Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data

The Integral Equation Model (IEM) is the most widely-used, physically based radar backscatter model for sparsely vegetated landscapes. In general, IEM quantifies the magnitude of backscattering as a function of moisture content and surface roughness, which are unknown, and the known radar configurations. Estimating surface roughness or soil moisture by solving the IEM with two unknowns is a classic example of under-determination and is at the core of the problems associated with the use of radar imagery coupled with IEM-like models. This study offers a solution strategy to this problem by the use of multi-angle radar images, and thus provides estimates of roughness and soil moisture without the use of ancillary field data. Results showed that radar images can provide estimates of surface soil moisture at the watershed scale with good accuracy. Results at the field scale were less accurate, likely due to the influence of image speckle. Results also showed that subsurface roughness caused by rock fragments in the study sites caused error in conventional applications of IEM based on field measurements, but was minimized by using the multi-angle approach.     

A new empirical model to retrieve soil moisture and roughness from C-band radar data

A new empirical model for the retrieval, at a field scale, of the bare soil moisture content and the surface roughness characteristics from radar measurements is proposed. The derivation of the algorithm is based on the results of three experimental radar campaigns conducted under natural conditions over agricultural areas. Radar data were acquired by means of several C-band space borne (SIR-C, RADARSAT) or helicopter borne (ERASME) sensors, operating in different configurations of polarization (HH or VV) and incidence angle. Simultaneously to radar acquisitions, a complete ground truth data base was built up with different surface condition measurements of the mean standard deviation (rms) height s, the correlation length l, and the volumetric surface moisture M_v. This algorithm is more specifically developed using the radar cross-section σ⁰ (HH polarization and 39° incidence angle off nadir), namely, σ_0HH,39, and the differential (HH polarization) radar cross-section Δσ₀=σ_0,23°−σ_0,39° in terms of an original roughness parameter, Z_s, namely Z_s=s²/l, and M_v. A good agreement is observed between model outputs and backscattering measurements over different test fields. Eventually, an inversion technique is proposed to retrieve Z_s and M_v from radar measurements.     

Segmentation of radar imagery using the Gaussian Markov random field model

Segmentation of highly speckled radar imagery is achieved by the use of the Gaussian Markov random field model. The model considers two regions to be different if one or more than one of the following conditions is true: (1) their first-order statistics are different, (2) their second-order statistics are different and (3) their spatial textures are different. It proves that the optimal model parameters can be obtained via solving linear algebraic equations for single-channel images and linear iterations for multi-channel images. Techniques of wavelet transforms and watershed process are used to obtain initial segmentation. Various grey and colour examples, including synthetic radar images, air-borne and space-borne synthetic aperture radar images, are tested, showing the accuracy and efficiency of the method. Regions whose mean differences are as small as 0.5dB and ratios of the standard deviation to the mean as high as 0.35, are separated with an accuracy of more than 95%. It would require their mean differences to be as large as 7.5dB to separate such speckled regions if only pixel values were used in segmentation.     

Likelihood parameter estimation for calibrating a soil moisture model using radar bakscatter

Land surface model parameter estimation can be performed using soil moisture information provided by synthetic aperture radar imagery. The presence of speckle necessitates aggregating backscatter measurements over large (> 100 m × 100 m) land areas in order to derive reliable soil moisture information from imagery, and a model calibrated to such aggregated information can only provide estimates of soil moisture at spatial resolutions required for reliable speckle accounting. A method utilizing the likelihood formulation of a probabilistic speckle model as the calibration objective function is proposed which will allow for calibrating land surface models directly to radar backscatter intensity measurements in a way which simultaneously accounts for model parameter- and speckle-induced uncertainty. The method is demonstrated using the NOAH land surface model and Advanced Integral Equation Method (AIEM) backscatter model calibrated to SAR imagery of an area in the Southwestern United States, and validated against in situ soil moisture measurements. At spatial resolutions finer than 100 m × 100 m NOAH and AIEM calibrated using the proposed radar intensity likelihood parameter estimation algorithm predict surface level soil moisture to within 4% volumetric water content 95% of the time, which is an improvement over a 95% prediction confidence of 10% volumetric water content by the same models calibrated directly to soil moisture information derived from synthetic aperture radar imagery at the same scales. Results suggest that much of this improvement is due to increased ability to simultaneously estimate NOAH parameters and AIEM surface roughness parameters.     

Surface current circulation estimation using NOAA/AVHRR images and comparison with HF radar current measurements

The method used to identify sea surface thermal features for estimating surface currents is presented. Results given have been obtained from investigations made in the Central Adriatic Sea off the coast of Monte Conero in the Marche Region, Italy, using Advanced Very High Resolution Radiometer (AVHRR) observations of NOAA-14, made simultaneously with the HF coastal radar system installed there.     

Laboratory and field measurements of the modification of radar backscatter by sand

Over the last two decades, the use of synthetic aperture radar (SAR) to address geologic problems has expanded as new applications for radar have been developed. One of the earliest and perhaps most surprising results from orbital SAR images of the Sahara was that, under certain conditions, radar signals penetrated up to several meters of sand to reveal subsurface features such as ancient river channels. Subsequent studies of radar penetration of arid sand deposits have dealt with factors that govern the ability of radar to penetrate a sand cover. This paper presents results from a laboratory experiment in which radar backscatter from a surface of rocks was measured under controlled conditions as a function of frequency, polarization, incidence angle, and sand cover thickness. The sand used in the experiment had a moisture content of 0.28 vol.% and caused calculated average attenuations of 4.2±1 dB/m for C-band and ∼11±2 dB/m for X-band. Results from the experiment were compared to field measurements of sand thickness during acquisition of airborne radar images. In AIRSAR images, the extent of dry sand in a dune field appears best in C-band because longer wavelength L- and P-band signals penetrate thinner sand deposits. Images of wet sand (4.9 vol.%) suggest that L-band was able to penetrate thin sand even though that sand was wet. Together, these laboratory and field measurements contribute towards a better understanding of how a sand cover modifies the radar backscatter of a surface.     

Robot calibration using the CPC error model

The complete and parametrically continuous (CPC) robot kinematic modeling convention has no model singularities and allows the modeling of the robot base and tool in the same manner by which the internal links are modeled. These two properties can be utilized to construct robot kinematic error models employing the minimum number of kinematic error parameters. These error parameters are independent and span the entire geometric error space. The BASE and TOOL error models are derived as special cases of the regular CPC error model. The CPC error model is useful for both kinematic identification and kinematic compensation. This paper focuses on the derivation of the CPC error models and their use in the experimental implementation of robot calibration.     

Analysis of radar images for rainfall forecasting using neural networks

This paper describes a new approach to the analysis of weather radar data for short-range rainfall forecasting based on a neural network model. This approach consists in extracting synthetic information from radar images using the approximation capabilities of multilayer neural networks. Each image in a sequence is approximated using a modified radial basis function network trained by a competitive mechanism. Prediction of the rain field evolution is performed by analysing and extrapolating the time series of weight values. This method has been compared to the conventional cross-correlation technique and the persistence method for three different rainfall events, showing significant improvement in 30 and 60 min ahead forecast accuracy.     

Suppression of speckle in synthetic aperture radar images using wavelet

A filter for suppressing speckle in synthetic aperture radar (SAR) images utilizing wavelet is proposed. The filter suppresses speckle by reducing the amplitude of the detail images in wavelet subspaces, while preserving edges by releasing the amplitude reduction around edges; information on edges, contained in the detail images, is utilized for edge detection. Simulations and application to SAR images have shown that the performance of the filter is satisfactory in both smoothing and edge preservation, and in generating visually-natural images as well.     

Geometric and elastostatic calibration of robotic manipulator using partial pose measurements

The paper deals with the geometric and elastostatic calibration of robotic manipulator using partial pose measurements, which do not provide the end-effector orientation. The main attention is paid to the efficiency improvement of identification procedure. In contrast to previous works, the developed calibration technique is based on the direct measurements only. To improve the identification accuracy, it is proposed to use several reference points for each manipulator configuration. This allows avoiding the problem of non-homogeneity of the least-square objective, which arises in the classical identification technique with the full pose information (position and orientation). Its efficiency is confirmed by the comparison analysis, which deals with the accuracy evaluation of different identification strategies. The obtained theoretical results have been successfully applied to the geometric and elastostatic calibration of a serial industrial robot employed in a machining work cell for aerospace industry.     

Investigating the potential for soil moisture and surface roughness monitoring in drylands using ERS SAR data

Multitemporal ERS-1 and ERS-2 SAR data were acquired for northern Jordan between 1995 and 1997 to investigate changes in the backscatter coefficients of a range of typical desert land surfaces. The changes in backscatter found were ascribed to variations in surface soil moisture, and changes in surface roughness caused by a range of natural and anthropogenic factors. Data collected from monitored sites were input into the Integral Equation Model (IEM). The model outputs were strongly correlated with observed backscatter coefficients (r ²=0.84). The results show that the successful monitoring of soil moisture in these environments is strongly dependent on the surface roughness. On surfaces with RMS height 0.5 cm, the sensitivity of the backscatter coefficient to changes in surface microtopography did not allow accurate soil moisture estimation. Microtopographic change on rougher surfaces has less influence on the backscatter coefficient, and the probability of soil moisture estimation from SAR imagery is greater. These results indicate that knowledge of the surface conditions (both in terms of surface roughness and geomorphology) is essential for accurate soil moisture monitoring, whether in a research or operational context. The potential benefits of these findings are discussed in the context of the Jordan Badia Research and Development Project.     

Model-based tool condition prognosis using power consumption and scarce surface roughness measurements

In machining processes, underusing and overusing cutting tools directly affect part quality, entailing economic and environmental impacts. In this paper, we propose and compare different strategies for tool replacement before processed parts exceed surface roughness specifications without underusing the tool. The proposed strategies are based on an online part quality monitoring system and apply a model-based algorithm that updates their parameters using adaptive recursive least squares (ARLS) over polynomial models whose generalization capabilities have been validated after generating a dataset using theoretical models from the bibliography. These strategies assume that there is a continuous measurement of power consumption and a periodic measurement of surface roughness from the quality department (scarce measurements). The proposed strategies are compared with other straightforward tool replacement strategies in terms of required previous experimentation, algorithm simplicity and self-adaptability to disturbances (such as changes in machining conditions). Furthermore, the cost of each strategy is analyzed for a given benchmark and with a given batch size in terms of needed tools, consumed energy and parts out of specifications (i.e., rejected). Among the analyzed strategies, the proposed model-based algorithm that detects in real-time the optimal instant for tool change presents the best results.     

Parameter measurements of moving targets using a 24-GHz radar

We designed a 24-GHz traffic surveillance radar (TSR-24) to monitor automobile and pedestrian. The range, radial velocity and direction-of-arrival (DOA) of the moving targets can be measured by the radar in real-time. The radar consists of a radar sensor, a waveform generation module, a filter and amplifier module and a digital signal processor (DSP) platform. The radar can be configured to work in Doppler mode or frequency modulated continuous wave (FMCW) mode through software configuration. The phase-monopulse approach is used for the target DOA estimation. A DOA error reduction method and the Rife method are proposed to improve the range and DOA accuracy. The parameter estimation algorithms are implemented in DSP. The radar is used to detect and track motorcycles, cars, trucks and even pedestrians. The measurement of an electric vehicle shows that the velocity error is 0.022 m/s. Various tests indicate that the range and DOA error is no more than 0.25 m and 1°, respectively.     

Retrieval of real-time live fuel moisture content using MODIS measurements

Live fuel moisture content (LFMC) is one of the most important fuel properties and a critical parameter for wildland fire danger rating estimation and fire behavior analysis. Direct ground measurement of live fuel moisture content has disadvantages of high cost and limited spatial distribution extent. This paper presents an algorithm to retrieve live fuel moisture content from multiple bands of MODIS measurements. We analyzed the physical relationship between surface reflectance and live fuel moisture content using simulated MODIS measurements of diverse leaf samples, derived approximate inversion models, and proposed a semi-physical approach for live fuel moisture retrieval employing multiple MODIS bands. Using simulated MODIS measurements, the correlation coefficients between the true LFMC and estimated LFMC with our inversion models are 0.7738, 0.8397, 0.9560 and 0.9576 respectively. For validation, we tested our inversion method with woody live fuel moisture measurements at fire weather stations in Georgia. The correlation coefficients between measured LFMC and estimated LFMC with our inversion models are 0.5727, 0.6522, 0.7551, and 0.7737 respectively. Both model simulation and station measurements demonstrated advantages of our approach in accuracy. Our study suggests the potential for near real-time applications of live fuel moisture.     

Soil moisture estimation using MODIS and ground measurements in eastern China

Recent technological advances in remote sensing have shown that soil moisture can be measured by microwave remote sensing under some topographic and vegetation cover conditions. However, current microwave technology limits the spatial resolution of soil moisture data. It has been found that the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) are related to surface soil moisture; therefore, a relationship between ground observed soil moisture and satellite NDVI and LST products can be developed. Three years of 1 km NDVI and LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been combined with ground measured soil moisture to determine regression relationships at a 1 km scale. Results show that MODIS NDVI and LST are strongly correlated with the ground measured soil moisture, and regression relationships are land cover and soil type dependent. These regression relationships can be used to generate soil moisture estimates at moderate resolution for study area.     

On the use of calibration sensors in source localization using TDOA and FDOA measurements

The accuracy of a source location estimate is very sensitive to the presence of the random noise in the known sensor positions. This paper investigates the use of calibration sensors, each of which is capable of broadcasting calibration signals to other sensors as well as receiving the signals from the source and other calibration sensors, to reduce the loss in the source localization accuracy due to uncertainties in sensor positions. We begin the study with deriving the Cramer–Rao lower bound (CRLB) for source localization using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements when a single calibration sensor is available. The obtained CRLB result is then extended to the more general case with multiple calibration sensors. The performance improvement due to the use of calibration sensors is established analytically. We then propose a closed-form algorithm that can explore efficiently the calibration sensors to improve the source localization accuracy when the sensor positions are subject to random errors. We prove analytically that the newly developed localization method attains the CRLB accuracy under some mild approximations. Simulations verify the theoretical developments.     

Effect of surface roughness on liquid property measurements using mechanically oscillating sensors

The resonant frequency and quality factor Q of a liquid immersed magnetoelastic sensor are shown to shift linearly with the liquid viscosity and density product. Measurements using different grade oils, organic chemicals, and glycerol-water mixtures show that the surface roughness of the sensor in combination with the molecular size of the liquid play important roles in determining measurement sensitivity, which can be controlled through adjusting the surface roughness of the sensor surface. A theoretical model describing the sensor resonant frequency and quality factor Q as a function of liquid properties is developed using a novel equivalent circuit approach. Experimental results are in agreement with theory when the liquid molecule size is larger than the average surface roughness. However, when the molecular size of the liquid is small relative to the surface roughness features molecules are trapped, and the trapped molecules act both as a mass load and viscous load; the result is higher viscous damping of the sensor than expected.     

On the influence of canopy structure on the radar backscattering of mangrove forests

This paper is the third of a series which aims to evaluate the effects of canopy structure on the polarimetric radar response of mangrove forests. It complements the experimental and theoretical study of closed canopies presented in the previous papers by analysing two different mangrove stands of equal biomass but which greatly differ in their structure. For the three considered frequencies (C-, L- and P-band), experimental observations show that the back-scattering from the open declining stand is higher than that of the closed forest. The corresponding enhancement factor increases with wavelength and shows maximum values for the HH polarization. The identification of the scattering mechanisms occurring between the incident radar wave and the forest components was performed with the assistance of a polarimetric scattering model based on a radiative transfer approach. For the co-polarizations, results of the simulation study confirm that the backscatter enhancement is mainly due to an increase of either the surface scattering or the interaction component. For the cross-polarization HV at L- and P-bands, the increase of the volume component, originating from a stronger interaction with bigger branches, is found to be responsible for the observed enhancement. These findings confirm the large effect of the canopy structure on the forest backscatter and give rise to two important applications. First, the mapping of open declining mangrove stands appears feasible by using either the backscattering coefficient values, especially at P-HH and P-HV, or the HH-VV phase difference at P-band. Second, the use of the σ °-biomass statistical relationships must be restricted to homogeneous closed canopies.     

Monitoring flooding in coastal wetlands by using radar imagery and ground-based measurements

Satellite radar was used in a Florida Juncus roemerianusmarsh to map tidal flooding, a critical control of coastal vegetation distribution. Radar images taken during a time of near-continuous recordings of ground-based hydrology measurements directly linked marsh flooding to lowered radar returns and indicated a negative covariation between flood frequency and radar return. Flood-extent contours extracted from the radar images and calibrated with point depth measurements showed marsh elevation could be estimated to about 8 cm compared to the 150 cm topographic contours currently available.