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Spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. There is growing interest in monitoring soil moisture in relation to soil- and land-based natural flood management (NFM), to understand the soil’s ability, via land-use and management changes, and to delay the arrival of flood peaks in nearby watercourses. This article monitors relative surface soil moisture (rSSM) across the Thames Valley, U.K., using Sentinel-1 data, and the Vienna University of Technology (TU-Wien) Change Detection Algorithm, with a novel exploration of monthly and annual normalization factors and spatial averaging. Two pairs of normalization factors are introduced to remove impacts from varying local incidence angles through direct and multiple regression slopes. The spatiotemporal distribution of rSSM values at various spatial resolutions (1000, 500, 250, and 100 m) is assessed. Comparisons with in situ soil moisture data from the COSMOS-UK network show that, while general temporal trends agree, the difference in effective depth of measurements, coupled with vegetation impacts during the growing season, makes comparison with soil moisture observations difficult. Temporal rSSM trends can be retrieved at spatial resolutions down to 100 m, and the rSSM RMSE was found to decrease as the spatial resolution increases. The vegetation effects upon the rSSM are further explored by comparing the two dominant land cover types: Arable and Horticulture, and Improved Grassland. It was found that, while the rSSM retrieval for these land covers was possible, and the general soil moisture trend is clear, overlying vegetation during the summer artificially increased the rSSM values. [ABSTRACT FROM AUTHOR]

A novel procedure based on filtering and interpolation approaches is proposed to estimate the sea state parameters, including significant wave height, peak wave direction, peak period, peak wavenumber, and peak wavelength in shallow waters using the X-band marine radars. The method compensates the distortions introduced by the radar acquisition process and the power decay of the radar signal along the distance applying image-enhancement techniques instead of empirical and semiempirical calibration methods that use signal-to-noise ratio and in situ measurements as external references. To determine the threshold value for the interpolation approach, the influence of the antenna height on shadowing modulation effects is examined through performing an analysis of variance (ANOVA) that uses data from two X-band radars deployed at 10 and 20 m above MSL. ANOVA results reveal that it is possible to explain the increment of intensities affected by shadowing throughout the distance using an adaptive threshold retrieved from a third-order polynomial function of the mean radar cross section (RCS). Finally, an X-band radar is installed at 13 m above MSL to test the proposed technique. During measurements, the wind and wave conditions varied, and the antenna-look direction remained constant. Errors for $H_{s}$ , $\theta _{p}$ , and $T_{p}$ calculated as the difference between estimated and true data show a mean bias and a relative value of 0.05 m (2.72%), 1.52° (5.94%), and 0.15 s (1.67%), respectively. The directional and wave energy spectra derived from radar estimates, acoustic wave and current, ADVs record, as well as JONSWAP formulation are presented to illustrate the improvement resulting from the proposed method over the frequency domain. [ABSTRACT FROM AUTHOR]

Microwave backscattering models play key roles in surface scattering modeling and soil moisture inversion in active microwave remote sensing. However, numerous evaluations indicate that significant discrepancies between the model simulations and radar observations remain, and these discrepancies are regarded to be attributed to inaccuracies in the models. What do such discrepancies originate from is unclear and has not been comprehensively analyzed. To this end, this paper presents an uncertainty analysis to explore the intrinsic reason for the discrepancies between the backscattering model simulations and radar observations. The probability distribution function and the corresponding statistical characteristics are introduced to describe the uncertainty in the model outputs. We find that the scale dependence of the key model inputs leads to significant uncertainties in the model inputs, and the uncertainties are transferred into the model outputs. Thus, the discrepancies between the model simulations and radar observations are intrinsically caused by the spatial scaling and related uncertainties of key model inputs. In short, the scale mismatch between the model inputs and remote sensing pixels is an intrinsic factor that causes the discrepancies between the model simulations and radar observations. This finding suggests that the scaling effect of model inputs should be carefully considered when using the backscattering models at the pixel scale, and equivalent inputs matched at the corresponding scales should be developed for remote sensing applications. Thus, this analysis insights into the scale dependence of inputs for backscattering models and suggests to provide scale-matched inputs where the models are applied at different scales. [ABSTRACT FROM AUTHOR]

A simple extrapolation technique is proposed for the intercalibration of the Ku- and Ka-band altimeter data based on a recent analytical scattering model referred to as “GO4.” This method is tested with AltiKa and Jason2-Ku altimeters using one year of reprocessed data with the improved retracking algorithm ICENEW. The variations of the normalized radar cross section with respect to the main oceanic parameters are investigated in the Ku and Ka bands; the latter band is shown to have an increased sensitivity to wind speed, significant wave height as well as sea surface temperature. As a by-product of this analysis, we derive an original expression for the swell impact on the mean square slope, which allows to correct the GO4 model for the contribution of long waves. We show that the Ku/Ka prediction agrees within 0.25 dB with the respective levels of AltiKa and Jason2-Ku cross sections at wind speed larger than 4 m/s. [ABSTRACT FROM PUBLISHER]

The rising food demand requires regular agriculture land-cover updates to support food security initiatives. Agricultural areas undergo dynamic changes throughout the year, which manifest varying radar backscatter due to crop phenology. Certain crops can show similar backscatter if their phenology intersects, but vary later when their phenology differs. Hence, classification techniques based on single-date remote sensing images may not offer optimal results for crops with similar phenology. Moreover, methods that stack images within a cropping season as composite bands for classification limit discrimination to one feature space vector, which can suffer from overlapping classes. Nonetheless, phenology can aid classification of crops, because their backscatter varies with time. This paper fills this gap by introducing a crop sequence-based ensemble classification method where expert knowledge and TerraSAR-X multitemporal image-based phenological information are explored. We designed first-order and higher order dynamic conditional random fields (DCRFs) including an ensemble technique. The DCRF models have a duplicated structure of temporally connected CRFs, which encode image-based phenology and expert-based phenology knowledge during classification. On the other hand, our ensemble generates an optimal map based on class posterior probabilities estimated by DCRFs. These techniques improved crop delineation at each epoch, with higher order DCRFs (HDCRFs) giving the best accuracy. The ensemble method was evaluated against the conventional technique of stacking multitemporal images as composite bands for classification using maximum likelihood classifier (MLC) and CRFs. It surpassed MLC and CRFs based on class posterior probabilities estimated by both first-order DCRFs and HDCRFs. [ABSTRACT FROM PUBLISHER]

Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g., due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection of in situ networks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m3/m3 over a variety land cover types. [ABSTRACT FROM PUBLISHER]

This paper deals with a ground experiment based on a P-band scatterometer that measured the evolution of the radar cross section (RCS) and coherence of a temperate tree in HV polarization, during four periods spread over nine months, over a cedar tree. Watering of the tree has limited influence in time on the RCS, i.e., limited to around 30 min, but impacts the coherence over a longer period of time. Analysis of the series shows that according to the season considered, clear daily cycles from 1 to 2 dB may appear on the coherence only (autumn) or on both coherence and RCS (spring), whereas in winter, they are absent on both results. It was analyzed in the literature that the variations in RCS are strongly correlated to the variations in the dielectric constant in trunks and branches. In addition, it was shown that the HV RCS presents seasonal trends with a yearly cycle of roughly 3 dB following similar trends reported for trunk moisture content time series. [ABSTRACT FROM PUBLISHER]

This paper deals with the method developed for rain flagging over oceans using radar backscatter and noise-equivalent brightness temperature (TB) measured by a Ku-band pencil-beam scatterometer launched onboard the Oceansat-2 (OS2) satellite. The OS2 scatterometer (OSCAT) measures radar backscatter and TB by its two pencil-beam antennas at local incidence angles of ∼49° (inner) and ∼57° (outer) in horizontal and vertical polarizations, respectively. These measurements are affected by rain and heavy clouds, in addition to the ocean surface winds, thus affecting retrieval of wind vectors. The characteristics of TB at OSCAT specifications have been studied based on radiative transfer simulations. The horizontally polarized TB is observed to be sensitive to both wind speed and rain, whereas the vertically polarized TB (TBV), typically at 57°, is found to be insensitive to ocean surface wind speed but is sensitive to rain, indicating its typical utility for rain flagging. As the retrievals of wind vectors are affected by rain, then so are the associated cost functions and their characteristics under rain-free and rainy situations. A combined use of TBV and retrieval cost functions has been made for rain flagging needed in wind directional-ambiguity removal, as well as tagging the retrieval quality. A data-driven dynamical method has been developed to specifically utilize TBV for supporting rain detection in view of the slow temporal drift found in the OSCAT TB data. Rain flags have been generated from OSCAT 25-km grid L2A (Version 1.4) data for the period from November 2009 to August 2010, and the results of comparison with Tropical Rainfall Measuring Mission Precipitation Radar 2A25 data are presented. [ABSTRACT FROM PUBLISHER]

We propose a new model for estimating subsurface soil moisture using P-band radar data over barren, shrubland, and vegetated terrains. The unknown soil moisture profile is assumed to have a second-order polynomial form as a function of subsurface depth with three unknown coefficients that we estimate using the simulated annealing algorithm. These retrieved coefficients produce the value of soil moisture at any given depth up to a prescribed depth of validity. We use a discrete scattering model to calculate the radar backscattering coefficients of the terrain. The retrieval method is tested and developed with synthetic radar data and is validated with measured radar data and in situ soil moisture measurements. Both forward and inverse models are briefly explained. The radar data used in this paper have been collected during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission flights in September and October of 2012 over a 100 km by 25 km area in Arizona, including the Walnut Gulch Experimental Watershed. The study area and the ancillary data layers used to characterize each radar pixel are explained. The inversion results are presented, and it is shown that the RMSE between the retrieved and measured soil moisture profiles ranges from 0.060 to 0.099 m3/m3, with a Root Mean Squared Error (RMSE) of 0.075 m3/m3 over all sites and all acquisition dates. We show that the accuracy of retrievals decreases as depth increases. The profiles used in validation are from a fairy dry season in Walnut Gulch and so are the accuracy conclusions. [ABSTRACT FROM PUBLISHER]

Helicopter-borne radar backscatter measurements are analyzed with respect to a multifrequency classification approach of sea ice. Measurements were carried out over the Arctic Ocean during August and September 2007 and represented unusually warm freeze-up conditions. Radar cross sections (RCSs) of totally ice-free wind-roughened water are used in combination with an ocean surface theoretical backscattering model for the calibration. The calibrated RCS \sigma^\circ agrees within 1 dB with nearly simultaneous Envisat Advanced Synthetic Aperture Radar measurements and literature values. Sea ice was classified using a Bayesian maximum likelihood approach. By including information from simultaneous infrared and visible video imagery of sea ice, four different surface types of sea ice could be identified in the resulting \sigma^\circ: old ice, gray ice, nilas, and open water. The most reliable classification was obtained through combination of copolarized C-, X-, and Ku-band data. The results degraded by only 7% in the case where the X-band information was dropped. On the other hand, a combination of the C- and X-bands or the X- and Ku-bands yielded a degradation of 13%. Given the remaining uncertainties in the approach, for sea ice classification during summer/fall conditions, our results suggest the complementary use of two of these three frequency bands instead of relying on just one frequency band. [ABSTRACT FROM AUTHOR]

Revisiting time constitutes a key constraint for continuous monitoring activities based on space- and airborne synthetic aperture radar (SAR) acquisitions. Conversely, the employment of terrestrial platforms overcomes this limitation and makes it possible to perform time-continuous observations of small space-scale phenomena. New research lines of SAR dealing with the backscattering evolution of different types of scenarios become hence possible through the analysis of ground-based SAR (gbSAR) data collections. The Remote Sensing Laboratory of the Universitat Politècnica de Catalunya drove a one-year measurements campaign in the village of Sallent, northeastern Spain, using its X-Band gbSAR sensor. The field experiment aimed at studying the subsidence phenomenon induced by the salt mining activity carried out in this area during the past decades. In this paper, the polarimetric behavior of an urban environment is investigated at different time scales. After a brief description of the test site and the measurement campaign, the analysis is focused on the stability on man-made structures at different time scales. PolSAR data monthly acquired from June 2006 to July 2007 are employed to stress the presence of nonstationary backscattering processes within the urban scene and the effect they have on differential phase information. Then, a filtering procedure aiming at reducing backscattering randomness in one-day and long-term data collections is then put forward. The improvements provided by the proposed technique are assessed using a new polarimetric descriptor, the time entropy. In the end, the importance of preserving the interferometric phase information from nonstationary backscattering contaminations using fully polarimetric data is discussed. [ABSTRACT FROM PUBLISHER]

Radar imaging of deserts suffers from insufficient radar backscatter at low microwave frequencies due to the low permittivity of dry sand and relatively smooth sand surface roughness. Operating at millimeter-wave (MMW) frequencies, however, rectifies this deficiency as significant radar backscatter is generated by surface and volume scattering. This is due to the fact that sand surface roughness is electrically large and signal penetration into the dry sand, which is a homogeneous mixture of air and sand particles with dimensions comparable to a fraction of a wavelength, generates considerable volume scattering. This paper investigates both surface and volume scattering from dry sand surfaces, subject to the peculiar physical properties of sand surfaces found in sand dune-covered regions. An incoherent model is proposed that characterizes the angular dependence of volume scattering from dry sand in the presence of a 1-D rippled air/sand surface. A set of indoor experiments conducted on smooth and 1-D rippled sand surfaces at Ka-band confirms that significant volume scattering is present at MMW frequencies and that the proposed model correctly captures the observed angular dependence when 1-D surface ripples are present. [ABSTRACT FROM AUTHOR]

L-band microwave backscatter and brightness temperature of sea surfaces acquired using the Passive/Active L-band Sensor during the High Ocean Wind campaign are reported in terms of their dependence on ocean surface wind speed and direction. We find that the L-band VV, HH, and HV radar backscatter data increase by 6-7 dB from 5 to 25 m/s wind speed at a 45° incidence angle. The data suggest the validity of Phased Array type L-band Synthetic Aperture Radar (PALSAR) HH model function between 5 and 15 m/s wind speeds, but show that the extrapolation of PALSAR model at above 20 m/s wind speeds overpredicts A0 and α1 coefficients. There is wind direction dependence in the radar backscatter with about 4 dB differences between upwind and crosswind observations at 24 m/s wind speed for VV and HH. The passive brightness temperatures show about a 5-K change for TV and a 7-K change for TH for a wind speed increasing from 5 to 25 m/s. Circle flight data suggest a wind direction response of about 1-2 K in TV and TH at 14 and 24 m/s wind speeds. The L-band microwave data show excellent linear correlation with the surface wind speed with a correlation better than 0.95. The results support the use of L-band radar data for estimating the wind-driven excess brightness temperature of sea surfaces. The data also support the applications of L-band microwave signals for high-resolution (kilometer scale) observation of ocean surface winds under high wind conditions (10-28 m/s). [ABSTRACT FROM AUTHOR]

A coherent scattering formulation is developed for radar remote sensing of Sahelian grassland. This African vegetation is mainly composed of annual grass and shrubs. In the proposed procedure, first, a temporal model for generation of grass and shrub structures, which includes important realistic botanical information, is implemented. Because we develop a coherent scattering model, preserving the relative position of plant elements in a statistical manner as accurately as possible is very important. Shrubs are reproduced using cylindrical elements which represent trunks, branches, and thin green stems that function as leaves for these shrubs. Their crown shape is highly irregular, but for the most part can be encompassed in an ellipsoidal or cylindrical volume; on the other hand, the grass is represented as a set of cylindrical stalks and blade leaves. The scattered power from each grass element is added because multiple scattering among adjacent elements can be neglected at microwave frequencies. We calculate the soil scattering using the Integral Equation Method and neglect the soil volume scattering which may become significant for dry soil condition at high incidence angles. Backscatter statistics are acquired via a Monte Carlo simulation over a large number of realizations. The accuracy of the model is verified using measured data acquired by the C-band environmental satellite advanced synthetic aperture radar instrument at different incident angles. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of the radar backscatter from a spatially extended geophysical target (SEGT) based on a semiempirical (SE) model. An SEGT is any geophysical object that is at least semitransparent to radar illumination (clouds, rain, snowfall in the atmosphere, thick snow cover of ground). The existing SE model does not take into account the statistical properties of the SEGT's media. To improve the SE model, a so-called "slice" approach is applied. In this approach, particles located close to the wavefront of the radar illumination are assumed to produce backscatter that is mainly coherent. This method allows the contribution of the microphysical parameters of the scattering media to the volume component of the radar cross section to be described more comprehensively than the SE model based on the incoherent approach. It is shown that the slice concept results in the original SE model in the particular case when the particle number fluctuation within the slices pertains the Poisson law. [ABSTRACT FROM AUTHOR]

Characteristics of the Ku-band polarimetric scat-terometer (POLSCAT) data acquired from five sets of aircraft flights in the winter months of 2006-2008 for the second Cold Land Processes Experiment (CLPX-II) in Colorado are described in this paper. The data showed the response of the Ku-band radar echoes to snowpack changes for various types of background vegetation in the study site in north central Colorado. We observed about 0.15-0.5-dB increases in backscatter for every I cm of snow-water-equivalent (SWE) accumulation for areas with short vegetation (sagebrush and pasture). The region with the smaller amount of biomass, signified by the backscatter in November, seemed to have the stronger backscatter response to SWE in decibels. The data also showed the impact of surface hoar growth and freeze/thaw cycles, which created large snow-grain sizes, ice crust layers, and ice lenses and consequently increased the radar signals by a few decibels. The copolarized HH/VV backscatter ratio seems to indicate double-bounce scattering between the ground surface and snow or vegetation. The cross-polarized backscatter [vertical-horizontal (VH)] showed not only the influence of vegetation but also the strong response to snow accumulation. The observed HV/VV ratio suggests the importance of multiple scattering or nonspherical scattering geometry of snow grain in the dense-media radiative transfer scattering model. Comparison of the POLSCAT and QuikSCAT data was made and confirmed the effects of mixed terrain covers in the coarse-resolution QuikSCAT data. [ABSTRACT FROM AUTHOR]

In the radiometric sensing of soil moisture through a forest canopy, knowledge of canopy attenuation is required. Active sensors have the potential of providing this information since the backscatter signals are more sensitive to forest structure. In this paper, a new radar technique is presented for estimating canopy attenuation. The technique employs details found in a transient solution where the canopy (volume-scattering) and the tree-ground (double-interaction) effects appear at different times in the return signal. The influence that these effects have on the expected time-domain response of a forest stand is characterized through numerical simulations. A coherent forest scattering model, based on a Monte Carlo simulation, is developed to calculate the transient response from distributed scatterers over a rough surface. The forest transient-response model for linear copolarized case is validated with the microwave deciduous tree data acquired the Combined Radar/Radiometer (ComRAD) system. The atteuation algorithm is applicable when the forest height is sufficient to separate the components of the radar backscatter transient response. The frequency correlation functions of double-interaction and volume-scattering returns are normalized after being separated in the time domain. This ratio simply provides a physically based system of equations with reduced parameterizations for the forest canopy. Finally, the technique is used with ComRAD L-band stepped-frequency data to evaluate its performance under various physical conditions. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is an important variable in determining streamflow, agricultural productivity, weather, and climate. An effective way to map SM over large areas on a regular basis is by using active microwave observations. This paper examines the influence of topography on radar backscatter measurements for a range of vegetation conditions and on the development of a normalization technique for the correction of topography-induced variability. Radar backscatter observations derived from the QuikSCAT sensor were analyzed to investigate the effect of sloping terrain over the North American Monsoon Experiment region that is characterized by heterogeneous surface conditions and complex topographic terrain. A digital elevation model, along with local incidence angle and slope, was used to investigate the backscatter dependence on topography variation for eight main vegetation classes. Pearson product-moment correlation (R) analysis showed strong backscatter dependence on the local incidence angle caused by changes in slope. The overall average reduction in variances after correction for August 2004 depended on vegetation type and ranged between -16% to -42% and -18% to -37% for horizontal and vertical polarizations, respectively. The corrected sigma-0 was also evaluated using in situ SM observations obtained during the Soil Moisture Experiment 2004 field campaign. The computed percent change in R between sigma-0 and SM demonstrated significant improvement after correction when using vertically observed sigma-0. The standard errors of estimate for these two vegetation classes were lowered by about 12% and 5%, respectively, after applying the proposed topographic normalization technique to the QuikSCAT observations. [ABSTRACT FROM AUTHOR]

We consider the exploitation of dielectric properties of saline deposits for the detection and mapping of moisture in arid regions on both Earth and Mars. We present simulated and experimental study in order to assess the effect of salinity on the complex permittivity of geological materials and, therefore, on the radar backscattering coefficient in the [1-7 GHz] frequency range. Laboratory measurements are performed on sand/sodium chloride aqueous mixtures using a vectorial network analyzer coupled to an open-ended coaxial dielectric probe. We aim at calibrating and validating semiempirical dielectric mixing models. In particular, we evaluated the dependence of the real and imaginary parts of complex permittivity on the microwave frequency, water content, and salinity. Our results confirm that if the real part is mainly affected by the moisture content, the imaginary part is more sensitive to salinity. In addition to the classic formulas of mixing models, the ionic-conductivity losses, which are due to mobile ions in the saline solution, are taken into account in order to better assess the effect of salinity on the dielectric properties of mixtures. Dielectric mixing models are then used as input parameters for the simulation of the radar backscattering coefficients by means of an analytical model: the integral equation model. Simulation results show that salinity should have a significant impact on the radar backscattering recorded in synthetic aperture radar data in terms of the magnitude of the backscattering coefficient. Moreover, our results suggest that VV polarization provides a greater sensitivity to salinity than HH polarization. [ABSTRACT FROM AUTHOR]

Focusing on a forest dominated by Poplar Box (Eucalyptus populnea) near Injune in Queensland, Australia, light detection and ranging (LiDAR) and optical remote sensing data are integrated with tree- and stand-level information to parameterize a coherent L-band synthetic aperture radar (SAR) imaging simulation that models microwave penetration and interaction with the canopy, understory, and ground. The approach used LiDAR data to generate a three-dimensional representation of the distribution of tree components (leaves and small branches) by species (based on 1-m³ voxels) and the ground surface. Tree trunks were mapped across a 7.5-ha forest stand using a LiDAR-derived height-scaled crown openness index. Primary and secondary branches were modeled as tapering cylinders and linked the canopy voxels to the LiDAR trunks. The dimensions of vegetation and soil components and their geometric and dielectric properties required by the model were calibrated with field-based measurements. Visual and numerical comparison between NASA JPL Airborne SAR data and the model simulation suggests the effective modeling of SAR imagery at L-band. The study provides a proof-of-concept approach for integrating LiDAR data in the parameterization of coherent SAR simulation models, and the model presents options for better understanding of the information content of SAR data in real forest situations. [ABSTRACT FROM AUTHOR]