The modeled ground contribution consists of attenuated surface scattering from the soil, the double-bounce scattering between the soil and tree elements such as trunk and branches, and volumetric scattering from a low layer of understory. The volume layer is dominated by diffuse volume scattering from the canopy elements, including multiple scattering effects. Forest canopy is expected to be a sparse medium, attenuating the signal with the distorted Born approximation. The vertical profile is represented by an effective scattering profile F(z), which takes into account extinction and the distribution of the scattering elements. Different forms were proposed for F(z) in the literature, including a uniform attenuated, Gaussian, or represented by Fourier or Legendre series. In this example, to represent the temperate and boreal forests, F(z) is characterized by two parameters, extinction σx and the canopy-fill-ratio rh, as outlined in Fig. 2.31.

The polarimetric coherency matrix of the volume component is characterized by two parameters related to the polarization entropy in the canopy (called orientation randomness Fig. 2.30 Information content of PolInSAR data, separated into polarimetric and interferometric sources in ground and volume layers

in Neumann et al. (2010), as it is more constrained than the generic entropy) and the effective scattering mechanism type. The particle anisotropy characterizes the effective shape of the average particle in the polarization plane in dependence of the particle and background permittivities The orientation randomness characterizes the width of the distribution of particle orientation angles in the polarization plane (Neumann et al. 2010). The polarimetry of the volume component can range from purely randomly oriented collection of scatterers with high polarization entropy to a less diffuse form with ordered branch structure.

Prior to parameter retrieval, data preparation consists of the following steps, starting with a stack of single-look complex (SLC) data sets for multiple acquisitions and polarizations: flat earth removal, range spectral filtering, polarimetric calibration, MB-PolInSAR covariance matrix estimation including pre-summing and speckle filtering, radiometric calibration, SNR compensation, polarimetric stationary calibration, local orientation angle compensation, ground topography phase shift, natural media (reflection symmetry) assumption, forest/non-forest pre-classification.

In the retrieval phase, most crucial is the estimation of the ground elevation phases and the linear ground-volume structure. Using one of the recently proposed least squares or maximum likelihood approaches (Tebaldini 2009) provides a parametric decomposition of the MB-PolInSAR covariance matrix. The determination of the decomposition parameters (which characterize the ground to volume ratio and the polarization responses at both layers) is based on a set of assumptions. Common assumptions are based on physical insights into the ground-volume distinctiveness and can include metrics on polarization entropy, scattering mechanism type, ranges for forest height and ground elevation, and temporal effects. The constrained optimization problem is solved in a least squares sense determining the decomposition of the MB-PolInSAR covariance matrix into two layers, providing the ground and volume PolSAR matrices, and a MB series of coherences for the ground and volume components. The physical structure indicators are extracted from the PolSAR matrices, such as canopy orientation randomness, scattering mechanism types, ground-to-volume ratio, etc. The final estimation of the vertical structure profile is performed in an additional step by fitting the coherence sets for the ground and volume to a model of the verticals structure distribution.

For the demonstration of biomass estimation, different regression frameworks (multiple linear regression (LR), support vector machines (SVM), and random forests (RF)) were evaluated by using field measurements of above-ground-biomass. Validation is performed using leave-one-out approach.

#### <IP_ADDRESS> Experimental Results

Test sites and corresponding radar and validation data sets selected for the generation of showcases on forest parameter estimation are summarized in Table 2.8 and further described in the Appendix.

MB-PolInSAR modeling and inversion provide many retrievable parameters including: forest height, ground elevation topography, vertical structure characterizing parameters (attenuation, canopy-fill-factor, differential extinction), temporal decorrelation, ground-to-volume ratio, individual power contributions, ground and volume scattering mechanism types and orientation randomness. For the Traunstein 2003 data, only the forest height can be related to the validation data, as projected from the 1998 inventory. In the following, we will present several retrieved forest structure parameters from the Traunstein 2003 data as obtained in the study (Neumann et al. 2010). Estimates are plotted in Fig. 2.32.

1. Vertical forest structure: forest heights are estimated close to projected field measurements with an average stand-wise root mean square error (RMSE) of about 5 m and standard deviation of 4.3 m. The heights were underestimated by 1.5 m. However, in some stands, the forest heights vary continuously, indicating non-homogeneity inside of the stands. Figure 2.33a shows the forest heights and the

individual canopy layer depths for the 20 evaluation stands. The red and green lines in this plot represent the field measured and PolInSAR estimated forest heights, respectively. The brown line delimits the canopy layer from the non-canopy layer above the ground and represents the canopy-fill-ratio, normalized to the total vegetation height. It is notable that the smallest error corresponds to the most homogeneous forest stand #2, which is largely dominated by a single species, Northern Spruce (bias: -0.3 cm; RMSE: 1.5 m). The highest errors correspond to stand #20: the heights are underestimated by 5 m with an RMSE of 7.6 m. However, examining the optical data, one can observe several clear cuts and roads inside of this forest stand, which we initially assumed to be homogenous. This indicates significant changes in the spatial structure of this forest stand between the forest inventory in 1998 and the date of radar acquisition in 2003.

Table 2.8 Test sites and corresponding radar and validation data selected for the generation of showcases on forest structure estimation

Fig. 2.32 Traunstein test site: optical image, PolSAR image with delineated forest stands, estimated forest height, and orientation randomness

covariance matrix, we estimated the differential extinction in the canopy between HH and VV polarizations to be up to 0.06 dB/m. This tree canopy orientation induced effect changes the PolInSAR coherence for the canopy. The induced effect on coherence magnitude and phase is at the order of 0.005 and 1, respectively.

7. Ground level contributions: Next to ground topography elevation, PolInSAR allows to estimate the ground-to-volume ratio (estimated to be in average about 0.2) and the individual PolSAR covariance matrices for the ground and volume contributions. We observed noteworthy crosspolarized contributions (about 20%) at the ground level, but the major scattering power at the ground was observed in the Pauli HH-VV component (about 50%). Though HH-VV is dominant, these results suggest that a simple first-order models for the ground component are insufficient to reliably represent the ground scattering inside the forest.

Fig. 2.33 Traunstein test site: Estimated forest structure, scatterplot of estimated height, temporal decorrelation, orientation randomness over incidence angle

Figures 2.34 and 2.35 represent the Krycklan catchment area and show forest structure parameters retrieved from MB-PolInSAR data: polarimetric image in Pauli matrix basis (2.34a), lidar top forest height (h100) (2.34b), forest height (2.34c), biomass using SVM regression (2.34d), ground and volume orientation randomness (2.35a, b) and ground and volume scattering mechanism types (2.35c, d). Investigating regression techniques using a combination of PolInSAR retrieved forest structure parameters allowed us to improve biomass estimation at both, L- and P-band frequencies. On the one hand, providing more structurerelated input parameters naturally improves the regression. However, due to spatial and temporal forest variability this regression usually does not generalize over large forest areas. We evaluated three approaches (Multiple Linear Regression (LR), Support Vector Machines (SVM), and Random Forests (RF)) using up to 14 parameters, independently at both frequencies. Using "leave-one-out" cross-validation approach, the best biomass RMSE were 19.7 and 22.7 t/ha at L- and P-band, respectively, using either SVM or LR. For comparison, without providing any forest structure information, the best RMSE values were 27.3 and 28.5 t/ha at L- and P-bands (both using SVM).

These were initial results on biomass regression from selected MB-PolInSAR forest structure estimates. It is to expect, that the results can be improved by more systematically evaluating the entire available parameter space and by performing a better regularization of the regression frameworks.

# <IP_ADDRESS> Discussion on the Role of Polarimetry, on the Maturity of the Application and Conclusions

This Section demonstrated a MB-PolInSAR approach for forest structure and biomass estimation on two airborne data sets at L- and P-band frequencies. We presented a model to characterize polarimetric interferometric radar response from vegetation. For the volume component, a novel polarimetric model was used to allow varying degrees of orientation randomness and scattering anisotropy inside the canopy.

The forest height estimation performance has been evaluated on real airborne L–band SAR data over the

Fig. 2.34 Krycklan catchment. From left: L-band PolSAR image, Lidar height (rh100), PolInSAR height and biomass estimates

Traunstein forest. Using three baselines, the forest height has been underestimated with an average bias of 1.51 m and RMSE of 4.97 m. In addition, polarimetric parameters and backscattered powers were estimated for both layers, the ground and the canopy. On the other hand, the estimations of the extinction and the canopy–fill–factor were not satisfactory, as there seems to rest a level of ambiguity and correlation between these two parameters. Furthermore, differential extinction and temporal decorrelation were estimated.

The same methodology was applied to boreal forest in the Krycklan Catchment to estimate forest structure parameters and to successively estimate biomass via regression. The combination of polarimetric canopy and ground indicators with estimated forest structure information (consisting of forest height and ground-to-volume ratio) improved the RMSE of biomass estimation up to 27% and 43% at L- and P-bands, respectively.

The limitations for the presented parameter retrieval framework are determined by model assumptions. Only a simplified vegetation structure is taken into account assuming vertically uniform and horizontally homogeneous layers. Next it is assumed, that the average effective particle shape is representative for all particles, and independent of height and polarization orientation. The variation of extinction with polarization is assumed to be insignificant for parameter retrieval. Also, a plane of reflection symmetry is assumed to exist for the illuminated vegetation area. The possibility to estimate the degree of temporal coherence over vegetation provides new opportunities for PolInSAR time series analysis, which might lead to competitive multi–temporal monitoring of ecosystem dynamics. However, further theoretical and experimental investigations need to be conducted to improve the understanding and to examine the possibilities and limits of radar remote sensing of the temporal behavior and the forest structure.

Interferometry and polarimetry provide complementary information on the illuminated forest and we only start to explore the potentials for forest monitoring using these techniques. Though, it is an actively developing field and over the last few years several important breakthroughs in information content understanding and processing were reported which should help making this technique operational.

Fig. 2.35 Krycklan catchment. From left: Ground and volume orientation randomness, and ground and volume scattering mechanism types

# 2.5 Biomass Estimation

# 2.5.1 Biomass Estimation: A Review

#### <IP_ADDRESS> Introduction, Motivation

Biomass is defined as the quantity of living organic matter per unit volume and is usually given as a mass, or a mass per unit area. Forest biomass refers to living and dead organic material of plants and trees in the forest including roots, stem, stump, branches, bark, seeds and foliage. Above ground forest biomass defines to the forest biomass above the soil (e.g. excluding the roots). About half of the forest biomass is carbon, so that biomass is often used as proxy for carbon and its spatial distribution characterises the distribution of carbon in the biosphere (Houghton et al. 2009; Houghton 2005; IPCC good practice guidance for land use, land use change and forestry 2003). Dynamic variations in the biomass are therefore a direct measure of the exchange of carbon between the terrestrial ecosystem and the atmosphere (GCOS 2003, 2004; GOFC-GOLD 2016). In this context, biomass was identified by the UNFCCC as an Essential Climate variable (GCOS 2003), whose determination is of great importance to reduce the uncertainties in our knowledge of the climate system. The importance of biomass manifests itself not only due to its role as a carbon sink or carbon source (Canadell et al. 2007; DeFries et al. 2002), but also due to its relevance for characterising forest or ecosystem productivity (Keeling and Phillips 2007).

Biomass inventories and biomass dynamics in local, regional and global scales are essential, though, up to now, largely unknown initialisation parameters of current climate models. A precise determination (on the order of 20%) of the spatial distribution of forest biomass would be sufficient to match the inaccuracy of terrestrial carbon fluxes to the other components of the carbon cycle (IPCC good practice guidance for land use, land use change and forestry 2003).

The significance of an accurate knowledge of biomass is further underlined by the increased importance of two other aspects: (1) the increasing use of biomass for generating energy and the resulting increase in the proportion of greenhouse gases emitted from biomass (Fargione et al. 2008), and (2) the increased interest in reducing greenhouse gases by preventing deforestation and forest damage (UNFCCC 2016; UN-REDD Programme 2008; Herold and Johns 2007).

Despite its importance, estimations of biomass at local, regional and global scales are today very inaccurate. One reason for this is the large effort required for a precise estimation of forest biomass. Direct biomass measurements are destructive and therefore costly in terms of resources, effort and time (Chave et al. 2003). Usually, biomass is estimated on a single tree basis from a number of measurements such as tree height and/or diameter and the use of appropriate allometric relationships (Chave et al. 2005).  In heterogeneous forests, this procedure is very arduous and often inaccurate, meaning that ground measurements of biomass in natural forests are never better than 20% (Chave et al. 2003). Particularly in tropical forest ecosystems there are large deviations also induced by allometric deviations. On larger scales, apart from the uncertainties arising from forest complexity, the different definitions, measurement protocols and used generalisation methods introduce additional inconsistencies in regional and national inventories and data bases. Finally, the Intergovernmental Panel on Climate Change (IPCC) recognises the lack of exact spatial forest biomass data (the measurement errors often exceed the estimated mean value) as one of the largest uncertainties in the global carbon budget.

Figure 2.36 demonstrates the actual stand of uncertainty. It compares four biomass maps of the Brazilian Amazon basin: on top left the Saatchi et al. 2007 map (Saatchi et al. 2007a), top right the Saatchi et al. 2011 map (Saatchi et al. 2011a), bottom left the Nogueira et al. 2008 map (Nogueira et al. 2008) and bottom right the Baccini et al. 2012 map (Baccini et al. 2012). All four are generated by combining ground and remote sensing data. However, each of them is using a different set of remote sensing data, different approaches to interpolate ground and remote sensing data, and/or different allometric relations. It is quickly evident that the four maps deliver strongly deviating mean biomass values, spatial distribution patterns and spatial biomass trends.

Biomass is an integrative forest parameter that depends on multiple tree or stand attributes. Starting from a single tree, the above ground stem biomass AGBT is given by the product of the stem volume VT with the (species specific) wood density ρT:

$$\text{AGB}\_{T} = V\_{T} \rho\_{T} = \frac{1}{4} H\_{T} \text{DBH}\_{T}^{2} F\_{T} \rho\_{T} \tag{2.25}$$

where HT is the (top) tree height, DBHT the diameter at breast height, FT is a (species dependent) form-factor accounting for the deviation from a pure cylinder shape. The wood density ρT typically varies between 0.5 and 0.69 g/cm-3 (GOFC-GOLD 2016). The so-called biomass expansion factor (BEF) is used to account for the total above ground biomass including branches, understorey, etc. contributions (Canadell et al. 2007).

However, single-tree measurements are ineffective for large areas and global applications. In contrast, forest stand parameters are an option with great potential as remote sensing systems can measure them globally. In order to develop methodologies that use forest stand parameter to estimate aboveground biomass, new allometric relationships accounting for stand rather single tree parameters need to be derived and investigated. In this sense, moving from a single tree to a stand, the above ground stand biomass AGB is obtained by summing up the biomasses of the N individual trees in the stand

$$\text{AGB} = \sum\_{i=1}^{N} \text{AGB}\_{Ti} = \sum\_{i=1}^{N} V\_{Ti} \rho\_{Ti}$$

$$= \sum\_{i=1}^{N} \frac{1}{4} H\_{Ti} \text{DBH}\_{Ti}^{2} F\_{Ti} \rho\_{Ti} \tag{2.26}$$

Substituting in (2.26) the basal area BA, that describes the amount of the stand area occupied by tree stems, the mean top height HM and a mean wood density ρM

$$\text{BA} = \frac{1}{4} \sum\_{i=1}^{N} \text{DBH}\_{Ti}^{2}, H\_{M} = \frac{1}{N} \sum\_{i=1}^{N} H\_{Ti},$$

$$= \frac{1}{N} \sum\_{i=1}^{N} F\_{Ti}, \rho\_{M} = \frac{1}{N} \sum\_{i=1}^{N} \rho\_{Ti} \tag{2.27}$$

follows

$$\text{AGB} = \text{BAH}\_M F\_M \rho\_M. \tag{2.28}$$

Equation (2.28) makes clear that an accurate estimation of stand biomass relies on the knowledge of three key parameters: the tree height and density (expressed for example by the basal area in (2.28)) which both together define the woody volume of the stem and the wood density. Today there is no remote sensing configuration able to measure directly all three parameters.

Radar, and particular SAR remote sensing techniques for the estimation of above ground forest biomass, can be divided into three categories: (1) direct, (2) model-based, and (3) allometric techniques. Direct techniques use empirical or statistical relationships to relate directly the radar observables (primarily radar backscatter measurements at a single or different polarisations but also polarimetric or interferometric correlations) to above ground forest biomass values. Model based approaches rely on electromagnetic scattering models to interpret the SAR data and to establish

Fig. 2.36 Four different biomass classification maps for the Brazilian Amazon. Top left: Saatchi et al. (2007a), top right: Saatchi et al. (2011a), bottom left: Nogueira et al. (2008), bottom right: Baccini et al. (2012)

the relationship to biomass. Finally, in allometric techniques, the radar observables are used for the estimation of one or more physical forest parameters, as for example forest height, and then these parameters are related to forest biomass through allometric relations. The different approaches and techniques will be reviewed in the following.

### <IP_ADDRESS> Methodology

#### <IP_ADDRESS>.1 Direct Biomass Estimation

The first approaches to estimate forest biomass directly from backscatter measurements at different frequencies and polarisations date back to the early days of SAR remote sensing, and are today still an active research area. The basis of these approaches is the fact that with increasing forest biomass the (measured) backscatter intensity increases. Accordingly the backscattered intensity from a young forest with low biomass is in general weaker than the backscattered intensity from an old taller forest with high biomass. This has been observed, investigated and reported in the frame of airborne SAR experiments (at different frequencies and polarisations) already back in the eighties – early nineties (Le Toan et al. 1992; Rignot et al. 1994b; Imhoff 1995b; Dobson et al. 1995; Kasischke et al. 1995; Rauste et al. 1994; Harrell et al. 1995) These early results initiated a big interest in exploring SAR measurements for forest biomass estimation. Following experiments and work focused on studying and establishing (empirical) relations between biomass and SAR intensity measurements at the conventional HV, VV and HV polarisations.

A number of experiments performed over different forests and at different frequencies, indicated that the HV polarised (back-) scattering coefficient is strongly correlated to biomass, that this is often the case also for the HH scattering coefficient, while the correlation between biomass and VV scattering coefficient is often weak. However, at lower frequencies and/or sparse forest conditions the HH scattering coefficient is contaminated by dihedral scattering contributions (characterised by a strong directional backscattering behaviour) and is therefore stronger affected by topographic variations. Accordingly, HV is identified as the best polarisation for biomass inversion because it is less affected by the disturbing ground scattering contributions. Figure 2.37 shows P-band HV backscattering coefficient plotted against aboveground biomass for four different sites. Calibration factors of 5 dB and -3 dB have been added to the datasets from La Selva and Remningstorp, respectively. The solid line is the regression curve derived from the combined data (Le Toan et al. 2011).

However, even if the experimental results appear in many cases convincing the theoretical interpretation relationships used where rather weak and largely based on simple forest scattering models. Today is known that that there are three critical limitations common to these studies/approaches:

3. The fact that the backscattered SAR intensity depends also on the acquisition geometry, terrain topography, seasonal and environmental (dielectric) conditions make the interpretation of the backscattered SAR intensity in terms of biomass levels ambiguous.

In order to overcome these limitations model-based approaches have been developed.

#### <IP_ADDRESS>.2 Model-Based Estimation

Model-based approaches use electromagnetic scattering models to establish the relation between SAR observables, primarily the (back-) scattering coefficients at one or more polarisations, and biomass. The main motivation behind their development was twofold: to better control the dependency of the biomass relation on the individual site characteristics by parameterising propagation and scattering processes and to reduce the distortion due to non-vegetation scattering contributions by decomposing the total scattering into elementary scattering contributions.

While they have been many approaches investigated and proposed addressing model based biomass estimation one can distinguish two different model families: The first family of models develop from the so called Water Cloud Model (Attema and Ulaby 1978), are in general two layer models (i.e. volume and ground layer) and are applicable primarily at higher frequencies where canopy attenuation is relevant (Henderson and Lewis 1998); The second one is derived by means of radiative transfer modelling, accounting in general up to three layers (i.e. grown, trunk and ground layer) and appears more suited for the lower frequency range (Saatchi and McDonnald 1997; Saatchi and Moghaddam 2000;

Fig. 2.37 P-band HV backscattering coefficient plotted against aboveground biomass for four different sites. (Le Toan et al. 2011)

Mitchard et al. 2011). Both approaches rely on the separation (or interpretation) of volume and ground (including surface and/or dihedral) scattering contributions either by means of polarimetric scattering coefficients or covariance matrices. This allows, depending on the implementation, to incorporate topographic corrections.

However, there are two critical points that constrain the applicability and/or performance of these models: the first one is the large number of model parameters required in relation to the low dimensionality of conventional SAR observation spaces. This results in a large number of parameters/ coefficients that have to be a-priori known or estimated from forest biomass reference data. The second one is the rather poor integration of the effect of forest biomass into EM scattering models at relevant frequencies. This is primarily because of our inability to describe in a realistic way the complex scattering interactions occurring within a forest under different conditions and at different scales. The fact that forest biomass by itself depends on a number of forest parameters makes this task even more complex.

The advantage of using multitemporal SAR acquisitions to improve the performance of biomass estimators was already recognized at a very early stage (Kurvonen et al. 1999). The availability of multiple SAR acquisitions increases the sensitivity of the SAR backscattered intensity to biomass and allows an estimation even beyond the saturation point as recent studies demonstrated (Santoro et al. 2011, 2013). More than 100 acquisitions per year are used to derive biomass estimates from C-band radar backscatter. Averaging across large number of acquisitions, performed under slightly different incidence angles in different environmental (i.e. weather) and seasonal conditions allows to overcome the saturation appearing in single image relationships. The price to be paid is the low spatial resolution resulting from the large number of samples required to obtain robust estimates. This has been demonstrated in Santoro et al. (2011) with a stock volume map of the boreal zone from ENVISAT-ASAR data with a resolution of 1 km 1 km. Up to 300 m3 /ha (approx. 150 tons/ha) no saturation effect could be detected.

The introduction of SAR interferometry in the late nineties had a significant impact on forest applications by improving the performance of existing applications when introducing both interferometric coherences and/or interferometric phases or triggering the development of new ones.

The interferometric coherence has been explored in terms of its temporal as well as in terms of its volumetric contributions. As the temporal decorrelation contribution dominated the early C-band repeat-pass interferometric spaceborne data available the first attempts where based on empirically relating the amount of change (i.e. temporal decorrelation) estimated in the interferograms to the biomass (or the stock volume, or the height) of the individual stands (Koskinen et al. 2001; Pulliainen et al. 2003; Eriksson et al. 2003b). In Wagner et al. (2003) ERS interferometric coherence in combination with JERS backscattering was used with success to classify the biomass levels across Siberia. The availability of repeat-pass space borne interferometric acquisitions with smaller temporal baselines and/or at longer wavelengths (L-band) with weaker temporal decorrelation contributions allowed the development of more physical interpretation and inversion models for the interferometric coherence able to distinguish between temporal and volume decorrelation contributions. This helped to improve biomass estimation and to obtain more robust estimates, especially in boreal forests (Santoro et al. 2002; Askne and Santoro 2009; Askne et al. 2013).

Besides the interferometric coherence, interferometric phase measurements have been also used to support biomass estimation in cases where the coherence level was allowing meaningful phase reconstruction. The so-called scattering center height, i.e. the height of the interferometric phase center with respect to the underlying ground has been used as a proxy for forest height in different biomass estimation schemas (Solberg et al. 2010, 2013; Treuhaft et al. 2015). However, the dependency of the phase center location on the acquisition geometry and terrain topography as well as on seasonal and environmental variation constrains its robustness especially at lower frequencies.

#### <IP_ADDRESS>.3 Allometric Biomass Estimation

Accurate forest height measurements can be used as a proxy for aboveground biomass estimates, especially in high biomass regions (Mette et al. 2003, 2004a, b). Differently than the conventional biomass estimates at stand level obtained by multiplying stock volume with mean wood density, biomass estimates from forest height measurements rely on the use of allometric relationships (Mette et al. 2003). A first approach in this direction was introduced in Mette et al. (2003) where a simple power law allometric relationship was proposed

$$\text{AGB} = l\_a \text{(1.66H)}^b. \tag{2.29}$$

where AGB is the stand biomass (Mg/ha), H is the forest dominant (top) height or H100 (height of the 100 largest trees per hectare (Reigber and Moreira 2000)), b is the allometric exponent and la a scaling factor known as allometric level. The allometric level accounts primarily for differences in stand densities but also tree species and site conditions and decreases from climax tree species to pioneer tree species. For estimating AGB from height measurements in the context of (2.29) the allometric level needs to be known. On regional (i.e. landscape) scale a mean allometric level can be assumed (under the assumption of similar tree species composition, logging practices, management concepts and growth conditions).

Height-to-biomass allometric relationships proved to be robust and able to provide accurate AGB estimates from forest height in homogenous forest conditions. This has been demonstrated for a number of boreal, and temperate sites with different conditions. However, the approach meets its limitations in forest of heterogeneous structure and density. This is shown in Fig. 2.38 where the height (i.e. H100) to biomass relationship for four European forest test sites. The results have been achieved by using a common allometric exponent of 0.52 and site adapted values for the allometric level. The three more heterogeneous sites, namely Traunstein, Ebersberger Forst and the Nationalpark Bayrischer Wald, are characterised by a lower height–biomass correlations indicating the insufficiency of a fixed allometric level to represent the height to biomass relationship. On the other hand, the more homogeneous, in terms of horizontal and vertical structure, boreal forest in Krycklan is well described by a single allometric level. The

Fig. 2.38 Height to biomass allometry for four European forest test sites. Top left: Traunstein Site in Germany; top right: Ebersberger Forst in Germany; bottom left: Bayrischer Wald in Germany; bottom right: Krycklan in Sweden

correspondences obtained for the four test sites between the biomass estimated from ground measurements and using the height to biomass allometry are shown in Fig. 2.39.

The high correlation between forest height and biomass has been explored in Toraño-Caicoya et al. (2016a) to use forest height estimated from TanDEM-X interferometric data to estimate successfully biomass at boreal forests by means of (2.29).

In order to improve the performance of the conventional forest height to biomass allometry that degrades in structurally heterogeneous forests the direct or indirect use of forest structure information in (2.29) has been proposed and is currently investigated by several groups. The forest structure information can be obtained from inventory data or even more relevant from 3-D radar reflectivity reconstructed from tomographic measurements. One such example has been recently presented in Toraño-Caicoya et al. (2015) where the use of vertical structure information derived from 3-D radar reflectivity reconstructed at L-band by means of tomographic SAR techniques has been used to improve the performance of the height to biomass allometry. First the vertical reflectivity profile F(z) is expressed in terms of a Legendre polynomial series

$$F(z) = \sum\_{n} a\_n P\_n(z)\text{ where } \qquad a\_n$$

$$= \frac{2n+1}{2} \int\_{-1}^{1} F(z)P\_n(z)dz\tag{2.30}$$

where Pn(z) are the Legendre polynomials and an the associated Legendre coefficients. The first four Legendre coefficients (a1, ...a4) are then used to define a structure ratio Srat that allows to distinguish between different allometric levels

$$S\_{\rm rat} = \frac{|a\_1|}{|a\_2 + a\_3 + a\_4|}. \tag{2.31}$$

The lower frequency contribution, expressed by the first Legendre coefficient (a1), is associated to the stem compartment while the higher frequency components, represented by the higher order coefficients a2, a3 and a4 are associated to the crown/canopy compartment. Accordingly, the structure ratio Srat Srat may be used as a proxy to stand density to improve the allometric biomass estimation (Toraño-Caicoya et al. 2016b)

$$\text{AGB} = \text{7S}^{0.8}\_{\text{rat}} H^b. \tag{2.32}$$

Thus, for a constant height, if the numerator of the ratio increases (indicating a higher proportion of stem biomass), biomass increases, and if the denominator increases (indicating a higher proportion of crown biomass) biomass decreases. Figure 2.46 shows the improvement achieved by using (2.35) instead of (2.32). The high variance produced by the "height to biomass" allometric relation in (2.32) can be corrected with the inclusion of forest structure information.

#### <IP_ADDRESS> Experimental Results

Test sites and corresponding radar and validation data sets selected for the generation of showcases on biomass estimation are summarized in Table 2.9 and further described in the Appendix.

Biomass maps have been obtained in the two selected test sites in two steps: first by estimating forest height from interferometric TanDEM-X and second by estimating biomass from height using following allometric relation provided by the Swedish National Forest Service Inventory (SLU)

$$\text{AGB} = 0.3H^2.\tag{2.33}$$

Results are shown in Fig. 2.40.

The accuracy of the estimated biomass depends on two error sources: the accuracy of the estimated height-to-biomass relationship (ΔAGB) and the accuracy of the forest height estimates (ΔH). ΔAGB is defined by the forest conditions and can only be improved when additional forest parameters (like forest density, structural parameters, etc.) are included in the allometric estimation process. Under the assumption of homogenous stand conditions across the boreal region, ΔAGB can be assumed constant and it is estimated from the bias observed in the height-to-biomass relationship applied to the reference data. ΔH depends on forest height estimation methodology and performance. Airborne lidar measurements provide height estimates with sub-meter accuracy and are therefore used as a reference.

For forest heights obtained from TanDEM-X data, ΔH depends, in general, on the acquisition mode (single-pol, dual-pol, dual-baseline) the number of available acquisitions (see Sect. 2.2) and on the associated range of spatial baseline.

Accordingly, the total biomass estimation sensitivity (ΔABGtot) can be expressed as

$$
\Delta \text{AGB}\_{\text{tot}} = \frac{\hat{\mathcal{Q}} AGB}{\hat{\mathcal{Q}}H} \Delta H + \Delta \text{AGB}. \tag{2.34}
$$

Using (2.33) in (2.34) it follows:

$$
\Delta \text{AGB}\_{\text{tot}} = 0.6H \Delta H + \Delta \text{AGB}. \tag{2.35}
$$

ΔH and ΔAGB determine the accuracy of biomass estimation. However, assuming the allometric error ΔAGB to be

Fig. 2.39 Height and structure to biomass allometry for four European forest test sites. Top left: Traunstein Site in Germany; top right: Ebersberger Forst in Germany; bottom left: Bayrischer Wald in Germany; bottom right: Krycklan in Sweden

independent from H, the biomass estimation accuracy is driven by ΔH only.

Figure 2.41 shows classification performance obtained by means of (2.33) for a relative height error of 0% (top left) and 10% (top right), and 1 (bottom left) and 3 m (bottom right) absolute errors. The real biomass is plotted on the y-axis, the estimated biomass, for a 90% confidence interval, is plotted on the x-axis in 10 Mg/ha biomass steps. The blue dashed lines indicate the class boundaries, estimated at the point in which two biomass intervals do not overlap. Assuming an error free height measurement (ΔH ¼ 0), up to 9 biomass classes can be obtained. This is the best performance that can be achieved, due to the residual error in ΔAGB. In case of a 10% height error five classes can be distinguished. For height errors larger than 20% only two biomass classes can be separated reducing the classification to a forest/non forest classification.

The analysis above makes clear that the biomass estimation performance reacts very sensitive to height errors. Consequently, reliable and robust biomass estimates require accurate and robust height estimates which again strongly depend on the polarimetric configuration of the observation space. The performance shortcomings of dual-polarimetric observations in terms of:

can be widely compensated by using quad-polarimetric observations.

Fig. 2.46 Scatter plot for model R1. For each stand/plot, all available acquisitions are shown

# <IP_ADDRESS> Discussion on the Role of Polarimetry, on the Maturity of the Application and Conclusions

Single-pol data interferometric data at a single baseline do not provide enough measurable to invert forest height from interferometric data without a-priori information on terrain topography. A single-pol inversion is possible when multiple baselines are available but the performance is compromised by the presence of temporal decorrelation and the ratio of the spatial baselines. On the other hand, forest height inversion is possible with dual-polarimetric interferometric data taking into account a reduced estimation performance when compared to the quad-polarimetric case. The availability of quadpolarimetric interferometric measurements - that allows the implementation of adaptive optimisation techniques - is critical when algorithm robustness and performance matters.

# 2.5.2 Biomass Estimation from Semi-empirical Relationships

# <IP_ADDRESS> Introduction, Motivation and Literature Review

Understanding the terrestrial carbon cycle and predicting future climate changes are important topics in climate research. One of the major uncertainties in the current carbon cycle models lies in terrestrial ecosystems, mainly forests (Solomon et al. 2007). Rather than estimating forest carbon directly, biomass can be used instead since about 50% of biomass is carbon. Furthermore, above-ground biomass B [t/ha] is often used as a proxy indicator which is estimated from remote sensing measurements, e.g. using P-band (ca 450 MHz) SAR as discussed in this Section. B is here defined as dry weight and includes stem, bark, branches, and needles/leaves, but excludes stump and roots.

Several studies of using P-band SAR for forest biomass retrieval have been performed in the past. The early studies (Ranson and Sun 1994; Imhoff 1995a; DeFries et al. 2002; Rauste et al. 1994; Beaudoin et al. 1994; Rignot et al. 1995) concluded that HV-polarized backscatter shows highest correlation with biomass. In later studies (Hoekman and Quiñones 2000; Rignot et al. 1995; Santos et al. 2003), retrieval models were extended to other polarisation channels, showing improved retrieval results. In Saatchi et al. (2007b, 2011b) and Sandberg et al. (2011), the need for topographic corrections was pointed out and models were

Table 2.9 Test sites and corresponding radar and validation data selected for the generation of showcases on biomass estimation with allometric relationships

Fig. 2.40 Biomass estimation using forest height-tobBiomass allometry. Left panel: Krycklan, TanDEM-X (27.02.2011), 13.5 18.8 km; right panel: Remningstorp, 9.3 11.3 km, 30.12.2011. From top to bottom: single look complex amplitude, coherence scaled from 0 (black) to 1 (white), forest height map scaled from 0 to 40 m, and biomass (classi fication) map. The 5 biomass classes are colored from light to dark green according to the mean biomass (center value) of each class

Fig. 2.41 Biomass estimation performance based on (2.36) for a 90% confidence interval for relative height errors on the top panel (0 left and 10% right) and absolute height errors on the bottom (1 m left and 3 m right) panel

improved. In Neumann et al. (2012), polarimetric and interferometric SAR observables were also used. Complementary approaches include polarimetric SAR interferometry (Cloude and Papathanassiou 1998; Papathanassiou and Cloude 2001; Cloude and Papathanassiou 2003; Neumann et al. 2010) or SAR tomography (Cloude 2006; Reigber and Moreira 2000; Tebaldini 2010). Although the models presented in the mentioned articles show promising results, the analysis were limited to a single test site and the diversity of the data was low. Therefore, the extrapolation capabilities of the models remain untested.

### <IP_ADDRESS> Methodology

Biomass retrieval using the following two polarimetric SAR models is analysed:

Table 2.10 Test sites and corresponding radar and validation data selected for the generation of showcases on biomass estimation

HV

$$(\text{M1})\,\ln B = \text{3.280} + 0.138 \left[ \boldsymbol{\uprho}\_{HV}^{0} \right]\_{dB} + \text{0.}\,\text{\AA}$$

$$(\text{M2})\,\ln B = \text{3.129} + 0.093 \left[ \boldsymbol{\uprho}\_{HV}^{0} \right]\_{dB} + \text{(0}\,\quad\text{\AA}$$

The coefficients were estimated by least squares using the 97 circular plots in Krycklan (for each plot, four data points representing each flight heading were used). The first model is a linear combination of the three like-pol backscatter channels. The second model is a model proposed in Soja et al. (2013), which includes HH/VV ratio (which was found to be less susceptible to temporal and topographic changes), and the ground-slope angle u, which introduces a first-order topographic correction. The models were tested in Solomon et al. (2007) for sensitivity to site, topography and temporal change. Furthermore, the models were evaluated using two sets of test data from two different test sites. The test sites are located 720 km apart and represent two different cases of boreal forest. Models fitted to data from one test site are evaluated on the other. In this manner, the model is validated independently of the training data set.

Biomass retrieval using a third model is also analysed, i.e. the single-pol model according to

$$(\mathbf{R}1)\,\,\ln B = C\_0 + C\_1 \left( \left[ \chi\_{HV}^0 \right]\_{dB} - 0.766 \right) \qquad (2.37)$$

where C0 ¼ 3.8914 and C1 ¼ 0.1301 (Le Toan et al. 2011).

#### <IP_ADDRESS> Experimental Results

Test sites and corresponding radar and validation data sets selected for the generation of showcases on direct biomass estimation with semi-empirical relationships are summarized in Table 2.10 and further described in the Appendix.

Averaged, stand-wise backscatter data were extracted from the geocoded SAR images for all stands and plots in both Remningstorp and Krycklan. A 50 50 m DEM was used for geocoding and normalisation. Although highresolution lidar DEMs were also available for both test sites, they were not used because the evaluation scenario would be less realistic as comparable DEM resolutions are

Fig. 2.42 Scatter plot for model M1. For each stand/plot, all available acquisitions are shown

not available on global scale. All normalisation procedures were performed before averaging, that is on high-resolution SAR data. A buffer zone of 10 m was also added to avoid border effects.

Quantitative results for model M1 are shown in Fig. 2.42. Scatter plots for biomass for the 58 lidar stands and 10 field plots in Remningstorp are shown. Model parameters were extracted in Krycklan. As it can be seen in the figure, biomass is overestimated by 25–50 t/ha. Root mean square error (RMSE) for all data (all acquisitions at all headings and all dates) is measured to 39% of the mean biomass of 181 tons/ ha (RMSE is estimated for field plots, for which biomass estimation error is a few per cent). For model M4, the corresponding results are shown in Fig. 2.44. RMSE is 25%, and bias is much lower than for model M1. It is thus concluded, that the inclusion of the HH/VV ratio and topography notably improves retrieval performance.

Biomass maps for model M1 and M4 are shown in Figs. 2.43, 2.44, and 2.45. For M1, overestimation can be observed in many places in the maps. For M4, overestimation can be observed in some limited regions. This overestimation Fig. 2.43 Remningstorp: biomass maps for model M1. One single image for the 179-degree heading was used for each date to create SAR-based biomass maps (IDs: 0110, 0206, and 0412, March to May, respectively)

Fig. 2.44 Scatter plot for model M4. For each stand/plot, all available acquisitions are shown

can been explained by physical scattering properties; see (Le Toan et al. 2011).

In Figs. 2.46 and 2.47, quantitative results and biomass maps are shown for single-pol model R1. The model overestimates biomass gravely for almost all stands and is thus not suitable for across-site biomass retrieval.

# <IP_ADDRESS> Discussion on the Role of Polarimetry, on the Maturity of the Application and Conclusions

The inclusion of polarimetry significantly improves biomass retrieval performance for regression-based models. Using the HH/VV-ratio, the geophysical variability common for both HH and VV is eliminated (for example, moisture, topography

Fig. 2.47 Remningstorp: biomass maps for model R1. One single image for the 179-degree heading was used for each date to create SAR-based biomass maps (IDs: 0110, 0206, and 0412, March to May, respectively)

Table 2.11 Summary of presented application, methods and preferred system configurations for forest monitoring

and structure). Using the slope angle u, the influence of topography is reduced.

# 2.6 Summary

# References

statistics. In: Proceedings of the 2006 European conference on synthetic aperture radar (EUSAR)

multitemporal SAR images. IEEE Trans Geosci Remote Sens 40:853–870

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/ licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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# Agriculture and Wetland Applications 3

J. M. Lopez-Sanchez, J. D. Ballester-Berman, F. Vicente-Guijalba, S. R. Cloude, H. McNairn, J. Shang, H. Skriver, T. Jagdhuber, I. Hajnsek, E. Pottier, C. Marechal, L. Hubert-Moy, S. Corgne, S. Wdowinski, R. Touzi, G. Gosselin, R. Brooks, Y. Yamaguchi, and G. Singh

#### Abstract

Based on experimental results, this chapter describes applications of SAR polarimetry to extract relevant information on agriculture and wetland scenarios by exploiting differences in the polarimetric signature of different

S. R. Cloude AEL Consultants, Cupar, Fife, UK

H. McNairn · J. Shang Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON, Canada

H. Skriver

Microwaves and Remote Sensing, National Space Institute, Technical University of Denmark, Lyngby, Denmark

#### T. Jagdhuber

Microwave and Radar Institute, German Aerospace Center (DLR), Wessling, Germany

#### I. Hajnsek

Microwaves and Radar Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany

Institute of Environmental Engineering, Swiss Federal Institute of Technology Zurich (ETH Zurich), Zürich, Switzerland

E. Pottier · C. Marechal Institut d'Électronique et de Télécommunications de Rennes, University of Rennes-1, Rennes, France

L. Hubert-Moy · S. Corgne LETG Rennes, University of Rennes-2, Rennes, France

S. Wdowinski Division of Marine Geology and Geophysics, University of Miami, Miami, FL, USA

R. Touzi · G. Gosselin · R. Brooks Canada Center for Remote Sensing, Natural Resources Canada, Ottawa, ON, Canada

Y. Yamaguchi · G. Singh Institute of Science and Technology, Niigata University, Niigata, Japan scatterers, crop types and their development stage depending on their physical properties. Concerning agriculture, crop type mapping, soil moisture estimation and phenology estimation are reviewed, as they are ones with a clear benefit of full polarimetry over dual or single polarimetry. For crop type mapping, supervised or partially unsupervised classification schemes are used. Phenology estimation is treated as a classification problem as well, by regarding the different stages as different classes. Soil moisture estimation makes intensive use of scattering models, in order to separate soil and vegetation scattering and to invert for soil moisture from the isolated ground component. Then, applications of SAR polarimetry to wetland monitoring are considered that include the delineation of their extent and their characterisation by means of polarimetric decompositions. In the last section of the chapter, the use of a SAR polarimetric decomposition is shown for the assessment of the damages consequential to earthquakes and tsunamis.

# 3.1 Introduction

There exists a wide variety of remote sensing applications in the context of agriculture. A possible classification is summarised in Table 3.1, including the end-users and their motivation for such applications.

Most of these applications have made use of quad-pol data, but recently several studies have been carried out using compact polarimetry, where a single transmit (usually circular) and dual linear coherent receive is used. These have shown some promising advances in agriculture applications, for instance, combining high classification accuracy from the polarimetric information with wide swath coverage. Among all these applications, the following ones demonstrate the role of polarimetry and the benefits of quad-pol data over singlepol or dual-pol approaches:

J. M. Lopez-Sanchez (\*) · J. D. Ballester-Berman · F. Vicente-Guijalba Institute of Computing Research, University of Alicante, Alicante, Spain

e-mail<EMAIL_ADDRESS>

Table 3.1 Remote sensing applications on agriculture

Regarding PolInSAR, the rapid rate of growth and change in most crops has undermined so far the development of operational applications based on space-borne SAR systems, since they only provide repeat-pass interferometry (with the notable exception of the recently launched TanDEM-X sensor, which has polarimetric modes available for future use) and agriculture requires single-pass acquisitions. To date, the only successful application based on PolInSAR data is the retrieval of crop height, as an indicator of crop condition or phenology, which has been demonstrated with airborne data.

Wetlands constitute an application scenario with increasing interest in the remote sensing community, especially due to their ecological importance and their clear role in biodiversity. The main applications of PolSAR in wetlands consist in their delineation and characterisation (i.e. condition assessment and vegetation-type classification), for which some approaches have been successfully tested recently. In addition, PolInSAR has been employed to monitor changes in the water level by exploiting the high coherence provided by the double-bounce mechanism produced by the interaction between the water layer and the plants.

Finally, PolSAR has been applied to assess the effect of earthquakes and tsunamis, thanks to the detection of changes in the physical properties of the scene when these events happen.

# 3.2 Crop Type Mapping

# 3.2.1 Evaluation of C-Band Polarimetric SAR for Crop Classification

# <IP_ADDRESS> Introduction, Motivation and Literature Review

As the global population increases at a rate of about 80 million per year (United Nations Population Fund 2011), the agriculture and agri-food sector must continue to innovate if it is to meet this demand with an ever-decreasing land base. Recognising the importance of timely information to support accurate national, regional and global agricultural production forecasts, the G20 Group on Earth Observations Global Agricultural Geo-Monitoring Initiative (GEO-GLAM) was launched in 2011 (European Space Agency 2012). Under this initiative, data from Earth observing satellites will be fundamental to characterise crop types and estimate acreages in order to monitor global food production. Yet as the sector adapts to meet the needs of this growing population, society will demand that food be grown in an environmentally sustainable manner. Thus, monitoring how crops are being grown and effects on the landscape and surrounding ecosystems is also important.

Synthetic aperture radars (SARs) can contribute significantly to this global monitoring effort. This is particularly true given improved access in the last decade to satellites with SARs which acquire data at multiple polarisations as well as those that are fully polarimetric-capable. And when agriculture monitoring agencies consider accessing data at multiple frequencies by tasking satellites from various international data providers, a radar-only solution to delivery of crop inventories is possible. Yet research and development are required to adapt existing classification methodologies to use data from SARs. In addition, global cropping systems vary significantly, and consequently, research is required to determine the optimal SAR configuration for different mixes of crops grown under diverse growth cycles.

In 2004, Agriculture and Agri-Food Canada (AAFC) began developing a classification method to inventory Canada's crops. This research led to the implementation of AAFC's annual crop inventory with the first national inventory available in 2011. Reliability in delivery of the AAFC inventory has been facilitated by acquiring RADARSAT-2 dual-polarisation (VV, VH) C-band data to fill gaps when adequate optical data are unavailable (Fig. 3.1). Although the proportion of SAR-to-optical data used in the inventory is growing each year, optical data are still required to meet individual crop accuracy targets. Thus AAFC research continues to explore how multi-frequency and polarimetric SAR data can help to move the inventory towards increased reliance on SAR sensors and less dependence on optical satellites.

SAR satellite data are attractive for mapping and monitoring agriculture as microwave frequencies are unaffected by cloud and haze, unlike their optical counterparts. Agricultural targets (soils and crops) are very dynamic, and thus optical sensors can miss crucial periods in the growing season when information is critically required. Early studies examined the use of single frequency-single polarisation SAR data and reported that these data had to be integrated with optical satellite images if adequate crop classification accuracies were to be achieved. This included integration of C-HH (Brisco and Brown 1995), C-VV (Ban 2003; Schotten et al. 1995) or C-HH and C-VV (Blaes et al. 2005) images with at least one optical image. Even when ENVISAT-ASAR and RADARSAT-1 were integrated to create a data set with all three noncoherent linear polarisations (C-VV, C-VH and C-HH), optical images were still needed to successfully classify individual crops in complex cropping systems (McNairn et al. 2009a). Yet these studies also reported that optically based classification accuracies benefited by the inclusion of SAR data (Brisco and Brown 1995; Ban 2003; McNairn et al. 2009a).

Of all the linear polarisations, many studies have confirmed that the cross-polarisation (HV or VH) is the single most important polarisation to identify the majority of crops (McNairn et al. 2000, 2009a, b; Foody et al. 1994; Lee et al. 2001). This polarisation is responsive to the multiple scattering from within the vegetation volume, and because

Fig. 3.1 Example of product from Agriculture and Agri-Food Canada's 2012 crop inventory which uses an integration of optical and RADARSAT-2 data

vegetation structure varies greatly among crops, crosspolarised backscatter provides the best discrimination. Nevertheless, classification accuracies are increased substantially with the inclusion of additional polarisations. The greatest incremental increase in accuracy is observed when adding a second polarisation (Foody et al. 1994; McNairn et al. 2000; Hoekman and Vissers 2003), yet a third polarisation will still result in additional improvements to the classification for some crops (McNairn et al. 2000, 2009a; Hoekman and Vissers 2003).

Less research has been completed on the additional benefit of coherent polarimetric parameters for crop classification. Integrating all three linear polarisations and several polarimetric parameters (the co- and cross-polarised pedestal and variation coefficients, e.g.) (Foody et al. 1994) reported a small increase in accuracies. This small incremental increase in accuracy was confirmed in a study where parameters generated from scattering decompositions provided slightly higher accuracies (McNairn et al. 2009b). The HH-VV correlation coefficient and the RR-RL ratio, integrated with HV, have also proven useful in classifying crops (Quegan et al. 2003).

A multi-frequency multi-polarisation approach is the best option for crop discrimination. Ground-based scatterometer and airborne SAR research campaigns describe improvements in vegetation discrimination using multi-frequency data. Researchers reported that relative to single frequency data, higher crop classification accuracies are achieved using Xand C-band (Thomson et al. 1990); C- and L-band (Skriver 2012); X-, C- and L-band (Brisco and Protz 1980; Guindon et al. 1984); as well as C- and L- and P-band (Chen et al. 1996; Ferrazzoli et al. 1997, 1999; Hill et al. 2005; Hoekman et al. 2011) data. The advantage of a multi-frequency data set for separating vegetation types has also been demonstrated using data acquired from multiple satellite platforms, specifically ERS (C-band) and JERS (L-band) (Bouman and Uenk 1992; Dobson et al. 1996). Lower-frequency (i.e. L-band) microwaves penetrate larger biomass crops, and the scattering from within the canopy where structure is quite different aids in separating these crops (McNairn et al. 2009b). Yet for smaller biomass crops, lower frequencies can penetrate too far into the canopy and result in significant soil contributions. For these crops, discrimination is achieved using higherfrequency microwaves where most interaction is limited to within the canopy.

#### <IP_ADDRESS> Methodology

The evaluation of C-band SAR for crop classification was accomplished using RADARSAT-2 data acquired for three study sites – two in Canada's western Prairie region and a third in eastern Canada. Field observations of crop type were collected to train the supervised classification model and to test the classification accuracy. Fields were randomly split with half used for training and the remainder used to independently evaluate the classification results. A 7 7 boxcar speckle filter was applied to the single-look complex RADARSAT-2 data for the eastern site. For the two western sites, a 9 9 boxcar filter was selected due to the larger field sizes typical of this region of Canada. The covariance matrix was then converted to a symmetrised 3 by 3 covariance matrix from which radar intensity backscatter (HH, HV/VH, VV) was generated. In addition, the Cloude-Pottier (Cloude and Pottier 1997) decomposition was applied generating entropy (H), anisotropy (A) and alpha angle (α) parameters. With the Freeman-Durden decomposition, the total power was partitioned into contributions from three scattering mechanisms: single-bounce, double-bounce and volume scattering (Freeman and Durden 1998). After generating these radar parameters, the data were orthorectified using platform ephemeris information and models of the internal sensor distortion, a set of ground control points (GCPs) and digital elevation models. Prior to classification an enhanced Lee filter was applied with a window size of 5 5 for the eastern site and 9 9 for the western sites.

Fully polarimetric sensors are limited in terms of swath coverage, making their use in large area monitoring a challenge. Specific to RADARSAT-2 the maximum swath for quad-pol (QP) is approximately 50 km, achieved with the wide QP mode. With compact polarimetric (CP) configurations, a larger swath is maintained relative to fully polarimetric SAR systems (Charbonneau et al. 2010), making CP an attractive option to assist with the production of national crop inventories. Although CP offers this operational advantage, the information generated by CP configurations is not well understood particularly as it relates to applications such as crop classification. To investigate the potential of CP for this application, a circular right transmitting-coherent linear receiving compact polarimetric mode (CL-pol) was simulated from the RADARSAT-2 fully polarimetric data acquired for the Casselman site. The simulated CL-pol data were stored in the Stokes vector format, and the Stokes vector parameters were subsequently used in the classification. A detailed description of the CP simulation in provided in (Charbonneau et al. 2010).

Upon evaluation of several classification models (McNairn et al. 2009a), AAFC chose a decision tree (DT) supervised classifier as it provided the most consistent and reliable classification accuracies particularly when SAR data were used in the classification. Decision boundaries and coefficients for the linear discriminate function used in the classification were estimated empirically from the training data. The DT was run using boosting over five trials to force the classifier to focus on poorly classified cases. To avoid over-fitting, the classifier used a global pruning of the model of 25%. The DT classifier was implemented with the software See5 (Rulequest Research 2008) and is currently the basis of AAFC's operational annual crop inventory.

#### <IP_ADDRESS> Experimental Results

Test sites and corresponding radar and validation data sets selected for crop classification are summarised in Table 3.2 and further described in the Appendix.

When a multitemporal data set with all linear polarisations (HH, VV, HV/VH) is available, high overall accuracies are achievable. With the simple crop mix (corn, soybeans, wheat, pasture) of the eastern site, accuracies of 90% are reached. Slightly lower accuracies are found in more complex cropping systems, such as those of Canada's western region. Here overall accuracies of 85.5% are reported for both the Manitoba and Saskatchewan sites. Using either the Cloude-Pottier (western sites) or Freeman-Durden (all three sites) decomposition parameters resulted in higher accuracies although gains were small. A classification using Cloude-Pottier entropy, alpha and anisotropy inputs increased overall accuracies by less than a percentage for the Manitoba site (86.3%) and less than 2% for the Saskatchewan site (87.3%). With the simple cropping mix in the eastern site, the linear polarisations outperformed the accuracies achieved with Cloude-Pottier (87.5%). The scattering parameters generated by the Freeman-Durden decomposition provided the best results for all three sites (Ontario, 91.3%; Manitoba, 87.1%; Saskatchewan, 88.7%) although increases in accuracy were limited to between 1 and 3%. For the Casselman site, the CL-pol Stokes vector parameters produced an overall accuracy (91.1%) similar to that achieved with the Freeman-Durden decomposition (91.3%).

Although high overall accuracies are an important indicator of a successful classification, crops must also be well classified individually in order to provide accurate production estimates. Reaching these accuracies has been a challenge for SAR-only classifications. For the Canadian inventory, achieving these individual crop accuracies has required integration of optical data particularly where the cropping mix is more complex. As presented in Fig. 3.2, when the three linear polarisations achieve high crop-specific accuracies (greater than 90%), little is gained in using fully polarimetric or CP parameters. However, the Cloude-Pottier or Freeman-Durden decompositions can improve accuracies when the linear polarisations are unable to reach accuracies above 90%. The results from the decompositions (QP) and the Stokes vector

# <IP_ADDRESS> Comparison with Single-/ Dual-Polarisation Data

As expected, the use of a single polarisation does not achieve satisfactory classification results. For the more complex cropping mix found in Carman, single VV and VH polarisations produced overall accuracies of 75.6% and 75.1%, respectively. Slightly higher accuracies were reached in the eastern site (77.9% for VV polarisation and 79.4% for VH). Only 62.5% (Casselman) and 68.1% (Carman) were attained with the HH polarisation. For the western cropping mix, while the VV polarisation provided better classification of pasture, corn and cereal crops, the remaining crops (canola, flax and beans) were best identified with the cross-polarisation. In Ontario, VH outperformed VV for all crops except corn. For both sites, HH always produced the lowest crop-specific accuracies.

As previously reported, the addition of a second polarisation (in this case adding VV to VH) provided significant improvements for many crops. For a more complex cropping mix (Carman), with these two polarisations, accuracies for corn, beans and cereals increased by 17.8%, 13.5% and 8.0%, respectively (Fig. 3.3). The addition of HH to these first two polarisations increased accuracies by less than 2%. In the eastern site, the integration of VV with VH increased individual accuracies by 7–8% for all crops except soybeans. For soybeans this increase was limited to less than 2%. For the Casselman site, there was value in adding the third HH polarisation for pasture and wheat where accuracies rose by a further 10–11% (Fig. 3.3).

In regions of Western Canada, approximately threequarters of the annual cropland is planted in cereals and canola. In the eastern part of the country, this is true for soybeans and corn. Early estimates of these specific crop acreages are important for production and yield forecasting.

Table 3.2 Test sites and corresponding radar and validation data selected for the generation of showcases on crop classification

Fig. 3.2 Producer's accuracies for the three study sites

Fig. 3.3 Comparison of crop-specific producer's accuracies using single, dual and multiple polarisations for Casselman (left) and Carman (right)

By mid-July, using only two RADARSAT-2 images (June 22 and July 16), accuracies above 70% (canola) and 80% (cereals) could be reached if either the Cloude-Pottier or Freeman-Durden decompositions were used in the classification of the western site. This represents a significant improvement in accuracies reached relative to those generated using single (VH), dual (VV, VH) or multiple (HH, VV, VH) polarisations (Fig. 3.4). For the eastern Ontario site, mid-season estimates (as well as by July 16) were significantly improved using the QP or CP configurations. Using either the simulated CL-pol data or one of the decompositions, accuracies close to 80% were achieved for corn and 93% for wheat. The Stokes vector parameters were far superior for mid-season identification of soybeans (79.9%), about 10% better than accuracies reached with either of the decompositions and 6% better than accuracies produced using the three linear polarisations.

Fig. 3.4 Mid-season classification accuracies for Casselman (left) and Carman (right)

# <IP_ADDRESS> Discussion on the Role of Polarimetry, on the Maturity of the Application and Conclusions

Accurate crop classification is the basis for generating production estimates and yield forecasts. Regardless of the source of satellite data, multitemporal acquisitions are necessary. These map products have traditionally been generated using optical satellite data. However, with improved access to SAR satellites with greater polarisation diversity, and in some cases polarimetric capabilities, the potential of this technology for agriculture monitoring is evident. The use of SAR for crop classification requires multitemporal acquisitions of dual polarisations (at a minimum) to meet acceptable overall classification accuracies. However, cropspecific accuracies still require the integration of additional data which has often come from optical sensors. Yet with increased access to polarimetric satellites, results are demonstrating that parameters generated from these complex data, such as radar decompositions, can improve cropspecific identification. In particular, fully and compact polarimetric data can assist in crop identification mid-season which is important if production estimates are required before end of season.

Repetition in results by many international researchers has brought confidence to the idea of radar-assisted crop identification. This has been put into practice by Agriculture and Agri-Food Canada where RADARSAT-2 (VV, VH) data are integrated with optical imagery to produce that country's annual inventory. Increasing the ratio of radar-to-optical data in such an inventory is now limited primarily by access to polarimetric swaths deemed to be operationally appropriate for national mapping. Missions which include wide swath compact polarimetry modes may provide a partial solution. Further, researchers have repeatedly demonstrated that a radar-only approach, with no optical data requirement, is possible with the integration of multi-frequency SAR. The primary impediment to implementing this approach is access to multi-frequency data over swaths appropriate for large area mapping. Without a single multi-frequency satellite, coordinating access from multiple sensors (from multiple agencies) is required. If this challenge can be met, monitoring agencies would have the classification tools and data to monitor national, regional and global agriculture production exclusively with SAR sensors.

# 3.2.2 Crop Classification Using Multitemporal L- and C-Band Airborne Polarimetric SAR

# <IP_ADDRESS> Introduction, Motivation and Literature Review

Remote sensing has the potential to provide estimates of inputs for land process models, for instance, the land cover type as well as quantitative geophysical parameters such as soil moisture and leaf area index (LAI). Such models can provide information that is crucial for a number of applications such as flood and drought prediction, crop irrigation scheduling and meteorology (Moran et al. 2004). Also, land cover maps provide fundamental information for many aspects of land use planning and policy development (Bocco et al. 2001), as a prerequisite for monitoring and modelling land use and environmental change (Shalaby and Tateishi 2007) and as a basis for land use statistics at all levels. The European Environmental Agency (EEA) has produced the CORINE Land Cover, and the requirement is a classification accuracy of 85% for the classification performance of the so-called level 3 classes, which are relatively broad classes (EEA 2007). An important part of land cover is the distribution of agricultural crops, and it is well-known that synthetic aperture radar (SAR) is able to perform such classification. The main advantage of SAR compared with optical and infrared sensors is its all-weather mapping capability. The discrimination potential of SAR data is based on the sensitivity of the radar backscatter to dielectric properties of the objects and to object structure (i.e. the size, shape and orientation distribution of the scatterers) (Skriver et al. 1999). The possibility of identifying individual classes is based on the fact that the dielectric properties and structure of different crop types vary. A distinct variation is also seen for these properties through the growing season due to the development of crops (Skriver et al. 1999). Therefore, the discrimination capabilities may vary through the year, and it may also be improved by performing multitemporal classification (Schotten et al. 1995; Skriver 2012; Skriver et al. 2011). The radar backscatter is also sensitive to other parameters, e.g. dielectric properties of the soil, surface roughness, terrain slope and vegetation canopy structure (e.g. row direction and spacing and cover fraction) (Moran et al. 2004). These properties are not necessarily specific for the individual classes and may therefore cause variability of the backscatter and other polarimetric observables within the classes. Also, differences in the development stages at a specific point in time due to, for instance, differences in sowing time may cause such variability (Skriver et al. 1999).

Polarimetric SAR measures all polarimetric information for a target in the form of the scattering matrix. Therefore, such data may be used to assess the capabilities of SAR for crop classification in various configurations, including single-polarisation, dual-polarisation and fully polarimetric modes. Various methods have been used for such assessments: statistical methods based on the Wishart distribution (Lee et al. 1994; 2001; Skriver 2012; Skriver et al. 2011) or covariance matrix elements transformed into backscatter coefficients (Lee et al. 2001; McNairn et al. 2009b; Hoekman and Vissers 2003; Skriver 2012; Skriver et al. 2011; Foody 1988; Freeman et al. 1994), methods based on scattering mechanisms (Lee et al. 1999, 2004) and knowledge-based methods (Ferrazzoli et al. 1999; Pierce et al. 1994; Skriver 2001). In the latter approaches, it is possible to include scattering model results and common knowledge about the targets. Hence, such methods are normally relatively robust and easy to adjust to different growing conditions caused by, for instance, different sowing time, soil and weather conditions. The number of different classes that can be determined is normally relatively small using this type of method. The statistically based methods, on the other hand, will normally provide a larger number of classes, but the classifiers will then normally be specifically adjusted to the data set at hand, and it is difficult to adapt the classifier to other environmental conditions and/or geographical locations.

#### <IP_ADDRESS> Methodology

The basis for our analysis and processing is the multi-look covariance matrix, which is formed from the coherent scattering vector made up of the backscatter responses at the linear polarisations, HH, VV and cross-polarisation. The diagonal backscatter elements in the covariance matrix are then normalised so they correspond to the γ 0 backscatter coefficient in order to reduce the influence of the incidence angle.

All acquisitions have been co-registered by identifying ground control points in the images. The covariance matrix data, corresponding to the original one-look scattering matrix data, have been averaged to reduce the speckle by a cosinesquared weighted 9 by 9 filter. After averaging the equivalent number of looks is estimated to be 9–11 from homogenous areas in the images in the Foulum data set and 10 in the

AgriSAR06 data set. Three different SAR modes are simulated using the same data set, i.e. single-polarisation, dual-polarisation and fully polarimetric data. For the polarimetric results, the full covariance matrix data has been used. For the single- and dual-polarisation modes, subsets of the covariance matrix data have been used, e.g. for the single-polarisation backscatter coefficients, γ0 hh and γ0 vv, the corresponding elements from the diagonal of the covariance matrix have been used, and for the dual-polarisation backscatter coefficients, pairs of these elements have been used, as well as the cross-polarised backscatter, γ0 xp . For the single- and dual-pol modes, the backscatter intensities have been used, and hence not the complex scattering matrix elements, in order to simulate satellite systems, like ENVISAT, RADARSAT, ALOS and Sentinel-1, where such modes are used to optimise the spatial and temporal coverage. Hence, the possibility of a coherent combination of the HH and VV polarisations has not been considered for this application.

For the single- and dual-polarisation data, the backscatter coefficients have been used in the classification. Backscatter coefficients follow the Gamma pdf for a multi-look SAR image. When the number of looks is large, the Gamma pdf can, however, be approximated by the Gaussian pdf. The number of looks in our case is at least 10. Hence, the Gaussian pdf is a valid approximation, and the multivariate Gaussian pdf is used for both single- and dual-polarisation data. The classification method used in all cases is the standard Bayesian maximum likelihood (ML) classifier for multivariate Gaussian statistics.

For the fully polarimetric cases, two methods are used: the standard Bayesian maximum likelihood Wishart classifier originally proposed by Lee et al. (1994) and the method introduced by Hoekman and Vissers using a reversible transform of the covariance matrix into backscatter intensities (Hoekman and Vissers 2003). The complex Wishart classifier is based on the classical Bayesian approach to classification, and the distance measure is derived based on the pdf of the covariance (or coherency) matrix for polarimetric SAR data, i.e. the complex Wishart pdf (Lee et al. 1994). Hoekman and Vissers (2003) introduced a reversible transform of the covariance matrix into backscatter intensities. The advantage is that the fully polarimetric information can be described by backscatter intensities alone. This description will, for instance, better than the Wishart distribution, describe the statistics of a collection of homogeneous areas for the same class but with some variability of the mean parameters due to, e.g. slightly different development stages for crops.

The methodology for the assessment of the classification accuracy that has been used is the following: one relatively large and representative field (more than 500 pixels) has been picked for each of the crop classes, and these areas have been used as training areas for the classifiers. A large number of test areas for the classes have been selected, and the classification accuracy has been estimated using these areas. The classification errors estimated are based on the number of pixels in all these test areas, and hence no results are shown using the same data set for training and for testing.

#### <IP_ADDRESS> Experimental Results

Test sites and corresponding radar and validation data sets selected for crop classification are summarised in Table 3.3 and further described in the Appendix.

The results of classifying the crops for different modes of the SAR acquisitions and for the multitemporal configurations are shown in Figs. 3.5 and 3.6. For each frequency and test site, all single-polarisation and incoherent dual-polarisation configurations have been assessed, as well as the two polarimetric classification methods when fully polarimetric data were available. In the two figures, only the configuration with the best performance, i.e. the lowest classification error, within these groups of configurations is shown. For the Foulum data set, it is seen in Fig. 3.5 that the polarimetric data always provide the best classification result, especially at L-band, where there is a relatively large improvement compared to the incoherent dual-polarisation results. At C-band, the difference between polarimetric results and the dual-polarisation results is smaller. At both frequencies, the Hoekman and Vissers classifier performs best, and the classification error is down to approximately 20%. Also at both frequencies, a clear improvement is seen using the multitemporal acquisitions.

At the AgriSAR06 test site, polarimetric data were available only at L-band, and in this case, as can be seen from Fig. 3.6, there is a clear advantage when only a few temporal acquisitions are available, whereas the performance is worse than the incoherent dual polarisation for more acquisitions. The best classification result is about 11%.

# <IP_ADDRESS> Comparison with Single-/ Dual-Polarisation Data

As mentioned above, also single- and incoherent dualpolarisation results are shown in Figs. 3.5 and 3.6. For both test sites at C-band, it is seen that the best single polarisation is VV and the best dual polarisation is VVXP, whereas at L-band the best single polarisation is HH and the best dual polarisation is HHXP.

Table 3.3 Test sites and corresponding radar and validation data selected for the generation of showcases on crop classification

Fig. 3.5 Classification errors for Foulum at C-band (left) and L-band (right) for the best modes for the three cases, single polarisation (red), dual polarisation (blue) and full polarimetry (green), as a function of the

multitemporal acquisition mode, where 4, 5, 6 and 7 correspond to April, May, June and July, respectively

80

Fig. 3.6 Classification errors for AgriSAR06 at C-band (left) and L-band (right) for the best modes for the three cases, single polarisation

(red), dual polarisation (blue) and full polarimetry (green) (only

L-band), as a function of the multitemporal series, where the day number corresponds to a series of acquisitions up to and including that day

For the Foulum test site, the smallest classification error for the C-band results is 41% for VV and 20% for VVXP, and for the L-band results, it is 43% for HH and 28% for HHXP (Skriver 2012). For the AgriSAR06 test site, the results are at C-band 19% for VV and 10% for VVXP, and at L-band 15% for HH and 3% for HHXP (Skriver et al. 2011). It is observed that the differences in performance between the C- and the L-band configurations are relatively small. For the Foulum test site, the C-band performs better than L-band, whereas it is the other way around for the AgriSAR06 test site. This probably has to do with the different set of crop types present at the two test sites.

In all cases, there is a large improvement from the singlepolarisation to the dual-polarisation results. It is clearly seen that the multitemporal acquisitions are necessary for these configurations in order to obtain a reasonable performance. Especially, the dual-polarisation modes including the crosspolarisation perform well, and at C-band they provide comparable results with the fully polarimetric data.

# <IP_ADDRESS> Discussion on the Role of Polarimetry on the Maturity of the Application and Conclusions

From the presented results, it is clear that polarimetry has a role in crop classification from SAR. Especially, for L-band an improvement is seen compared to the single and incoherent dual polarisations. At C-band, the polarimetric SAR still performs best, but the improvement compared to the simpler modes is small. The classification results for C- and L-band are relatively similar, and there is no clear preference for one or the other, except that L-band probably requires the polarimetric modes to obtain a sufficient classification performance, whereas simpler modes may be sufficient at C-band.

The application of using multitemporal SAR for crop classification is considered to be relatively mature. Multitemporal acquisitions are considered to be the most important requirement for this application, whether polarimetric or simpler modes are available or not. For the polarimetric modes, it may be less critical if a lot of multitemporal acquisitions are not available, whereas it is essential with multitemporal acquisitions for the single- and dual-polarisation modes.

In conclusion, the performance of the multitemporal SAR data is seen to be very close and even better for some of the modes, compared with the requirements on, e.g. the CORINE land cover of 85% classification accuracy on level 3 classes. Hence, it will be possible with future polarimetric and/or short revisit systems to obtain a sufficient classification performance. This could, for instance, be possible with the future Sentinel-1 satellites, where single- and dualpolarisation acquisitions at C-band will be available with short revisit times for large parts of the continents, or with future L-band polarimetric missions, such as ALOS-2.

# 3.3 Soil Moisture Estimation Under Vegetation Using SAR Polarimetry

# 3.3.1 Introduction, Motivation and Literature Review

Several hydrological processes, like surface runoff, infiltration, plant water uptake and groundwater drainage, are affected by the spatial and temporal variations of soil moisture within the pedosphere (Bronstert et al. 2012; Krajewski et al. 2006). These moisture changes are still very difficult to simulate by environmental/hydrological models due to the multiple and multi-dimensional dependencies of this highly varying soil parameter. Therefore they require a continuous and long-term monitoring (Meehl et al. 2007).

Monitoring of soil moisture content reveals, for instance, the retention capabilities of the soil to store the infiltrated precipitation for a delayed and uncritical release in the discharge system (Bronstert and Bardossy 1999). In this way hazard precautions, like flood forecasting and dam management, can be supported to identify critical catchment states before flooding events (Minet et al. 2011). Besides flood prevention in humid regions, also agricultural management practices in semi-arid regions, like irrigation, benefit from the incorporation of soil moisture information leading to crop yield optimisation in terms of precision farming (Robinson et al. 2008).

In practice, information about the soil moisture status is mostly acquired by single point measurements (Robinson et al. 2003), which might be combined to wireless sensor networks in order to enlarge the monitoring to field scale (Cardell-Oliver et al. 2005). In addition, soil moisture sensing on this scale can be also approached by geophysical methods, like ground-penetrating radar (GPR) (Huisman et al. 2003) or ground albedo neutron sensing (GANS) with cosmic ray probes (Zreda et al. 2008). As the spatial upscaling from point or field measurements to large scales is highly non-linear (Merz and Plate 1997), remote sensing extends soil information retrieval from local point or field studies to spatial investigations on sub-catchment or entire catchment areas, including also very isolated regions (Western et al. 2002).

Since optical remote sensing of soil properties (Katra et al. 2006) relies on daylight and clear weather conditions, RADAR-based methods turn out to be weather- and illumination-independent supporting a continuous soil monitoring strategy. Therefore passive microwave sensors (radiometers) (Jackson 1993) as well as active microwave sensors (scatterometers (Wagner et al. 2007), synthetic aperture radars (SAR) (Ulaby et al. 1982)) are used on airborne or space-borne platforms.

Space-borne radiometers and scatterometers, like MIRAS on SMOS (Kerr et al. 2001) and AMI on ERS, operate with low spatial resolution in the order of kilometres and high temporal resolution in the range of one acquisition every second or third day (in mid-latitudes), which is favourable for global monitoring purposes.

In contrary, SAR sensors, like PALSAR on ALOS (Rosenqvist et al. 2007) or ASAR on ENVISAT, acquired data in the order of decametres with a temporal resolution in the range of one acquisition every first to second week (in mid-latitudes), which fits to regional imaging purposes (Wagner et al. 2008). In order to combine both spatial scales for a spatially enhanced soil moisture retrieval on a global scale, an active-passive microwave sensor will be launched for the SMAP mission (Entekabi et al. 2010).

However, only SAR remote sensing enables a monitoring of the soil and its variability down to the local scale due to its imaging capabilities in high spatial resolution compared to the other microwave sensors. The sensitivity of SAR for soil characteristics is based on the fact that the transmitted electromagnetic (EM) wave interacts with two key parameters of the soil system: soil moisture and soil roughness. SAR sensors, used for soil moisture retrieval, acquire in different wavelengths (ranging from X-band (λ ¼ 3 cm, TerraSAR-X (Anguela et al. 2010)), C-band (λ ¼ 5 cm, ENVISAT-ASAR (Löw et al. 2006)), L-band (λ ¼ 23 cm, ALOS (Koyama and Schneider 2011)) to P-band (λ ¼ 80 cm, E-SAR (Jagdhuber et al. 2012a)).

Moreover, SAR remote sensing has the capability to penetrate through natural media, like vegetation layers, especially with increasing wavelength (Cloude et al. 2004). Hence, also agricultural regions, which are most of the year covered by vegetation, can be investigated for their soil moisture conditions. This enables a continuous soil monitoring along the entire plant growth cycle, if the increasing scattering contribution from the vegetation is considered (Hajnsek et al. 2009).

Concerning SAR remote sensing approaches, the first soil moisture analyses were carried out on single-polarisation intensities over bare soils leading to ill-posed inversion problems, because the soil moisture and roughness influences are enclosed in one acquired signal (Bernard et al. 1982). With the emerging establishment of fully polarimetric SAR sensors, the observation space has been enlarged allowing to separate soil roughness from soil moisture influence for an unambiguous inversion of both soil parameters over bare soils (Hajnsek et al. 2003).

For bare soils, three different categories of EM scattering models, which relate the natural soil conditions with the characteristics of the scattered RADAR signature, can be distinguished: empirical models, semi-empirical models and theoretical models. As remote sensing relies on the understanding of electromagnetic (EM) scattering on ground, only theoretical models can be considered as test site independent and form the basis of the analyses. Among these models, the integral equation method (IEM) is widely applied for EM scattering of bare soils because of its broad range of applicability in terms of soil roughness classes (Fung 1994).

So far the investigation of soil parameters, like soil moisture, with polarimetric SAR remote sensing was constrained on bare soil areas due to the lack of retrieval algorithms including the influence of a vegetation cover (Hajnsek et al. 2003). The dielectric discontinuities in vegetation volumes, which can be, for example, stems, branches, twigs and leaves, scatter the waves in a complex manner (Ulaby and Wilson 1985).

In order to incorporate the influence of vegetation cover, different EM models have been developed to describe vegetation scattering (Lang and Sidhu 1983), whereby the level of detail varies from a simple layer of uniformly shaped spheres (Attema and Ulaby 1978) until sophisticated threedimensional models of the vegetation volume, including cylinders, dipoles and discs with their respective orientation distributions to simulate stems, branches and leaves (Karam et al. 1992). Especially for the latter models, the amount of required input parameters exceeds by far the potential observable space of SAR remote sensing for direct inversion.

Unlike traditional retrieval approaches evaluating the total backscattered signature, an innovative soil information retrieval under vegetation cover should focus on the separation of the individual scattering contributions from the soil and the vegetation in order to extract only the soil properties for inversion. Polarimetric decompositions represent an established technique for separation of elementary scattering processes and have quite a history in polarimetric SAR remote sensing. Originally, these decomposition methods were designed for forested environments including scattering interactions with the ground, with the tree trunk and the ground as well as with the tree canopy. Recently, the description of the polarimetric vegetation volume component was considerably extended by Freeman (Freeman 2007), Neumann (Neumann et al. 2010) and Arii (Arii et al. 2010) using shape parameters for the scattering components (leaves, twigs, branches) and different orientation distributions within the tree canopy to characterise the polarimetric volume scattering of various tree species in a more detailed way. Besides the polarimetric decomposition models for forests, several (multi-angular) polarimetric decomposition methods for the more complex scattering scenario of agriculture, including a depolarising surface component due to soil roughness or an oriented vegetation component due to plant structure, are under development and provide the methodical foundation for this application showcase (Hajnsek et al. 2009; Jagdhuber et al. 2012b, 2013a).

# 3.3.2 Methodology

The retrieval of soil characteristics by polarimetric SAR remote sensing is investigated for agricultural areas using C-band wavelength (λ ¼ 5 cm) to assess a continuous soil parameter estimation with a growing vegetation cover along the phenological cycle. Due to polarimetric decomposition techniques, the separation of vegetation from soil/ground information should allow a continuous monitoring of soil moisture under vegetation cover. A modular polarimetric decomposition for retrieval of soil moisture is introduced to provide a profound basis for representation of different natural scattering conditions.

The received SAR signal of almost any natural media is a mixed response of different scattering processes occurring within the resolution cell. Therefore polarimetric decomposition techniques have been developed and used to separate the different scattering contributions. Equation (3.1) describes a generic example of an incoherent decomposition using the coherency matrix notation (Cloude 2009). A simple and generic case of model-based decompositions considers a non-penetrable surface superimposed by a volume of particles. This can be modelled with three components defined as surface TS, dihedral TD and volume TV scattering. The first component in Eq. (3.1) represents direct backscatter from a smooth surface (ks < 0.3), which can be expressed by the SPM or Bragg scattering model (Yamaguchi et al. 2006):

$$\mathbf{T}\_S = f\_S \begin{bmatrix} 1 & \beta\_S^\* & 0\\ \beta\_S & |\beta\_S|^2 & 0\\ 0 & 0 & 0 \end{bmatrix} \text{ with}$$

$$f\_S = \frac{m\_S^2}{2} |\mathbf{R}\_h + \mathbf{R}\_v|^2 \text{ and } \beta\_S = \frac{\mathbf{R}\_h - \mathbf{R}\_v}{\mathbf{R}\_h + \mathbf{R}\_v} \quad (3.1)$$

where the coefficients Rh and Rv are the horizontal and vertical Bragg scattering coefficients and mS represents the soil roughness influence on the intensity component fS. Both Bragg scattering coefficients depend only on the dielectric constant of the soil εS and the local incidence angle θl.

The dihedral scattering component can be modelled as a double Fresnel reflection on smooth dielectric media leading to the following rank-1 coherency matrix (Yamaguchi et al. 2006) and including the dihedral scattering intensity fD and the dihedral scattering mechanism ratio αD:

$$\mathbf{T}\_D = f\_D \begin{bmatrix} \left| a\_D \right|^2 & a\_D & 0\\ a\_D^\* & 1 & 0\\ 0 & 0 & 0 \end{bmatrix}, \text{with}$$

$$f\_D = \frac{m\_D^2}{2} \left| R\_{sh} R\_{th} + R\_{sv} R\_{lv} e^{i\varphi\_D} \right|^2 \text{ and,}$$

$$a\_D = \frac{R\_{sh} R\_{th} - R\_{sv} R\_{tv} e^{i\varphi\_D}}{R\_{sh} R\_{th} + R\_{sv} R\_{tv} e^{i\varphi\_D}} \tag{3.2}$$

where the horizontal and vertical Fresnel coefficients of the soil plane (Rsh, Rsv) and of the trunk plane (Rth, Rth) depend on the soil and trunk dielectric constant (εS and εt) and the respective incidence angle θs ¼ θl and θt ¼ π/2 θl. The phase φD incorporates differential propagation in the case of an orientated vegetation layer, while mD represents the scattering loss on the intensity component fD.

The most challenging component with respect to EM modelling is the vegetation volume. A widely used approach deals with the vegetation volume as a cloud of equally shaped particles with a certain orientation distribution. In one of the simplest ways, the volume component is modelled as randomly oriented cloud of dipoles. The initial vertical dipole can be expressed with the following scattering matrix Svdi:

$$\mathbf{S}\_{\rm vdi} = \begin{bmatrix} \mathbf{0} & \mathbf{0} \\ \mathbf{0} & \mathbf{1} \end{bmatrix}. \tag{3.3}$$

After expansion to the coherency matrix, the matrix is rotated around the LoS by an angle of 2ψ to account for the orientation of the scattering particles:

$$\mathbf{T}\_{\rm rad} = \mathbf{R}\_2 \mathbf{T}\_{\rm rad} y \mathbf{R}\_{2\mu}^T = \begin{bmatrix} 1 & 0 & 0 \\ 0 & \cos 2\mu & \sin 2\mu \\ 0 & -\sin 2\mu & \cos 2\mu \end{bmatrix} \begin{bmatrix} 1/2 & -1/2 & 0 \\ -1/2 & 1/2 & 0 \\ 0 & 0 & 0 \end{bmatrix} \begin{bmatrix} 1 & 0 & 0 \\ 0 & \cos 2\mu & -\sin 2\mu \\ 0 & \sin 2\mu & \cos 2\mu \end{bmatrix} . \tag{3.4}$$

In order to respect all the different orientations occurring within vegetation, the rotated coherency matrix Trot is integrated together with the specific probability density function pψ(ψ) of orientations assuming a uniform angular distribution pψ(ψ) ¼ 1/(2π) and a distribution width Δψ ¼ ψ2ψ1 ¼ 2π in the case of a randomly oriented vegetation volume (Yamaguchi et al. 2006):

$$\begin{split} \mathbf{T}\_{V} &= f\_{V} \int\_{\mathcal{W}\_{1}}^{\mathcal{W}\_{2}} p\_{\mathcal{Y}}(\boldsymbol{\mu}) \mathbf{T}\_{\text{rot}} d\boldsymbol{\mu} \\ &= f\_{V} \begin{bmatrix} 1/2 & 0 & 0 \\ 0 & 1/4 & 0 \\ 0 & 0 & 1/4 \end{bmatrix} . \end{split} \tag{3.5}$$

In a first step, the modelled vegetation volume component TV with its volume intensity fV is subtracted from the measured polarimetric SAR data T to separate the volume TV and the ground TG components (TS + TD):

$$
\langle \mathbf{T} \rangle - f\_V \mathbf{T}\_V = \mathbf{T}\_G = \mathbf{T}\_S + \mathbf{T}\_D. \tag{3.6}
$$

However, if the volume modelling is imperfect, Eq. (3.6) leads to biased or even non-physical results for the ground component TG. To avoid this effect, a mathematical method using the positive semi-definiteness of the Hermitian ground component was proposed by Van Zyl et al. to correct the volume intensity fVcorr (Van Zyl et al. 2008). With this corrected volume intensity, the linear system of Eq. (3.2) is solved for the single ground components (αD, βS, fS, fD) of the three scattering contributions obeying the dominance criterion of Freeman et al. for the ground components (Freeman and Durden 1998).

# 3.3.3 Experimental Results

Test sites and corresponding radar and validation data sets selected for crop classification are summarised in Table 3.4. The Indian Head test site is further described in the Appendix.

The presented polarimetric methodology was employed on the fine quad-polarimetric RADARSAT-2 data sets of Flevoland and Indian Head in order to decompose the SAR data into their respective scattering components (surface, dihedral, volume) and afterwards to invert the volumetric soil moisture from the ground components (surface, dihedral).

Figure 3.7 displays exemplarily the RGB composite of the normalised decomposed scattering power components from the three-component model-based decomposition introduced in Sect. 3.3.2 for the two test sites and two different acquisition dates (Flevoland, in the beginning of the observation cycle, 11th of June 2009, towards the end of the observation cycle, 29th of August 2009; Indian Head, 4th of August and 4th of September 2009). For the Flevoland scenes, the water bodies in blue colour and the urban areas predominantly in red colour are clearly visible in both scenes due to their temporal scattering stability. However, the agricultural areas are mostly indicated by the rectangular "patch work" structures across the centre of the scenes, which change significantly between the two acquisitions. For the Indian Head scenes, the agricultural fields show two different patterns for each date (August, September) due to the phenological plant development along the growing cycle. While the strongly vegetated fields exhibit a strong volume scattering, the low vegetated (catch crops) or bare fields (after harvest) indicate predominantly surface scattering.

Table 3.4 Test sites and corresponding radar and validation data selected for the generation of showcases on soil moisture estimation under vegetation

In June, after the start of the crop cycle (Fig. 3.7 left), the fields appear in all three colours, indicating a mixture of surface (bare soil), dihedral (stalk/leave-ground) and volume (complex vegetation) scattering due to the different phenologies of the different crop types, but also the different sowing dates on different fields.

In August, within the final stage of the crop cycle (Fig. 3.7 right), a part of the agricultural fields (mostly the winter crops) are already harvested and appear in blue colour indicating predominantly surface scattering, while the other part of the agricultural fields (summer crops, like corn) still exhibit green colours, indicating complex volume scattering before harvesting.

Figure 3.8 exhibits the trends of the three normalised scattering power components (red, dihedral; green, volume; blue, surface) for Flevoland and Indian Head test sites. The general trend is similar for both test sites at Flevoland, where volume is dominant most of all times with values around 0.6–0.7 peaking in July/August during the maturity stage of the vegetation. The ground components always stay below 0.4–0.5 except for the April dates in Flevoland.

Moreover, dihedral scattering is mostly present within the June dates in Flevoland, when the vegetation is active and fresh for a distinct double bounce and almost vanishes afterwards, while surface scattering dominates over dihedral scattering for Flevoland in July and August 2009. For the Indian Head test site, the time series in Fig. 3.8 reveals a clear dominance of volume scattering (level ¼ 0.8 along all August) due to a strong agricultural vegetation cover of canola crops (vegetation height > 1.0 m) on the test field, where some minor ground scattering occurs as single-bounce surface and/or as double-bounce dihedral scattering (level < 0.2). Hence, the dry moisture regime (mean soil moisture level lower than 8vol.%) together with the thick vegetation layer of canola will be a big challenge for soil moisture inversion under vegetation at C-band.

In conclusion, the decomposition led to a physically meaningful separation of the scattering contributions within the C-band RADARSAT-2 scenes, which reflects the agricultural scattering scenario reasonably well. After the model-based polarimetric decomposition into the single scattering components, the surface and dihedral components were inverted for soil moisture retrieval under vegetation cover as described in Sect. 3.3.2. Only for Flevoland very low inverted moisture ranges (soil moisture <5.6vol.%) compared to the in situ measured moistures (~30–40vol.%) are not representative and are filtered out in the end of the inversion process.

Figure 3.9 presents exemplarily the combination of inverted soil moisture values from both ground components (surface, dihedral) for the Flevoland (11th of June 2009 and 29th of August 2009) and Indian Head (4th of August and 4th of September 2009) test sites. The inversion rates for the whole Flevoland scene are 33.12% for the June date and 32.70% for the August date, while the inversion rates for both test sites and all scenes are displayed in Fig. 3.10. For Flevoland the inversion rates vary between 22% and 33% along time, whereby the minimum is reached in July, when the agricultural vegetation is in its maturity stage. For Indian Head the inversion rate varies between 11% and 25% growing slightly higher in the end of the vegetation growth period. Hence, in all cases, the inversion exhibits gaps of non-invertible points (white areas in Fig. 3.9). The inversion mostly failed within the agricultural regions due to the variety of scattering scenarios (typical field sizes at Flevoland: 10–15 ha), which are difficult to describe in detail with one type of model-based polarimetric decompositions assuming just a randomly oriented vegetation volume.

However, distinct field heterogeneities are visible within the soil moisture results, which fit the expectations of a highly varying soil moisture content on the agricultural fields. For Indian Head, a higher inversion rate can be stated for the scene acquired in September due to a diminished vegetation cover caused by harvesting activities and senescence.

In general, the inversion result at Flevoland for the 11th of June 2009 reveals a higher soil moisture level on the dihedral dominant fields than on the fields with surface scattering. In addition, the dominance of surface scattering for the 29th of

Fig. 3.7 RGB composite of normalised decomposed scattering power components from model-based polarimetric decomposition for the beginning of the observation cycle at 11th of June 2009 (Top left) and the end of the observation cycle at 29th of August 2009 (Top right) at the Flevoland test site and for 4th of August in the main growth period

August 2009 seems to result in a lower moisture level on most of the fields. In contrast to Flevoland, the Indian Head test site exhibits generally a lower soil moisture level, but also states relatively a higher moisture regime for fields with dihedral compared to surface scattering.

Figure 3.11 presents the validation of the inverted soil moisture from the ground scattering components (surface, dihedral) with the in situ measurements of the 2 test sites and all continuously recording moisture stations for 11 different dates from 11th of June until the 29th of August 2009 at

(Bottom left) and 4th of September 2009 in the end of the main growth period (Bottom right) at the Indian Head test site (red, even bounce/ dihedral scatterers; green, volume scatterers; blue, odd bounce/surface

scatterers)

Fig. 3.8 Normalised decomposed scattering power components (red, dihedral; green, volume; blue, surface) from the model-based polarimetric decomposition for the Flevoland test sites (site 1, Plus sign; site 2, Star sign from the beginning of the observation cycle at 11th of June

2009 until the end of the observation cycle at 29th of August 2009) and for the Indian Head test site (site 3, Plus sign) (R, even bounce/dihedral scatterers; G, volume scatterers; B, odd bounce/surface scatterers)

Flevoland and for 9 dates from 4th of August until 4th of September 2009 at Indian Head. A clear underestimation has to be stated at Flevoland for all dates of the observation cycle. In contrary, a distinct overestimation is reported by the comparison with in situ measurements at Indian Head. The estimated soil moisture values for the two measurement locations at Flevoland are underestimating the measured soil moisture values by a RMSE of 13.32vol.% (r 2 ¼ 0.12), whereas the comparison with the field measurements for Indian Head overestimates with a RMSE of 22.84vol.% (r 2 ¼ 0.02) including all analysed dates.

In the past, the soil moisture retrieval method was developed and implemented for L-band data (see Jagdhuber et al. 2012b, 2013a, b), and with the transfer to the C-band scattering scenario, the algorithm needs further adaptions especially concerning the ground scattering models. First analyses on the surface component inversion blame the low moisture values at Flevoland on the low T12 correlation term, which leads to small βS values and the underestimation of the soil moisture level.

In addition, the measured soil moisture of the two in situ stations at Flevoland ranges between 24vol% and 42vol%. This represents a significantly high soil moisture level, which is an additional challenge for the inversion algorithm, because the sensitivity of the inversion models decreases with increasing soil moisture level (Jagdhuber 2012).

For Indian Head, the distinct overestimation of the soil moisture might be traced back to a very challenging scattering scenario of a large canola vegetation cover (>100 cm) and a very dry soil underneath, which might be hardly detectable at C-band (wavelength: 5 cm).

Moreover, it has to be stated that the validation could only be conducted over time (continuously recording moisture stations) and not over space (no soil moisture network or spatial sampling), since only three measurement locations were available for Flevoland and Indian Head in 2009.

Fig. 3.9 Inverted soil moisture [vol.%] from both ground scattering components (surface, dihedral) for the Flevoland test site (at the beginning of the observation cycle at 11th of June 2009 (Top left) and the end of the observation cycle at 29th of August 2009 (Top right)) and for the

Indian Head test site (on the 4th of August (Bottom left) and the 4th of September (Bottom right) 2009); non-invertible areas are masked white; image smooth: 4 4

Fig. 3.11 Validation of inverted soil moisture [vol.%] from ground scattering components (surface, dihedral) with in situ measurements of soil moisture at the two continuously recording moisture stations (site 1, Plus sign; site 2, Star sign) at Flevoland (left) for 11 different dates

# 3.3.4 Comparison with Single-/Dual-Pol Data

The developed algorithm is directly and solely based on the fully polarimetric observation space and therefore directly affected by the reduction to single- or dual-polarimetric data. This means that only in the case of fully polarimetric data, the vegetation- and/or soil roughness-induced depolarisation can be assessed, quantified and removed in the best way. A decomposition, separating the vegetation

the observation cycle at 29th of August 2009 and at Indian Head (right) from 21st of July until 4th of September 2009

from 11th of June until the 29th of August 2009 (colours) and at Indian head (right) for 12 dates from 4th of August until 4th of September 2009; validation box: 7 7 pixels

volume and the soil ground scattering components, is not/only partly feasible with single-/dual-polarimetric approaches.

Hence, there is a trade-off between decomposing fully polarimetric data and then solving a lower parameterised inversion problem or using single-/dual-polarimetric data and modelling the whole backscatter signal by solving an ambiguous, highly parameterised inversion problem.

Fig. 3.12 Multi-angular polarimetric decomposition for soil moisture retrieval under vegetation cover at L-band applied to E-SAR data of the OPAQUE 2008 campaign: (Top) Local incidence angle and soil moisture inversion results for a single-, bi- and tri-angular model-based decomposition and inversion are depicted. The inversion rate constantly

increases, when moving to a multi-angular decomposition and inversion. (Bottom) Validation of the SAR-based moisture estimates with in situ measurements from different crop types for each single measurement location (left) and for the mean of field values (right) (Jagdhuber et al. 2013a)

Fig. 3.13 Hybrid polarimetric decomposition for soil moisture retrieval under vegetation cover at L-band applied to E-SAR data of the AgriSAR 2006 campaign: (Top) Land use and soil moisture inversion results for a time series covering the entire vegetation cycle (April–July 2006) are depicted. Urban and forested areas are masked grey. (Bottom)

# 3.3.5 Discussion on the Role of Polarimetry, on the Maturity of the Application and Conclusion

Polarimetry and the acquisition of fully polarimetric SAR data are essential for the retrieval of soil moisture under vegetation. Only the polarimetric observation space with its

Validation of the SAR-based moisture estimates with in situ measurements from different crop types for each single measurement locations (left) and for the mean of field values (right). Symbology: Diamond, 5th of July; Stars, 7th of June; Plus, 19th of April) (Jagdhuber et al. 2013b, 2014)

capability of polarimetric decompositions provides and reveals the different properties (dielectric content, object geometry) of the scatterers on ground in the most complete way compared to backscatter intensity analyses or interferometric techniques. Therefore SAR polarimetry plays an essential role in this application.

In the last years, the application of soil moisture retrieval under vegetation advanced to a distinct level of maturity by using longer wavelength SAR (L-band) (Jagdhuber et al. 2012b, 2013a, b). Compared to C-band, L-band provides a deeper penetration into the vegetation canopy together with a convenient signal-to-noise ratio (SNR) for reliable polarimetric signal analyses (Jagdhuber 2012). Figures 3.12 and 3.13 show exemplarily two novel and innovative types of polarimetric decompositions for soil moisture inversion under vegetation at longer wavelength (L-band): a multi-angular, model-based decomposition and a hybrid (combined model-/eigen-based) decomposition. The multi-angular decomposition in Fig. 3.12 leads to the increase in inversion rate from 40% to 71%, when moving from a single-angular to a tri-angular model-based decomposition, which also reflects in the quality of the inversion with an RMSE of 5.85vol.% in contrast to the C-band results (in Fig. 3.11) (Jagdhuber et al. 2013a). In comparison to all presented inversion approaches, Fig. 3.12 reveals the best inversion and validation results of the novel hybrid decomposition technique using a modelbased decomposition for the volume removal and an eigenbased decomposition to retrieve the single ground scattering components for soil moisture inversion under vegetation cover for an entire vegetation growth period sensed during the AgriSAR 2006 campaign (Jagdhuber et al. 2013b). The inversion rate is always higher than 95% including the entire vegetation cycle. Moreover, the RMSE for all acquisition dates (April–July 2006) lies in the favourable range of 4,6vol.% (mean of field values) to 6.5vol.% (single scattering locations) for ecosystem applications.

Furthermore, first attempts were also made for retrieving soil moisture under boreal forest using P-band in order to enlarge the moisture retrieval to forested environments (Jagdhuber et al. 2012a). In addition, also X-band fully polarimetric data of TerraSAR-X are currently investigated for soil moisture retrieval on bare and sparsely vegetated soils for shorter wavelength SAR (Jagdhuber et al. 2013c, d). In this way, the fully polarimetric soil moisture estimation using decomposition techniques is approached covering the frequency range from X- to P-band considering the wavelength-dependent sensitivity for vegetation cover and the soil below.

# 3.4 Crop Phenology Estimation Using SAR Polarimetry

# 3.4.1 Introduction, Motivation and Literature Review

All agricultural crops present a continuous development, from sowing or transplanting to harvest, in which they grow and evolve in accordance with their biophysical characteristics and the farming practices applied to them. Phenology denotes such a succession of stages during the cultivation cycle and is commonly expressed using numerical scales (Meier 2001).

Tracking phenology of agricultural fields by remote sensing is useful for farmers with extensive fields because it provides key information for planning and triggering cultivation practices, so the main application of this EO product is precision farming. These cultivation practices (e.g. irrigation, fertilisation, effective germination counting, harvest, etc.) require timely inputs about the status of the plants and, specifically, about their condition or situation along the expected cultivation cycle.

Besides precision farming, timely information of phenology can contribute to agencies and institutions involved in market predictions, insurance policies, subsidies claims, etc. since such information complements their own data sources and provides a temporal schedule for the crop production and yield calendar.

Most of the applications require phenology information at parcel (field) level, but in some cases it may be necessary to provide values at pixel (sub-parcel) level, especially when dealing with detection of heterogeneities produced by cultivation problems (e.g. water salinity used of irrigation) and plant diseases (e.g. pests and plagues). Unfortunately phenology monitoring by satellite remote sensing has not attracted much attention in the past due to both the lack of time series of images and the cost of the ground campaigns. Nevertheless, this situation has changed in recent times, thanks to the launch of satellites with shorter revisit times (e.g. 11 days for TerraSAR-X) and reconfigurable acquisitions (different beams can be operated for more frequent observation of a particular area). Importantly for us these are also available in multiple polarisation channels, which prompted this research into phenology retrieval using polarimetric imaging radar (PolSAR).

Algorithms for phenology monitoring have to be devised individually for each crop type, based on the expected response of each crop to the sensor at its different stages, which can be extracted from the data themselves (training sets) or from previous experiments, models and simulations. In addition, approaches based on the availability of time series of images can improve importantly the performance of these algorithms, since phenology is intrinsically related to time.

The starting point of this application is the knowledge of which crop type or plant species is cultivated at the monitored fields, which is provided by the users or can be obtained from a crop type map. Then, the general objective of this product consists in estimating the current phenological stage of the plants in the parcel by exploiting a single PolSAR acquisition.

The topic of vegetation phenology monitoring by means of remote sensing has been mostly addressed in the past by analysing temporal variations of vegetation indices acquired by optical sensors. These vegetation indices are formed by combining different spectral bands which exhibit different sensitivities to biochemical plant constituents and canopy biophysical parameters. These indices are then used as a proxy for monitoring the plant growth stage by means of empirical relationships with structural parameters. Among all of them, the most important one is LAI (leaf area index) which plays a key role in the design of crop models for crop condition assessment and final yield prediction (Bach and Mauser 2003).

In addition to different airborne sensors operating worldwide (CASI, ROSIS, DAIS-7915), there are several operative satellite missions which have been providing multispectral data for large-scale Earth dynamics studies. Some of these instruments are the MODerate-resolution Imaging Spectroradiometer (MODIS) by NASA, the Advanced Very High Resolution Radiometer (AVHRR) by NOAA and the VEG-ETATION sensor on board SPOT satellite by CNES. In general, the main purpose of all these projects has been primarily focused on crop yield prediction and crop mapping/classification, being these tasks directly related to the estimation of phenology (Nellis et al. 2009).

Early studies by Badhwar and Henderson in 1981 (Badhwar and Henderson 1981) made use of Landsat Multispectral Scanners (MSS) in order to design a crop model able to determine development stage of corn and soybean. Further improvement was proposed by Tilton and Hollinger in 1982 (Tilton and Hollinger 1982) when they proposed an algorithm to predict the development stages early in the growing season by means of a principal component analysis of Landsat images. In 1990, Lloyd exploited the approximately linear relationship between solar energy absorption of plants and NDVI measurements acquired by NOAA-AVHRR in order to describe phenological events (Lloyd 1990). Likewise, in 1994 Reed et al. (1994) analysed time series of NOAA-AVHRR acquisitions as well, and they found a high correlation between NDVI temporal trends and phenological variations in case of spring wheat at a continental scale.

The work by Tucker in 1979 (Tucker 1979) is considered as one of the first contributions that proposed the systematic use of NDVI for characterising vegetation. Since then, NDVI has been the most widely used spectral index for vegetation monitoring. It combines the reflectance values of plants in red (low reflectance) and near-infrared (high reflectance) bands. In general, temporal variations of NDVI exhibit a high correlation with the main growth stages of plants, i.e. vegetative, reproductive, maturity and senescence stages.

The combination of other spectral bands has led to the development of refined optical indices that could yield different results depending upon the type and conditions of vegetation. Studies conducted in (Haboudane et al. 2004) demonstrated that LAI values for soybean, corn and wheat can be consistently estimated from vegetation indices at different phenological stages.

Further improvements have been proposed, such as the enhanced vegetation index (EVI) which was designed in order to increase the sensitivity to vegetation changes by considering the canopy background contributions as well as the atmosphere influences (Huete et al. 2006).

Contrary to the valuable and long experience acquired in hyperspectral systems, the use of active microwave remote sensing for agriculture monitoring activities is a relatively new topic (Lopez-Sanchez and Ballester-Berman 2009), also due to the scarcity of well-established and long-term research programmes developing both air- and space-borne missions capable of providing the required radar data sets. Fortunately, this situation has entirely changed in recent years after the initiation of a large number of radar remote sensing campaigns promoted by different aerospace agencies and institutions around the world such as ESA, CSA, NASA, DLR and JAXA.

After some pioneer works on the potential of radar for characterising natural targets (see, e.g. Ulaby et al. 1987), the interest on the use of active microwave sensors was greatly stimulated during the 1990s (Henderson and Lewis 1998) as a consequence of the launch of three satellites operating at C-band, i.e. ERS, ENVISAT-ASAR and RADARSAT-1.

Among other applications, vegetation phenology monitoring has been also tackled, but the literature on this topic is still very scarce. First contributions demonstrating the relationship between radar backscatter and growth stages of rice were presented by Le Toan et al. (1989), Kurosu et al. (1995) and Le Toan et al. (1997) in 1989, 1995 and 1997, respectively. Other follow-up works to this topic were also based on the analysis of incoherent radar measurements and can be found in (Inoue et al. 2002; Chen and McNairn 2006; Koay et al. 2007; Bouvet et al. 2009). These works have shown the potential of C-band HH/VV ratio for rice field discrimination and that a clear intensity signature appears as a consequence of phenological development of rice plants. More recently in (Lopez-Sanchez et al. 2012a), an algorithm to estimate the phenological stages throughout the whole growing season was designed on the basis of a coherent approach. However, the phenology retrieval issue has not been explicitly studied for other crops in the literature. Indeed, the only clear example we have found on this topic is the work by Wegmüller and Werner in 1997 (Wegmüller and Werner 1997) where they described the sensitivity of interferometric coherence to the fraction of vegetation cover and the potential for tracking phenological events for corn, sugar beets, potatoes and rapeseed.

Alternatively, the interest has been focused on estimating parameters related to crop growth, such as biomass and LAI (which in turn are intrinsically related to each other), as well as crop condition and type assessment. Early contributions by Brown et al. (1992) and Bouman and Hoekman (1993) made use of different frequency bands for separating crop types. Also the estimation of biomass from olive groves, sunflower, corn, sorghum, rape, wheat and alfalfa was studied in (Ferrazzoli et al. 1997). Estimation of LAI with saturation effect at 2–3 m2 /m2 has been also reported in (Ferrazzoli et al. 1992) by means of radar backscatter at C-band. The use of time series of backscattering coefficients, together with electromagnetic models and neural networks, for such a purpose has been also addressed in the literature (Del Frate et al. 2004). Backscattering coefficients at different polarimetric channels were also correlated with phenology and cultivation practices (e.g. irrigation) for several crop types in (Moran et al. 2012).