# A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison

Xiangzhuo Liu, Wigneron J.-P., Wolfgang Wagner, Frédéric Frappart, Lei Fan, Mariette Vreugdenhil, Nicolas Baghdadi, Mehrez Zribi, Thomas Jagdhuber, Shengli Tao, et al.

► To cite this version:

Xiangzhuo Liu, Wigneron J.-P., Wolfgang Wagner, Frédéric Frappart, Lei Fan, et al. A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison. Remote Sensing of Environment, 2023, 299, pp.113850. 10.1016/j.rse.2023.113850. hal-04246194

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Distributed under a Creative Commons Attribution 4.0 International License
1 A new global C-band vegetation optical depth product

2 from ASCAT: description, evaluation, and inter-

3 comparison

4 Xiangzhuo Liuª, Jean-Pierre Wigneronª,*, Wolfgang Wagnerᵇ, Frédéric Frappartª, Lei Fanᶜ,

5 Mariette Vreugdenhilᵇ, Nicolas Baghdadiᵈ, Mehrez Zribiᵉ, Thomas Jagdhuberf,g, Shengli Taoh,

6 Xiaojun Liª, Huan Wangª,h, Mengjia Wangi, Xiaojing Baij, B. G. Mousak, Philippe Ciais¹

7 a INRAE, UMR1391 ISPA, Université de Bordeaux, Villenave d'Ornon 33140, France

8 b Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna 1040, Austria

10 c Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station,

11 School of Geographical Sciences, Southwest University, Chongqing 400715, China

12 d TETIS, Université de Montpellier, CIRAD/CNRS/INRAE, Montpellier 34093, France

13 e CESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, Toulouse 31401, France

14 f Microwaves and Radar Institute, German Aerospace Center (DLR), Münchener Strasse 20,
15 82234 Weßling, Germany

16 g University of Augsburg, Institute of Geography, Augsburg 86159, Germany

17 h Key Laboratory for Earth Surface Processes of the Ministry of Education, Institute of
18 Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871,
19 China

20 i School of Geoscience and Technology, Zhengzhou University, Henan 450001, China

21 j School of Hydrology and Water Resources, Nanjing University of Information Science and
22 Technology, Nanjing 210044, China
23 k Department of Mining and Petroleum Engineering, Faculty of Engineering, Al-Azhar

24 University, Cairo 11884, Egypt

25 l Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-

26 UVSQ, Université Paris Saclay, Gif-sur-Yvette 91191, France;

27 *Corresponding Author: J.-P. Wigneron (jean-pierre.wigneron@inrae.fr)

28

29 **Abstract:**

30 Active microwave measurements have the potential to estimate vegetation optical depth
31 (VOD), an indicator related to vegetation water content and biomass. The Advanced
32 SCATterometer (ASCAT) provides long-term C-band backscatter data at vertical-vertical
33 (VV) polarization from 2007. So far, very few studies have considered retrieving VOD from
34 this active sensor. This study presents a new publicly released global long-term and
35 continuous (2007-2020) C-band VOD dataset retrieved from the ASCAT observations, named
36 the ASCAT INRAE-BORDEAUX or ASCAT IB VOD product. The retrieval algorithm is
37 based on the Water Cloud Model (WCM) including the Ulaby bare soil model. The algorithm
38 takes advantage of a multi-temporal (MT) retrieval method relying on a cost function where
39 constraints to the retrieved parameters are implemented and a reanalysis soil moisture (SM)
40 dataset from ERA5-Land is used as an input. The performance of ASCAT IB VOD was
41 evaluated by inter-comparing it with ASCAT Technische Universität Wien (TUW), the
42 Advanced Microwave Scanning Radiometer 2 (AMSR2), and VOD Climate Archive
43 (VODCA) VOD products (the last two products are estimated from passive microwave
44 observations). Results showed that ASCAT IB VOD presented the highest spatial correlation
45 with aboveground biomass (R~0.83) and with the Global Ecosystem Dynamics Investigation
46 (GEDI) canopy height (R~0.84-0.85). In terms of temporal performance, ASCAT IB VOD
47 had the highest correlation R values with leaf area index (LAI) and Normalized Difference
Water Index (NDWI) in most parts of the globe from 2013 to 2018. This contrasts with AMSR2 VODs which correlated better with Normalized Difference Vegetation Index (NDVI). The new ASCAT-based VOD product on a global scale highlighted the potential benefit of combining active (namely ASCAT) and passive (namely AMSR2) VOD products for vegetation studies.

Keywords: VOD; biomass; vegetation phenology; ASCAT; Active microwave; multi-temporal (MT); AMSR2

# 1. Introduction:

Microwave vegetation optical depth (VOD) has proven to be a useful indicator for the study of terrestrial ecosystems based on its sensitivity to vegetation biomass as well as water content of vegetation (Chaparro et al., 2019; Frappart et al., 2020; Rodríguez-Fernández et al., 2018a; Vittucci et al., 2019; Wigneron et al., 2021). To date, long-term VOD retrieved from K-, X-, C- and L-band passive microwave brightness temperatures (Kerr et al., 2012; Konings et al., 2017; Liu et al., 2011; Moesinger et al., 2020; Wang et al., 2021a; Wigneron et al., 2017) have been used to monitor the global vegetation biomass changes (Liu et al., 2013), the carbon dynamics in tropical and Siberian regions (Fan et al., 2022; Fan et al., 2019; Wigneron et al., 2020), resilience of the Amazon rainforest (Boulton et al., 2022), vegetation dynamics in drylands over West African Sahel (Tian et al., 2016) and vegetation phenology over the mid- and high-latitudinal Northern Hemisphere (Li et al., 2023). However, some deficiencies, including the discontinuity in time series between observations from satellites and their successor (the Advanced Microwave Scanning Radiometer for EOS, AMSR-E, and the Advanced Microwave Scanning Radiometer 2, AMSR2), as well as data quality issues related to radio frequency interference (Li et al., 2021; Wang et al., 2021b), affect the quality of the above-mentioned passive long-term VOD datasets. Most importantly, a long-term freely available global active VOD product is still lacking and very few studies have focused on this
73 topic. The development of a new active global product could be very interesting as it could
74 provide useful data to monitor the vegetation characteristics, in a way complementary to
75 passive VODs (Liu et al., 2021b).

76 The active microwave observations show a different sensitivity to vegetation and soil
77 compared with the passive ones (Link et al., 2021; Prigent et al., 2022). Using a discrete
78 radiative transfer model to simulate the relationship between active and passive signals in
79 three vegetation types (wheat, corn, and forest), Link et al. (2021) found the coupling between
80 active and passive signals decreases with increasing vegetation water content because of the
81 decreasing sensitivities of the radar (active) and radiometer (passive) observations to soil
82 moisture. The sensitivity of the radar systems to SM over vegetation areas decreases more
83 quickly than that of the radiometers due to the two-way vegetation attenuation (forward and
84 backward) (Piles et al., 2015) and active systems are more sensitive to the effects of the
85 vegetation structure (Ferrazzoli et al., 1989; Fung and Eom, 1985; Wigneron et al., 1999).
86 Prigent et al. (2022) found the diurnal and seasonal cycles of backscatter and emissivity
87 collected from Global Precipitation Mission (GPM) at a 1° × 1° resolution tend to be in phase
88 opposition in the tropical forest region, while backscatter and emissivity vary more in phase
89 during the dry season in the less densely forested regions of southeast Amazon. The dissimilar
90 information obtained from active and passive microwave signals motivates us to explore the
91 retrieval of VOD from active microwave observations.

92 Active microwave data provide complementary observations to passive observations because
93 1) they are sensitive to specific vegetation characteristics, 2) are less affected by radio
94 interference than passive observations (Liu et al., 2021b) and 3) can also provide long-term
95 records, such as observations from three series of Advanced SCATterometer (ASCAT)
96 Meteorological Operational (MetOp -A/B/C) satellites which start in 2007 and can be
97 extended to the 2020s (Srivastava et al., 2016). Radar observations have been used in many
studies for vegetation monitoring (phenology, structure, or productivity) (Canisius et al., 2018; Chang et al., 2022; Hosseini et al., 2015; Meroni et al., 2021; Neumann et al., 2010; Wagner et al., 1999), while studies on active VOD retrievals are scarce and only conducted at the site scale or over small regions (El Hajj et al., 2019a; Grippa and Woodhouse, 2003; Magagi and Kerr, 1997; Quast et al., 2019; Zhou et al., 2022). The first active VOD research at a continental scale (Vreugdenhil et al., 2016) was conducted for assessing the vegetation correction in the Technische Universität Wien (TUW) method of the C-band ASCAT soil moisture retrieval (Wagner et al., 2013). The TUW VOD is retrieved by the change detection method using the Water Cloud Model (WCM) (Attema and Ulaby, 1978). The TUW VOD value is mainly dependent on two parameters (slope and curvature) which are the coefficients of a second-order Taylor expansion function established to describe the angular ASCAT backscatter dependency (Hahn et al., 2017). The preliminary TUW VOD data set did not consider inter-annual variability as the climatology of slope and curvature are computed in the algorithm (Vreugdenhil et al., 2016; Vreugdenhil et al., 2017). With a novel calculation of slope and curvature, the updated TUW VOD can capture vegetation dynamics in Australia (Vreugdenhil et al., 2017). However, it is not yet public and there are few studies on the global assessment of TUW VOD (Teubner et al., 2018; Vreugdenhil et al., 2016).

Recently, INRAE-BORDEAUX (IB) developed a new active VOD product from ASCAT in Africa (hereafter ASCAT IB V1 VOD) (Liu et al., 2021b). The algorithm is based on the WCM integrated with the Ulaby bare soil scattering model (Ulaby et al., 1978) as these simple models can be used efficiently to simulate the ASCAT backscattering signals (Lievens et al., 2021; Santoro et al., 2022; Shamambo et al., 2019). To retrieve VOD from the VV polarization and mono-angular (incidence angle at 40°) normalized ASCAT observations, the ERA5-Land soil moisture (SM) dataset was used as an input to the algorithm. The evaluation of ASCAT IB V1 VOD showed a high spatial correlation with Climate Change Initiative
(CCI) and Saatchi above-ground biomass (AGB) (Carreiras et al., 2017; Saatchi et al., 2011; Santoro and Cartus, 2019), with R values generally higher than for ASCAT TUW VOD and C-band passive VOD, namely the Advanced Microwave Scanning Radiometer 2 (AMSR2) and VOD Climate Archive (VODCA) VOD. ASCAT IB V1 VOD was also found to be highly temporally correlated to Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and leaf area index (LAI). However, the algorithm used in ASCAT IB V1 VOD has been applied globally with imperfect results (Liu et al., 2021a), mainly because the hypothesis that the vegetation scattering parameter (ω) consists globally of two constant values (distinguishing very densely vegetated regions from the other vegetated regions) did not hold.

In the present study, to improve the quality of the global ASCAT IB VOD product, we evaluated the possibility of retrieving VOD and ω simultaneously from the ASCAT normalized backscatter at 40° by adding constraints in the retrieval process. The first constraint was applying a multi-temporal (MT) method which is generally based on the assumption that VOD varies relatively slowly in time as was first developed by Wigneron et al. (2000) for the SM and VOD retrieval algorithm of the Soil Moisture and Ocean Salinity (SMOS) mission (Kerr et al., 2010). This assumption is now widely used in the development of passive VOD retrievals. For instance, in the multi-temporal dual channel retrieval algorithm (MT-DCA), VOD was assumed to be nearly constant between every two consecutive overpasses (Konings et al., 2016). Based on MT-DCA, (Konings et al., 2017; 2016) successfully retrieved VOD and SM from L-band Aquarius (with a seven-day revisit time) and SMAP (with a three-day revisit time). This assumption was also adopted in the different operational VOD and SM retrieval algorithms of the SMOS observations (Al Bitar et al., 2017; Wigneron et al., 2021). Similarly in the active domain, El Hajj et al. (2019a) assumed that VOD remains stable during four consecutive overpasses (18 days) of Sentinel-1
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and hence obtained the very first retrievals of VOD from Sentinel-1 images in southern France.

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Incorporating a *priori* information into the algorithm is another interesting way to constrain the retrieved parameters. A key feature of the widely used L-band microwave emission of the biosphere (L-MEB) inversion method is integrating a *priori* information into the retrieval (Wigneron et al., 2021; Wigneron et al., 2017). In the SMOS-IC Version 2 algorithm (Wigneron et al., 2021), by using the mean value of VOD retrieved in the previous ten days as a first guess value of VOD for subsequent SM and VOD retrievals, results showed a considerable improvement compared with the preceding versions. In the SMAP official SM baseline algorithm (O'Neill et al., 2015), the MODIS NDVI was converted to vegetation water content and then multiplied by a factor to represent VOD. Based on the optimization of the *a priori* information, Li et al. (2022) developed a new SMAP SM and VOD product (called SMAP-IB) using the L-MEB algorithm. In addition, Wang et al. (2021a) calibrated a relationship between VOD and the Microwave Polarization Difference Index (MPDI) and then used the VOD based on MPDI as the initial value to retrieve X-band VOD from the AMSR2.

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In this context, the main objective of this study is to retrieve simultaneously a global ASCAT product including VOD and the vegetation scattering parameter (ω). The retrieval algorithm used the model-based soil moisture (SM) data from the ERA5-Land product as it offers long-term data at a time close to the ASCAT satellite observations owing to its hourly availability. The use of SM data from satellite missions (e.g. SMOS, SMAP and AMSR2) is more difficult: observation time is not concurrent to the ASCAT observations; the time period is not long enough; and accuracy may be limited for some satellite products in some areas (Liu et al., 2021b; Xing et al., 2021). To overcome the challenge of retrieving two parameters from a single-angle and polarization ASCAT observation, the concept of “slow VOD time variations”
introduced previously and the *a priori* information obtained from ASCAT IB V1 VOD
retrievals over Africa were incorporated into the algorithm. Similar to previous VOD
assessment studies (Chaparro et al., 2019; Li et al., 2021; Liu et al., 2021b), thirteen years of
global ASCAT IB VOD (2007-2020) were retrieved and evaluated using different AGB, tree
height and optical vegetation index products. The key characteristics of the global ASCAT IB
VOD product are illustrated by performing comparisons with ASCAT TUW VOD and two
other passive C-band VOD products from the AMSR2 satellite, namely AMSR2 VOD and
VODCA VOD.

181 2. Data description

182 2.1 ASCAT backscatter

183 The ASCAT sensors are equipped with fan-beam (mid, fore, and aft) antennas which conduct
184 VV backscatter observations with incidence angles varying from 25 to 65 degrees at a carrier
185 frequency of 5.255 GHz (C-band) covering two 550 km wide swaths separated by a gap of
186 about 360 km with a 30–50 km spatial resolution and twice a day temporal resolution (Figa-
187 Saldaña et al., 2014). In the present study, we used the backscatter measurements normalized
188 at the standard reference angle (40°) and the historically wettest measurements (referred to as
189 wet reference), which were extracted from the ASCAT Soil Moisture near-real time (NRT)
190 product at 12.5 km Swath Grid. This product provides 15 scene acquisitions per day and
191 covers the globe approximately every two days. The stack of the ASCAT backscatter data
192 used in this study consisted of all data acquired by MetOp-A descending orbits (9:30 am)
193 from 2007 to 2020. We used data measured at morning because vegetation water stress is
194 typically lower in the morning making “morning” VOD better suited for monitoring biomass
195 (Frappart et al., 2020; Liu et al., 2021b). The data with good quality (probability flags for
196 frozen soil and snow equal to zero, topographic complexity flags and wetland probability
197 flags lower than 30% and 10%, respectively) were resampled to a 0.25° grid (~ 25 km × 25
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km) by the inverse distance weighting algorithm (Lievens et al., 2017). The ASCAT data is
freely available at https://archive.eumetsat.int/.

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## 2.2 ERA5-land data

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In this study, we used the 0-7 cm SM and soil temperature (ST) datasets of ERA5-Land (Muñoz-Sabater et al., 2021). This dataset is on a 0.1-degree grid with hourly time steps. The new surface model (Carbon Hydrology-Tiled ECMWF Scheme for Surface Exchanges over Land) used in ERA5-Land is able to better simulate the SM dynamics than the older scheme (Wagner et al., 2022). The evaluation of ERA5-Land SM also shows good performance on the global scale (Beck et al., 2020; Chen et al., 2021; Lal et al., 2022; Muñoz-Sabater et al., 2021). More importantly, as the ERA5-Land does not assimilate land surface observations (whereas ERA5 assimilates the ASCAT SM), ERA5-Land SM and the ASCAT backscatter measurements can be treated as quasi-independent variables (Wagner et al., 2022). In the present study, ERA5-Land SM is used as an input to the WCM model to retrieve ASCAT IB VOD, and ERA5-Land ST is used for simulating the Ulaby bare soil model parameters. ERA5-Land SM and ST were aggregated to the 0.25° grid using area-weighted averaging, and then temporally matched with the ASCAT descending (~9:30 local time) observations.

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## 2.3 Vegetation variables for evaluating VOD

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Three kinds of vegetation variables widely used in VOD evaluation (Grant et al., 2016; Li et al., 2021; Rodríguez-Fernández et al., 2018b; Tian et al., 2016) were used to evaluate the retrieved ASCAT IB VOD: AGB, canopy height, and optical vegetation indices. The rationale for using those parameters is that 1) there is no consensus on *in-situ* VOD reference values, so proxies of VOD as vegetation height and vegetation biomass are often used to evaluate the performance of the VOD retrieval algorithms (Li et al., 2022; Li et al., 2021); 2) VOD can provide information on AGB at the inter-annual scale and on the vegetation water status at the

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seasonal scale (Lyons et al., 2021; Wang et al., 2023). Since total vegetation biomass (AGB) is generally well related to vegetation height (Asner et al., 2012), utilizing canopy height may confirm the anticipated relationship between VOD and AGB. We also refer the reader to the inter-comparison study of nine commonly used VOD for more details (Li et al., 2021).

### 2.3.1 AGB datasets

Three AGB products, the Saatchi AGB in 2015 (Carreiras et al., 2017; Saatchi et al., 2011), the Climate Change Initiative (CCI) AGB dataset version 1 in 2017 (Santoro and Cartus, 2019) and the Global Ecosystem Dynamics Investigation (GEDI) L4B AGB in 2020 (Dubayah et al., 2022), were used to evaluate the ability of ASCAT IB VOD to monitor AGB. Those three datasets are all at 1-km spatial resolution and were resampled to the 0.25° grid by spatial averaging.

### 2.3.2 Canopy height datasets

Two canopy height products (Lang et al., 2022; Potapov et al., 2020) used in this study were derived from the GEDI LIDAR measurement. The product developed by Potapov et al. (2020) is produced by extrapolating the GEDI footprint-level forest canopy height to a 30 m spatial resolution map using a regression tree algorithm and Landsat analysis-ready data for the year 2019. Lang et al. (2022) used the GEDI Level 1B waveforms and Bayesian deep learning algorithm to develop a global 0.5° resolution canopy height map, which has a lower root mean square error (RMSE = 2.7m) than the product (RMSE = 6.6m) from Potapov et al. (2020).

### 2.3.3 Optical vegetation indices datasets

Three vegetation indices (VIs) products, the 10-day NDVI and LAI data from Copernicus Global Land Service (CGLS) (https://land.copernicus.eu/global/) and 8-day Normalized Difference Water Index (NDWI) calculated from the MODIS MOD09A1 product (Gu et al., 2008), were used to assess the temporal variations in ASCAT IB VOD. LAI and NDVI are
good proxies of phenology and vegetation greenness. NDWI represents the vegetation water dynamics which is expected to be important in terms of temporal changes. Therefore, those three VIs can be used to test the ability of ASCAT IB VOD to monitor the seasonal and interannual variations of vegetation phenology and vegetation water status. After quality control, the dataset was resampled to a spatial resolution of a 0.25° grid (Fuster et al., 2020).

## 2.4 Other C-band VOD products

To better illustrate the performance of the ASCAT IB VOD product, we inter-compared it with three other C-band VOD products, namely ASCAT TUW, AMSR2 and VODCA VOD.

ASCAT TUW VOD was provided by Vreugdenhil et al. (2016). TUW VOD is calculated from the loss in sensitivity to soil moisture (i.e., attenuation of the bare soil backscatter). The sensitivity is calculated as the difference between the wet and dry reference, and more details on the product are given in (Vreugdenhil et al., 2016; Vreugdenhil et al., 2017). AMSR2 and VODCA VOD can be freely downloaded at the Goddard Earth Sciences Data and Information Services Center (GES DISC) website and the Zenodo repository (Moesinger et al., 2020).

These two products are retrieved from the passive sensor AMSR2 (providing data from July 2012) but with different algorithms. The AMSR2 VOD retrieval algorithm is based on the Land Parameter Retrieval Model (LPRM) version 5 (Owe et al., 2008), while VODCA VOD is retrieved via the LPRM version 6 and then a cumulative distribution function (CDF) matching technique is used to scale VOD to the AMSR-E VOD (Moesinger et al., 2020). The key differences between those three products and ASCAT IB VOD are presented in the discussion section.

## 2.5 Ancillary datasets

This study also utilized several ancillary datasets, including ISRIC SoilGrids250m data (Hengl et al., 2017), Digital Elevation Model (DEM)-related data (Danielson and Gesch,
2011), and MODIS land cover map (Sulla-Menashe et al., 2019). The SoilGrids250m dataset provides global standard numeric soil properties at seven standard depths with a 250 m spatial resolution. DEM-related data were calculated from the 1km Global Multi-resolution Terrain Elevation Data 2010 by using the System for Automated Geoscientific Analyses (SAGA) GIS software (Conrad et al., 2015). The SoilGrids250m (15 types, in Table S1), and DEM-related (9 types, in Table S2) data were used in the estimation of the soil parameters of the Ulaby linear model (see Section 3.1). As several previous studies showed that VOD varies as a function of the land cover (Li et al., 2021; Vreugdenhil et al., 2016), the land cover map extracted from the MODIS MCD12Q1 product based on the International Geosphere-Biosphere Programme (IGBP) scheme was used to analyze the different VOD products.

## 3. Methodology

The ASCAT IB retrieval approach is illustrated in the flowchart of Fig.1. The retrieval model is based on a vegetation backscatter model (Water Cloud Model) integrated with the Ulaby bare soil model (Section 3.1). Instead of the common procedure in which VOD and SM are retrieved simultaneously, which may lead to an ill-posed problem (Wigneron et al., 2000), we focused on retrieving the two vegetation parameters, VOD and vegetation scattering parameter (ω), which can be assumed to be relatively constant over a short time-window, while model-based dynamic SM data is used as an input in the retrieval algorithm. One prototype study undertaken to retrieve VOD from AMSR2 in Africa supports the validity of this idea (Wang et al., 2021a). The estimation of the Ulaby linear model parameters is described in Section 3.2. The retrieval of the VOD and ω from the integrated retrieval model is presented in Section 3.3. The methods implemented to quantify the performance of ASCAT IB VOD and to qualify the ω parameter are described in Section 3.4.
<figure><img src="image_2.png" /><figcaption>Fig. 1. Flowchart presenting the development and assessment of global ASCAT IB VOD.</figcaption></figure>

## 3.1. Vegetation backscatter model

The radar backscatter from vegetation was described by the Water Cloud Model (WCM) (Attema and Ulaby, 1978). The sensor-received backscattering coefficient ($\sigma_{obs}^{\circ}$, in m$^2$/m$^2$) at the incidence angle $\theta$ (40° in this study) can be denoted as a combination of two components: the direct vegetation backscatter signal ($\sigma_{veg}^{\circ}$, in m$^2$/m$^2$) and the backscatter from the soil surface ($\sigma_{soil}^{\circ}$, in m$^2$/m$^2$) attenuated by the vegetation canopy ($\gamma^2$, called the two-way vegetation attenuation factor). This leads to the following equations:

$$ \sigma_{obs}^{\circ} = \sigma_{veg}^{\circ} + \gamma^2 \sigma_{soil}^{\circ} \qquad (1) $$

$$ \sigma_{veg}^{\circ} = \omega \cdot \cos \theta \cdot (1 - \gamma^2) \qquad (2) $$

$$ \gamma^2 = \exp(-2 \cdot \tau / \cos \theta) \qquad (3) $$

where $\omega$ is the vegetation scattering parameter related to the single scattering albedo, and $\tau$ is the vegetation optical depth (VOD).

A simple linear approach (Eq. (4)) (Ulaby et al., 1978) was used to model the bare soil backscatter ($\sigma_{soil}^{\circ}$, in dB) as a function of soil moisture (SM, in m$^3$/m$^3$):
$$ \sigma_{\text{soil(dB)}}^{\circ} = 10 \cdot \log_{10} \sigma_{\text{soil(m}^2\text{/m}^2)}^{\circ} = C + D \cdot \text{SM} \quad (4) $$

where C is the radar backscatter of the very dry bare soil, D represents the radar backscatter sensitivity to soil moisture changes, and SM comes from the ERA5-land SM product.

The Eqs. (2)–(4) were finally inserted into Eq. (1) to express $\sigma_{\text{obs}}^{\circ}$ as a function of vegetation optical depth ($\tau$):

$$ \sigma_{\text{obs}}^{\circ} = \omega \cdot \cos \theta \cdot (1 - \exp(-2 \cdot \tau / \cos \theta)) + \exp(-2 \cdot \tau / \cos \theta) \cdot 10^{0.1 \cdot (C + D \cdot \text{SM})} \quad (5) $$

## 3.2 Calibration of the model parameters

The soil-vegetation radar model in Eq. (5) contains three parameters ($\omega$, C and D) that needed to be calibrated on each pixel in space and time. As there are no “true” VOD samples, the three parameters cannot be calibrated at the same time. In this study, we calibrated the bare soil parameters (C and D) and then retrieved $\omega$ and VOD simultaneously.

The soil parameter calibration consisted of three steps (1) selecting so-called “bare soil” pixels globally where vegetation effects can be neglected during a specific period (which varies over each pixel) (2) computing the C and D parameters over the “bare soil” pixels during that specific period (3) training machine learning models by using the retrieved C and D values over “bare soil” pixels obtained in the step (2) and then estimating the two parameters (C and D) globally. These three steps are detailed below:

**Step1:** Globally selecting the “bare soil” pixels based on LAI and land cover data. Two kinds of “bare soil” pixels can be identified: i) vegetation-free pixels during the entire year (desert area), and ii) pixels with sparse vegetation (LAI<0.5) seasonally. Some “bare soil” pixels have very low moisture condition (< 0.05 m³/m³) and almost stable $\sigma_{\text{obs}}^{\circ}$. In those pixels, only C can be calculated. Therefore, we divided the “bare soil” pixels into two categories, namely category 1: where soil moisture and $\sigma_{\text{obs}}^{\circ}$ show a significant correlation; category 2: where soil moisture is very low and there are very low time variations in $\sigma_{\text{obs}}^{\circ}$.
Step2: calculating the parameters C and D over the “bare soil” pixels. For the pixels in category 1, the C and D values were obtained with linear fitting on Eq. (4). For the pixels in category 2, only the C value can be computed, and it was estimated from the averaged σ°obs. At the same time, the corresponding soil properties (Table S1) and DEM-related (Table S2) data were extracted.

Step3: estimating C and D globally. The pixels in step 2 were used to train two random forest (RF) regression models, in which the parameters C and D computed over the “bare soil” pixels were treated as the target and the soil properties (Table S1) and DEM-related (Table S2) data were selected as the predictors. More details about the implementation of the RF model are presented in Appendix A. Then, the selected global predictors were used as input to the trained RF models to estimate C and D globally.

Two years (2015-2016) of ASCAT σ°obs and ERA5-Land SM were used to select the “bare soil” pixels. For more details about each step, readers can refer to the same calibration work done in Africa (Liu et al., 2021b).

### 3.3 Retrieval of VOD and ω

The simultaneous retrieval process of two variables, VOD and ω, was performed by using a cost function (CF) (Eq. (6)). CF integrates the squared weighted differences between the backscatter simulations (σ°sim) and the observations (σ°obs), taking into account the constraint information on the retrieved model parameters (Wigneron et al., 2007). This strategy was also implemented in the development of SMOS IC V2 and SMAP IB (Li et al., 2022; Wigneron et al., 2021).

$$CF = \frac{\sum (\sigma_{\text{obs}}^{\circ} - \sigma_{\text{sim}}^{\circ})^2}{\text{StdDev}(\sigma^{\circ})^2} + \sum_{i=1}^{2} \frac{(P_{i}^{\text{ini}} - P_{i}^{*})^2}{\text{StdDev}(P_{i})^2} \quad (6)$$
where the sum of the differences between observed ($\sigma^°_{obs}$) and simulated ($\sigma^°_{sim}$) backscattering coefficient is computed from VV polarization observations over a w-day window. In this window, the value of retrieved $P_i^*(VOD^*$ and $\omega^*$, respectively for i = 1, 2) are time-invariant. StdDev($\sigma^°$) is the standard deviation associated with $\sigma^°_{obs}$. $P_i^{ini}$ (VOD$^{ini}$ and $\omega^{ini}$, respectively for i = 1, 2) is the initial value of VOD or $\omega$ in the retrieval process and corresponds to a constraint information (or “priori information”) estimate; StdDev($P_i$) is the standard deviation associated with this estimate.

Two sources of constraint information were used in the retrieval algorithm: (1) the ASCAT IB V1 VOD developed in Africa (Liu et al., 2021b) was used to estimate the a priori information on VOD, and (2) the wet reference of the ASCAT observations was used to estimate the a priori information on $\omega$. Specifically, due to the diversity of the vegetation conditions in Africa, the VOD$^{ini}$ and StdDev(VOD) parameters used in Eq. (6) were simply estimated for the forest and non-forest regions in Africa. In this study, VOD$^{ini}$ was set to 0.87 and 0.16 for forest and no-forest pixels, respectively. StdDev(VOD) was set to 0.40 for forest pixels and 0.15 for no-forest pixels. According to the definition of $\omega$, the $\omega^{ini}$ and StdDev($\omega$) parameters were calculated from the ASCAT wet reference ($\sigma^°_{WetRef}$) data which is computed from the maximum backscattering coefficient for each pixel during the peak growth season (Fig. 1 in Steele-Dunne et al. (2019) and Fig. 2 in Vreugdenhil et al. (2016)) or during times with high values of ERA5-Land SM (Fig. S1). $\sigma^°_{WetRef}$ is provided over each pixel and is almost constant on a daily basis. The yearly mean value of $\sigma^°_{WetRef}$ in each pixel was used to estimate $\omega^{ini}$. Like the calculation of StdDev(VOD), StdDev($\omega$) was estimated from the yearly mean value of $\sigma^°_{WetRef}$ over the forested and non-forested areas. It was set to 0.01 for the forested areas and 0.03 for the non-forested areas.

In order to ensure a good quality of $P_i^*(VOD$ and $\omega)$ and to consider that $P_i^*$ did not change much over the period of w-days, the w value cannot be too small or too large (Wigneron et al.,
2021). An 18-day window was used in this study. This window size allows for robust results with more than three consecutive observations and it corresponds to the time-period used for Sentinel-1 VOD (El Hajj et al., 2019a). We also checked that this window size led to the best results when evaluating the performance of ASCAT IB VOD against AGB (Table S3).

## 3.4. Evaluation metrics for ASCAT IB retrievals assessments

Evaluating the ASCAT IB retrievals, we mainly focused on the VOD parameter as there are few studies evaluating the $\omega$ parameter at a large scale (Du et al., 2016; Fernandez-Moran et al., 2016; Konings et al., 2017; Vittucci et al., 2017). As in (Li et al., 2021), the global ASCAT IB VOD retrievals were evaluated in time and space. For the temporal performance, the Pearson correlation coefficient (R) between optical vegetation indices (i.e., LAI, NDVI and NDWI and ASCAT IB VOD was calculated. To evaluate ASCAT IB VOD in terms of spatial performance, we estimated the spatial correlation between ASCAT IB VOD and different AGB products (Eq. (7)). Meanwhile, the spatial correlation between VOD-predicted-AGB and AGB was also computed: the aim is to evaluate the performance of ASCAT IB VOD and other VOD in estimating AGB. To compute the VOD-predicted AGB as a function of ASCAT IB VOD, two functions (Eq. (8)-(9)) were chosen to fit the relationship between VOD and AGB (Li et al., 2021; Wang et al., 2021a). The root mean square deviation (RMSE) between VOD-predicted-AGB and AGB was also calculated (Eq. (10)). The same calculation was also done for ASCAT IB VOD and different canopy height products.

$$R = \frac{\sum_{i=1}^{n}(VOD_i - \overline{VOD})(Y_i - \bar{Y})}{\sqrt{\sum_{i=1}^{n}(VOD_i - \overline{VOD})^2} \sqrt{\sum_{i=1}^{n}(Y_i - \bar{Y})^2}} \quad (7)$$

$$Y = \frac{a}{1 + \exp(-b * (VOD - c))} + d \quad (8)$$

$$Y = a * \exp(b * VOD) + d \quad (9)$$
$$402 \qquad RMSE = \sqrt{\frac{\sum_{i=1}^{n}(y_i - Y_i)^2}{n}} \qquad (10)$$

where Y is AGB or canopy height, a, b, and c are best-fit parameters, y is the predicted AGB or predicted canopy height, n is the valid number of pixels.

The spatial and temporal performance of three other C-VOD products were also evaluated for inter-comparing with ASCAT IB VOD. In order to keep consistency for each product, the following criteria were used: 1) for each product, we used VOD data from the same year (or close in time) as the vegetation-related proxies; 2) since the VODCA data set is only available until 2018 and AMSR2 provides data from July 2012, the temporal evaluation was made from 2013 to 2018; and 3) over each pixel and each year, the number of VOD data should be larger than 60 per year to avoid statistical under-representation. The retrievals of the VOD and ω parameters were also analysed considering different classes of vegetation types defined by the MODIS IGBP classification scheme.

## 4. Results

### 4.1 Calibration results of the C and D parameters

The “bare soil” pixels used for the computation of the C and D parameters of the Ulaby bare soil scattering model (Eq. (4)) are presented in Fig. 2. The red pixels, corresponding to the “bare soil” regions where the backscatter signals were observed in a prolonged state of dryness, are in the desert area. For the blue pixels in non-desert regions, the C and D values were computed from Eq. (4), keeping only pixels where the correlation coefficient (R) is larger than 0.25. Those pixels are mainly distributed in the north, center, and south of Africa, the south of Australia, south of Southern America, Central Asia, and the western regions of the United States. The main land cover types associated with the blue pixels are grassland, cropland, and open shrubland which have a clear seasonality. As a result, the C value could be
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estimated over 23,482 pixels and the *D* value over 24,205 pixels. The corresponding soil properties (18 types) and terrain-related indexes (9 types) of these pixels were extracted, and then they were used as predictors to train two random forest models (referred to as the *C* and *D* models, respectively) for simulating, separately, the *C* and *D* parameters at the global scale.

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<figure><img src="image_3.png" /></figure>

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<caption>**Fig.2.** Spatial distribution of the “bare soil” pixels (represented by points) and MODIS IGBP land cover map. The “bare soil” pixels are shown in a 2° × 2° grid if the total number of *C* or *D* pixels is larger than six.</caption>

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In total, 18 predictor variables were selected to train the *C* model and 19 predicator variables were selected to train the *D* model after applying the recursive feature elimination (RFE) method. Fig. 3 (a-b) shows the variable importance of each *C* and *D* model. Soil property indices (group SPI) played a key role in estimating the target variables in both the *C* and *D* models, with a total importance of 79.39 % and 65.67 %, respectively. The soil organic carbon density (Ocdens) ranked first in SPI for both models. The terrain-related features (group TI) contributed more in the *C* model than in the *D* model, which is consistent with the results obtained in Africa (Liu et al., 2021b).
<figure><img src="image_4.png" /><figcaption>(b)</figcaption></figure>

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

Fig.3. Predictive variable importance of the C (a) and D (b) model values. SPI and TI mean soil property indices (in orange) and terrain-related feature variables (in dark blue), respectively. The definition of each variable is given in Table S1 and S2.

The results of the 10-fold cross-validation of two RF models trained by the selected indices (Fig. 4) are better than those obtained in Africa (Liu et al., 2021b). For the C model, $R^2$ increased from 0.85 to 0.87, and RMSE decreased from 1.31 dB to 0.97 dB. The $R^2$ metric of the D model improved from 0.61 to 0.79, but the RMSE showed a slight increase (from 2.37 dB/ m³·m⁻³ to 2.43 dB/ m³·m⁻³). The overestimation of the lower values and underestimation of the higher values obtained in Africa still occurred for the two models calibrated here at the global scale. This is likely due to the limitation of the RF regression model that cannot be extrapolated; namely when the range of the test fold is not included in the training fold, values beyond the range of the training data set will be overestimated or underestimated.
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Performance of the *C* (a) and *D* (b) soil model based on the 10-fold cross-validation.</figcaption></figure>

The spatial patterns of the global predicted *C* and *D* parameters are illustrated in Fig. 5. The 5th–95th percentiles of the *C* and *D* values among all pixels varied from -18.05 dB to -11.82 dB, and from 5.12 dB/m³·m⁻³ to 13.97 dB/m³·m⁻³, respectively. These values are in the usual range for C-band SAR observations (Baghdadi et al., 2008; Baghdad et al., 2016; Verhoest et al., 2008). The distribution of the *D* values is more discrete than that of the *C* values. The pixels with a value ranging from -15 dB to -10 dB account for 76.20 % of the pixels in the *C* map, while 66.62 % of them are concentrated in the range of -15 dB to -12.5 dB. Comparatively, 75.26 % of the pixels have a *D* value in the 5-10 dB/m³·m⁻³ range and are evenly distributed in 5-7.5 dB/m³·m⁻³ (accounting for 38.79 %) and 7.5-10 dB/m³·m⁻³ (accounting for 36.46 %). In addition, the distribution of the pixels with a *C* value lower than -15 dB in the *C* map matches well with the distribution of pixels with a *D* value larger than 10 dB/m³·m⁻³ in the *D* map, especially in South and North America, Eastern Europe, and Central Asia. Considering each IGBP landcover (Fig. 5 (c)), we can see that the *C* and *D* parameters have a wide range of values in the low vegetation areas. A wide range of *C* values can also be found in mixed forest (MFO). The median *C* values were similar across
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all vegetation types, while the median *D* values varied across the different land cover types.

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More details about the statistical description of *C* and *D* are given in Appendix Table 1-3.

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

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**Fig. 5.** Global maps of the *C* (a) and *D* (b) soil model parameters estimated from the RF models and (c) their boxplots for different IGBP land types.

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**4.2 Vegetation scattering parameter (ω) retrieval**

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A global map of the thirteen-year time-average retrieved ω (based on w=18 days) is shown in Fig. 6 (a). The highest values (ω > 0.32) were obtained in Western Australia, northern Mexico, northwest Canada, eastern Lena River in Russia, southern Somalia, western Arabian
Peninsula, southwest Iran, and northern Pakistan. Those regions are in areas with complex terrain of hills and gullies. High values of ω were also retrieved in most pixels of the tropical regions. A clear gradient of increasing average ω value from north to south can be seen across the Sahel. The maximum values of ω reach up to 0.97, and the pixels with values in the range of 0.08-0.32 account for 97.36 % of all pixels. A boxplot of ω for each land cover type sorted by decreasing median values are shown in Fig 6 (c). Evergreen broadleaf forests (EBF) have the highest ω values, which is consistent with previous results from a calibration of the ω values over southern France (Shamambo et al., 2019). Interestingly, high ω values were obtained in the Cropland/Natural vegetation mosaics (CVM) where roughly half of the pixels (53.48%) have a value greater than 0.24. The standard deviation of the ω value in low vegetation is larger than that in the forest and woody savannas areas.
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Global distribution of (a) temporal average and (b) standard deviation of the retrieved ω parameter from 2008 to 2020, and (c) the mean value per vegetation type ordered by decreasing values and the corresponding standard deviation.</figcaption></figure>

## 4.3 ASCAT IB VOD evaluation

### 4.3.1 Global spatial patterns

The spatial distribution of the four VODs (Fig. 7) shows that the passive VOD products (AMSR2 VOD and VODCA VOD) have a larger coverage as the active VOD products (ASCAT IB VOD and ASCAT TUW VOD) failed to obtain values in some areas (algorithm
failure). In the ASCAT IB VOD retrieval algorithm, we did not perform VOD retrievals in bare soil areas when the bare soil fraction is greater than 99 % and in pixels where we failed to simulate the bare soil backscatter (i.e. when the simulated bare soil backscatter value is larger than the observed backscatter value). The retrieval of TUW VOD was not performed when the difference between the wet and dry reference exceeds the maximum range in the backscatter signal over bare soils (Vreugdenhil et al., 2016). The maximum values of all the four VODs were retrieved in the tropics with VOD ~ 1.2 for AMSR2 and ASCAT TUW VOD, ~ 1.0 for ASCAT IB VOD, and ~ 0.75 for VODCA VOD. The two passive VOD products (AMSR2 and VODCA) have a similar spatial distribution, with higher values in the boreal forests similar to those in the tropical region because they are retrieved from the same sensor (AMSR2) but with a different version of the algorithm. The zonal averaged VOD per latitude (Fig. 7 (e)) shows two peaks at the latitudes of ~ 0°N and ~ 60°N for all three VODs, except for ASCAT TUW VOD. At the latitude of ~ 0°N corresponding to regions of dense tropical forests, both ASCAT VODs decrease rapidly from north to south, while both AMSR2 VODs show a plateau at ~ 0°N and decrease from a latitude of about 13°S.
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Global distribution of time-averaged VOD maps over the period from years 2013-2018, (a) ASCAT IB, (b) ASCAT TUW, (c) AMSR2, (d) VODCA and (e) corresponding zonal average.</figcaption></figure>

In order to quantitatively describe the differences among the four VODs, we computed the spatial correlation and residuals of the relationship between ASCAT VOD IB and the three other VOD products (Fig 8). ASCAT IB VOD has the highest correlation with AMSR2 VOD,
followed by VODCA VOD and ASCAT TUW VOD. AMSR2 and VODCA VOD show a piecewise linear correlation with ASCAT IB VOD, while the correlation between TUW VOD and ASCAT IB VOD is quite low when the ASCAT IB VOD value is lower than 0.6. The residual plots show that the value of ASCAT IB VOD is lower than that of AMSR2 VOD in almost all pixels. Compared with VODCA VOD, ASCAT IB VOD values are larger when their values exceed 0.8. ASCAT IB VOD values are lower than the ASCAT TUW VOD values except for values in the range of 0.7-0.8.

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Density scatter plots and residuals of the spatial relationship between ASCAT VOD IB and the three other VOD products. VOD is time-averaged over 2013-2018, with residuals calculated as ASCAT IB VOD minus the three other VODs.</figcaption></figure>

### 4.3.2 Spatial correlation with aboveground biomass and canopy height

Fig. 9 shows the density scatter plots of the four VODs with the three aboveground biomass (AGB) products. The density of points in the scatter plots of GEDI AGB is lower than that of the two other AGB products because the range of GEDI biomass did not include areas with latitudes above 53.99°N. The distribution density plot of VOD versus AGB differs between
the ASCAT and AMSR2 VOD products. The shape of the density distribution of ASCAT VOD shows a gradual slope rise, while that of AMSR2 VOD presents first a slow slope increase and then a steep increase for AGB greater than ~100 Mg ha⁻¹. Therefore, two different parameterization functions (Eq. (8) for ASCAT VOD; Eq. (9) for AMSR2 VOD) were used to fit the relationship between VOD and AGB. The results show that ASCAT IB VOD exhibits the best performance to predict all three AGB products, with a correlation of 0.83-0.87 and RMSE values (34.62-36.86 Mg ha⁻¹) computed between reference and predicted AGB. AMSR2 VOD achieved the second-best performance in predicting the Saatchi and GEDI AGB. Similarly, ASCAT TUW VOD achieved the second-best performance in predicting CCI AGB. ASCAT TUW with Saatchi and GEDI AGB and VODCA with CCI AGB presented the lowest correlation values (0.63-0.66). Note that there is some scatter in these relationships and at low height levels, it cannot be concluded that the C-band VOD product will be a good proxy of the vegetation biomass. In particular, further studies based on specific data sets will be necessary to better evaluate the potential capabilities of ASCAT IB VOD in low vegetation canopies as croplands and grasslands.
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Density scatter plots of the four global C-VOD (yearly averaged value) vs Saatchi AGB (first row), CCI AGB (second row) and GEDI AGB (third row) datasets. R₁ is the spatial Pearson correlation coefficient calculated between C-VOD and AGB (Eq. (7)), while R₂ is calculated between VOD-predicted AGB and AGB. The solid blue lines are the predictive fits obtained using Eqs. (8) or (9).</figcaption></figure>

Considering the density scatter plot (Fig. 10) between VOD and the two GEDI canopy height (CH) (Lang and Potapov) products, ASCAT IB VOD obtained the best correlation (R = 0.84-0.85). The saturation of VOD to CH is more serious for AMSR2 and VODCA VOD than for ASCAT IB and TUW VOD. AMSR2 and VODCA VOD saturate at a value of CH exceeding 15 m, while ASCAT IB and TUW VOD saturate at a higher value (> 20 m). Regarding the potential to predict canopy height, ASCAT IB VOD presents again the best results with the two canopy height (CH) products at different spatial resolutions (Potapov: from 30 m resampled to 0.25 degrees, Lang: 0.5 degrees). The lowest RMSE values were obtained in the
568 ASCAT IB VOD predictions of CH: RMSE = 4.07 m for Potapov's CH and RMSE = 4.38 m
569 for Lang's CH. The R<sub>2</sub> value (computed using a predictive fit) is lower than the R<sub>1</sub> value for
570 the ASCAT IB VOD. This is mainly because we forced the predictive fitted line to start from
571 (0, 0). The quality of the relationship between VOD and CH is slightly weaker for the Lang
572 product (Fig. 10) probably due to the coarse resolution of the latter product.

<figure><img src="image_12.png" /></figure>

574 **Fig. 10.** Density scatter plots of the four global C-VOD (yearly value) vs the Potapov et al. canopy height (first row), and Lang et al. canopy height (second row) datasets. R<sub>1</sub> is the spatial Pearson correlation coefficient calculated between C-VOD and canopy height (CH) (Eq. (7)), while R<sub>2</sub> is calculated between VOD-predicted CH and CH. The solid blue lines are the fits obtained using Eq. (8) or (9).

579 **4.3.3 Temporal correlation with vegetation indices**

580 The regions where the four VODs products presented the highest absolute temporal
correlation with VIs are shown in Fig. 11. The two ASCAT VODs had more pixels with a
581 higher correlation with LAI and NDWI, with ASCAT IB VOD showing the highest
582 percentage. Passive VODs have a higher correlation with NDVI over more pixels, with
583 AMSR2 VOD having the highest percentage. Similar features can be seen in Fig. 11 (a)-(c)
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for the three vegetation indices, with ASCAT IB VOD obtaining the highest correlation values over more pixels in the subtropics regions, same for TUW VOD in the eastern United States, and same for the two AMSR2 VODs in Europe and Australia. The main differences were found in Alaska and central and eastern Russia where ASCAT IB VOD vs. LAI (Fig. 11 (a)) and TUW VOD vs. NDWI (Fig. 11 (c)) showed the highest correlation values, while the two AMSR2 VODs showed highest correlation values with NDVI (Fig. 11 (b)). In central Russia, the two AMSR2 VODs (Fig. 11 (b)-(c)) achieved the highest correlation values with NDWI and NDVI, while the two ASCAT VODs showed the highest correlation values with LAI (Fig. 11 (a)). The pixel-wise temporal correlations between the four VODs and the three VIs are shown in Fig S2. Overall, AMSR2 VODs obtained significant correlations with VIs in more pixels than ASCAT VODs. Specifically, more significant correlations were obtained (i) between AMSR2 VODs and NDVI, and (ii) between ASCAT VODs and LAI. Both the ASCAT VOD and AMSR2 VOD have the lowest number of values significantly associated with NDWI. The spatial distribution of correlation values between the two AMSR2 VODs and the three different VIs is very similar, but the results for the active VODs are significantly different. The correlation values between the two active VODs and the VIs are in opposite directions in some specific regions. In the Amazon and Congo forest regions, ASCAT IB VOD shows negative correlations with the three different VIs, while ASCAT TUW VOD shows positive correlations. In the Central African Republic, northern Argentina and southern Bolivia, ASCAT IB VOD correlates positively with the three different VIs, while ASCAT TUW VOD correlates negatively with the same VIs. For further details, see Fig. S2
<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Maps of the highest absolute temporal correlation (R) values between four VOD datasets and three VIs based on six-year datasets (2013-2018) and corresponding percentage of coverage (bottom left). “Not significant,” represented by shaded areas, means the P-value of the correlation is larger than 0.05. White areas mean “no valid data.”</figcaption></figure>
To illustrate the seasonal dynamics of the active and passive VODs, the time series of VOD and NDVI (LAI) are plotted over five pixels with relatively homogeneous land cover conditions but different vegetation types (Fig. 12). The fractional coverage of the land cover class and location information of each pixel is presented in Table S4. In the cropland site over northern France, ASCAT IB VOD showed a negative correlation with NDVI and LAI, while the three other VOD products showed the same seasonal dynamics as NDVI and LAI (the ASCAT IB VOD changes were similar to the three other VODs and VIs only from May to July). Fig. 12 (a) illustrates the negative temporal correlations obtained by ASCAT IB VOD with all three VIs in Eastern Europe (Fig. S2). The soil moisture and backscattering coefficient data for both periods (Feb.-May and Aug.-Nov.) were checked (Fig. S3). The backscattering coefficient changes are in phase with the time changes in soil moisture. However, the backscattering coefficient has a high value at the beginning of the vegetation development and the end of senescence, even larger than the value at the time when the LAI was at its peak. Shan et al. (2022) have also identified the same phenomenon that they attributed to human activity before and after sowing and to vegetation senescence in agricultural fields that increased soil roughness resulting in the high value of the observed backscatter signals. In addition, previous studies have observed a decrease in the backscatter signals as LAI increased in narrow-leaved crops (Fontanelli et al., 2013; Macelloni et al., 2001).
<figure><img src="image_14.png" /><figcaption>**Fig. 12.** Time series of the five VOD products (including the old ASCAT IB V1 version in (d)), NDVI, and LAI over five types of vegetation cover in the period from January 2013 to December 2018 (AMSR2 and VODCA VOD were smoothed by an 18-day moving window).</figcaption></figure>

Interestingly, over the evergreen broadleaf forest site in southern Vietnam, ASCAT IB VOD presents large seasonal variations similar to those of LAI, while the three other VODs and NDVI showed slight time variations. In the high latitude grasslands site (Fig. 12 (c)), the temporal availability of ASCAT IB VOD is shorter than that of the passive VODs, but ASCAT IB VOD still monitored well the vegetation growth. The AMSR2 VODs have a
longer temporal availability than the active VODs in the high latitude regions, which is clearly shown in the Hovmöller diagrams (Fig. S4). In the savanna site in Africa (Fig. 12 (d)), the peak of ASCAT IB VOD is well synchronized with that of LAI, while the AMSR2 VODs and NDVI are more synchronized. Compared to the ASCAT IB V1 VOD version (only available in Africa) (Liu et al., 2021b), it can be clearly seen that the new global ASCAT IB VOD product is smoother and solves the noise issue mentioned in the previous study in Africa. In the open shrublands site (Fig. 12 (e)), the values of LAI and NDVI were higher in 2017 than in the other three years, and ASCAT IB VOD also captured well this inter-annual variation in the vegetation cycle. ASCAT TUW VOD presents weak temporal variations over all the different vegetation types, because a time window ranging from 2 to 12 weeks were used to calculate VOD (Vreugdenhil et al., 2016).

## 5. Discussion

### 5.1 Uncertainty in the VOD retrieval

There are three main uncertainties associated with ASCAT IB VOD. The first one is the simplified scattering process considered in the retrieval algorithms, and the other two are the soil parameters estimated from the random forest models and the ERA5-Land SM input. The water cloud model that is used to simulate the backscattering coefficient in the present study neglected the multiple scattering effects as it is hard to quantify this part in the model. Some studies showed that the multiple scattering effects can be generally ignored for low levels of the vegetation cover at the C- and L-bands (El Hajj et al., 2019b; Hosseini et al., 2015; Zribi et al., 2019). One study (Li et al., 2020) confirmed also this conclusion by comparing the VOD and SM parameters retrieved from L-band SMOS TB based on Tau-Omega and a higher order radiative transfer model, named the Two-Stream emission model (Schwank et al., 2018), which holds a stronger physical background with consideration of multiple scattering and reflection. However, the VOD values retrieved from the two models vary over
dense vegetation areas and boreal regions. A study that explored the contribution of double-bounce to the total signal of the P-, L- and C-bands in forest regions showed that the double-bounce component at C-band is lower than that at the L- and P-bands and accounts for a small contribution (1%) to the total signal in forests (Freeman and Durden, 1993). Quast et al. (2019) used a generalization of the WCM that incorporates an estimate of first-order (i.e., double-bounce) interaction-effects to simulate the ASCAT backscattering signals. The results also showed the interaction-contributions are very small. In the passive domain, (it is likely it is the same in the active domain), several studies showed that multiple scattering effects can be well accounted for by zero-order radiative transfer models that neglect multiple scattering effects, provided that the model parameters (VOD and scattering parameter) are considered as effective parameters (Kurum, 2013; Li et al., 2020). Here, we roughly simulated the effect of neglecting multiple scattering, by accounting for the impact of a 0 to 5 % change in the total signal (the value of 0 to 5 % was estimated from Freeman and Durden (1993) and Quast et al. (2019)), on VOD retrievals for the three vegetation types (Fig. S5). Assuming multiple scattering accounts for 5 % of the total signal (worst-case scenario), the results showed that the relative variation of VOD in the evergreen broadleaf forest reached 19 % when ignoring multiple scattering. The relative change of VOD in grasslands also showed a higher value (17 %) than in savannah (12 %) even though their VOD value changed little (~ 0.02). These results show that neglecting multiple scattering effects in ASCAT VOD retrievals has a non-negligible effect, potentially leading to a change in VOD values by around 10-20 % in the worst case and that better consideration of multiple scattering effects should be considered in future improvements to our retrieval approach. However, the relative change in VOD caused by neglecting multiple scattering is relatively small compared to the large uncertainties associated with global AGB datasets: AGB values can vary by up to 50 % in some forest regions for different AGB data sets (Araza et al., 2022; Urbazaev et al., 2018). Considering
that there is no simple equation to quantitatively describe the multiple scattering effects in WCM, the traditional WCM equations were used in this study. Although there are some discrete models (Bracaglia et al., 1995; Ulaby et al., 1990) that considered the multiple scattering effects, it is difficult to apply these models over large regions as they need too many input parameters for a rigorous retrieval (Bai et al., 2018; Bai et al., 2022).

Another possible uncertainty associated with ASCAT IB VOD is related to the fact we ignored the subsurface scattering in the simplified Ulaby soil model. Wagner et al. (2022) showed that the backscattering coefficient negatively correlates with SM when subsurface scattering is the main contribution of the soil backscatter and positively correlates with SM when the surface scattering effects dominate. When both types of scattering are present, the backscattering coefficient will exhibit a negative correlation with SM in dry conditions and a positive correlation as SM gradually increases, showing a U-shape (Liu et al., 2016; Wagner et al., 2022). Based on the correlation with AGB, the ASCAT IB VOD retrieved using the soil parameters predicted by different models trained with different positive correlation thresholds were evaluated, with the threshold $R > 0.25$ achieving the best results (Table S5). When a higher threshold value is selected, pixels with mixed scattering that may be U-shaped with a lower R-value are excluded from the model training, resulting in more errors in the soil parameters prediction and finally causing errors in the ASCAT IB VOD retrieval. Although some studies have showed that the subsurface scattering effects usually happen in dry desert areas (McColl et al., 2014), the correlation values between the ASCAT backscatter measurements and ERA5-land SM revealed that pixels with negative correlation values are also located in some sparsely vegetated areas (Fig. 1 in (Wagner et al., 2022)). Because of the negative correlation, the $\sigma_{\text{WetRef}}^{\circ}$ parameter fails to represent the time period when SM values are high (Fig. S1). Therefore, neglecting subsurface scattering in soil scattering modeling and in estimating initial values of the vegetation parameter from $\sigma_{\text{WetRef}}^{\circ}$ will introduce
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uncertainty to the ASCAT IB VOD retrievals, especially when the subsurface effects are
dominant.

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716

Random forest models were used in this study to simulate the global *C* and *D* soil model
parameters. The underestimation of the high values and overestimation of the low values of
the soil parameters (*C* and *D*) can be noted in the 10-fold cross-validation (Fig. 4 (a)-(b)).

719

This is caused by the limitations of the tree-based (e.g. random forest) models to perform
extrapolations (Hengl et al., 2018). The predictions for test data outside the range of training
data are often underestimated or overestimated. During the 10-fold cross-validation, the data
are divided into ten groups with 9 groups used for training and 1 group for testing, and each
group is tested once for the model. The range of data in the training and testing folds may be
similar if the data has a normal distribution, where the mean is equal to the median value. In
the histogram of the *C* and *D* values extracted in “bare soil” pixels (Fig. S6), there is a larger
difference between the averaged and median value of the *D* values than for the *C* values. As a
result, the underestimation and overestimation of the *D* model are more severe than that of the
*C* model (Fig. 4 (a)-(b)). The effect of the uncertainty associated with the parameters *C* and *D*
on the VOD retrievals was evaluated on three vegetation types based on RMSE in Fig. 4. The
results (Fig. S7) show that the most significant impact is on grasslands, followed by savannas
and evergreen broadleaf forests. Following the method of Liu et al. (2021b), the uncertainty
associated with ERA5-Land’s SM on the VOD retrievals was analyzed by examining changes
in SM values in relation to relative changes in VOD values over three vegetation types. The
selected change in SM value, derived from an estimate of ERA5-Land’s ubRMSE, is 0.05
m³/m³. The results (Fig. S8) show that the relative change in VOD values is ± 17 % on
grassland, ± 8 % on savannah and ± 3 % on evergreen broadleaf forest when SM values vary
by ± 0.05 m³/m³. As expected, the impact of errors on SM is weaker as VOD values increase.

717

718

720

721

722

723

724

725

726

727

728

729

730

731

732

733

734

735

736

737
738

These results are similar to the sensitivity analysis of the uncertainty associated with ERA5-

739

Land SM on ASCAT IB V1 VOD retrievals (Liu et al., 2021b).

740

## 5.2 Inter-comparison of the active and passive VOD

741

The assessment and comparison results indicate that there are some discrepancies between

742

ASCAT IB VOD and AMSR2 VODs. Regarding spatial relationships, ASCAT IB VOD

743

showed the highest correlation with AGB and canopy height (Fig. 9-10). It was also found

744

that ASCAT IB VOD had the best temporal correlation with LAI and NDWI for most of the

745

pixels at the global scale, while AMSR2 VODs showed best correlation with NDVI (Fig. 11

746

(b)). The statistics of the temporal correlation between VODs and VIs in each land cover type

747

show similar results (Table S6), especially for short vegetation. Physical radiative transfer

748

mechanisms may help to understand why LAI and NDWI have a better spatial and temporal

749

correlation with active ASCAT IB VOD than passive AMSR2 VODs. As active microwave

750

sensors emit energy, the observed backscattering coefficient is a measurement of the

751

microwave radiation scattered in a single backward direction, while the passive microwaves

752

sensors detect the naturally emitted microwave energy which corresponds to the integration of

753

the bi-static coefficient considering the incident radiations over the whole hemisphere

754

(Ferrazzoli et al., 1989; Wigneron et al., 1999). Therefore, compared to passive observations,

755

active observations are more sensitive to volume scattering within the canopy. This is because

756

passive microwave remote sensing integrates over all of the possible directions of incident

757

radiation, while active microwave remote sensing only samples a single direction, resulting in

758

less averaging of coherent effects. Consequently, active microwave remote sensing is more

759

sensitive to vegetation canopy structure and specifically temporal changes in leaf water and

760

orientation than passive microwave remote sensing.

761

Even though ASCAT IB VOD performed well in space and time when compared with the

762

three other C-band VOD products, we should be aware that the algorithms and input datasets
of those four products differ and also make the VOD values and ranges different (Fig. 8). We summarized these main differences in Table 1. For the two active VOD products, the retrieval algorithms are both based on the water cloud model, but the inputs are different. TUW VOD was retrieved by using the ASCAT dry and wet references which are the mean value of the 10% highest and lowest normalized backscatter data at 25° and 40° (Pfeil et al., 2018), while the ASCAT normalized backscatter data at 40° was used to retrieve ASCAT IB VOD. The hypotheses made in the two active VOD retrieval algorithms are also very different. In the ASCAT IB VOD retrieval algorithm, the vegetation scattering (ω) and VOD parameters were assumed to be constant within a period of 18 days and retrieved together using a model-based SM as input to the algorithm. In contrast, the TUW VOD retrieval algorithm assumes that the maximum range of backscatter values (- 6.68 dB) over bare soil is the same around the world, excluding desert areas. The auxiliary data used by both algorithms mainly concern the modelling of soil scattering. The ASCAT IB VOD retrieval algorithm employed many ancillary data sets (e.g. SoilGrids250m and DEM-related datasets) for calibrating the parameters of the soil model and used a modelled reanalysis SM product from ERA5-land as input to the soil model. Conversely, the TUW VOD retrieval algorithm utilized only the Koppen-Geiger climate classification data to model the bare soil scattering. The different ancillary data used in the two algorithms will affect the VOD retrievals. The most obvious difference is that the two active VODs are retrieved using ancillary information for soil moisture, while VOD and SM are simultaneously retrieved in the passive domain (Owe et al., 2008; Wigneron et al., 2017). The two passive VOD are retrieved from AMSR2 brightness temperature measurements at 55° (Imaoka et al., 2012) but with different versions of the LPRM algorithm. VODCA VOD was retrieved based on LPRM version 6 (van der Schalie et al., 2017) which update the setting of the effective scattering albedo and of the soil roughness parameter, compared with LPRM version 5 used in the AMSR2 VOD retrievals. Those
788 discrepancies among the four VOD products may make the interpretation of the inter-
789 comparison more challenging.

<table><caption>Table 1. Summary of the key differences in the retrieval algorithms of the active ASCAT VODs (ASCAT IB, TUW) and the passive VODs (AMSR2 and VODCA).</caption><thead><tr><th>Algorithm</th><th>ASCAT IB</th><th>ASCAT TUW (Vreugdenhil et al., 2016)</th><th>LPRM version 5 (AMSR2 VOD) (Owe et al., 2008)</th><th>LPRM version 6 (VODCA VOD) (Moesinger et al., 2020; van der Schalie et al., 2017)</th></tr></thead><tbody><tr><td>Observations</td><td>ASCAT σ<sup>o</sup><sub>obs</sub> at VV polarization</td><td>ASCAT σ<sup>o</sup><sub>WetRef</sub> and σ<sup>o</sup><sub>DryRef</sub> at VV polarization</td><td>AMSR2 T<sub>B</sub> at dual (V, H) polarization</td><td>AMSR2 at dual (V, H) polarization</td></tr><tr><td>Vegetation scattering modelling</td><td>Water cloud model (Attema and Ulaby, 1978)</td><td>Water cloud model (Attema and Ulaby, 1978)</td><td>τ-ω<sub>p</sub> model (Mo et al., 1982)<br>ω<sub>p</sub>= 0.05</td><td>τ-ω<sub>p</sub> model (Mo et al., 1982)<br>ω<sub>p</sub>= 0.075</td></tr><tr><td>Soil scattering modelling</td><td>Ulaby linear model (Ulaby et al., 1978)</td><td>Δσ<sup>o</sup><sub>s</sub>: maximum range in backscatter values over bare soils</td><td>H-Q-N modelling (Wang and Choudhury, 1981)<br>H<sub>R</sub>= 0.09</td><td>H-Q-N modelling (Wang and Choudhury, 1981)<br>H<sub>R</sub>= 1.2*(1-2*SM)</td></tr><tr><td>Hypothesis</td><td>VOD is the same over a short time window (18 days)</td><td>Δσ<sup>o</sup><sub>s</sub> is the same everywhere in the world, except in desert areas.</td><td>VOD is the same at H and V polarizations</td><td>VOD is the same at H and V polarizations</td></tr><tr><td>Ancillary data</td><td>SoilGrids250m (Hengl et al., 2017) and DEM-related data (Danielson and Gesch, 2011) are used to estimate the soil parameters of the Ulbay linear model;<br>ERA5-Land SM (Muñoz-Sabater et al., 2021) is used as input to simulate the bare soil backscatter coefficient</td><td>Koppen-Geiger climate classification was used to set the Δσ<sup>o</sup><sub>s</sub> parameter</td><td>Vertically polarized Ka-band (36.5 GHz) observations from AMSR2 are used to derive the effective temperature (Holmes et al., 2009);<br>Soil texture (clay, silt and sand) from FAO is used to estimate the soil dielectric constant (Wang and Choudhury, 1981)</td><td>Same with LPRM version 5</td></tr><tr><td>VOD retrieval</td><td>VOD is retrieved simultaneously with the vegetation scattering parameter (ω)</td><td>Only VOD is retrieved</td><td>VOD is retrieved simultaneously with SM</td><td>VOD is retrieved simultaneously with SM, VOD is re-scaled to AMSR-E VOD by a CDF matching technique</td></tr></tbody></table>
<table><thead><tr><th>Limitations</th><th>The multi-temporal method may result in VOD not being sensitive to dry-down periods (Li et al., 2021) and has larger uncertainties over grasslands and croplands where vegetation growth can be very fast (Jackson et al., 2004).</th><th>The use of a kernel smoother with a half-width window of 21 days (Vreugdenhil et al., 2020) when calculating σ<sup>°</sup><sub>WetRef</sub> and σ<sup>°</sup><sub>DryRef</sub> decrease the seasonal amplitude of VOD; σ<sup>°</sup><sub>WetRef</sub> and σ<sup>°</sup><sub>DryRef</sub> are not optimized for regional conditions (Pfeil et al., 2018).</th><th>The global constant value of ω<sub>p</sub> and H<sub>R</sub> may cause uncertainties in VOD retrieval (Baur et al., 2019).</th><th>Same with LPRM version 5</th></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

T<sub>B</sub> = brightness temperature; ω<sub>p</sub> = Effective scattering albedo; H<sub>R</sub> = roughness parameter

## 6. Conclusion and Outlook

The objective of this work was to retrieve the first global ASCAT IB VOD product and the vegetation scattering parameter (ω) from the single-channel (VV) active microwave observations of ASCAT. The spatial distribution of the retrieved ω map matches well vegetation distribution, which is in line with the previous findings of the literature. When compared to the other C-VOD products, ASCAT IB VOD had a competitive advantage. In spatial terms, ASCAT IB VOD presented the highest correlation values with AGB and tree height, with R values of ~0.83 and ~ 0.84, respectively. In comparison with the previous ASCAT IB V1 VOD developed only over Africa, the global ASCAT IB VOD has shown strong improvement in terms of temporal evolution which are much less noisy pixel-based time-series than those of the previous version. Some interesting discrepancies were also observed between ASCAT and AMSR2 VODs. The non-linear density distribution presented by ASCAT VOD and AGBs is different from that presented by AMSR2 VOD. ASCAT VODs have a linear spatial relationship with LAI and a non-linear one with NDWI and NDVI, while AMSR2 VODs presented opposite results (Fig. S9). Similar results were obtained in terms of temporal variations: ASCAT VODs show greater synchronization with LAI and NDWI, while AMSR2 VODs are more closely aligned with NDVI. So, the different characteristics of the two ASCAT VODs (more closely related to changes in LAI) and the two AMSR2 VODs (more closely related to changes in NDVI and NDWI) showed appealing complementarity for conducting joint vegetation studies.
The strong ability of ASCAT observations to predict AGB as found in this work and Santoro et al. (2022) encourages us to apply this long-term VOD to investigate the time changes in aboveground biomass at continental scales since 2007. Recently, Tao et al. (2022) developed the first global C-band scatterometer dataset which dates back to 1992. Applying the new ASCAT IB algorithm to this dataset would allow obtaining a 30-year data set of the VOD parameter which is very promising for studies of vegetation resilience (Forzieri et al., 2022). Considering that global ASCAT IB VOD showed a good sensitivity to NDWI, some applications (like wildfire prediction or post-fire recovery) related to vegetation water content can be explored in the future (Bousquet et al., 2022; Fan et al., 2018). Furthermore, a specific version of our algorithm could be developed for Sentinel-1 (S1): less uncertainty in the retrievals could be obtained by combining the S1 observations at the two VV and VH polarisations, instead of using only one (VV) for ASCAT. In addition, improved bare soil models and the use of machine learning methods that can be extrapolated (e.g. ensemble RF, Cubist) (Lin et al., 2017; Pouladi et al., 2019) could lead to a decrease in the uncertainty associated with the simulation of the bare soil backscattering.

## Data availability

Global ASCAT IB VOD and vegetation scattering parameter ($\omega$) can be downloaded from the INRAE Bordeaux remote sensing lab website (https://ibremote-sensing.inrae.fr) after registration.

## Appendix A

### A.1 Random forest model

In this study, the random forest (RF) regression model was used to simulate the global soil parameters (C and D) of the Ulaby bare soil model. RF is a tree-based machine learning
method and has been widely adopted in many geographic-related studies (Liao et al., 2022; Wang et al., 2022). RF works by training many regression trees and reporting the mean response over all the trees. In each tree, approximately two-thirds of the samples in the dataset are used for training and the remaining third (called the out-of-bag data (OOB)) are used for internal validation of the model. The r-square values (R²) and RMSE calculated from the OOB samples are used to assess the model performance. The python sklearn package (Pedregosa et al., 2011) was used for the implementation of RF, including the establishment of the model, the selection of the predictor of the model, and the validation of the model.

The 'RandomForestRegressor' was used as the estimator and its two parameters (n_estimators and max_features) were optimized by the 'GridSearchCV' function. The 'random_state' has been defined to guarantee the reproducibility of the model. The predictor variables of the model were selected using a recursive feature elimination (RFE) method (Guyon et al., 2002). We first fitted the model with all the predictors and then removed the least important predictor from the model. This process was iterated until only one predictor variable was left. The selected predictors are those that lead to the highest R² and lowest RMSE. Finally, the 10-fold cross-validation (CV) (Stone, 1974) was used as an independent validation to assess the accuracy of the selected model. The mean value of the R² and RMSE of 10-fold CV and the importance of each predictor were reported.

## Appendix Table 1

Statistics of C and D at the global scale.

<table><thead><tr><th>Parameter</th><th>Max</th><th>95th percentiles</th><th>Min</th><th>5th percentiles</th><th>Mean</th><th>Median</th></tr></thead><tbody><tr><td>C(dB)</td><td>-7.60</td><td>-11.82</td><td>-26.01</td><td>-18.05</td><td>-14.27</td><td>-13.92</td></tr><tr><td>D (dB/m³·m⁻³)</td><td>34.12</td><td>13.97</td><td>1.75</td><td>5.12</td><td>8.46</td><td>9.76</td></tr></tbody></table>

## Appendix Table 2

Statistics of C and D over different ranges of values.

<table><thead><tr><th>Parameter</th><th>Range [Min, Max]</th><th>Percentage</th><th>Mean</th><th>Median</th></tr></thead><tbody><tr><td>C (dB)</td><td>[-25,-20]</td><td>1.51 %</td><td>-21.05</td><td>-20.78</td></tr></tbody></table>
<table><tr><td></td><td>[-20,-15]</td><td>21.98 %</td><td>-16.64</td><td>-16.38</td></tr><tr><td></td><td>[-15,-12.5]</td><td>66.62 %</td><td>-13.73</td><td>-13.72</td></tr><tr><td></td><td>[-12.5,-10]</td><td>9.58 %</td><td>-11.65</td><td>-11.85</td></tr><tr><td></td><td>[-10,-5]</td><td>0.31 %</td><td>-9.53</td><td>-9.70</td></tr><tr><td rowspan="5">D<br>(dB/m<sup>3</sup>·m<sup>-3</sup>)</td><td>[0,5]</td><td>3.96 %</td><td>4.57</td><td>4.67</td></tr><tr><td>[5,7.5]</td><td>38.79 %</td><td>6.38</td><td>6.43</td></tr><tr><td>[7.5,10]</td><td>36.47 %</td><td>8.63</td><td>8.59</td></tr><tr><td>[10,15]</td><td>17.31 %</td><td>11.74</td><td>11.47</td></tr><tr><td>[15,25]</td><td>3.39 %</td><td>17.67</td><td>16.95</td></tr><tr><td></td><td>[25,35]</td><td>0.08 %</td><td>26.92</td><td>26.32</td></tr></table>

## 858 Appendix Table 3

## 859 Statistics of C and D for different IGBP land cover classes.

<table><thead><tr><th rowspan="2">IGBP<br>land cover</th><th colspan="2">Min</th><th colspan="2">Max</th><th colspan="2">Mean</th><th colspan="2">Median</th><th rowspan="2">Percentage</th></tr><tr><th>C</th><th>D</th><th>C</th><th>D</th><th>C</th><th>D</th><th>C</th><th>D</th></tr></thead><tbody><tr><td>ENF</td><td>-16.48</td><td>3.88</td><td>-9.83</td><td>12.88</td><td>-13.12</td><td>7.12</td><td>-13.34</td><td>6.83</td><td>2.17 %</td></tr><tr><td>EBF</td><td>-16.81</td><td>4.52</td><td>-9.79</td><td>16.14</td><td>-14.02</td><td>8.86</td><td>-14.21</td><td>8.84</td><td>7.32 %</td></tr><tr><td>DBF</td><td>-17.28</td><td>4.10</td><td>-10.09</td><td>18.00</td><td>-13.80</td><td>8.59</td><td>-13.90</td><td>8.51</td><td>1.80 %</td></tr><tr><td>DNF</td><td>-15.34</td><td>5.42</td><td>-12.35</td><td>12.42</td><td>-13.90</td><td>9.11</td><td>-13.92</td><td>8.86</td><td>0.11 %</td></tr><tr><td>MFO</td><td>-16.53</td><td>3.83</td><td>-9.91</td><td>16.82</td><td>-13.42</td><td>8.82</td><td>-13.43</td><td>8.34</td><td>4.84 %</td></tr><tr><td>CSH</td><td>-17.39</td><td>4.08</td><td>-10.76</td><td>13.19</td><td>-14.51</td><td>7.22</td><td>-14.55</td><td>6.64</td><td>0.23 %</td></tr><tr><td>OSH</td><td>-21.04</td><td>1.75</td><td>-9.17</td><td>30.59</td><td>-13.85</td><td>6.76</td><td>-13.44</td><td>6.38</td><td>13.15 %</td></tr><tr><td>WSA</td><td>-20.09</td><td>4.13</td><td>-9.90</td><td>16.91</td><td>-13.69</td><td>8.21</td><td>-13.69</td><td>8.07</td><td>10.03 %</td></tr><tr><td>SAV</td><td>-19.61</td><td>3.05</td><td>-10.25</td><td>17.46</td><td>-13.82</td><td>8.04</td><td>-13.78</td><td>7.95</td><td>13.44 %</td></tr><tr><td>GRA</td><td>-26.01</td><td>2.06</td><td>-8.72</td><td>34.12</td><td>-14.43</td><td>9.02</td><td>-14.01</td><td>8.00</td><td>23.48 %</td></tr><tr><td>CRO</td><td>-22.01</td><td>2.81</td><td>-1.08</td><td>26.35</td><td>-14.67</td><td>10.88</td><td>-14.53</td><td>10.86</td><td>9.64 %</td></tr><tr><td>CVM</td><td>-18.40</td><td>4.63</td><td>-12.31</td><td>22.20</td><td>-14.34</td><td>9.84</td><td>-14.17</td><td>9.65</td><td>0.61 %</td></tr><tr><td>BSV</td><td>-20.43</td><td>2.23</td><td>-7.60</td><td>24.42</td><td>-15.76</td><td>7.81</td><td>-15.99</td><td>7.31</td><td>13.18 %</td></tr></tbody></table>

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1179 Wang, M., Wigneron, J.-P., Sun, R., Fan, L., Frappart, F., Tao, S., Chai, L., Li, X., Liu, X., Ma, H., Moisy, C., & 1180 Ciais, P. (2021b). A consistent record of vegetation optical depth retrieved from the AMSR-E and AMSR2 X- 1181 band observations. *International Journal of Applied Earth Observation and Geoinformation*, 105

1182 Wang, N., Peng, J., Xue, J., Zhang, X., Huang, J., Biswas, A., He, Y., & Shi, Z. (2022). A framework for 1183 determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest- 1184 temporal convolution network. *Geoderma*, 409
1185 Wigneron, J.-P., Li, X., Frappart, F., Fan, L., Al-Yaari, A., De Lannoy, G., Liu, X., Wang, M., Le Masson, E., & Moisy, C. (2021). SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives. *Remote Sensing of Environment, 254*

1188 Wigneron, J.P., Fan, L., Ciais, P., Bastos, A., Brandt, M., Chave, J., Saatchi, S., Baccini, A., & Fensholt, R. (2020). Tropical forests did not recover from the strong 2015-2016 El Nino event. *Sci Adv, 6*, eaay4603

1190 Wigneron, J.P., Ferrazzoli, P., Calvet, J.C., & Bertuzzi, P. (1999). A parametric study on passive and active microwave observations over a soybean crop. *IEEE Transactions on Geoscience and Remote Sensing, 37*, 2728-2733

1193 Wigneron, J.P., Jackson, T.J., O'Neill, P., De Lannoy, G., de Rosnay, P., Walker, J.P., Ferrazzoli, P., Mironov, V., Bircher, S., Grant, J.P., Kurum, M., Schwank, M., Munoz-Sabater, J., Das, N., Royer, A., Al-Yaari, A., Al Bitar, A., Fernandez-Moran, R., Lawrence, H., Mialon, A., Parrens, M., Richaume, P., Delwart, S., & Kerr, Y. (2017). Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. *Remote Sensing of Environment, 192*, 238-262

1199 Wigneron, J.P., Kerr, Y., Waldteufel, P., Saleh, K., Escorihuela, M.J., Richaume, P., Ferrazzoli, P., de Rosnay, P., Gurney, R., Calvet, J.C., Grant, J.P., Guglielmetti, M., Hornbuckle, B., Mätzler, C., Pellarin, T., & Schwank, M. (2007). L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields. *Remote Sensing of Environment, 107*, 639-655

1203 Wigneron, J.P., Waldteufel, P., Chanzy, A., Calvet, J.C., & Kerr, Y. (2000). Two-Dimensional Microwave Interferometer Retrieval Capabilities over Land Surfaces (SMOS Mission). *Remote Sensing of Environment, 73*, 270-282

1206 Xing, Z., Fan, L., Zhao, L., De Lannoy, G., Frappart, F., Peng, J., Li, X., Zeng, J., Al-Yaari, A., Yang, K., Zhao, T., Shi, J., Wang, M., Liu, X., Hu, G., Xiao, Y., Du, E., Li, R., Qiao, Y., Shi, J., Wen, J., Ma, M., & Wigneron, J.-P. (2021). A first assessment of satellite and reanalysis estimates of surface and root-zone soil moisture over the permafrost region of Qinghai-Tibet Plateau. *Remote Sensing of Environment, 265*

1210 Zhou, Z., Fan, L., De Lannoy, G.D., Liu, X., Peng, J., Bai, X., Frappart, F., Baghdadi, N., Xing, Z., Li, X., Ma, M., Li, X., Che, T., Geng, L., & Wigneron, J.-P. (2022). Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin. *Remote Sensing, 14*

1213 Zribi, M., Muddu, S., Bousbih, S., Al Bitar, A., Tomer, S.K., Baghdadi, N., & Bandyopadhyay, S. (2019). Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics. *Remote Sensing, 11*

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