Modelling temporal variations in microwave backscattering from reed marshes

A full understanding of radar backscattering characteristics and their seasonal variations is one of the important ways to analyse the growth conditions in wetlands. This research simulated seasonal C-band and L-band synthetic aperture radar (SAR) backscattering from reed marshes using a refined version of the electromagnetic (REM) model, which was first validated by time series of multimode SAR observations at the experimental site used. Then, two factors including sensor parameters and vegetation structure, which influence the temporal evolution of the radar response from reed marshes, were assessed. The results demonstrate that the radar response is closely related to growth processes in the reed marsh. At the early growth stage when reed marshes are sparse, the double-bounce mechanism was dominant at all the incidence angles of C-band radar, but for L-band radar, strong specular reflection was produced from the smooth water if the incidence angle is lower than 25°. It was also found that the sensitivity to the density and height of reed marshes is greater for L-band radar than at the C-band, indicating that L-band backscattering may be useful for reed marsh biomass retrieval.     

Fully polarimetric airborne SAR and ERS SAR observations of snow: implications for selection of ENVISAT ASAR modes

Snow cover has a substantial impact on processes involved in the interaction between atmosphere and surface, and the knowledge of snow parameters is important in both climatology and weather forecasting. With the upcoming launch of Advanced Synthetic Aperture Radar (ASAR) instruments on Envisat, enhanced snow-mapping capabilities are foreseen. In this paper fully polarimetric C- and L-band airborne SAR data, ERS SAR and auxiliary data from various snow conditions in mountainous areas are analysed in order to determine the optimum ASAR modes for snow monitoring. The data used in this study are from the Norwegian part of the snow and ice experiment within the European Multi-sensor Airborne Campaign (EMAC'95) acquired in the Kongsfjellet area, located in Norway, 66°?N, 14°?E. Fully polarimetric C- and L-band SAR data from ElectroMagnetic Institute SAR (EMISAR), an airborne instrument operated by the Danish Center for Remote Sensing (DCR), were acquired in March, May, and July 1995. In addition, several ERS SAR, airborne photos, field and auxiliary data were acquired.

A larger separation between wet snow and bare ground in EMISAR C-VV polarisation data was found at high incidence angle (55°) compared to lower incidence angle (45°). Cross-polarized observations from bare ground, dry and wet snow in the incidence angle range 35° to 65° are below the specified Envisat ASAR noise floor of –20–22 dB. The backscattering angular dependency for wet snow and bare ground derived from EMISAR C-VV and ERS SAR data corresponds well, and agrees to some extent with volume and surface scattering model results. The C-band is more sensitive to variation in snow properties than the L-band.     

Microwave electromagnetic modelling of Sahelian grassland

In this paper radar scattering models based on coherent and incoherent formulations for an African grassland (Sahelian) are examined. The coherent model is used to account for the structure of the grass plants and the results are compared with the same model assuming random placement and orientation of scatters, and the radiative transfer model. The validity of the three models applied to grass vegetation is determined by comparing the model predictions with Envisat Advanced Synthetic Aperture Radar (ASAR) data gathered in 2005 over Sahelian grassland. The Agoufou site, as defined in the African Monsoon Multidisciplinary Analysis (AMMA) project, is selected as the test target and a set of ground data was collected during 2004 and 2005. Through a comprehensive data comparison, it is shown that the coherent scattering model with a generator considering botanical information is the best model to predict the backscattering data that matches Envisat measurements well (correlation?=?0.92). At low incidence angles (<30°), the radar backscatter shows a strong dependence on soil moisture variations. The analysis of the different contributions leads to a study of the main scattering mechanisms. For high incidence angles, the backscattering coefficient at HH polarization shows a marked seasonal variation associated with grass presence.     

Surface soil moisture estimation over dense crop using Envisat ASAR and Landsat TM imagery: an approach

This study focuses on developing a new method of surface soil moisture estimation over wheat fields using Environmental Satellite Advanced Synthetic Aperture Radar (Envisat ASAR) and Landsat Thematic Mapper (TM) data. The Michigan Microwave Canopy Scattering (MIMICS) model was used to simulate wheat canopy backscattering coefficients from experiment plots at incidence angles of 23° (IS2) and 43.9° (IS7). Based on simulated data, the scattering characteristics of a wheat canopy were first investigated in order to derive an optimal configuration of polarization (HH) and incidence angle (IS2) for soil moisture estimation. Then a parametric model was developed to describe wheat canopy backscattering at the optimal configuration. In addition, direct backscattering and two-way transmissivity of wheat crowns were derived from the TM normalized difference vegetation index (NDVI); direct ground backscattering was derived from surface soil moisture and TM NDVI; and backscattering from double scattering was derived from total backscattering. A semi-empirical model for soil moisture estimation was derived from the parametric model. Coefficients in the semi-empirical model were obtained using a calibration approach based on measured soil moisture, ASAR, and TM data. A validation of the model was performed over the experimental area. In this study, the root mean square error (RMSE) for the estimated soil moisture was 0.041 m³ m^?3, and the correlation coefficient between the measured and estimated soil moisture was 0.84. The experimental results indicate that the semi-empirical model could improve soil moisture estimation compared to an empirical model.     

Investigation of C- and X-band backscattering signatures of Baltic Sea ice

Backscattering signatures of various Baltic Sea ice types and open water leads were measured with the helicopter-borne C- and X-band Helsinki University of Technology scatterometer (HUTSCAT) during six ice research campaigns in 1992–1997. The measurements were conducted at incidence angles of 23° and 45°. The HUTSCAT data were assigned by video imagery into various surface type categories. The ground data provided further classification of the HUTSCAT data into different snow wetness categories (dry, moist and wet snow). Various basic statistical parameters of backscattering signature data were used to study discrimination of open water leads and various ice types. The effect of various physical parameters (e.g. polarization, frequency, snow condition) on the surface type discrimination was investigated. The results from the data analysis can be used to help the development of sea ice classification algorithms for space-borne SAR data (e.g. Radarsat and Envisat). According to the results from the maximum likelihood classification it is not possible to reliably distinguish various surface types in the SAR images only by their backscatter intensity. In general, the best ice type discrimination accuracy is achieved with C-band VH-polarization σ° at an incidence angle of 45°.     

Analysis of TerraSAR-X data and their sensitivity to soil surface parameters over bare agricultural fields

The sensitivity of TerraSAR-X radar signals to surface soil parameters has been examined over agricultural fields, using HH polarization and various incidence angles (26°, 28°, 50°, 52°). The results show that the radar signal is slightly more sensitive to surface roughness at high incidence (50°–52°) than at low incidence (26°–28°). The difference observed in the X-band, between radar signals reflected by the roughest and smoothest areas, reaches a maximum of the order of 5.5 dB at 50°–52°, and 4 dB at 26°–28°. This sensitivity increases in the L-band with PALSAR/ALOS data, for which the dynamics of the return radar signal as a function of soil roughness reach 8 dB at HH38°. In the C-band, ASAR/ENVISAT data (HH and VV polarizations at an incidence angle of 23°) are characterised by a difference of about 4 dB between the signals backscattered by smooth and rough areas.Our results also show that the sensitivity of TerraSAR-X signal to surface roughness decreases in very wet and frozen soil conditions. Moreover, the difference in backscattered signal between smooth and rough fields is greater at high incidence angles. The low-to-high incidence signal ratio (Δσ° = σ_26°–28°/σ_50°–52°) decreases with surface roughness, and has a dynamic range, as a function of surface roughness, smaller than that of the backscattering coefficients at low and high incidences alone. Under very wet soil conditions (for soil moistures between 32% and 41%), the radar signal decreases by about 4 dB. This decrease appears to be independent of incidence angle, and the ratio Δσ° is found to be independent of soil moisture.     

Radar estimates of soil moisture over the Konza Prairie

Abstract

Radar backscatter measurements were made as a part of the First International satellite land surface climatology project Field Experiment (FIFE) to estimate soil moisture for use by other investigators. The helicopter-mounted radar was flown along selected transects that coincided with soil moisture measurements. The radar operated at microwave frequencies of 5-3 and 9 6 GHz and at selected incidence angles between 0° and 60°. Vertical polarization was used for two days in June of 1987 and horizontal polarization was used for three days in July and October of 1987.

The scattering coefficient data from different days were grouped by frequency and antenna angles and then related to soil moisture along the flight paths using linear regression. A measure of linearity for the regression, R², ranged between 0·9 and 0·5. The larger coefficients were for X -band measurements made at large antenna incidence angles, and the smaller coefficients were for C-band measurements made: at incidence angles near vertical.     

Simulated and observed L-HH radar backscatter from tropical mangrove forests

Abstract

We applied the Santa Barbara canopy backscatter model to model radar backscatter from mangrove forest stands in the Ganges delta of southern Bangladesh, and assessed the feasibility of delineating flooding boundaries within the stands. Modelled L-band (0-235 m wavelength) HH backscatter showed that canopy volume scattering dominated for stands under nonflooded ground surface. Double bounce trunk-ground term were enhanced by the presence of water under trees. For flooded mangrove forest, the trunk-ground term was dominant at small radar incidence angles; the trunk-ground term dominancy reduced as the incidence angle increased. Shuttle Imaging Radar (SIR-B) data and model results showed that for the mangrove forest, radar data with small incidence angles should be used to delineate the flooding boundaries.     

The use of a microwave backscatter model for retrieving soil moisture over bare soil

Abstract

Most attempts at predicting soil moisture from C-band microwave backscattering coefficients for bare soil are made by fitting experimental calibration relations obtained for limited ranges of incidence angle and soil surface roughness. In this paper, a more general approach is discussed using an inversion procedure to extend the use of a single experimental calibration relation to a wider range of incidence angle and surface roughness. A correcting function is proposed to normalize the backscattering coefficients to the conditions (incidence angle and surface roughness) of the calibration relation. This correcting function was derived from simulated data using the physical optics or KirchhofTs scatter model using the scalar approximation. Before discussing the inversion procedure, the backscattering coefficients calculated by the model have been compared with experimental data measured in the C-band, HH polarization and three incidence angles (Θ= 15°, 23°, 50°) under a wide range of surface soil moisture conditions (0.02H_v  0.35cm³ cm^-3) and for a single quite smooth soil surface roughness (0–011 _s  OOI4/n)m. The model was found to be experimentally validated from 15° to 23° of incidence and for surface soil moistures higher than 0-I0cm³cm^-3. For the inversion procedure, it is assumed to have a wider range of validity (15°  Θ 35° ) for ihc incidence angle. A sensitivity analysis of the model to errors on roughness parameter and incidence angle was performed in order to assess the feasability and suitability of the described inversion procedure.     

Radar response of periodic vegetation canopies

Vector radiative transfer theory is used to model the scattered intensity from a layer of randomly oriented particles over a periodic rough surface. To account for the periodic nature of row-structured vegetation, the number density of particles within the layer is assumed to be varying periodically in the horizontal direction. Using Fourier series expansions and orthogonality properties, the radiative transfer equation is solved for the transformation matrix relating the incident and scattered intensities, from which the backscattering coefficient of the layer can be computed for any incidence direction and polarization configuration. The experimental component of this investigation consisted of radar observations at 1–5,4–75, and 9–5 GHz made by a truck-mounted system for a field of corn under three conditions: (a) full, which means that the corn plants were in their natural state, (b) defoliated, which was accomplished by stripping off the leaves and removing them, thereby leaving behind only bare vertical stalks, and (c) bare soil, which corresponds to the soil surface after having removed the stalks. The soil surface is modelled as a composite consisting of a deterministic periodic component and a random roughness component. A two-scale polarimetric scattering model is formulated and used to compare with the experimental observations. Excellent agreement between theory and measurements is realized as a function of both incidence and azimuth angles at all three microwave frequencies. The canopy model was then applied to the corn canopy under the two other conditions: stalks alone and full canopy. The model results were compared with radar backscatter measurements made for each of three look directions, including perpendicular and parallel to the row direction and at 45° relative to the row direction. For the stalk canopy, it was observed that the quasi-periodic arrangement of the stalks within the row enhances the backscatter at L-band when looking perpendicular to the row direction, which is attributed to a coherent-scattering effect associated with the stalks. A heuristic approach is used to model the quasi-periodic structure of the stalks by deriving a coherency factor which multiplies the first-order radiative solution for randomly located stalks. A similar coherency factor was also introduced for the leaves of the full canopy. The modified model was found to provide good agreement with experimental observations at L-band for all polarizations and at all look directions.     

Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data

A study was carried out to investigate the utility of L-band SAR data for estimating aboveground biomass in sites with low levels of vegetation regrowth. Data to estimate biomass were collected from 59 sites located in fire-disturbed black spruce forests in interior Alaska. PALSAR L-band data (HH and HV polarizations) collected on two dates in the summer/fall of 2007 and one date in the summer of 2009 were used. Significant linear correlations were found between the log of aboveground biomass (range of 0.02 to 22.2 t ha^-1) and σ° (L-HH) and σ° (L-HV) for the data collected on each of the three dates, with the highest correlation found using the L-HV data collected when soil moisture was highest. Soil moisture, however, did change the correlations between L-band σ° and aboveground biomass, and the analyses suggest that the influence of soil moisture is biomass dependent. The results indicate that to use L-band SAR data for mapping aboveground biomass and monitoring forest regrowth will require development of approaches to account for the influence that variations in soil moisture have on L-band microwave backscatter, which can be particularly strong when low levels of aboveground biomass occur.     

Characterisation of surface roughness and sediment texture of intertidal flats using ERS SAR imagery

High resolution, synoptic information on sediment characteristics of intertidal flats is required for coastal management, e.g., for habitat mapping and dredging studies. This study aims to derive such information from space-borne Synthetic Aperture Radar (SAR). Estimates of the backscattering coefficient were extracted from ERS-1 SAR and ERS-2 SAR PRI imagery of four intertidal flats in the Westerschelde (southwest Netherlands). They were related to field measurements of surface roughness, moisture conditions and sediment texture. The field data were also used as input to the backscattering model IEM. The data and model predictions show that on the intertidal flats, backscattering depends mainly on vertical surface roughness, with rougher surfaces associated with more backscattering. Surface roughness, mainly determined by the ripple structure of the bed, decreased with the amount of mud in the sediment. This resulted in a significant negative correlation between backscattering and mud content, and a significant positive correlation between backscattering and median grain-size of the sediment. Sediment texture was also correlated with the volumetric moisture content of the sediment, with finer sediments being associated with higher moisture contents. However, moisture contents were generally high, and therefore the backscatter signal was not sensitive to differences in moisture content. The relationships allowed the development of regression models for the prediction of surface characteristics from SAR imagery, from which maps of, for example, mud content, have been derived.     

Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields

The objective of this investigation is to analyze the sensitivity of ASAR (Advanced Synthetic Aperture Radar) data to soil surface parameters (surface roughness and soil moisture) over bare fields, at various polarizations (HH, HV, and VV) and incidence angles (20°-43°). The relationships between backscattering coefficients and soil parameters were examined by means of 16 ASAR images and several field campaigns. We have found that HH and HV polarizations are more sensitive than VV polarization to surface roughness. The results also show that the radar signal is more sensitive to surface roughness at high incidence angle (43°). However, the dynamics of the radar signal as a function of soil roughness are weak for root mean square (rms) surface heights between 0.5 cm and 3.56 cm (only 3 dB for HH polarization and 43° incidence angle). The estimation of soil moisture is optimal at low and medium incidence angles (20°-37°). The backscattering coefficient is more sensitive to volumetric soil moisture in HH polarization than in HV polarization. In fact, the results show that the depolarization ratio σ_HH⁰/σ_HV⁰ is weakly dependent on the roughness condition, whatever the radar incidence. On the other hand, we observe a linear relationship between the ratio σ_HH⁰/σ_HV⁰ and the soil moisture. The backscattering coefficient ratio between a low and a high incidence angle decreases with the rms surface height, and minimizes the effect of the soil moisture.     

Monitoring soybean growth using L-, C-, and X-band scatterometer data

A ground-based fully polarimetric scatterometer operating at multiple frequencies was used to continuously monitor soybean growth over the course of a growing season. Polarimetric backscatter data at L-, C-, and X-bands were acquired every 10 min. We analysed the relationships between L-, C-, and X-band signatures, and biophysical measurements over the entire soybean growth period. Temporal changes in backscattering coefficients for all bands followed the patterns observed in the soybean growth measurements (leaf area index (LAI) and vegetation water content (VWC)). The difference between the backscattering coefficients for horizontally transmitted horizontally received (HH) and vertically transmitted vertically received (VV) polarizations at the L-band was apparent after the R2 stage (DOY 224) due to the double-bounce scattering effect. Results indicated that L-, C-, and X-band radar backscatter data can be used to detect different soybean growth stages. The results of correlation analyses between the backscattering coefficient for specific bands/polarizations and soybean growth data showed that L-band HH-polarization had the highest correlation with the vegetation parameters LAI (r = 0.98) and VWC (r = 0.97). Prediction equations for estimation of soybean growth parameters from the L-HH were developed. The results indicated that L-HH could be used for estimating the vegetation biophysical parameters considered here with high accuracy. These results provide a basis for developing a method to retrieve crop biophysical properties and guidance on the optimum microwave frequency and polarization necessary to monitor crop conditions. The results are directly applicable to systems such as the proposed NASA Soil Moisture Active Passive (SMAP) satellite.     

Effect of surface roughness,wavelength, illumination,and viewing zenith angles on soil surface BRDF using an imaging BRDF approach

Compared to non-imaging instruments, imaging spectrometers (ISs) can provide detailed information to investigate the influence of scene components on the bidirectional reflectance distribution function (BRDF) of a mixed target. The research reported in this article investigated soil surface reflectance changes as a function of scene components (i.e. illuminated pixels and shaded pixels), illumination and viewing zenith angles, and wavelength. Image-based BRDF data of both rough and smooth soil surfaces were acquired in a laboratory setting at three different illumination zenith angles and at four different viewing zenith angles over the full 360° azimuth range, at an interval of 20°, using a Specim V10E IS (Specim, Spectral Imaging Ltd., Oulu, Finland) mounted on the University of Lethbridge Goniometer System version 2.5 (ULGS-2.5). The BRDF of the smooth soil surface was dominated by illuminated pixels, whereas the shaded pixels were a larger component of the BRDF of the rough soil surface. As the illumination zenith angle was changed from 60° to 45° and then to 30°, the shadowing effect decreased, regardless of the soil surface. Soil surface reflectance was generally higher at the backscattering view zenith angles and decreased continuously to forward scattering view zenith angles in the light principal plane, regardless of the wavelength, due to the Specim V10E IS seeing more illuminated pixels in the backscattering angles than in the forward scattering angles. Higher soil surface reflectance was observed at higher illumination and viewing zenith angle combinations. For both soil surface roughness categories, the BRDF exhibited a greater range of values in the near-infrared than at the visible wavelengths. This research enhances our understanding of soil BRDF for various soil roughness and illumination conditions.     

Active microwave measurement of soil water content

Measurements of radar backscatter from bare soil at 4.7, 5.9, and 7.1 GHz for incident angles of 0–70° have been analyzed to determine sensitivity to soil moisture. Because the effective depth of penetration of the radar signal is only about one skin depth, the observed signals were correlated with the moisture in a skin depth as characterized by the attenuation coefficient (reciprocal of skin depth). Since the attenuation coefficient is a monotonically increasing function of moisture density, it may also be used as a measure of moisture content over the distance involved, which varies with frequency and moisture content. The measurements show an approximately linear increase in scattering with attenuation coefficient of the soil at angles within 10° of vertical and all frequencies. At 4.7 GHz this increase continues relatively large out to 70° incidence, but by 7.1 GHz the sensitivity is much less even at 20° and practically gone at 50°.An inversion technique to determine how well the moisture content can be estimated from the scattered signal indicates good success for near-vertical angles and middle ranges of moisture density, with poorer success at smaller moisture densities and an anomaly in the data at the highest moisture density that must be resolved by further experimentation.     

Correlation Analysis between L-band SAR and ForestStem Volume in Northeast China

Because Synthetic Aperture Radar(SAR)can penetrate into forest canopy and interact with the primary stem volume contents of the trees (trunk and branch),SAR data are widely used for forest stem volume estimation.This paper investigated the correlation between SAR data and forest stem volume in Xunke,Heilongjiang using the stand-wise forest inventory data in 2003 and ALOS PALSAR data for five dates in 2007.The influences of season and polarizations on the relationship between stem volume and SAR data were studied by analyzing the scatterplots;that was followed by interpretation of the mechanisms primarily based on a forest radar backscattering model-water cloud model.The results showed that the relationship between HV polarization backscatter and stem volume is better than HH polarization,and SAR data in summer dry conditions are more correlated to stem volume than the data acquired in other conditions.The interferometric coherence with 46-day temporal baseline is negatively correlated to the stem volume.The correlation coefficients from winter coherence are higher than those from summer coherence and backscatter.The study results suggest using the interferometric coherence in winter as the best choice for forest stem volume estimation with L-band SAR data.     

A simple method to account for topography in the radiometric correction of radar imagery

This article presents a method to study and correct radiometric distortions caused by topography in SAR images. The method is easy to implement and requires neither sophisticated software nor code-level programming. It also considers the case of a flat surface having an elevation different from the one for which calibration parameters were derived. An ortho-image of the slant range distance is used with a Digital Elevation Model to generate images of the local incident angle along the range and azimuth directions. The method compensates for variations in the terrain area of each pixel and for the angular dependence of backscatter, allowing the choice of either an empirical or semi-empirical scattering model. The method is applied to high-resolution C-SAR subsets of an agricultural area in the Central Cordillera of Costa Rica. The removal of topographic features appears excellent for local incident angles up to 80°, but small-scale structures have pronounced effects on the radar return for higher local incident angles and are not adequately corrected.     

Appropriate scale of soil moisture retrieval from high resolution radar imagery for bare and minimally vegetated soils

This research investigates the appropriate scale for watershed averaged and site specific soil moisture retrieval from high resolution radar imagery. The first approach involved filtering backscatter for input to a retrieval model that was compared against field measures of soil moisture. The second approach involved spatially averaging raw and filtered imagery in an image-based statistical technique to determine the best scale for site-specific soil moisture retrieval. Field soil moisture was measured at 1225 m² sites in three watersheds commensurate with 7 m resolution Radarsat image acquisition. Analysis of speckle reducing block median filters indicated that 5 × 5 filter level was the optimum for watershed averaged estimates of soil moisture. However, median filtering alone did not provide acceptable accuracy for soil moisture retrieval on a site-specific basis. Therefore, spatial averaging of unfiltered and median filtered power values was used to generate backscatter estimates with known confidence for soil moisture retrieval. This combined approach of filtering and averaging was demonstrated at watersheds located in Arizona (AZ), Oklahoma (OK) and Georgia (GA). The optimum ground resolution for AZ, OK and GA study areas was 162 m, 310 m, and 1131 m respectively obtained with unfiltered imagery. This statistical approach does not rely on ground verification of soil moisture for validation and only requires a satellite image and average roughness parameters of the site. When applied at other locations, the resulting optimum ground resolution will depend on the spatial distribution of land surface features that affect radar backscatter. This work offers insight into the accuracy of soil moisture retrieval, and an operational approach to determine the optimal spatial resolution for the required application accuracy.