3D modelling of forest canopy structure for remote sensing simulations in the optical and microwave domains

A detailed 3D structural model of a conifer forest canopy was developed in order to simulate the reflectance (optical) and backscatter (microwave) signals measured remotely. We show it is feasible to model forest canopy scattering using detailed 3D models of tree structure including the location and orientation of individual needles. An existing structural growth model of Scots pine (Pinus sylvestris L.), Treegrow, was modified to simulate observed growth stages of a Scots pine canopy from age 5 to 50 years. The 3D tree models showed close structural agreement with in situ measurements. Needles were added to the structural models according to observed phyllotaxy (distribution). Individual trees were used to generate model canopies, which in turn were used to drive optical and microwave models of canopy scattering. Simulated canopy radiometric response was compared with airborne hyperspectral reflectance data (HyMAP) and airborne synthetic aperture RADAR (ASAR) backscatter data. Model simulations agreed well in general with observations, particularly at optical wavelengths where model simulations of low and high density canopy stands were shown to bracket observations. Relatively small sensitivity of observed reflectance to canopy age was captured reasonably well by the simulations. The choice of needle shape and phyllotaxy was shown to have a significant impact on multiple scattering behaviour at the branch scale. In the microwave domain, simulated backscatter values agreed reasonably well with observations at L-band, less so at X-band. L-band simulated backscatter significantly underestimated observed backscatter at younger canopy ages, probably as a result of inappropriate modelling of soil/understory. It is demonstrated that a combined structural and radiometric modelling approach provides a flexible and powerful method for simulating the remotely sensed signal of a forest canopy in the optical and microwave domains. This is particularly useful for exploring the impact of canopy structure on the resulting signal and also for combined retrievals of forest structural parameters from optical and microwave data.     

Assessing the influence of vegetation cover on soil-moisture signatures in fire-disturbed boreal forests in interior Alaska: Modelled results

A microwave backscattering model for shrub clumps was presented. The modelling approach was to treat the clumps as scatterers and attenuators. Three major model components were defined: surface backscattering, clump volume scattering, and multiple path interactions between clumps and ground. Total backscatter was computed by incoherent summation of the components. We then used the model to study the effects of variations in surface and willow properties (soil moisture content, and surface roughness rms height and correlation length, and willow ground coverage, clump height, and stem density) on backscatter from willows in Alaskan boreal forest region. We examined the sensitivity to variations of the six parameters combined and to variation of each parameter alone from willows of three clump sizes representing different stages of vegetation regrowth after fire. Modelled C-band backscatter was more sensitive to the variations of the surface and willow parameters than L-band backscatter at incidence angles between 20° and 60°. At incidence angles of 20-60°, C-HH and C-VV backscatter was sensitive to the variations of the three surface parameters. L-HV and L-VV backscatter were only sensitive to the moisture variation. Among the three willow parameters, change of willow ground coverage produced more sensitive cases than variations of clump height and stem density combined at C- and L-band.     

Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia

In Queensland, Australia, forest areas are discriminated from non-forest by applying a threshold (∼ 12%) to Landsat-derived Foliage Projected Cover (FPC) layers (equating to ∼ 20% canopy cover), which are produced routinely for the State. However, separation of woody regrowth following agricultural clearing cannot be undertaken with confidence, and is therefore not mapped routinely by State Agencies. Using fully polarimetric C-, L- and P-band NASA AIRSAR and Landsat FPC data for forests and agricultural land near Injune, central Queensland, we corroborate that woody regrowth dominated by Brigalow (Acacia harpophylla) cannot be discriminated using either FPC or indeed C-band data alone, because the rapid attainment of a canopy cover leads to similarities in both reflectance and backscatter with remnant forest. We also show that regrowth cannot be discriminated from non-forest areas using either L-band or P-band data alone. However, mapping can be achieved by thresholding and intersecting these layers, as regrowth is unique in supporting both a high FPC (> ∼ 12%) and C-band SAR backscatter (> ~ − 18 dB at HV polarisation) and low L-band and P-band SAR backscatter (e.g. < =∼ 14 dB at L-band HH polarisation). To provide a theoretical explanation, a wave scattering model based on that of Durden et al. [Durden, S.L., Van Zyl, J.J. & Zebker, H.A. (1989). Modelling and observation of radar polarization signature of forested areas. IEEE Trans. Geoscience and Remote Sensing, 27, 290-301.] was used to demonstrate that volume scattering from leaves and small branches in the upper canopy leads to increases in C-band backscattering (particularly HV polarisations) from regrowth, which increases proportionally with FPC. By contrast, low L-band and P-band backscatter occurs because of the lack of double bounce interactions at co-polarisations (particularly HH) and volume scattering at HV polarisation from the stems and branches, respectively, when their dimensions are smaller than the wavelength. Regrowth maps generated by applying simple thresholds to both FPC and AIRSAR L-band data showed a very close correspondence with those mapped using same-date 2.5 m Hymap data and an average 73.7% overlap with those mapped through time-series comparison of Landsat-derived land cover classifications. Regrowth mapped using Landsat-derived FPC from 1995 and JER-1 SAR data from 1994-1995 also corresponded with areas identified within the time-series classification and true colour stereo photographs for the same period. The integration of Landsat FPC and L-band SAR data is therefore expected to facilitate regrowth mapping across Queensland and other regions of Australia, particularly as Japan's Advanced Land Observing System (ALOS) Phase Arrayed L-band SAR (PALSAR), to be launched in 2006, will observe at both L-band HH and HV polarisations.     

Estimation of tree growth in a conifer plantation over 19 years from multi-satellite L-band SAR

The general capability of synthetic aperture radar (SAR) for monitoring forest ecosystems is well documented. However, the majority of SAR studies of forest dynamics use only imagery acquired by one SAR system and are thus limited to the lifecycle of a particular satellite. The synergistic analysis of SAR data from one of the earliest spaceborne SAR missions, the SEASAT mission, with the Japanese JERS-1 satellite-borne SAR is presented. Biophysical parameters frequently retrieved from SAR are tree biomass using backscatter and tree height from the interferometric phase. One potential application that has not been thoroughly examined is mapping of incremental tree growth from SAR backscatter changes. Tree growth measures biomass changes over time, and is correlated to the amount of carbon sequestered by the trees. This paper examines the retrieval of tree growth from multitemporal spaceborne L-band SAR. A SEASAT SAR image from 1978 and a JERS-1 SAR image from 1997 over Thetford forest, UK are used to retrieve tree growth of Corsican Pine stands. Incremental growth was estimated from the changes in backscatter coefficient, and compared to the expected tree growth from general yield class models used by the UK Forestry Commission. The accuracy of the retrieval algorithm depends on the minimum forest stand size included in the analysis. For managed forest plantations, multitemporal L-band SAR has some potential for detecting incremental biomass to support sustainable forest management.     

An explanation of enhanced radar backscattering from flooded forests

Abstract

A simpie structural backscatter model for a forest stand, suitable for use with L-band HH polarized radar imagery, is used to explain the increased level of backscattering observed from flooded forests. Measurements made of relative levels of backscatter from SIR-B image data of a flooded Australian forest are consistent with an interpretation based upon scattering mechanisms involving both the tree components and the understorey or forest floor. The change in Fresnel power reflection coefficient of the ground with flooding is advanced as the cause of the enhancement in backscattered power levels.     

L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest

At present, the greatest source of uncertainty in the global carbon cycle is in the terrestrial ecosystems. In order to reduce these uncertainties it is necessary to provide consistent and accurate global estimates of the world forest biomass. One of the most promising methods for obtaining such estimates is through polarimetric SAR backscatter measurements at low frequencies. In this paper, the relation between polarimetric SAR backscatter at L- and P-bands and forest biomass is investigated using data acquired within the BioSAR-I campaign in southern Sweden during 2007. Methods for estimating biomass on stand level using these data are developed and evaluated, and the results for the two frequency bands are compared. For L-band data, the best results were obtained using HV-polarized backscatter only, giving estimation errors in terms of root mean square errors (RMSE) between 31% and 46% of the mean biomass for stands with biomass ranging from 10 to 290 t/ha, and an (adjusted) coefficient of determination (R²) between 0.4 and 0.6. For P-band data, the results are better than for L-band. Models using HV- or HH-polarized P-band backscatter give similar results, as does a model including both HV and HH. The RMSEs were between 18 and 27%, and the R² values were between 0.7 and 0.8.     

Analysis of L-band SAR backscatter and coherence for delineation of land-use/land-cover

In this study, we investigated the potential improvement of land-use/land-cover (LU/LC) classification using multidate backscatter intensity as well as interferometric coherence images derived from Advanced Land Observing Satellite phased array L-band synthetic aperture radar data. Four interferometric synthetic aperture radar data pairs in horizontal–horizontal polarizations were processed to obtain backscatter intensity and coherence images. From the analysis of these images, it was observed that backscatter values alone are not sufficient to separate certain LU/LC classes, e.g. forest and mining areas, due to similarities in the associated scattering mechanisms producing similar backscatter values. However, the temporal coherence values from these LU/LC features were found to be distinctly different. Supervised classifications using maximum-likelihood distance were performed with various combinations of data (three-date backscatter intensity and two-date backscatter intensity with corresponding coherence data) to generate LU/LC maps of the study area. The comparison of classification accuracies obtained for different combinations of data indicates that the classification accuracy is improved by adding coherence information to the backscatter intensity data compared to using the multidate backscatter intensity data alone. Thus, the analysis of backscatter intensity along with coherence is a better alternative than using backscatter intensity alone to improve the accuracy in LU/LC classification.     

Inversion of a forest backscatter model using neural networks

A neural network approach was used to develop acccurate algorithms for inverting a complex forest backscatter model. The model combines a forest growth model with a radar backscatter model. The forest growth model captures natural variations of forest stands (e.g., growth, regeneration, death, multiple species and competition for light). This model was used to produce vegetation structure data typical of transitional/northern boreal hardwood forests in Maine. These data supplied inputs to the radar backscatter model which simulated the polarimetric radar backscatter (C, L, P, X bands) above the forests. Using these simulated data, various neural networks were trained with inputs of different backscatter bands and output parameters of above ground biomass, total number of trees, mean tree height and mean tree age. These trained neural networks act as efficient algorithms for inverting the complex forest backscatter model. The accuracies (r.m.s. and R2 values) for inferring various parameters from radar backscatter were above ground biomass (1.6kg m -2, 0.94), number of trees (48 ha -1, 0.94), tree height (0.47 m, 0.88) and tree age (24.0 years, 0.83). The networks that used only AIRSAR bands (C, L, P) had a high degree of accuracy. The inclusion of the X band with the AIRSAR bands did not seem to increase significantly the accuracy of the networks. The networks that used only the C and L bands still had a relatively high degree of accuracy for all forest parameter (R2 values from 0.75 to 0.91). Modest accuracies (R2 values from 0.65 to 0.84) were obtained with networks that used only the L band and poor accuracies (R2 values from 0.36 to 0.46) were obtained with networks that used only the C band. Several networks were shown to be relatively insensitive to the addition of random noise to radar backscatter. The results demonstrate that complex, forest backscatter models can be efficiently inverted using neural networks that use only radar backscatter data.     

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.     

A radar ocean imaging model for small to moderate incidence angles

Based on a first-principles scattering theory applicable to small to moderate incidence angles, a new imaging model for ocean features is proposed. In contrast to the Alpers and Hennings Bragg-based model, the new model incorporates the full ocean wave spectrum, utilizes Hughes' suggested spectral decay rate formula and contains no adjustable parameters. For typical ocean currents, the new model predicts realistic values for L-band cross section modulations and comparable L-band and X-band cross section modulations. These latter results are shown to be due to backscatter from ocean waves that are significantly longer than the Bragg resonant wave. By comparison the Bragg-based imaging model is shown to predict that X-band modulations will be at least one order of magnitude weaker than L-band modulations.     

Relationships between polarimetric SAR backscatter and forest canopy and sub-canopy biophysical properties

This paper highlights the potential of multiwaveband polarimetric SAR data for the estimation of both canopy (percentage canopy closure) and sub-canopy (stem biomass) biophysical variables of a Sitka spruce forest in upland Wales. Stand stem biomass was estimated using forest survey data on diameter at breast height (DBH) and tree height from 0.01 ha plots. Photographs of the forest canopy were taken using a camera fitted with a wide-angle fisheye lens from a number of locations within a stand. The photographs were later digitized and estimates of stand percentage canopy closure were derived using image processing software. It was found that C-band HV and VV, and L-band HV and VV polarization backscatter were significantly related to stem biomass. There was no sensitivity to percentage canopy closure using single polarization backscatter but highly significant relationships were obtained using ratios of single polarization backscatter and variables derived from the polarization signatures. The strong correlations between C-band backscatter and stem biomass indicated a relationship between the structure of the top crown layer and sub-canopy biomass.     

Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests

Disturbed forests may need decades to reach a mature stage and optically-based vegetation indices are usually poorly suited for monitoring purposes due to the rapid saturation of the signal with increasing canopy cover. Spaceborne synthetic aperture radar (SAR) data provide an alternate monitoring approach since the backscattered microwave energy is sensitive to the vegetation structure. Images from two regions in Spain and Alaska were used to analyze SAR metrics (cross-polarized backscatter and co-polarized interferometric coherence) from regrowing forests previously affected by fire. TerraSAR-X X-band backscatter showed the lowest sensitivity to forest regrowth, with the average backscatter increasing by 1-2 dB between the most recent fire scar and the unburned forest. Increased sensitivity (around 3-4 dB) was observed for C-band Envisat Advanced Synthetic Aperture (ASAR) backscatter. The Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) L-band backscatter presented the highest dynamic range from unburned to recently burned forests (approximately 8 dB). The interferometric coherence showed low sensitivity to forest regrowth at all SAR frequencies. For Mediterranean forests, five phases of forest regrowth were discerned whereas for boreal forest, up to four different regrowth phases could be discerned with L-band SAR data. In comparison, the Normalized Difference Vegetation Index (NDVI) provided reliable differentiation only for the most recent development stages. The results obtained were consistent in both environments.     

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.     

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.     

Effective tree scattering and opacity at L-band

This paper investigates vegetation effects at L-band by using a first-order radiative transfer (RT) model and truck-based microwave measurements over natural conifer stands to assess the applicability of the τ ? ω (tau–omega) model over trees. The tau–omega model is a zero-order RT solution that accounts for vegetation effects with two vegetation parameters (vegetation opacity and single-scattering albedo), which represent the canopy as a whole. This approach inherently ignores multiple-scattering effects and, therefore, has a limited validity depending on the level of scattering within the canopy. The fact that the scattering from large forest components such as branches and trunks is significant at L-band requires that zero-order vegetation parameters be evaluated (compared) along with their theoretical definitions to provide a better understanding of these parameters in the retrieval algorithms as applied to trees.This paper compares the effective vegetation opacities, computed from multi-angular pine tree brightness temperature data, against the results of two independent approaches that provide theoretical and measured optical depths. These two techniques are based on forward scattering theory and radar corner reflector measurements, respectively. The results indicate that the effective vegetation opacity values are smaller than but of similar magnitude to both radar and theoretical estimates. The effective opacity of the zero-order model is thus set equal to the theoretical opacity and an explicit expression for the effective albedo is then obtained from the zero- and first-order RT model comparison. The resultant albedo is found to have a similar magnitude as the effective albedo value obtained from brightness temperature measurements. However, both are less than half of the single-scattering albedo estimated using the theoretical calculations (0.5?0.6 for tree canopies at L-band). This lower observed effective albedo balances the scattering darkening effect of the large theoretical single-scattering albedo with a first-order multiple-scattering contribution. The retrieved effective albedo is different from theoretical definitions and not the albedo of single forest elements anymore, but it becomes a global parameter, which depends on all the processes taking place within the canopy, including multiple-scattering and canopy ground interaction.     

On the relationship between the circumference of rubber trees and L-band waves

Despite substantial research conducted within the forestry domain, detailed assessments to monitor plantations and support their sustainable management have been understudied. This article attempts to fill this gap through coupling fully polarimetric L-band data and contemporary data mining methods for the estimation of tree circumference as: (1) a primary dataset for biomass accumulation studies; and, (2) critical information for operational management in rubber plantations. We used two rubber plantation sites in Subang (West Java) and Jember (East Java), Indonesia, to evaluate the capability of L-band radar data. Although polarimetric features derived from polarimetric decomposition theorems have been advocated by others, we show that backscatter coefficients, especially HV polarization, remain an important dataset for this research domain. Using Subang data to build the model, we found that modern machine learning methods do not always deliver the best performance. It appears that the data being ingested plays a significant role in obtaining a good model, hence careful selection of datasets from multiple forms of polarimetric SAR data needs to be further considered. The highest coefficient of determination (R² = 0.79) was achieved by Yamaguchi decomposition features with the aid of partial least squares regression. Nonetheless, we note that the R² gap was insignificant to the backscatter coefficient when random forests regression was used (R² = 0.78). Overall, only the backscatter coefficient dataset delivered fairly consistent results with any regression model, with the average R² being about 0.67. When tuning parameters were not assessed, random forests consistently outweighed support vector regressions in all forms of datasets. The latter generated a substantial increase in R² when a linear kernel was used instead of the popular radial basis function. The issue of transferability of the model is also addressed in this article. It appears that similarity of terrain characteristics substantially influences the model’s performance. Models developed in Subang, which has gentle slopes, seem valid only in plantations with similar terrain. Validation attempts in very flat terrain within two plantation sectors in Jember delivered a poor result, although they have similar elevations to the Subang site. In contrast, validation in a plantation sector with similar, gently sloping terrain achieved an R² of about 0.6 using some datasets.     

Radar observations of basaltic lava flows,Craters of the Moon,Idaho

Abstract

Radar images were assessed to determine the backscatter characteristics of basaltic lava flows of predominantly pahoehoe textures and the ability to detect fissure vents. The images were obtained from synthetic aperture side-looking airborne radar systems—X-band HH, X-band HV, L-band HH and L-band H V. Smooth, collapsed blisters of shelly pahoehoe have weak returns in all four radar images. These returns are identical to those from pahoehoe surfaces covered with smooth mantles of windblown sediments. Hummocky pahoehoe flows have strong backscatter in all four images, most likely due to the large range in surface roughness causing multiple scattering at both radar wavelengths. Aa lava flows show the greatest variation in backscatter intensities—strong XHH, weak XHV, strong LHH and very strong LHV returns. This variation is due to an increase in multiple scattering at the L-band scale. Although smooth and rough surface textures can be differentiated in the radar images, there are constraints in tracing textural changes back to a particular fissure vent, in part because near-vent flows do not have unique radar signatures. Eruptive fissures are detectable in the radar images by virtue of associated parallel spatter ramparts which have diagnostic, strong backscatter in the X-band images that are in contrast to the weak backscatter of the surrounding shelly pahoehoe lava. However, spatter ramparts are not delineated in the L-band images. The centimetre-scale relief of the agglutinate spatter may cause scattering of the X-band energy more than the L-band energy. Although the structures are several metres high, the look directions for both imaging systems are approximately parallel to the trend of the ramparts. The rampart walls do not serve as reflectors. Such findings emphasize the importance of look direction in the use of radar images to characterize terrains.     

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.     

NEWS SECTION

Abstract

The action balance equation is solved numerically using the method of characteristics. The algorithm can handle any functional form of the current velocity and the source function. It therefore allows a comparison of the hydrodynamic parts of the models of Alpers and Hennings (1984), Shuchman el al. (1985) and Holliday et al. (1986,1987), all describing the imaging mechanism of mapping bottom topography with side-looking airborne radar (SLAR) and synthetic aperture radar (SAR). The effects of advection and second-order terms, neglected in the original work of Alpers and Hennings (1984), are included and studied. It is shown that advection is important, notably at L-band for features smaller than 1 km, such as sand waves, while the second-order terms shift the modulation of the wave spectrum upward. All models studied give similar results for L-band and X-band, showing that the dependence of the relaxation rate on wave number and wind speed variations due to variations in current velocity has only a small influence. The predicted modulations at L-band are of the order of 10 per cent, in agreement with the Seasat data. At X-band, however, the predicted modulations are an order of magnitude smaller than at L-band. This disagrees with the experimental data which seem to indicate that modulations at X-band are of the same order of magnitude as those at L-band. Advection is important for the positioning of modulations in the radar backscatter relative to the bottom topography. However, the positional accuracy of existing experimental data is not good enough to allow comparison with theoretical predictions.     

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.