature and GPR antenna under varying complex environment is, therefore, ideal for extraction of subtle interpretation information from the radargram.
Since PMWE are also weaker in absolute reflectivity than PMSE the small number of coherent integrations used in the scanning experiments makes it difficult to detect echoes. The event from 31 December 2010 at around 19:00 UT was strong enough to backscatter signals from all transmitted di- rections down to 15 ◦ off-zenith. Figure 5 shows horizon- tally resolved structures of a PMWE at different heights. The maximum scaling of the color coded SNR is lower by 21 dB compared to the plots in Fig. 3 demonstrating the much weaker strength of PMWE compared to PMSE. The verti- cal slice along the east-west direction shows a layered struc- ture with a thickness of 2–3 km tilted by approximately 30 ◦ .
Soil moisture is an important parameter to understand the hydrologic cycle. The soil moisture measured data is scarce and alternative approaches are needed for its spatial mapping. Radarbackscatter over land depends upon the soil moisture and vegetation characteristics of the land surface. In the case of arid regions, such as LCRB, due to sparse vegetation, backscatter primarily depends on the soil moisture charac- teristics. A new technique that relates σ ◦ response to NDVI and soil moisture data in arid regions is presented. TRMMPR σ ◦ is modeled as a function of incidence angle, NDVI, and soil water content. The model calibration is performed using the known soil moisture data from VIC estimates and gage measurements; and NDVI data during 1998. The model is tested over selected study sites in the LCRB with varying vegetation cover. The model parameters reflect the surface characteristics and the σ ◦ sensitivity to soil water content and NDVI. The model is used to derive the soil water con- tent during 1999–2006 and provides results consistent with the VIC estimated and in-situ measurements. The results are temporally consistent with the time series of measured pre-
During the timeframe of this study, 1993–2011, we no- ticed that many lakes flipped from springtime grounded-ice to floating-ice status and a few changed from floating-ice to grounded-ice status. Some lakes in the ACP region froze to the bottom in the 1990s but no longer freeze to the bottom in the late 2000s (based on recent C-band SAR data). One of the study lakes, West Twin Lake, was frozen to the lake bed in the 1992 and 1998 spring images, but in 2008 spring images, C-band radarbackscatter of about −6 dB signified floating ice. This could be explained by warmer winters, or winters with more insulating snowfall in the more recent past (Walsh et all., 1998; Duguay et al., 2003; Brown and Duguay, 2010; Arp et al., 2012; Surdu et al., 2013). Consequently, pixels from West Twin Lake were classified as grounded ice until 2008, but as floating ice thereafter. Another study lake on the ACP, Kimouksik Lake, was a floating-ice lake in 1993, but low SAR backscatter evinced Kimouksik ice was freezing to the bottom in 2008. This switch from floating ice to grounded ice was a result of a change in water level due to the draining of an adjacent lake between 1992 and 2002. The hydrologi- cal changes of this lake are well documented in Jones (2006). Pixels from Kimouksik Lake were classified as floating ice in 1993, but as grounded ice thereafter.
integrated for 2 s. The HF pump frequency during this time interval was 2.85 MHz. Persistent plasma line enhance- ments were seen clearly during O-mode transmissions, except for 0337 to 0338 UT. No plasma lines were seen during X-mode pumping. The purely growing mode can be clearly seen at 2.85 MHz, and the decay and the first cascade lines are also clearly identified. Persistent plasma lines contained broad peaks from 2.84 to 2.85 MHz. The broad peaks were not seen in the persistent plasma line of 3 February. Figure 4 shows temporal variations in foF2 measured with the ionosonde and height-averaged SNR of the diagnostic-radarbackscatter power for the 4 February 2005 case. Enhancements of the plasma line did not persist after 0336 UT as shown in Figure 4. The operational mode of the diagnostic radar was switched from the upshifted plasma line measurement to the downshifted one at 0340 UT. Persistent plasma line enhancements were not clearly seen after the fortuitous measurement switch; but we think that the mode change is not the significant reason to stop the persistent enhancement. This is because the pulse beginning at 0337 UT did not show persistent enhance- ments, although the diagnostic radar was still operated in the mode of upshifted plasma line measurement. The principal reason we consider is the background ionospheric change as shown in Figure 4.
Through numerical simulations, he showed that such tur- bulence was not caused by shear instability of the Kelvin-Helmholtz type, but it was more likely due to pre- cipitation below the bases of mid-level clouds, which trig- gered Rayleigh-Taylor instability due to the sublimation of ice/snow particles and consequent cooling of the drier air mass below the cloud base. Kudo et al. (2015) followed up and presented observations of MCT during the Tanuki 2011 campaign at the MU radar site in Shigaraki, Japan, when some radiosondes launched during the campaign managed to sample the MCT, with the MU radar operat- ing simultaneously in a high-resolution mode to provide vivid images of radarbackscatter depicting the MCT structure. MU radar measurements of vertical velocities and turbulence kinetic energy (TKE) were presented along with in situ measurements by the sondes, and compared with numerical simulations.
portions of the areas or objects are. Radar images are composed of many dots, or picture elements. Each pixel in the radar image represents the radarbackscatter for an area on the ground: Bright areas represent high backscatter (bright features mean that a large fraction of the radar energy was reflected back to the radar), while darker areas in the image represent low backscatter (dark features imply that very little energy was reflected back to the antenna). Backscatter for a target area at a particular wavelength varies because of several conditions, as the size of the scatters in the target area, the moisture content of the target area, the polarization of the pulses, the values of emitted wavelengths, and the observation angles. A rule that helps interpreting the radar images is that the brighter the backscatter on the image, the rougher the surface being imaged. Flat surfaces that reflect little microwave energy always appear dark in radar images. Vegetation is usually moderately rough on the scale of most radar wavelengths and appears as gray in a radar image. Some areas not illuminated by the radar, like the back slope of mountains, are in shadow, and appear dark. Roads and freeways are at surfaces so they appear dark. Backscatter is also sensitive to the target's electrical properties, such as water content: Wetter objects appear bright and drier targets appear dark (with the exception of smooth bodies of water, which behave as at surfaces and reflect incoming pulses away, thus they appear dark). Backscatter also varies depending on the use of different polarization and observations angles: Low incidence angles (perpendicular to the surface) will result in high backscatter, while it decrease with increasing incidence angle.
ABSTRACT: This study reports the operational implementation of technique for the exploitation of TOPSAR data in the framework of soil condition applications. Three empirical and theoretical radarbackscatter models were examined in this study to characterize radarbackscatter of TOPSAR data over coastal lowland of Sadong Simunjan River Basin, Sarawak, Malaysia. The main objective of this study is to analyze relationship between radarbackscatter of TOPSAR data to (i) degree of wetness of drained peatland, (ii) depth of water table and, (iii) peat decomposition. The analysis of these models were examined using varying terrain-and-sensor related parameters namely surface roughness, dielectric constant, incidence angle, polarization and frequency, respectively. These simulated backscatters were used to understand the interaction of SAR with the above three methods before mapping the soil conditions using TOPSAR data. Results of this study indicate good relationship (RMSE<5.0) exists between radar backscatters and moisture content, even in the relatively moist drained peatland, which good correlationship with the depth of water table and peat decomposition at 0.89 and 0.88 respectively.
The distribution of 1σ 0 values in the flooded region is perhaps best explained in terms of what may be observed in Fig. 8. The images centre on the segment of the Indus river running between Sukkur and Dadu, where its course changes from a south-west to a south-east direction. The image on the left shows radarbackscatter values acquired on 20 Au- gust 2010. The image on the right shows the same values, with those of a previous cycle deducted. The regions la- belled D and F represent sections of the river characterised by a large flood channel superimposed with the meander- ing and anabranching main Indus channel (see Fig. 9). At the time of acquisition, this large channel was completely flooded, and appears as radar dark in the first image. How- ever, due to the fact that alluvial sediment can also act as a specular reflector in the same way as water, much of the D region is punctuated with mid-range value pixels in the dif- ference image, and region F is all but indistinguishable from non-flooded land. The large area at A with low backscat- ter values in the first image shows that part of the lowlands which protrudes into the Sulaiman mountains of Balochistan, comprising mainly the districts of Bolan and Sibi. This re- gion is normally dry, with an annual rainfall of 200–250 mm. The low backscatter is considered to be the result of attenua- tion and absorption of the signal, rather than of specular re- flection. The low backscatter values are clearly offset in the difference image, leaving only those low values representing
In contrast to the percolation zone, the ablation zone is typically conceptualized as homogenous solid ice with uniform electromagnetic properties. Ku-band backscatter over the bare ice ablation zone is dominated by surface scattering from the rough (and seasonally saturated) ice surface but volume scattering has received little direct study and may be important over superimposed ice, seasonally-snow covered surfaces, or areas with multi-modal surface roughness distributions . Penetration depths at C- and L-band exhibit an order of magnitude range over bare ice surfaces on the GrIS, possibly reflecting variations in ice thermal structure and surface roughness . Similar comparisons at Ku-band do not exist to our knowledge, but could be facilitated by dual-frequency radar altimeters such as SRAL or RA-2 (Table 1). Factors that affect bare ice microwave permittivity include its grain size, temperature, porosity, water content, crystal structure, and chemical and physical impurity content [146–149]. Although slope errors dominate elevation retrieval uncertainty in the ablation zone, seasonal and spatial variations in ablation zone surface properties and their effect on radarbackscatter may be an overlooked source of uncertainty [117,135]. Conversely, this variation and its effect on waveform shape could provide new opportunities for understanding the ablation zone, such as detecting water surface elevation change in supraglacial lakes, or inferring changes in near-surface ice structure related to physical weathering of solid ice [119,149].
Based on significant wave height modeled by using velocity bunching and radarbackscatter cross section across front, Fuzzy numbers are generated. In doing so,two basic notions that we combined together: confidence interval and presumption level. A confidence interval is a real values interval which provides the sharpest enclosing range for significant wave height values. An assumption level µ -level is an estimated truth value in the [0,1] interval on our
Havnes et al.  used a heating cycle with 20 s on and 160 s off to create the ﬁ rst overshoot feature. Havnes  called the time variation of radarbackscatter the “ overshoot characteristic curve ” (OCC). The “ classical ” OCC observed with the EISCAT VHF and UHF radars [Havnes et al., 2003; Kassa et al., 2005; Næsheim et al., 2008] is characterized by an abrupt weakening as the heater is switched on [Chilson et al., 2000]. There can be some recovery of the backscatter during the time the heater is kept on. When the heater is initially switched off, one often observes a sudden increase (overshoot) of the backscatter by a factor that can be up to ~6 above the backscatter observed after the subsequent relaxation back to the normal conditions for an unheated region in a steady state [Kassa et al., 2005]. When the heater is switched on, the electrons are heated practically instantaneously but ions and neutrals are not affected. The dust density distribution is assumed to be unaffected by the heating, but the charge carried by the dust will in ﬂ uence the electron density. The heated electrons will initiate two processes: dust will become more charged and the heating of the electrons will affect the plasma density, which will readjust. If readjustment is the faster process, the main effect will be that electron density gradients are weakened and the radarbackscatter therefore also weakens. If the charging is the faster process, the increased charges on dust particles can lead to the elec- trons being forced into forming steeper density gradients causing the radarbackscatter to increase. During the time that the heater is on, the dust will continue to be charged. When the heater is switched off, the dust will be more charged than before the heater was switched on and force the now cooler electrons into a distribution with stronger density gradients than before the heater was switched on. This also leads to a stronger radarbackscatter — an overshoot.
Abstract: Observed sea surface Ka-band normalized radarbackscatter cross section (NRCS) and Doppler velocity (DV) exhibit energy at low frequencies (LF) below the surface wave range. It is shown that non-linearity in NRCS-wave slope Modulation Transfer Function (MTF) and inherent NRCS averaging within the footprint account for the NRCS and DV LF variance with the exception of VV NRCS for which almost half of the LF variance is attributable to wind fluctuations. Although the distribution of radar DV is quasi-Gaussian suggesting virtually little impact of non-linearity, the LF DV variations arise due to footprint averaging of correlated local DV and non-linear NRCS. Numerical simulations demonstrate that MTF non-linearity weakly affects traditional linear MTF estimate (less than 10% for |MTF| < 20). Thus the linear MTF is a good approximation to evaluate the DV averaged over large footprints typical of satellite observations.
An algorithm for retrieval of soil moisture from radar imagery for bare soil surfaces is introduced as the second step establishing a relationship between radarbackscatter and three governing surface parameters namely local incidence angle, soil moisture and soil roughness. Then, surface soil moisture distribution of the catchment is calculated using three different methods. Similar to the analyses held with soil roughness, these methods are first developed with the point measurements of soil moisture and then applied to other areas of the basin by re-sampled kriging technique. The Backscatter Correction (BS) Factors as method I, which does not include vegetation effect on radarbackscatter, is built for bare or sparsely vegetated farmland and pasture fields of the basin. The step three is followed to propose an algorithm that would be used for microwave remote sensing of soil moisture on vegetation covered areas of the basin. For this reason, the second method of soil moisture estimation, a delta index approach combined with the Water Cloud Model (WCM) for the farmland and pasture land use classes under dense vegetation cover condition.
We proposed Passive IP Traceback (PIT) which tracks spoofers based on path backscatter messages and public available information. We specified how to apply PIT when the topology and routing are both known, or the routing is unknown, or neither of them are known. We presented two effective algorithms to apply PIT in large scale networks and proofed their correctness. We demonstrated the effectiveness of PIT based on deduction and simulation. We showed the captured locations of spoofers through applying PIT on the path backscatter dataset.