SMOS L2 algorithm over forests: Description and generation of global maps

Rachid Rahmoune, Paolo Ferrazzoli

Tor Vergata University, DICII, Via del Politecnico 1, Roma, Italy

Yann Kerr, Philippe Richaume

CESBIO, Avenue. Edouard Belin 18, Toulouse, France

Corresponding author:

Rachid Rahmoune

Tor Vergata University, DICII,

Via del Politecnico, 1

Rome, Italy

Tel. +39 06 72597425

Fax +39 06 72597460

E-mail: rahmoune@disp.uniroma2.it

SMOS L2 algorithm over forests: Description and generation of global maps

Rachid Rahmoune, Paolo Ferrazzoli

Tor Vergata University, DISP, Via del Politecnico 1, Roma, Italy

Y. Kerr, P. Richaume

CESBIO, Avenue. Edouard Belin 18, Toulouse, France

Abstract This paper shows global maps of optical depth and soil moisture over land, obtained using the SMOS Level 2

algorithm. The focus is on forested areas, where the approach adopted to develop the algorithm can be subdivided into

different steps. First a theoretical model, which was previously developed and tested using ground based and airborne

measurements, generated parametric outputs. By fitting this output data set, the albedo and the optical depth of a simple

first order radiative transfer model were estimated. Then, this simplified forest model was included in the general Level

2 algorithm described in the Algorithm Theroretical Baseline Document (ATBD). The paper describes the details of this

procedure and shows some retrieval results. First, the algorithm was run with three free parameters: Soil moisture,

optical depth, and albedo. The retrieved albedo resulted to be close to the initial estimate (0.08) for Boreal forests, while

it was lower for Tropical forests. Running again the algorithm with the albedo fixed, a global map of optical depth was

generated. The spatial features of the map follow the global information about forest biomass available in the literature.

Finally it was found that, on average, the influence of seasonal effects on optical depth is moderate.

Keywords: Microwave radiometry, Forests, Optical depth

I INTRODUCTION

Over land, the Soil Moisture and Ocean Salinity (SMOS) mission has the objective to retrieve soil moisture over several

kinds of land cover, including low vegetation and forests [1]. Developing a reliable forward model and a valid retrieval

algorithm for forests is important for several reasons. First of all, forest cover dominates several SMOS pixels.

Moreover there are mixed pixels, and even when other land covers (e.g. agricultural fields, grass) are dominant, a

correct characterization of forest emission improves the retrieval accuracy [2].

It is well established that soil moisture retrieval is difficult under forest cover [3]-[5]. In fact the attenuation related to

the high wood biomass reduces the contribution of the soil surface to the overall emission, thus reducing the sensitivity

to variations of soil moisture. A further reduction of sensitivity is associated to litter cover [3], [5]-[6]. At C band and

higher frequencies, crown attenuation makes soil moisture retrieval very difficult in most of cases. At L band some

sensitivity to soil moisture is expected, since crown attenuation is lower than at the higher frequencies, but establishing

quantitative models is not simple. While for low vegetation the optical depth can be simply related to biomass with a

reasonable accuracy [7], the same relationship cannot be easily extended to arboreous biomass, which is characterized

by different dielectric and geometrical properties.

Important ground-based and airborne experiments have been undertaken, providing an insight into this problem.

Multitemporal measurements of brightness temperature were carried out over coniferous and broadleaf forests using L

band radiometers mounted on a tower [3]-[5], [8]-[11], and were accompanied by detailed measurements of ground

variables, such as soil moisture, wood biomass, and forest geometrical properties. In some cases, outputs were used to

test theoretical models [5], [11], or fit the parameters of semi-empirical models [8], [10]. Upward looking radiometric

data were also collected in some forests [8], [10]. Moreover, airborne campaigns have provided useful data for

investigating the relationship between emissivity and forest biomass [12]-[14], and soil conditions [15]. Although these

efforts are very important, they are not yet sufficient to reach a complete understanding of the forest emission problem,

and to draw conclusive considerations. While some studies indicate the radiometric sensitivity to variations of soil

moisture to be poor in developed forests [3]-[5], others over moderately dense forests with thin litter show the

sensitivity to be appreciable [9]-[10], [15], although still lower than over low vegetation. Moreover, further efforts are

required to establish if the sensitivity to soil moisture is mostly reduced by crown attenuation or by litter cover. In

summary, modelling forest attenuation is an important issue which requires further research.

This paper investigates the problem using SMOS data and the L2 algorithm, which is described in the Algorithm

Theroretical Baseline Document (ATBD) [16], [17]. The algorithm is based on two main components. The first consists

of a forward model, which estimates the brightness temperature emitted by land nodes of SMOS using a priori

information (auxiliary data) about land cover, surface temperature, Leaf Area Index (LAI), etc., and initial estimates of

soil moisture taken by the data base of the European Centre for Medium-Range Weather Forecasts (ECMWF). The

second consists of an inversion process, which estimates the actual soil moisture and optical depth by using

multiangular measurements, forward model outputs and a Cost Function [17]. For all SMOS nodes classified as soil

covered by vegetation, the brightness temperature is estimated by a simple first order radiative transfer model [7]. In

order to keep the algorithm consistent, and to consider the case of mixed pixels, the same approach is kept for both low

vegetation and forests [17]. For the case of forests, the radiative transfer parameters (optical depth) and (albedo) are

estimated using the synthetic outputs of the theoretical model described in [18], according to the procedure which was

further developed in [19]. The theoretical model was tested and revised using sets of ground based and airborne

measurements carried out over various forests in different environments [20]-[22].

This paper describes the procedure adopted to obtain the parameters of the simple first order model, which will be

named ATBD forest model. Then, global maps of variables retrieved by L2 algorithm will be shown. Initially, the

algorithm was run with three free variables to be retrieved: Soil Moisture (SM), vegetation optical depth (), and

vegetation albedo (). Histograms of retrieved will be shown, and compared against the first estimate of this variable

based on the theoretical model. Then, the algorithm was run keeping fixed the previously retrieved values, in order to

generate global maps of and SM. Finally, seasonal effects were evaluated by comparing maps of obtained in July and

November.

II. THE FORWARD MODEL

A. The basic theoretical model

As stated in the Introduction, the initial values of and for forests were estimated by fitting the outputs of a

theoretical model, which is based on the radiative transfer theory and adopts a discrete approach [18]. Trunks and

branches are represented as dielectric objects with the shape of cylinders, while leaves are represented as needles or

disks. The bistatic scattering cross sections and extinction cross sections are computed by using suitable

electromagnetic approximations [18]. The bistatic scattering coefficient of the soil is computed by using the Integral

Equation Model, in its bistatic version [23]. Single contributions are combined by using the matrix doubling algorithm,

which allows us to compute the overall bistatic scattering coefficient in all directions, including multiple scattering

effects. The overall reflectivity is finally computed by integration over the upper half space and the emissivity is

obtained by the energy conservation law. Details about the electromagnetic model are given in [18].

For litter, the model published in [20], which considers the litter as a dielectric layer over the soil, was applied. At first,

air-litter and soil-litter interfaces are assumed to be flat. The layer is a mixture of air and dielectric material. The weight

of the dielectric material is the basic input, and can be related to the litter-fall by using relationships available in the

literature [24]. The litter moisture is related to soil moisture, using an empirical relationship based on field

measurements [3]. The permittivity of the dielectric material is computed as a function of moisture, and the permittivity

of the layer mixture is computed by means of the quadratic refractive model for mixtures [25]. The overall reflectivity

of this composite medium, made by soil and litter, is computed using a coherent multiple reflection model. Further

details about the procedure, which leads to computing the bistatic scattering coefficient of the ensemble litter/soil

included it in the general model are given in [20].

This theoretical forest model was previously tested against ground based and airborne measurements [20]-[22]. Rms

errors were in the range 2.5 K 4.0 K. The objective of this paper is not to make further direct tests of this theoretical

model, but to show how its outputs were used to compute the parameters of the simplified ATBD forest model [16]-

[17] and to estimate how the SMOS algorithm performs at large scale over forests.

B. Input variables and estimate of parameters

The procedure assumes to know, besides soil variables, the following inputs:

Leaf Area Index contributed by forest leaves LAIF

Leaf Area Index contributed by herbaceous vegetation LAIV

distribution of trunk diameters at breast height dbh [cm]: p(dbh)

moisture [g/g] and dry matter density D [g/cm3] of trunks, branches and leaves.

A general scheme of the procedure is given in Figure 1.

Figure 1. General scheme of the modeling procedure.

For specific forest plots, the dbh distribution depends on the age and the type of trees. At the scale of SMOS pixels, the

differences are smoothed. A Gaussian distribution is assumed here, with an average value of 20 cm and a

standard deviation of 5 cm. A parametric study indicated that the selection of this parameter is not critical, provided the

woody biomass is lower than about 400 m3/ha, and the cases of very young forests (dbh =10 cm) are excluded [21]. For

the range of dbh values, a lower limit dbhmin equal the average minus three times the standard deviation, and an

upper limit dbhmax equal to plus three times the standard deviation is imposed. The input data set required by the

theoretical model is obtained according to the procedure outlined in Appendix A. A key variable is the actual maximum

yearly value of Leaf Area Index LAIFmax.

The theoretical model is used to predict the trends of emissivity (in full leaf development) as a function of LAIFmax.

Examples for the case of broadleaf forests are given in Figure 2. The case of no litter is also considered. As expected,

the dynamic range is reduced by increasing LAIFmax, while it is still significant for LAIFmax lower than about 3. The

effects of litter are low for dry soils, but become important for moist soils. Litter properties are also related to LAIFmax

using the relations described in Appendix A.

Figure 2. Simulated trends of emissivity vs. soil moisture content (m3/m3) for broadleaf forests in full leaf development.

Polarization: vertical (left), and horizontal (right). Maximum yearly value of forest LAI: 1, 2, 3, 4. Continuous lines:

with litter. Dashed lines: without litter.

The simulation outputs are used to fit the equivalent parameters F (forest optical depth) and F (forest albedo),

contributed by arboreous vegetation and assumed to be independent of polarization, of the ATBD forest model [16],

[17]. The fitting procedure was subdivided into various steps. First, a height standard deviation of soil roughness equal

to 1.5 cm was selected. The roughness factor HR of the ATBD model was set by imposing the surface emissivity of the

ATBD model to be equal to the surface emissivity of the theoretical model. The theoretical model was run at both

polarizations for an angular range of 5-55 and a SMC range of 5%-30%. Then, the ATBD model was run for the same

conditions, and F and F were selected in order to yield the minimum rms difference between brightness temperature

(K) outputs of the two models. We verified that the obtained results are scarcely affected by the initial choice about

roughness, provided the same roughness is selected for the two models.

The trends of F and F as a function of LAIFmax , for the case of broadleaf forests, are shown in Figure 3. Values of F

lower than about 0.6 are achieved for sparse forests with LAIFmax

Figure 3. Trends of fitted radiative transfer parameters F and F as a function of maximum yearly value of forest LAI,

for broadleaf forests.

Figure 4. Rms error in the estimate of F (albedo) as a function of maximum yearly value of forest LAI, for broadleaf

forests.

developed forests, the fitting of the albedo is less reliable, in that the emisivity is more influenced by soil properties.

A linear regression was then applied to the relationship between the optical depth and LAIFmax, in order to compute the

coefficients of a linear relationship such as:

F = bF LAIFmax (1)

The resulting coefficients are:

bF = 0.29 for broadleaf forests.

bF = 0.36 for coniferous forests.

Equation (1) gives the maximum yearly value of F, which is achieved in full leaf development. If the forest is

deciduous, F decreases in late autumn and winter due to the absence of leaves. Further model simulations, not shown

here, indicated that the corresponding decrease of bF coefficient is 0.03 units. Therefore, a general expression is:

F = bF LAIFmax 0.03 ( LAIFmax - LAIF) (2)

The total optical depth, including contributions of both forest vegetation and herbaceous vegetation is given by:

= F + bV LAIV (3)

The bV coefficient is set equal to 0.06, which is the value adopted by the ATBD for low vegetation [17].

Then, we assumed:

LAIF = fF LAI

LAIV = fV LAI

LAI is the total Leaf Area Index, available by ECOCLIMAP data base [26].

fF and fF represented the fractions of LAI contributed by arboreous leaves and herbaceous vegetation, respectively.

Some data are available in the literature [27]-[29], showing some dispersion. In order to simplify the initial guess of

we made an assumption which was verified in some broadleaf and coniferous forests [28]-[29]: fF = 0.6, fV = 0.4

For the albedo, the value achieved fitting the outputs of the theoretical model (as described above) for the dense forests

(higher values of LAIFmax in Figure 3) is used:

F = 0.08

This value represented a good fit for both broadleaf and coniferous forests.

The roughness parameter HR [17] has been set equal to 0.3. This value corresponds to a height std. of about 1.0 cm,

which is quite realistic [30].

The procedure described in this Section leads to compute the optical depth and the albedo of the ATBD forest model.

The performance of this model against SMOS signatures will be estimated in the next Sections.

III THE L2 ALGORITHM

The SMOS SM L2 algorithm [17] is iterative. It estimates the soil moisture and the optical depth by minimizing the rms

difference (Cost Function) between the measured multiangular brightness temperatures (L1C data) and the forward

model simulations. In this process, the algorithm uses as initial values the soil moisture predicted by ECMWF and the

optical depth predicted by the procedure illustrated in Section II.B

In this work, the algorithm was run for the SMOS data collected in the time intervals form July 1 to July 4, 2011, and

from November 1 to November 4, 2011. For each SMOS node [17], all the passes for which the retrieval was successful

were considered and the results were averaged.

A. Estimate of the albedo

Initially, the algorithm was run with three free parameters: Soil moisture, optical depth and albedo. The histograms of

retrieved albedo, computed using data collected from July 1, 2011 to July 4, 2011 and subdivided into main continental

regions, are reported in Figure 5. The value initially estimated by the forward model (=0.08) is reported for

comparison. For Boreal forests of North America and Europe/Asia, this value is close to the maximum of the

histograms. However, for tropical and Subtropical forests of South America and Polynesia the maximum of the

histograms is close to 0.06. Explaining in detail this difference is the subject of future work. At present time, it can be

stated that the forward model described in Section II and Appendix A is based on assumptions and allometric equations

mostly derived for Boreal forests [31]-[34]. Parametric investigations [21] showed that the emissivity simulated for the

higher biomass, which is directly related to , can be appreciably influenced by geometrical properties, such as

distribution of branch dimensions.

Figure 5. Histograms showing number of SMOS nodes with retrieved albedo equal to abscissa value. The dashed

vertical bar indicates the initial value (0.08) predicted by the theoretical model.

B. Global maps of optical depth and soil moisture

Figure 6. Global map of retrieved optical depth.

Using again the data collected from July, 1, 2011 to July, 4, 2011, global maps of optical depth and soil moisture were

generated. In this running of the algorithm, was set equal to 0.08 for boreal forests and 0.06 for tropical and

subtropical forests, considering the results reported in Figure 5. The algorithm described in [17] was used for low

vegetation. The resulting map of optical depth is shown in Figure 6.

As expected, the optical depth in the areas covered by forests is higher than in low vegetation. In general, the map

shows a correspondence with forest biomass maps available in the literature [35]-[36]. More detailed quantitative

comparisons are in progress. values higher than 1.0 are obtained in large part of the dense and thick Amazon forest,

while a decrease is observed moving towards the subtropical Chaco forest, mostly covering Paraguay and Northern

Argentina, where the vegetation is less dense. A high optical depth is also observed in Polynesia. Moderate values,

typically in the range 0.7-0.9 are obtained in Boreal forests of Canada, US, and Siberia. The retrieval is unsuccessful in

the Congo forest, where the forest is dense and the quality of L1C data resulted to be poor. In many regions of Europe

and Asia, the retrieval was hampered by heavy RFI effects [37], as confirmed by the specific flag included in L1C data.

Figure 7. Ratios between retrieved and initial values of optical depth for forest nodes.

The initial estimate of is based on the procedure depicted in Section II, which contains several approximations. The

iterative L2 algorithm starts from this initial estimate and uses SMOS L1C data in order to find the minimizing the

Cost Function for each node [17]. For forest nodes, a map of the ratio between retrieved and initial values is reported in

Figure 7. In general, the ratio is close to the unity over Amazonia, while is appreciably lower than 1 are in most of

Boreal forests, particularly in the case of Coniferous trees. These variations are partially due to approximations of the

model, but can be also attributed to inaccuracies of ECOCLIMAP data base.

Figure 8. Global map of retrieved soil moisture.

The world map of retrieved soil moisture is reported in Figure 8. When the retrieved optical depth is moderate, as in

Canada and most of Siberia, SM is retrieved. When the optical depth is high, there are areas where the retrieval fails,

since the sensitivity to variations of SM can be too low with respect to the noise of L1C data.

This global map confirms some expected spatial trends, such as the decrease of SM from Atlantic to Pacific coast of

South America (for a latitude lower than about -20). Detailed ground truth of SM below forests are available in few

sites only. Multitemporal tests are in progress.

C. Seasonal effects

Figure 10. Map of differences between values retrieved in November and July, 2011, for North America.

Figure 11. Histogram of differences between values retrieved in November and July, 2011, for North America.

Finally, seasonal effects were investigated. The retrieval of optical depth was made again using SMOS data averaged in

the time interval from November 1, 2011 to November 4, 2011, using previous assumptions about albedo. A map of the

differences between values obtained in November and July is reported in Figure 10 for the North American continent,

where large forests are present and seasonal effects are expected. Positive and negative changes are mixed in a complex

way, probably due to combined effects of leaf cycle and change of litter biomass. Of course, variations can be also due

to errors in the estimate. Histograms of variations is reported in Figure 11. The maximum is close to zero, which

confirms the previous considerations. Most of variations are in a range +/- 0.2, which is low with respect to optical

depth values of July (0.7-0.9). This result confirms that an important contribution to forest optical depth is constant with

time, since is due to branches, in agreement with previous experimental results [9]-[10], and model simulations [21].

V. CONCLUSIONS

The forward model used for forests in the SMOS L2 algorithm has been described. The coefficients were obtained by

fitting the outputs of a theoretical model.

The retrieval algorithm was run to estimate the forest albedo and to generate global maps of retrieved optical depth and

soil moisture. The retrieved albedo is close to the initial estimate for Boreal forests, while is lower for Tropical and

Subtropical forests. Explaining this result is the subject of future investigations.

The global map of optical depth follows the spatial trends expected on the basis of available information about forest

biomass. The SM map follows some expected spatial patterns, although more in depth quantitative investigations are

needed.

Seasonal effects on forest optical depth are moderate. This variable shows positive and negative variations from July to

November, but a general tendency is not observed.

APPENDIX A

THE INPUT DATA SET

Let the minimum and maximum values of diameter at breast height dbh be defined as dbhmin and dbhmax, respectively.

The dbhmax-dbhmin range is subdivided into MT intervals, of width given by:

dbh=dbh maxdbhmin

M T (A1)

For the i-th interval, the number NTi of trees per unit area [ha-1] with dbh value within the interval is given by:

dbh+dbh

d(dbh)p(dbh)N=Ni

idbhTTi (A2)

NT is the tree density [ha-1], and dbhi is the value of dbh at the lower limit of the interval.

Using allometric equations [31], the total dry biomass and its subdivision into trunk, branch and leaf fractions, can be

related to the diameter at breast height (dbh). For each interval i, the tree dry biomass DBi (kg) is estimated by means of

the allometric equation [31]:

)](dbhb+[b=DB i1i lnexp 0 (A3)

b0 and b1 are coefficients depending on tree species.

For broadleaf forests, average coefficients are:

b0 = - 2.48

b1 = 2.483

For coniferous forests, average coefficients are:

b0 = - 2.54

b1 = 2.481

Other allometric equations [31] give the biomass components (kg) contained in trunks (DBti ), leaves (DBli ), and

branches (DBbi ).

For trunks:

)dbha+(aDB=DB

i

t1t0iti exp (A4)

For broadleaf forests:

at0 = - 0.30

at1 = - 5.42

For coniferous forests:

at0 = - 0.37

at1 = - 1.8

For leaves:

)dbha+(aDB=DB

i

l1l0ili exp (A5)

For broadleaf forests:

al0 = - 4.08

al1 = 5.88

For coniferous forests:

al0 = - 2.96

al1 = 4.47

For branches:

DBbi =DB iDB tiDB li (A6)

The maximum yearly value of leaf dry biomass per unit area DBL (t/ha) can be derived as a function of the maximum

yearly value of arboreous Leaf Area Index LAIFmax by means of empirical formulas.

For broadleaf forests, we have adopted the formula given in [30]:

DBL=LAI Fmax

1 .72 (A7)

For coniferous forests, the coefficient has been estimated by fitting the data made available by [24]:

DBL=LAI Fmax

0 .43 (A8)

The tree density NT can be obtained by using (A2) and by imposing:

dbh+dbh

d(dbh)p(dbh)DBN=DBN=DBLi

idbh

N

=iliT

N

=iliTi

11

0.0010.001 (A9)

The required variables for trunks, branches, leaves, and litter can be computed as indicated below.

1) Trunks

Trunks are modelled as equivalent cylinders. For the i-th interval of dbh values, the cylinder diameter is assumed to be

equal to dbhi. The dry biomass DBti (kg) is computed by (A3) and (A4), and converted into woody volume WVti (m3),

according to the procedure described in Appendix A. The height of the cylinders is given by:

22/0.01 )dbh(WV

=Hi

titi

(A10)

Since there is a trunk per tree, the number of trees with diameter equal to dbhi is given by equation (A3). Trunks are

assumed to be vertical.

2) Branches

Branches are subdivided into sections, and sections are modelled as cylinders. For the i-th interval of dbh values, the

branch dry biomass DBbi can be computed by (A3)-(A6) and converted into woody volume WVbi. Branches are

subdivided into primary and secondary branches. The latter are assumed to contain 30% of the overall branch volume,

according to the estimate given by [33].

For primary branches belonging to a tree with dbh equal to dbhi, the maximum diameter db_maxi has been related to dbhi..

For coniferous forests, the relationship made available by [34] has been used. For broadleaf forests, the maximum

diameter db_maxi has been assumed to be equal to 0.25 dbhi. This coefficient, which was aleady adopted in [21], agrees

with available photos of some broadleaf forests. The branch diameters are assumed to follow a Gaussian pbi distribution,

centered at db_maxi/2. The range between 0 and db_maxi is subdivided into Mbi intervals of width d. For the j-th interval, dj

is the central value, and pbij gives the fraction of total branch volume filled by branches with diameter internal to the

interval. This assumption is in general agreement with the measured data reported by [33], although they were fitted by

different functions. The length of the cylinder is equal to 50 dj, with an upper limit at 25 cm [33].

Also for secondary branches the distribution of filled volumes pbsi is assumed to be Gaussian. The average diameter is

equal to 0.6 cm and the length is computed similarly to the case of primary branches.

Finally, the total number of cylinders with diameter dj is given by:

)p+(pN=N bsijbijTM

=iTibj

1 (A11)

This distribution is used by the electromagnetic model as input. Both primary and secondary branches are assumed to

be randomly oriented.

3) Leaves

Broad leaves are described as circular disks with a radius of 2 cm and a thickness of 0.02 cm. Coniferous leaves are

decribed as needles with a height of 10 cm and a diameter of 2 mm. The number of elements is obtained as the ratio

between LAIF and element area. Elements are randomly oriented.

4) Litter

For litter, the dry biomass DBLI (kg/m2) is related to litter-fall. Typically, 2 years of litter-fall are considered. By fitting

data of broadleaf forests provided by [24] we have assumed:

DBLI=1.25 DBL for broadleaf forests

DBLI=0.25 DBL for coniferous forests

DBL is the maximum yearly value of dry leaf biomass, and can be computed by (A7), (A8).

The litter thickness is related to the litter biomass using the formula given in [38].

ACKNOWLEDGMENTS

This work was partially supported by ESA, European Space Agency, under Contracts 21527/08/I-LG and

4000101152/LO/I-AM

REFERENCES

[1] Y. H. Kerr, P. Waldteufel, J.-P. Wigneron, J.-M. Martinuzzi, J. Font, and M. Berger, "Soil Moisture Retrieval from

Space: The Soil Moisture and Ocean Salinity. (SMOS) Mission", IEEE Trans. Geosci. Remote Sensing, vol. 39, pp.

1729-1735, 2001.

[2] A.A. Van de Griend, J.-P. Wigneron, and P. Waldteufel, Consequences of Surface Heterogeneity for Parameter

Retrieval from 1.4 GHz Multiangle SMOS Observations, IEEE Trans. Geosci. Remote Sensing, vol. 41, pp. 803-811,

2003.

[3] J. P. Grant, J.-P. Wigneron, A. A. Van de Griend, A. Kruszewsky, S. S. Sbjrg, and N. Skou, A field experiment on

microwave forest radiometry: L-band signal behaviour for varying conditions of surface wetness, Remote Sensing

Environ., vol. 109, pp. 1019, 2007.

[4] M. Guglielmetti, M. Schwank, C. Mtzler, C. Oberdrster, J. Vanderborght, and H. Flhler, FOSMEX: Forest soil

moisture experiments with microwave radiometry, IEEE Trans. Geosci. Remote Sensing, vol. 46, pp. 727735, 2008.

[5] M. Kurum, P. ONeill, R. Lang, M. Cosh, A. Joseph, and T. Jackson, Impact of Conifer Forest Litter on Microwave

Emission at L-Band, IEEE Trans. Geosci. Remote Sensing, vol. 50, pp. 1071-1084, 2012.

[6] J.P. Grant, A.A. Van de Griend, M. Schwank, and J.P. Wigneron Observations and modeling of a pine forest floor

at L-band, IEEE Trans. Geosci. Remote Sensing, vol. 47, pp. 20242034, 2009.

[7] J-P Wigneron, Y. Kerr, P. Waldteufel, K. Saleh, M.-J. Escorihuela, P. Richaume, P. Ferrazzoli, P. de Rosnay, R.

Gurney, J.-C. Calvet, J.P. Grant, M. Guglielmetti, B. Hornbuckle, C. Matzler, T. Pellarin, and M. Schwank, L-band

Microwave Emission of the Biosphere (L-MEB) Model: description and calibration against experimental data sets over

crop fields, Remote Sensing Environ., vol. 107, pp. 639655, 2007.

[8] J. P. Grant, K. Saleh, J.P. Wigneron, M. Guglielmetti, Y.H. Kerr, M. Schwank, N. Skou, N. and A. A. Van de Griend,

Calibration of the L-MEB Model Over a Coniferous and a Deciduous Forest, IEEE Trans. Geosci. Remote Sensing,

vol. 46, pp. 808-818, 2008.

[9] M. Guglielmetti, M. Schwank, C. Mtzler, C. Oberdrster, J. Vanderborght, and H. Flhler, Measured microwave

radiative transfer properties of a deciduous forest canopy, Remote Sensing Environ., vol. 109, no. 4, pp. 523532,

2007.

[10] E. Santi, S. Paloscia, P. Pampaloni, and S. Pettinato, Ground-based microwave investigations of forest plots in

Italy, IEEE Trans. Geosci. Remote Sensing, vol. 47, pp. 30163025, 2009.

[11] M. Kurum, R. Lang, P. ONeill, A. Joseph, T. Jackson, and M. Cosh A First-Order Radiative Transfer Model for

Microwave Radiometry of Forest Canopies at L-band, IEEE Trans. Geosci. Remote Sensing, vol. 49, pp. 31673179,

2011.

[12] R. H. Lang, P. de Matthaeis, D. M. LeVine, S. Bidwell, M. Haken, and N. Chauhan, L-band radiometer

measurements of coniferous forests, in Proc. International Geoscience and Remote Sensing Symposium, Honolulu,

2000.

[13] K. Saleh, J. P. Wigneron, J. C. Calvet, E. Lopez-Baeza, P. Ferrazzoli, M. Berger, P. Wursteisen, L. Simmonds, and

J. Miller, The EuroSTARRS airborne campaign in support of the SMOS mission: first results over land surfaces, Int.

J. Remote Sensing, vol. 25, pp. 177-194, 2004.

[14] G. Macelloni, S. Paloscia, P. Pampaloni, and R. Ruisi Airborne multifrequency L- to Ka-band radiometric

measurements over forests, IEEE Trans. Geosci. Remote Sensing, vol. 39, pp. 2507-2513, 2001.

[15] J. P. Grant, A. A. Van de Griend, J.-P. Wigneron, K. Saleh, R. Panciera, and J. P. Walker On the Influence of Forest

Cover Fraction on L-band Soil Moisture Retrievals from Heterogeneous Pixels using Multi-Angular Observations,

Remote Sensing Environ., vol. 114 (5), pp. 1026-1037, 2010.

[16] Y. H. Kerr, P. Waldteufel, P. Richaume, I. Davenport, P. Ferrazzoli, and J.-P. Wigneron, J.-P. SMOS level 2

processor for soil moisture, algorithm theoretical based document (ATBD). CESBIO, IPSL-Service d'Aronomie,

INRA-EPHYSE, Reading University, Tor Vergata University. SO-TN-ESL-SM-GS-0001 Issue 3.e, 09/12/2010, 2010.

[17] Y. H. Kerr, P. Waldteufel, P. Richaume, J. P. Wigneron, P. Ferrazzoli, A. Mahmoodi, A. Al Bitar, F. Cabot, C. Gruhier, S. E.

Juglea, D. Leroux, A. Mialon, S Delwart, The SMOS Soil Moisture Retrieval Algorithm, IEEE Trans. Geosci. Remote Sensing,

vol. 50, pp. 1367-1383, 2012.

[18] P. Ferrazzoli and L. Guerriero, ``Passive microwave remote sensing of forests: a model investigation'', IEEE Trans.

Geosci. Remote Sensing, vol. 34, pp. 433-443, 1996.

[19]P. Ferrazzoli, L. Guerriero, and J. P. Wigneron, Simulating L-band emission of forests in view of future satellite

applications, IEEE Trans. Geosci. Remote Sensing, vol. 40, pp. 2700-2708, 2002.

[20] A. Della Vecchia, P. Ferrazzoli, J.-P. Wigneron, and J. Grant, Modeling forest emissivity at L band and a

comparison with multitemporal measurements, IEEE Geosci. Remote Sensing Letters, vol. 4, pp. 508-512, 2007.

[21] A. Della Vecchia, P. Ferrazzoli, L. Guerriero, R. Rahmoune, S. Paloscia, S. Pettinato and E. Santi, "Modeling the

Multifrequency Emission of Broadleaf Forests and Their Components," IEEE Trans. Geosci. Remote Sensing, vol.48,

pp. 260-272, 2010.

[22] R. Rahmoune, P. Ferrazzoli, J. P. Walker and J. P. Grant, L-band emission from a Eucalyptus forest in various soil

conditions during the NAFE campaign, Proc. of Microrad 2010 Symposium, Washington, pp.81-85, 2010, IEEE 978-1-

4244-8121-7/10.

[23] A.K. Fung , Microwave scattering and emission models and their applications, Artech House, Norwood, USA,

1994.

[24] M. G. R. Cannell, Ed., World Forest Biomass and Primary Production Data, Academic, New York, 1982.

[25] F.T. Ulaby, R.K. Moore, and A.K. Fung, Microwave Remote Sensing. Active and Passive Vol. I. Reading

(USA): Addison Wesley, ch. 4, pp. 232-237, 1982.

[26] J. L. Champeaux, V. Masson, and F. Chauvin, "ECOCLIMAP: a global database of land surface parameters at 1 km

resolution" Meteorol. Appl., vol. 12, pp. 2932, 2005.

[27] L. Misson, D.D. Baldocchi, T.A. Black, P.D. Blanken, Y. Brunet, J. Curiel Yuste, J.R. Dorsey, M. Falk, A. Granier,

M.R. Irvine, N. Jarosz, E. Lamaud, S. Launiainen, B.E. Law, B. Longdoz, D. Loustau, M. McKay, K.T. Paw, T. Vesala,

D. Vickers, K.B. Wilson, and A.H. Goldstein, Partitioning forest carbon uxes with overstory and understory eddy-

covariance measurements: A synthesis based on FLUXNET data, Agricultural and Forest Meteorology , vol. 144, pp.

1431, 2007.

[28] A. Peduzzi, H. Lee Allen, and R. H. Wynne, Leaf Area of Overstory and Understory in Pine Plantations in the

Flatwoods, South. J. Appl. For., vol. 34, pp. 154-160, 2010.

[29] P. D. Blanken, T. A. Black, P. C. Yang, H. H. Neumann, Z. Nesic, R. Staebler, G. den Hartog, M.D. Novak, and X.

Lee, Energy balance and canopy conductance of a boreal aspen forest: Partitioning overstory and understory

components, Journal of Geophysical Research, Vol. 12, pp. 28915-28927, 1997.

[30] J-P Wigneron, A. Chanzy, Y. Kerr, H. Lawrence, J.-C. Shi, M-J Escorihuela, V. Mironov, A. Mialon, F. Demontoux,

P. de Rosnay, and K. Saleh, Evaluating an improved parameterization of the soil emission in L-MEB, IEEE Trans.

Geosci. Remote Sensing, vol. 49, pp. 1177-1189, 2011.

[31] J. C. Jenkins, D. C. Chojnacky, L. S. Heath, and R. A. Birdsey, National-scale biomass estimators for United

States tree species, Forest Science, vol. 49, pp. 12-26, 2003.

[32] H.H. Bartelink, "Allometric relations for biomass and leaf area of Beech (Fagus sylvatica L.)," Ann.Sci. For., vol.

54, no. 1, pp. 39-50,1997

[33] K. Saleh, A. Porte, D. Guyon, P. Ferrazzoli, and J.-P. Wigneron, A geometric description of a Marittime Pine

forest suitable for discrete microwave models, IEEE Trans. Geosci. Remote Sens.ing, vol. 43, pp. 2024-2035, 2005.

[34] E.S. Kasischke, N.L. Christensen, and E.M. Haney, Modeling of geometric properties of loblolly pine tree stand

characteristics for use in radar backscatter studies, IEEE Trans. Geosci. Remote Sensing, vol. 32, pp. 800-822, 1994.

[35] FAO, The status of forests: the Global Forest Resources Assessment 2000,

http://www.fao.org/docrep/003/y0900e/y0900e05.htm

[36 ] NASA and Jet Propulsion Laboratory, NASA Map Sees Earths Trees in New Light, 2011

http://www.jpl.nasa.gov/news/news.cfm?release=2012-044

[37] R. Oliva, E. Daganzo-Eusebio, Y. H. Kerr, S. Mecklenburg, S. Nieto, P. Richaume, and C. Gruhier, SMOS Radio

Frequency Interference scenario: Status and actions taken to improve the RFI environment in the 1400-1427-MHz

passive band, IEEE Trans. Geosci. Remote Sensing, vol. 50, pp. 1427-1439, 2012.

[38] Y. Sato, T. Kumagai, A. Kume, K. Otsuki, S. Ogawa, "Experimental analysis of moisture dynamics of litter layers -

the effects of rainfall conditions and leaf shapes," Hydrological Processes, vol. 18, pp. 3007-3018, 2004.