Including vegetation scattering effects in a radar based soil moisture estimation model

Transcription

1 354 Remote Sensing and Hydrology 2000 (Proceedings of a symposium held at Santa Fe, New Mexico, USA, April 2000). IAHS Publ. no. 267, Including vegetation scattering effects in a radar based soil moisture estimation model RAJ AT BINDLISH* SSAI, USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, BARC-W, Maryland 20705, USA bindlish@hvdrolab.arsusda.gov ANA P. BARROS* Division of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA Abstract Previously, the IEM (Integral Equation Model) was successfully used in conjunction with an inversion model to retrieve soil moisture using multi-frequency and multi-polarization data from Spaceborne Imaging Radar C-band (SIR-C) and X-band Synthetic Aperture Radar (X-SAR), without the need to prescribe time-varying land surface attributes as constraining parameters. The retrieved values were compared against in situ observations from the Washita'94 field experiment. The RMS error in the estimated soil moisture was of the order of 0.05 cm 3 cm" 3, which is comparable to the effect of noise in the SAR data. The JEM was originally developed for scattering from a bare soil surface, and therefore the vegetation scattering effects are not explicitly incorporated in the model. In this study, we couple a semi-empirical vegetation scattering model, modified after the water-cloud model, to the existing radar based soil moisture inversion model. This approach allows for the explicit representation of vegetation backscattering effects without the need to specify a large number of parameters. Although the use of this parameterization resulted in modest improvements (roughly 4% overall), it does provide a general framework that can be used for other applications. Key words inverse modelling; microwave remote sensing; SAR; soil moisture; vegetation INTRODUCTION The presence of vegetation canopy complicates the retrieval of soil moisture because the canopy contains moisture of its own. The backscatter intensity in a SAR image at L-band is proportional to the biomass of the forest stands (Cimino et ai, 1986; Wu, 1987). Some of the factors that are known to govern backscattering behaviour are: (a) dielectric constant of the vegetation material, which is strongly influenced by moisture content; (b) size distribution of the scatterers in a canopy; (c) the shape distribution of the scatterers; (d) orientation distribution of the scatterers; (e) geometry of the canopy cover (aligrrment and spacing, cover fraction, etc.); and (f) roughness and dielectric constant of the underlying soil surface. The radar backscatter signal at high frequencies is particularly sensitive to vegetation (Ulaby et al., 1986; Prevot et al., 1993a), whereas at low frequencies, it is particularly sensitive to soil moisture (Ulaby et al., 1986; Fung, 1994). Thus, a combination of high and low frequency SAR data has been used to improve the * Formerly at: Department of Civil and Environmental Engineering, The Pennsylvania State University, Pennsylvania 16802, USA

2 Including vegetation scattering effects in a radar based soil moisture estimation model 355 estimation of soil moisture (Prevot et al, 1993b; Taconet et al, 1994). These studies demonstrated that the vegetation scattering for low frequency SAR data, was important only when vegetation density was high. In sparsely vegetated areas, the contribution from vegetation in the scattering process was assumed to be significantly smaller than that from soil, so that vegetation induced scattering can be neglected. However, in arid and semiarid regions, soil moisture content rarely exceeds 20%, indicating that the soil contribution may be small or approximately of the same magnitude as the vegetation contribution. MODELLING BACKSCATTER FROM VEGETATION CANOPIES To use radar data for such purposes, a common approach is first to develop direct models simulating the backscattering coefficient of a given canopy. These models may then be inverted to estimate canopy characteristics. Several direct modelling approaches have been presented in the literature, which can be grouped together in three general classes that will be referred to as empirical, theoretical and semi-empirical. Empirical models Most studies have concentrated on a single tree structural type (i.e. monospecies). Observed data show that, for a given species (structural type), the radar backscattering coefficient (G at frequency /, polarization p and incidence angle 9) increases with biomass in a power-law relationship. Backscatter becomes insensitive to increase in biomass at a threshold level (saturation level) that scales with wavelength for a given species. The hv- and hh-polarized backscatter are found to be most sensitive to vegetation and hence yield the highest correlations. The vv-polarized backscatter tends to saturate at lower levels of Normalized Difference Vegetation Index (NDVI). Imhoff (1995) and Dobson et al. (1995) concluded that these saturation points define the upper limits for accurate estimation of forest biomass. This indeed is the case when only single frequency and polarization data are available. Theoretical models Most models for radar scattering from vegetation (Attema & Ulaby, 1978; Fung & Ulaby, 1978; Tsang & Kong, 1981; Lang & Sidhu, 1983; Eom & Fung, 1984; Karam & Fung, 1988) treat the canopy as a uniform layer of some specified height containing a random distribution of scatterers. Models based on the field approach (Fung & Ulaby, 1978; Tsang & Kong, 1981) incorporate the effects of heterogeneity of the vegetation scatterers (different shape, size and orientation) through the correlation function representing the fluctuating component of the dielectric constant of the medium. The models based on the radiative transfer approach (Eom & Fung, 1984; Ulaby et al, 1986; Tsang et al, 1985; Ulaby et al, 1990) include these effects by integrating the radar response function over the statistical distributions of the scatterers. In general, the field approach is appropriate for weakly scattering media in

3 356 Rajat Bindlish & Ana P. Barros which the ratio of the fluctuating component of the dielectric constant to the mean value of the medium is small (Lee & Kong, 1985; Ulaby et al, 1986). In practice, two types of problems are encountered when modelling the backscatter behaviour of a vegetation canopy. The first relates to the difficulties encountered in specifying model parameters that adequately describe the canopy. The second type of problem relates to mathematical complexity. In terms of the geometry of a given scatterer, the scattering phase function usually is more complicated when the scatterer's dimensions are comparable to the wavelength X, which is true of most vegetation canopies at microwave frequencies. Thus, if one were to take all these factors into account, the scattering model would become both very complicated and difficult to use. Semi-empirical models In order to circumvent these problems, a simpler approach based on the so-called water-cloud models was developed by Attema & Ulaby in 1978 and modified, or extended subsequently by various authors (Hoekman et al., 1982; Ulaby et al., 1984; Paris, 1986). In these models, the power backscattered by the whole canopy is represented as an incoherent sum of the contributions of vegetation and soil. These models are simple and use few parameters and variables. The canopy is represented by "bulk" variables such as Leaf Area Index (LAI) or total water content, and it has been shown that they can easily be inverted (Bouman, 1991). They are therefore good candidates for use in inversion algorithms. Yet, because of their simplicity, they lack generality, and require calibration on scarcely available experimental datasets. In the semi-empirical models (including water-cloud models), the vegetation layer is modelled by assuming that its dielectric constant, or permittivity is a random process, the moments of which (i.e. mean and correlation functions) are known. The microwave dielectric constant of dry vegetative matter is much smaller than the dielectric constant of water. Because the "green" water-rich portion of the canopy (the leaves) constitutes one percent or less of the overall volume. Attema & Ulaby (1978) proposed that the canopy can be modelled as a water cloud whose droplets are held in place by structurally dry matter. In the water cloud model, the power backscattered by the whole canopy rj is represented as the incoherent sum of the contribution of the vegetation, o veg, and the contribution of the underlying soil, a so u. The latter is attenuated by the vegetation layer: where: o = o veg+ T 2 o soil (1) a veg =Am v cosq(l-x 2 ) (2) x 2 = exp(-2bm v sec 9) (3) and m v is the vegetation water content (kg nf 2 ). A and B are parameters depending on the canopy type. The geometrical structure of the canopy is implicitly accounted for through A and B, which are always determined by fitting the models against experimental datasets.

4 Including vegetation scattering effects in a radar based soil moisture estimation model 357 DATA SYNOPSIS This study was conducted using data collected during Washita'94, an experiment in the Little Washita watershed (southwest Oklahoma, USA). The watershed has a subhumid climate with an average rainfall of 750 mm. During the experiment, the land was covered by rangeland, pasture, winter wheat, corn and alfalfa. A complete description of the watershed is available in Allen & Naney (1991) or Starks & Humes (1996). RELATIONSHIP BETWEEN OBSERVED BACKSCATTER AND NDVI The linear dependence of a 0 on biomass of forests is found to decrease with frequency as scattering and attenuation by the crown layer of foliage and small branches becomes more significant (Table 1). The polarizations most sensitive to specular scattering mechanisms by the trunk and ground surface (hh and hv) show the highest sensitivity to biomass, whereas the linear dependence of CT on biomass tends to saturate at biomass levels that scale with wavelength (Dobson et al, 1992). Cross-polarization signatures have shown more sensitivity to the crown structure than the likepolarization signature. Table 1 Regression constant for linear and exponential fit to NDVI (winter wheat) in Washita '94 study area. Frequency polarization ratio Linear fit Exponential fit _ C hv L hh L hv L vv _ Based on observations from SAR data analysis and radar backscatter modelling, the ratio of hv backscattering from a longer wavelength (P or L) to that from a shorter wavelength (Q appears to be a good combination for mapping the forest biomass. As discussed by Sun & Ranson (1995) this ratio enhances the correlation of the image signature to the standing biomass, and compensates for part of the variations in backscattering attributed to radar incidence angle. The correlation coefficient increases to 0.5 (maximum value of 0.6) from around 0.3 (maximum value of 0.4), when an exponential function is used instead of a linear relationship. Table 1 shows the values of correlation coefficient for both different frequencies and polarizations (both for linear and exponential fit).

5 358 Rajat Bindlish & Ana P. Barros VEGETATION BACKSCATTERING MODEL We interpreted the backscattering coefficients of the canopies in the framework of semi-empirical water-cloud models. The backscattering in the presence of vegetation is approximated as a combination of the individual backscatter from the vegetation canopy and the underlying soil layer as given by equation (1). As mentioned previously, the orientation and geometry of the vegetation are key governing factors for vegetation backscatter. It is possible that two tree canopies overlap and see the same radar beam. The water-cloud model however, does not account for vegetation overlap. The effect of "radar shadow" or "layover" is modelled using an exponential vegetation correlation function. Layover occurs when two tree canopies of different heights are located at the same range distance and the vegetation backscatter from one is affected by the other and vice versa. Thus, the current model will account for scattering of the same beam by two different trees, which would lead to over-estimation of vegetation backscatter. The geometric effect of the tree spacing can be accounted by introducing a vegetation correlation length, which is a function of the distance between the plant canopies at which they can be considered as independent scatterers. The proposed vegetation correlation length is a function of plant type or land use as follows: <*=a ^[l-exp(-a)] (4) where a * veg is the corrected vegetation contribution, and a is the radar shadow coefficient. The concept of radar shadow coefficient can be interpreted as a function of the vegetation correlation length (L veg ), which is a function of land use, and the average distance between the discrete vegetation canopies within a pixel (x veg ): a = ^ ; oc>0 (5) Keg ~ X veg Thus, x veg is a spatially distributed variable within the area of study and describes the shape, size and distribution of the vegetation at the site. The concept of vegetation correlation length is similar to the correlation length for soils. After the threshold value of x veg = L veg is reached, there is no radar shadow and the coefficient becomes zero. A maximum damping of one-fold is assumed for densely vegetated areas. The vegetation attenuation generally increases with increasing frequency, whereas the volume scattering coefficient is proportional to the fourth power of frequency (f) (Ulaby et al, 1984). Ulaby et al. (1984) also showed that the combined effect in the water-cloud model varies approximately as /, as well as the geometric scattering effect. Thus, the effect of frequency in the multi-frequency inversion algorithm was incorporated as follows: <eg*f 2 (6) The IEM model was run in the forward mode with the site characteristics to obtain the contribution from the underlying soil layer, by using the site observed characteristics. Using this soil contribution, the vegetation contribution at each of these sites was determined. The vegetation parameters were obtained by using a simple multiparameter regression. Table 2 shows the values of the vegetation constants obtained

6 Including vegetation scattering effects in a radar based soil moisture estimation model 359 based on this semi-empirical vegetation parameterization formulation. All the landuses were grouped together, and a single value for vegetation parameters was obtained for the entire study area. Table 2 Values of vegetation constants used in the semi-empirical vegetation model. _ B a 2.12 The use of an explicit water-cloud model formulation for vegetation correction results in the increase in the correlation coefficient with respect to the observed backscatter values to These vegetation parameters values were used in the semiempirical model to obtain the vegetation contribution at each point. The soil moisture can then be obtained by using the multi-frequency soil moisture inversion methodology (Bindlish & Barros, 2000). Figure 1 shows values of estimated soil moisture before and after using the explicit vegetation parameterization. In Bindlish & Barros (2000) an ad hoc perturbation approach was used to study the sensitivity of soil moisture estimates to vegetation effects. The results obtained in this study are similar to those obtained with the ad hoc approach, but the use of the semi-empirical water-cloud model results in the formulation of a simple parameterization that can be applied to any other data. In particular, we believe that using this parameterization should provide significantly more accurate soil moisture

7 360 Raj at Bindlish & Ana P. Barros estimates for areas with dense vegetation cover, which is not the case for Little Washita. The next research step is to incorporate explicitly a measure of vegetation structure in the parameterization. CONCLUSIONS This work presented the use of a semi-empirical vegetation model in conjunction with the multi-frequency soil moisture inversion algorithm developed earlier. The watercloud model facilitates the incorporation of an explicit vegetation correction introducing only three vegetation parameters. Although the use of this parameterization resulted in modest improvements (roughly 4% overall), a much better performance should be expected in regions of dense vegetation cover. Acknowledgements This research was funded by NASA under contract NAGW-5254 with the second author, and contract NAGW-2686 with the Earth System Science Center at The Pennsylvania State University. REFERENCES Allen, P. B. & Naney, J. W. (1991) Hydrology of the Little Washita River watershed, Oklahoma: data and analysis. USDA/ARS-90. National Agricultural Water Quality Laboratory, Durant, Oklahoma, USA. Attema, E. P. W. & Ulaby, F. T. (1978) Vegetation modeled as a water cloud. Radio Science 13, Bindlish, R. & Barros, A. P. (2000) Multifrequency soil moisture inversion from SAR measurements with the use of IEM. Remote Sens. Environ. 71(1), Bouman, B. (1991) Crop parameter estimation from ground based X-band (3 cm wave) radar backscattering data. Remote Sens. Environ. 37, Cimino, J., Casey, A. D., Rabassa, J. & Wall, S. D. (1986) Multiple incidence angle SIR-B experiment over Argentina: mapping of forest units. IEEE Trans. Geosci. Remote Sens. 24, Dobson, M. C, Ulaby, F. T., Le Toan, T., Beaudoin, A., Kasischke, E. S. & Christensen, N. (1992) Dependence of radar backscatter on coniferous forest biomass. IEEE Trans. Geosci. Remote Sens. 21, Dobson, M. C, Ulaby, F. T., Pierce, L. E., Sharik, T. L., Bergen, K. M., Kellndorfer, J., Rendra, J. R., Li, E., Lin, Y. C, Nashashibi, A., Sarabandi, K. & Siqueira, P. (1995) Estimation of forest biophysical characteristics in northern Michigan with SIR-C/X-SAR. IEEE Trans. Geosci. Remote Sens. 33, Eom, H. J. & Fung, A. K. (1984) A scatter model for vegetation up to Ku-band. Remote Sens. Environ. 19, Fung, A. K. (1994) Microwave Scattering and Emission Models and Their Application. Artech House, Boston, USA. Fung, A. K. & Ulaby, F. T. (1978) A scatter model for leafy vegetation. IEEE Trans. Geosci. Remote Sens. 16, Hoekman, D., Krul, L. & Attema, E. (1982) A multi-layer model for radar backscattering by vegetation canopies. In: Proc. IGARSS'82 vol. 2. IEEE. Imhoff, M. L. (1995) Radar backscatter and biomass saturation: ramifications for global biomass inventory. IEEE Trans. Geosci. Remote Sens. 33(2), Karam, M. A. & Fung, A. K. (1988) Electromagnetic scattering from a layer of finite length, randomly oriented, dielectric circular cylinders over a rough interface with application to vegetation. Int. J. Remote Sens. 9, Lang, R. H. & Sidhu, J. S. (1983) Electromagnetic backscattering from a layer of vegetation: a discrete approach. Trans. Geosci. Remote Sens. 21, Lee, J. K. & Kong, J. A. (1985) Active microwave remote sensing of an anisotropic random medium layer. IEEE Geosci. Remote Sens. 23, Paris, J. (1986) The effect of leaf size on the microwave backscattering by corn. Remote Sens. Environ. 19, Prevot, L., Dechambre, M., Taconet, O., Vidal-Madjar, D., Normand, M. & Galle, S. (1993) Estimating the characteristics of vegetation canopies with airborne radar measurements. Int. J. Remote Sens. 14, Starks, P. J. & Humes, K. S. (1996) Hydrology Data Report Washita, 94. NAWQL Sun, G. & Ranson, K. J. (1995) A three-dimensional radar backscattering model of forest canopies. IEEE Trans. Geosci. Remote Sens. 33(2), IEEE Trans.

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