# SMOS L-VOD Retrieved by Level 2 Algorithm and its Correlation With GEDI LIDAR Products

Cristina Vittucci ID, Leila Guerriero ID, Member, IEEE, Paolo Ferrazzoli ID, Senior Member, IEEE, Philippe Richaume ID, and Yann H. Kerr ID, Senior Member, IEEE

**Abstract**—In this article, L-band vegetation optical depth (L-VOD) retrieved by Soil Moisture and Ocean Salinity (SMOS) in four large continents is compared against vegetation parameters (RH100 and PAI) retrieved by Global Ecosystem Dynamics Investigation (GEDI) LIDAR instrument, recently launched by NASA. In order to manage the different spatial resolutions, GEDI parameters were averaged within SMOS pixels and a threshold to the minimum number of GEDI samples per SMOS pixel was applied. Spatial correlations between monthly averages were investigated from May 2019 to April 2020. For continents mostly covered by tropical vegetation (Africa and South America), the Pearson correlation coefficients between L-VOD and RH100 are higher than 0.8 in all months of the year. Conversely, seasonal effects are observed in North America and Asia, producing a lower correlation in colder months. RMS differences between L-VODs retrieved by SMOS and the ones obtained using a linear regression over RH100 are lower than 0.2 for all cases, and close to 0.1 for most cases. Using PAI in place of RH100 slightly lower spatial correlations are generally achieved. Overall, the obtained results confirm the good potential of L-VOD to monitor vegetation height in different environments.

**Index Terms**—LIDAR, microwave radiometry, vegetation height, vegetation optical depth.

## I. INTRODUCTION

THE key parameters provided by the Soil Moisture and Ocean Salinity (SMOS) mission over land are soil moisture (SM) and L-band vegetation optical depth (L-VOD) [1]. Although the retrieval of SM was the low hanging fruit of the mission, the information about vegetation has reached maturity causing a growing interest in testing the L-VOD product and using it in applications.

Direct ground measurements of L-VOD are not possible, since it is a model parameter used in the radiative transfer equation. However, as it is strictly connected to the attenuation of the electromagnetic wave propagating within the vegetation layer, it is related to the total amount of vegetation matter, as well as

geometrical properties and moisture of vegetation elements. In consideration of these dependencies, several studies have investigated the correlation between spatial and temporal variations of L-VOD and vegetation properties made available by direct measurements or global data bases. For crops, L-VOD proved to be basically dependent on growth cycles. The correlations with vegetation water content (VWC, in kg/m²) and leaf area index (LAI) were investigated in several studies based on experiments before SMOS launch. A linear dependence was found since the 80's [2] and thorough reviews are available in the literature [3], [4].

For forests, it was found that SMOS L-VOD retrieved by level 2 (L2) retrieval algorithm is spatially correlated with forest height provided by [5] (see [6]–[8]) particularly for forests of South America and Africa. Other studies using L2 [9] and SMOS-IC [10], [11] found significant correlations with above ground biomass (AGB), made available by data bases of [12]–[14]. In parallel, significant results were obtained using L-VOD products obtained by processing brightness temperature data acquired by Aquarius and SMAP [15], [16].

These promising results encouraged a direct use of SMOS L-VOD for forest applications. Multitemporal L-VOD data were used to investigate carbon losses occurred in Africa [17] and all tropical regions [18]. Similarly, L-VOD proved to be effective in monitoring the recent biomass increase occurred in Southern China [19]. Seasonal variations, and their relation with plant water storage and leaf cycles, were investigated in [20]. Other studies related L-VOD to gross primary production [21] and other ecosystem functional properties [22].

Several studies already use vegetation optical depth (VOD) retrieved at higher frequencies, since it is available for long time intervals and allows us monitoring of long-term environmental changes [23], [24]. A relation between the Ku band VOD from AMSR-E and MODIS vegetation indices was found in [25]. More recently, AMSR2 signatures were used to estimate VWC, further investigating the relationship between VOD and microwave measurements, and obtaining valid results in a 0–8 kg/m² VWC range [26]. Higher frequency VOD increases with biomass, but reaches saturation for biomass values lower with respect to L-band case [8], [10], [26]. Therefore, L-VOD is more effective in monitoring forest properties and their change in areas characterized by high biomass.

In spite of the promising performances of L-VOD as a vegetation monitoring tool, the problem of improving the quality of the product and testing the correlation with vegetation properties

Manuscript received June 12, 2021; revised October 26, 2021; accepted November 8, 2021. Date of publication November 15, 2021; date of current version December 2, 2021. This work was supported by the European Space Agency under Contract 10.1111/gcb.15072. (Corresponding author: Cristina Vittucci.)

Cristina Vittucci, Leila Guerriero, and Paolo Ferrazzoli are with the Department of Civil Engineering and Computer Science Engineering, Tor Vergata University of Rome, 00133 Rome, Italy (e-mail: vittucci@disp.uniroma2.it; leila.guerriero@uniroma2.it; ferrazzo@uniroma2.it).

Philippe Richaume and Yann H. Kerr are with the CESBIO, Bâtiment CTS, 31401 Toulouse, France (e-mail: philippe.richaume@cesbio.cnes.fr; yann.kerr@cesbio.cnes.fr).

Digital Object Identifier 10.1109/JSTARS.2021.3128022
deserves further efforts. Since SMOS launch in 2009, the L2 algorithm for retrieval of SM and L-VOD underwent several modifications [27]. The recently introduced v700 version shows major changes, including the merging of low-vegetation and forest areas to a single fraction for retrieval. It is urgent to test this new version over extended time intervals, in order to verify whether significant improvements were obtained. From the validation point of view, the forest height data base made available by [5] is based on ICESAT GLAS LIDAR measurements carried out in 2005 and cannot account for recent changes. Most of available data bases of AGB are still based on ICESAT GLAS measurements, in synergy with other optical instruments and with subsequent application of different types of allometric equations [12]–[14]. These data bases are different from each other, and establishing the most reliable one for testing L-VOD is a critical task. Recently, AGB maps were obtained using signatures collected by spaceborne SAR, in particular L-band ALOS-PALSAR and C-band Envisat-ASAR [28]. A cross-comparison between results obtained by passive and active microwave instruments will be interesting in the future, but the products are not yet consolidated, at present time.

In this article, SMOS L-VOD data collected from May 2019 to April 2020 using the recent v700 version of L2 retrieval algorithm [27] are compared against products of NASA Global Ecosystem Dynamics Investigation (GEDI) instrument [29]. GEDI is a LIDAR system, whose outputs are processed in order to retrieve several properties of observed vegetation. Signatures collected during the first months of GEDI's life were already used to produce a global forest height map, in synergy with Landsat data [30]. In this article, we will consider in particular the maximum above ground height of the canopy (RH100) and the plant area index (PAI) made available in the level 2B data [29]. The aim of the present article is to compare L-VOD against a dataset which is updated and produced by a recent advanced instrument which makes important vegetation variables available.

Section II summarizes the main properties of SMOS L2 and GEDI data adopted in the article, and the method used to associate the different datasets. Section III illustrates and discusses the method adopted to fill the wide SMOS pixels with data collected by a higher resolution instrument such as GEDI. Section III illustrates the obtained results, in terms of scatterplots between L-VOD and RH100, and their seasonal variations. The implications in testing the reliability of L-VOD and possibly improving the forward model used in SMOS L2 algorithm are discussed. The tests are repeated against PAI. Finally, the improvements achieved by v700 version of L2 algorithm are investigated by comparing statistical results against ones achieved by previous v650 version. Section IV discusses the results.

## II. DATASETS

### A. SMOS L-VOD

This study was conducted using L-VOD retrieved from v700 version of L2 UDP SMOS data [27]. SMOS data have an average resolution of 43 km (hereinafter, referred as SMOS pixel) which corresponds to the average radiometer footprint width at half maximum of the synthesized beam. L-VOD is a

fundamental parameter of the radiative transfer equation adopted in the retrieval algorithm and it is always estimated along with SM. In that equation it is defined as $τ$, and the transmissivity of vegetation is given by $exp(-τ/cos θ)$, where θ is the off-nadir angle of observation [1].

All SMOS data, including the L2 ones, are provided by ESA on the Icosahedral Equal Area 4H9 grid in swath mode, with equal internode distance of 15 km. In the context of the SMOS mission, this grid is referred as the Discrete Global Grid (DGG). DGG is alternatively also used to refer to specific grid points. L2 data are organized in orbits per day and are quality flagged. Details about L2 algorithm are provided in [27].

The choice of the timeframe of our analysis was constrained by that of GEDI data freely available on NASA website at the time of this article, namely from mid-April 2019 to March 2020.

In this article, L-VOD data were monthly averaged at each node. The extraction of L-VOD values at ascending and descending nodes was conducted after checking SMOS L2 quality flags, regarding the probability of Radiofrequency Interference occurrence (RFI-Prob). Nodes having RFI-Prob > 5% were filtered out, and acquisitions with less than 100 incidence angles (too far away from the satellite subtrack) were removed.

A major change of the v700 of the SMOS L2SM processor with respect to the previous version (v650) is the merging of low-vegetation and forest areas to a single fraction for retrieval of the free parameters (SM and L-VOD). This produces changes in the retrievals for mixed pixels, with a spatial smoothing effect and reduction of retrieval failures. Other changes regard the consideration of organic soil fraction in the dielectric model of soil and the adoption of a water fraction derived by ESA CCI-Radar data base [27]. Moreover, the single scattering albedo for low vegetation is changed from 0 to 0.06. Finally, a different algorithm is introduced to manage image reconstruction errors [27].

### B. Global Ecosystem Dynamics Investigation

The GEDI is a NASA mission aimed at monitoring the Earth ecosystems status with particular focus on deforestation and its impact to the increase of the atmospheric $CO_2$ concentrations. The GEDI mission is based on a full-waveform LIDAR instrument installed on-board the International Space Station to provide the first global, high-resolution observations of forest vertical structure [29]. The GEDI mission is operational since April 2019 and is planned to last for two years, with the capability of producing billions of full waveform observation per year [31].

GEDI is not an imaging instrument, but there are gaps among its ground tracks and between adjacent swaths. The gaps decrease as a function of latitude, mission length, and at orbital cross-over points. Anyway, during the planned lifecycle, GEDI will provide a fine mesh of transect observations in order to ensure the mission goal of collecting the first high-resolution LIDAR observations of the three-dimensional structure of forests on the Earth.

During GEDI's 2-year mission lifetime, waveform measurements are planned to cover the Earth's surface between 51.6°N and 51.6°S latitudes, resulting in approximately 10 billion observations of the Earth's land surface in absence of clouds. The sampling pattern is designed to maximize the geographic
coverage of these observations by exploiting the GEDI config-
uration which is based on three lasers (two full power and a
coverage one). The GEDI lasers generate four beams that are
dithered to achieve data collection along a swath made of 8
tracks, of 25 m diameter footprints, separated by 600 m in across
track direction.

The “coverage” laser is split into two transects that are then
each dithered producing four ground transects. The other two
lasers are only dithered producing two ground transects each
[29]. The fundamental observations made by the GEDI instru-
ment are waveforms of energy as a function of receive time.
The footprint observations are combined with laser pointing and
positioning information in order to establish ranges from reflect-
ing surfaces within the footprint with accurate geolocation. The
recorded waveform shapes are influenced by the characteristics
of canopy and its structure, in addition to the ground slope, the
pulsewidth, and the spatial distribution of impinging energy [32].

The first and last modes along the profile of a recorded
waveform within the footprint are usually correlated with the
highest and lowest reflecting surfaces, respectively. In order to
precisely geolocate the corresponding reflecting surfaces and
measure statistical quantile metrics from the integrated wave-
form between those ranging points, it is important to classify
these modes inside the integrated waveform. These statistical
quantiles are referred to as relative height metrics which are
the primary parameters measured by the GEDI instruments.
In addition to the LIDAR statistical metrics, the short GEDI
pulsewidth enables the estimation of biophysical metrics, since
they are a direct measurement of the vertical distribution of the
interacting surfaces between the canopy top and the ground.
In this article, we will consider RH100 and PAI parameters
measured by GEDI.

The RH100 metric represents the maximum above-ground
height of the canopy within a footprint. This parameter has a
key role in the analysis of the present article. The PAI is defined
as one-half of the total area of all canopy structural elements
(e.g., leaves, branches, and trunks) per unit ground surface. PAI
is closely related to the LAI and is closely linked to canopy cover
through the gap distribution within canopy. In dense broadleaf
forests, the numeric difference between LAI and PAI is usually
small (e.g., LAI ≈ 93% PAI) [33].

The GEDI L2B data are provided by NASA as granule prod-
ucts [29]. Each granule contains one orbit in a ±52° range of
latitudes. The L2B products are provided as HDF files which
contain the LIDAR parameters and the ancillary information
(e.g., geographic coordinates for each footprint, quality flags,
etc.) for the four beams of the instruments. The different beams
are identified by some specific codes and only the four full power
beams are considered in the analysis. The codes of the considered
full power beams are BEAM0101, BEAM0110, BEAM1000,
and BEAM1011. Every L2B product contains information sam-
pled on both land and sea and this aspect has been considered
in the preprocessing of the data.

C. GEDI vs SMOS Association

The aim of the preprocessing performed in this article is
the production of aggregated values of GEDI parameters at the

<figure><img src="image_1.png" /><figcaption>Fig. 1. Workflow of the applied processing.</figcaption></figure>

SMOS spatial resolution and the computation of monthly aver-
ages of these aggregated datasets. To ensure the comparability
of the GEDI L2B data and the SMOS L2 VOD data, a dedicated
workflow has been applied.

The workflow is represented in Fig. 1. The procedure was
applied for every month. Starting from GEDI L2 data of all
orbits of the month, full power beams were selected, and the
values of RH100 and PAI were extracted. A filtering procedure
was applied, in order to eliminate the samples collected over
sea or large inland water areas, and LIDAR beams observations
flagged as unsuccessful retrievals.

SMOS L-VOD was extracted from SMOS L2 data, again considering all orbits of the given month. Some filterings were applied at DGG level. As specified in Section II-A, nodes with probability of RFI-Prob higher than 5% were eliminated. In addition, we only considered SMOS nodes for which the sum of low vegetation (FNO) and forest (FFO) fractions was higher than 0.95. In this way, the effects of open water, urban areas, barren, and lands under snow/ice cover throughout the year are minimized.

For all SMOS DGG grid points selected by the filtering
procedure, their coordinates were extracted and used as input for
further processing of GEDI data. Circles with 45 km diameter,
which on average represent the SMOS footprint, centered in
each DGG were taken. The average values of RH100 and PAI
were computed considering the intersection of the circles with
all GEDI orbits of the month. For each DGG monthly average
values of RH100, PAI, and L-VOD were computed for further
comparisons and statistical investigations.

In addition, the total number of monthly GEDI samples was
computed for each DGG. Results of Section III-A were obtained
after applying thresholds to this number.

The article was done for four large continental areas, with
coordinate limits specified in Table I. Within the latitude range
covered by GEDI, Europe and Oceania showed a low number
of SMOS pixels, particularly for dense forests, and were not
included.
<table><caption>TABLE I
CONSIDERED CONTINENTAL AREAS AND RELEVANT COORDINATE LIMITS</caption><thead><tr><th>Area</th><th>Lower latitude (°)</th><th>Upper latitude (°)</th><th>Western longitude (°)</th><th>Eastern longitude (°)</th></tr></thead><tbody><tr><td>North America</td><td>20</td><td>52</td><td>-140</td><td>-50</td></tr><tr><td>South America</td><td>-52</td><td>20</td><td>-90</td><td>-30</td></tr><tr><td>Africa</td><td>-52</td><td>20</td><td>-20</td><td>50</td></tr><tr><td>Asia</td><td>-15</td><td>52</td><td>70</td><td>150</td></tr></tbody></table>

### III. RESULTS

#### A. Characterization of SMOS Pixels Using GEDI Samples

Although a correlation between SMOS L-VOD and the GEDI parameters used here (such as RH100 and PAI) is justified by physical considerations, the fundamental problem of spatial resolution must be taken into account. The SMOS footprint is large, about 43 km on average. Conversely, GEDI footprints are narrow, with about 25 m resolution, 60 m spacing, and a large distance between subsequent orbits.

After applying the procedure described in Section II-B, the spatial correlation coefficients (Pearson coefficients) between SMOS L-VOD's and the average values of GEDI parameters were computed for each month and different continents. In order to ensure a sufficient coverage of the SMOS pixels, computations were carried out after excluding SMOS pixels for which the total number of GEDI samples was lower than a given thresholds. A quantitative summary of the effects of the threshold is reported in Fig. 2, in which a regression between SMOS L-VOD and GEDI RH100 was computed for July 2019. The Pearson correlation coefficients and the number of available SMOS nodes are plotted as a function of the threshold applied to the number of GEDI footprints per SMOS pixel.

As expected, increasing the threshold improves the statistical characterization of the extended SMOS pixels but produces a decrease in the number of available SMOS nodes. However a significant number (about 10000 SMOS nodes for one month) is still available for a threshold of 2400.

The correlation coefficients increase with the threshold, particularly in the range of lower values. For a threshold of 2400, correlation coefficients are higher than 0.8 for all continents in July 2019. We did not force the thresholds to values higher than 2400 since the number of SMOS nodes would be dramatically reduced, with consequent increase in the uncertainty of the correlation statistics.

Results reported in the subsequent sections, showing scatterplots for two representative months (Section III-B) and linear correlation parameters for one year (Section III-C) were obtained after eliminating SMOS pixels for which the total number of GEDI samples was lower than 2400.

#### B. L-VOD vs RH100 Scatterplots and Cumulative Distribution Functions

Fig. 3 reports scatterplots of SMOS L-VOD vs GEDI RH100 (cm) obtained considering SMOS pixels available after filtering and thresholding procedures, for July 2019 and January 2020,

<figure><img src="image_2.png" /><figcaption>Fig. 2. Regression between SMOS L-VOD and GEDI RH100 for July 2019. Top: Pearson correlation coefficient of a SMOS L-VOD vs GEDI RH100 (cm) regression as a function of the threshold applied to the number of GEDI samples per SMOS pixel. Bottom: Number of SMOS DGGs.</figcaption></figure>

and the continental areas indicated in Table I. Relevant correlation coefficients will be given in following sections.

In the scatterplots of Fig. 3, the color codes represent the values of density (in fraction) of the SMOS pixels involved.

In July 2019, all scatterplots show a significant increasing trend of L-VOD vs RH100. The best results are achieved over South America, where several samples are available for both low vegetation and dense forests.

For Africa and Asia, the dense forests are located at the equatorial latitudes (Congo basin and Indonesian islands, respectively), for which GEDI orbits provide a limited number of samples, as already found in [30]. For North America, the RH100 and L-VOD ranges are limited. Seasonal changes, from July to January, mostly produce a lowering of L-VOD in some Boreal forests of North America and Asia. This effect can be contributed by various reasons. Low temperatures produce an appreciable reduction in wood permittivity, as illustrated in [34], with consequent reduction of L-VOD. The retrieval can be also affected by seasonal occurrence of snow and soil freezing. In addition, defoliation of deciduous forests could affect both L-VOD and RH100. Specific investigations over sites with detailed ground truth available can contribute to clarify these issues. An important experiment is in progress [35] but direct comparisons
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 3. Density scatterplots SMOS L-VOD vs RH100 (cm). From top to bottom: (a–b) South America, (c–d) Africa, (e–f) North America, and (g–h) Asia. Left: July 2019. Right: January 2020.</figcaption></figure>

with our results are not easy since the site is located at high latitude, outside the coverage of the present article.

The cumulative distribution functions of RH100 and L-VOD for the four continental areas in July 2019 are compared in Fig. 4. A comparison between the two diagrams indicates

that RH100 and L-VOD distributions show markedly similar properties.

For South America, both RH100 and L-VOD show significantly higher values for all fractions of considered samples, and the distribution shows a bimodal shape.
<figure><img src="image_11.png" /><figcaption>Fig. 4. Cumulative distribution functions of RH100 (cm) and L-VOD for July 2019.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 5. Scatterplots of L-VOD vs RH100 (cm) labeled with ecoregions. July 2019. Top: South America. Bottom: Africa.</figcaption></figure>

In order to understand these features, the scatterplots of Fig. 3(a) and (c) are plotted again in Fig. 5 labeled with ecoregions defined in [36]. For South America, two groups of samples, showing high and low values of L-VOD and RH100, can be identified. The first one is dominated by dense and moist forests of Amazonia, while the second one is dominated by subtropical vegetation of Cerrado and Dry Chaco. Conversely, many African samples correspond to Savannahs and woodlands, with a wide spread of vegetation height and density that produce a continuous distribution of L-VOD and RH100.

### C. Regression Analysis Over Different Continents

The present article can be used to investigate a possible role of GEDI parameters in the estimation of the prior value of L-VOD, which is used in SMOS L2 algorithm [1], or in the downscaling of the retrieved L-VOD. To this aim, we have computed the

<figure><img src="image_13.png" /><figcaption>Fig. 6. Temporal trends of b' (cm⁻¹) coefficient from (1) for four continental areas.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 7. Temporal trends of b'' coefficients from (1) for four continental areas.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig. 8. Temporal trends of Pearson correlation coefficient of (1) for four continental areas.</figcaption></figure>

coefficients of a regression such as

$$L-VOD = b'_{1}RH100 + b''_{1} \quad (1)$$

The investigation was carried out for each month and each continent. Only cases in which the number of GEDI samples per SMOS pixel was higher than 2400 and the number of available SMOS pixels was higher than 5000 were considered.

Results obtained for b', b'', and Pearson correlation coefficient are reported in Figs. 6–8, respectively. December 2019 was not included since the number of available SMOS pixels was lower than 5000 for all continents. In the time interval from May to September, the b' coefficients of the different continents are relatively close to each other. In the subsequent months, seasonal effects are observed in Boreal Forests of North America and Asia, with b' reaching a minimum in January (Fig. 6).

According to Fig. 7, b'' values (representing the intercept of the linear trend) are inside a limited 0–0.1 range in almost all cases. This indicates that the linear trend is followed and saturation effects are moderate for all cases.
<table><caption>TABLE II
ANNUAL MEANS OF b' AND b'' COEFFICIENTS</caption><thead><tr><th>Area</th><th>b' (cm<sup>-1</sup>)</th><th>b''</th></tr></thead><tbody><tr><td>North America</td><td>2.75 10<sup>-04</sup></td><td>0.074</td></tr><tr><td>South America</td><td>3.64 10<sup>-04</sup></td><td>0.087</td></tr><tr><td>Africa</td><td>3.39 10<sup>-04</sup></td><td>0.048</td></tr><tr><td>Asia</td><td>2.89 10<sup>-04</sup></td><td>0.045</td></tr></tbody></table>

<figure><img src="image_16.png" /><figcaption>Fig. 9. Temporal trends of rms differences between L-VOD's retrieved by SMOS and the ones obtained using (1) with b' and b'' reported in Table II.</figcaption></figure>

<table><caption>TABLE III
PEARSON CORRELATION COEFFICIENTS OF THE REGRESSION OF L-VOD (RETRIEVED BY V700) VS RH100 AND PAI FOR JULY 2019 AND JANUARY 2020</caption><thead><tr><th>Area</th><th>RH100<br>July 2019</th><th>RH100<br>January 2020</th><th>PAI<br>July 2019</th><th>PAI<br>January 2020</th></tr></thead><tbody><tr><td>North<br>America</td><td>0.86</td><td>0.73</td><td>0.78</td><td>0.64</td></tr><tr><td>South<br>America</td><td>0.91</td><td>0.89</td><td>0.89</td><td>0.88</td></tr><tr><td>Africa</td><td>0.81</td><td>0.83</td><td>0.80</td><td>0.83</td></tr><tr><td>Asia</td><td>0.85</td><td>0.68</td><td>0.83</td><td>0.75</td></tr></tbody></table>

Correlation coefficients reported in Fig. 8 are higher than 0.7, except for Asia in January 2020, due to issues discussed above (Fig. 3). Considering the results reported in Figs. 6 and 7, we investigated the trends of rms differences between L-VOD's retrieved by SMOS and the ones obtained using (1) with the values of b' and b'' computed as the annual means for each geographical area, as reported in Table II.

Results are shown in Fig. 9. RMS differences values are lower than 0.2 for all cases and close to 0.1 for most cases. Temporal variations are low.

## D. Correlation Coefficients of L-VOD With RH100 and PAI

Pearson correlation coefficients of the regression of L-VOD vs RH100 and PAI for July 2019 and January 2020 are summarized in Table III.

In July 2019, all coefficients are higher than 0.80 for RH100 and 0.77 for PAI. The best results are achieved in South America, characterized by a large fraction of pixels with large forests, well captured both by SMOS L-VOD and GEDI parameters. In all continents, the replacement of RH100 with PAI does not produce any improvements.

For North America and Asia, seasonal effects are clearly visible, producing an appreciable worsening in the correlation

coefficients of January, with respect to the ones of July. This issue was already discussed in Section III-B. Conversely, results are stable for South America and Africa, mostly covered by evergreen vegetation.

It was checked that all correlation coefficients computed using the recent v700 version of SMOS L2 algorithm show an improvement with respect to previous v650 version, computed under the same conditions.

## IV. CONCLUSION

In this article, the spatial correlation between L-VOD retrieved by the latest version of L2 SMOS retrieval algorithm (v700) and two parameters retrieved by GEDI (RH100 and PAI) was investigated. In order to manage the spatial resolution problem, we joined several GEDI footprints within each SMOS pixel, and applied a monthly average, taking benefit of the slow variations of SMOS L-VOD and GEDI parameters versus time.

Moreover, thresholds were fixed regarding the minimum number of GEDI footprints to consider per SMOS pixel to derive accurate statistics. For all considered continents, the Pearson correlation coefficient between monthly averages of L-VOD and RH100 regularly increases by increasing the threshold. A limit of 2400 was selected.

A 1-year interval was considered. For South America and Africa, which are significantly covered by tropical vegetation, the Pearson correlation coefficients of an L-VOD vs RH100 linear regression are higher than 0.8 for all months. Seasonal effects are observed in North America and Asia, producing a lower correlation for colder months. Cumulative distributions of RH100 and L-VOD show markedly similar properties, confirming the significant physical relationship between these two quantities.

RMS differences between L-VOD values retrieved by SMOS and the ones obtained using the linear regression over RH100 are lower than 0.2 for all cases, and close to 0.1 for most cases.

Correlations of L-VOD with PAI are slightly lower than with RH100.

A first outcome of this article consists in a successful comparison of SMOS L-VOD against a LIDAR dataset which is multitemporal, differently from static ones used in previous works. Moreover, all correlation coefficients between v700 L-VOD and RH100 are better with respect to L-VOD from previous L2 versions. v700 was validated by several tests before its approval, but the tests were mostly focused on SM retrieval improvement and did not consider comparisons of L-VOD against LIDAR data. Therefore, the results further confirm the validity of the v700 algorithm and encourage the use of L-VOD as a tool for monitoring vegetation.

A second outcome consists in the possibility of using GEDI RH100 data with a unique set of linear coefficients to estimate L-VOD for each continent. This result suggests investigating a new method to assign the prior values of L-VOD in the iterative retrieval algorithm adopted by SMOS. L-VOD is an output of the retrieval procedure, but the accuracy can be improved if the prior value is closer to the final one.
The synergic use of GEDI and SMOS L-VOD datasets can improve the accuracy and/or the timeliness in monitoring vegetation changes occurring at yearly or monthly time scales, such as deforestation, regrowth, and desertification.

The multitemporal investigation identifies a marked difference between the behaviors of tropical and boreal forests, the latter showing a decrease of L-VOD in cold months. This effect can be contributed by several possible factors, such as a change in wood permittivity, retrieval problems due to changes of soil properties, and defoliation in deciduous forests.

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<figure><img src="image_17.png" /></figure>

Cristina Vittucci received the M.Sc. degree in environmental engineering from the University "La Sapienza" of Rome, Rome, Italy, in 2007, the master's degree in remote sensing and GIS for environmental planning from the University of Roma Tre, Rome, Italy, in 2010, and the Ph.D. degree in geoinformation from Tor Vergata University of Rome, Rome, Italy, in 2015.

In the same year, she joined the Microwave Remote Sensing Group, Tor Vergata University of Rome. She is currently a Researcher with Tor Vergata University of Rome, and since 2014 she has been with the European Space Agency SMOS Mission algorithm developing team, as a Developer and Validator for the SMOS L2 products retrieval algorithm over forests, working in close collaboration with CESBIO, Toulouse, France. Her research interests include the use of multifrequency radiometric features to monitoring environmental variables such as ecosystem functional properties or the change of the land cover and forest degradation, the application of machine learning inversion methods for the retrieval of global forest AGB and height information by means of radiometric parameters, like L-band vegetation optical depth or SAR backscattering, as well as the modeling of the electromagnetic interaction of microwave active and passive and LIDAR measurements with the forest structure.
<figure><img src="image_18.png" /></figure>

**Leila Guerriero** (Member, IEEE) received the laurea
degree in physics from Sapienza University of Rome,
Rome, Italy, in 1986, and the Ph.D. degree in elec-
tromagnetism from Tor Vergata University of Rome,
Rome, Italy, in 1991.

Since 1994, she has been a Permanent Researcher
with Tor Vergata University, where she is currently
an Associate Professor holding the courses on Earth
Satellite Observation and on Geoinformation. She
participated in several international projects, among
them: the ESA projects Soil Moisture and Ocean
Salinity Satellite, Development of SAR Inversion Algorithms for Land Appli-
cations, use of Bistatic Microwave Measurements for Earth Observation, and
SAOCOM-CS Bistatic Imaging, Radiometry and Interferometry Over Land.
Lately, she has been involved in the modeling of Global Navigation Satellite
System Reflectometry signals for ESA projects and in the European FP7
and H2020 Programmes. Her research interests include modeling microwave
scattering and emissivity from agricultural and forested areas.

Prof. Guerriero is a member of the Permanent Steering Scientific Committee of MicroRad. She is Secretary and Treasurer of the GRSS North-Central Italy Chapter.

<figure><img src="image_19.png" /></figure>

**Paolo Ferrazzoli** (Senior Member, IEEE) received the graduate degree in electronic engineering from the University “La Sapienza” of Rome, Rome, Italy, in 1972.

In 1974, he joined Telespazio S.P.A., Rome, Italy, where he was mainly active in the fields of antennas, slant-path propagation, and advanced satellite telecommunication systems. In 1984, he joined Tor Vergata University of Rome, Rome, where he taught microwaves, radiowave propagation, and electromagnetic fields. He was involved in international experimental remote sensing campaigns such as AGRISAR, AGRISCATT, MAESTRO-1, MAC-Europe, and SIR-C/X-SAR. He participated in the co-ordinating team of the ERA-ORA Project, funded by EEC, establishing an assemblage among several European researchers working in radar applications. His research interests include microwave remote sensing of vegetated terrains, with particular emphasis on electromagnetic modeling.

Mr. Ferrazzoli was a member of Science Advisory Group and a member of Quality Working Group in the framework of the European Space Agency SMOS Project. He was Chair of MICRORAD 2012 International Meeting, and a member of the Technical Program Committee of IGARSS Conferences from 2012 to 2018.

<figure><img src="image_20.png" /></figure>

**Philippe Richaume** received the engineering degree in computer, electronic, and automatic from the Ecole Supérieure d'Informatique, Electronique et Automatique, Paris, France, in 1990, the M.Sc. degree in computer sciences and artificial intelligence from Paul Sabatier University, Toulouse, France, in 1991, and the Ph.D. degree in computer sciences and applied mathematics from CNAM, Paris, France, in 1996.

For the last 25 years, he has worked for various
geophysical laboratories, putting to stress advanced
computer science and applied mathematics paradigms against real problems,
particularly in the context of inverse problems applied on remote sensing
observations. He is working currently with the Centre d'Etudes Spatiales de la
BIOSphère, Toulouse, France. His research interests include signal processing,
nonlinear modeling, and inverse problem, particularly using artificial neural
networks such as for real-time signal processing controller of a radio receiver
dedicated to solar wind plasma line tracking onboard the WIND/WAVES
spacecraft, or for direct-inverse modeling of ocean surface wind from ERS
1-2 C-band/NSCAT Ku band scatterometer or biophysical parameters, LAI,
chlorophyll, etc., from POLDER optical directional multi spectral reflectances,
or using more traditional iterative minimization approaches like for soil moisture
retrieval from SMOS L-band brightness temperatures, its validation and future
improvements he has been working on since 2005.

<figure><img src="image_21.png" /></figure>

**Yann H. Kerr** (Senior Member, IEEE) received the engineering degree in radar and telecommunications from Ecole Nationale Supérieure de l'Aronautique et de l'Espace, Toulouse, France, in 1977; the M.Sc. degree in electronics and electrical engineering from Glasgow University, Glasgow, U.K., in 1981, and the Ph.D. degree in astrophysics, geophysics and space techniques from the Université Paul Sabatier, Toulouse, France, in 1992.

From 1980 to 1985, he was employed with CNES.
In 1985, he joined LERTS, for which he was director
in 1993–1994. He spent 19 months with JPL, Pasadena, CA, USA, in 1987–1988.
He has been working with CESBIO since 1995 (Deputy Director 1995–1999
and Director 2007–2016). He has been involved with many space missions. He
was an EOS Principal Investigator (interdisciplinary investigations), and PI and
precursor of the use of the SCAT over land. In 1989, he started to work on the
interferometric concept applied to passive microwave earth observation and was
subsequently the science lead on the MIRAS project for ESA with MMS and
OMP. His research interests include theory and techniques for microwave and
thermal infrared remote sensing of the Earth, with emphasis on hydrology, water
resources management, and vegetation monitoring.

Dr. Kerr was also a Coinvestigator on IRIS, OSIRIS, and HYDROS for NASA. He was a Science Advisor for MIMR and Co I on AMSR. He is a member of the SMAP Science Team. In 1997, he first proposed the natural outcome of the previous MIRAS work with what was to become the SMOS Mission to CNES, proposal which was selected by ESA in 1999 with him as the SMOS mission Lead-Investigator and Chair of the Science Advisory Group. He is also in charge of the SMOS science activities coordination in France. He has organized all the SMOS workshops, and was guest editor on three IEEE Special issues and one RSE. He is currently involved in the exploitation of SMOS data, in the Cal Val activities and related level 2 soil moisture and level 3 and 4 development and SMOS Aquarius SMAP synergistic uses and on the soil moisture essential climate variable. He is also working on the SMOS-Next SMOS-HR concepts and is involved in both the Aquarius and SMAP missions. He was nominated highly cited scientist by Thomson Reuters/Publons in 2015, 2019, and 2020.