# Seasonal variations in vegetation water content retrieved from microwave remote sensing over Amazon intact forests

Huan Wang, Wigneron J.-P., Philippe Ciais, Yitong Yao, Lei Fan, Xiangzhuo

Liu, Xiaojun Li, Julia Green, Feng Tian, Shengli Tao, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Huan Wang, Wigneron J.-P., Philippe Ciais, Yitong Yao, Lei Fan, et al. Seasonal variations in vegetation water content retrieved from microwave remote sensing over Amazon intact forests. Remote Sensing of Environment, 2023, 285, pp.113409. 10.1016/j.rse.2022.113409. hal-04095055

HAL Id: hal-04095055

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Submitted on 23 Jul 2025

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<figure><img src="image_2.png" /></figure>
1 Seasonal variations in vegetation water content retrieved from microwave remote

2 sensing over Amazon intact forests

3 Huan Wang<sup>a,b,c</sup>, Jean-Pierre Wigneron<sup>b,*</sup>, Philippe Ciais<sup>c</sup>, Yitong Yao<sup>c</sup>, Lei Fan<sup>d</sup>, Xiangzhuo Liu<sup>b</sup>, Xiaojun Li<sup>b</sup>, Julia

4 K. Green<sup>c,e</sup>, Feng Tian<sup>f</sup>, Shengli Tao<sup>a</sup>, Wei Li<sup>g</sup>, Frédéric Frappart<sup>b</sup>, Clément Albergel<sup>a</sup>, Mengjia Wang<sup>i</sup>, Shuangcheng

5 Li<sup>a</sup>

6 a. College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of

7 Education, Peking University, Beijing 100871, China

8 b. INRAE, UMR1391 ISPA, Université de Bordeaux, F-33140, Villenave d'Ornon, France

9 c. Laboratoire des Sciences du Climat et de l'Environnement, CEA/CNRS/UVSQ/Université Paris Saclay, Gif-sur-

10 Yvette, France

11 d. Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical

12 Sciences, Southwest University, Chongqing 400715, China

13 e. The University of California, Berkeley, California, USA

14 f. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

15 g. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute

16 for Global Change Studies, Tsinghua University, Beijing, China

17 h. European Space Agency Climate Office, ECSAT, Harwell Campus, OX11 0FD Didcot, Oxfordshire, UK

18 i. School of Geoscience and Technology, Zhengzhou University, 450001, China

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

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Vegetation optical depth (VOD) is seasonally sensitive to plant water content and aboveground biomass. This index has a strong penetrability within the vegetation canopy and is less impacted by atmosphere aerosol contamination effects, clouds and sun illumination than optical vegetation indices. VOD is thus increasingly applied in ecological applications, e.g., carbon stock, phenology and vegetation monitoring. However, VOD retrieval over dense forests is subject to uncertainties caused by the thick canopy and complex multiple scattering effects. Thus, a comprehensive evaluation of VOD products over dense forests is needed for effective and accurate applications. This study evaluated the seasonal variations of eight recently developed/reprocessed VOD products at different frequencies (e.g., Ku-, X-, C- and L-band) over Amazon intact forests, supported by the ORCHIDEE-CAN-NHA model-simulated vegetation water content. Furthermore, we also explored the potential causes of VOD retrieval issues, in terms of retrieval algorithm uncertainties.

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We first confirmed that soil water availability dominated seasonal dynamics of vegetation water content over Amazon intact forests. This was verified by model-simulated vegetation water content and by C-band radar backscatter observations. Generally, evening or midday vegetation water content shows higher correlations with soil moisture than morning or midnight vegetation water content. In terms of ability of morning or midnight VOD products to follow the seasonality of soil moisture, active microwave ASCAT-IB C-VOD (median seasonal correlation with soil moisture (R) ~ 0.50) outperforms the passive microwave VOD products, followed by passive microwave AMSR2 X-VOD (R ~ 0.26) and VODCA X-VOD (R ~ 0.16). However, SMOS-IC L-VOD (R ~ -0.15) and AMSR2 C1-VOD (R ~ -0.20) show obviously negative seasonal correlations with soil moisture across most pixels. This implausible behavior is likely to be caused by the inappropriate setting of time-invariant scattering effects in the
43 passive microwave VOD retrieval algorithms, which could lead to an overestimation of the VOD
44 amplitude during dry seasons. Thus, we recommend that the seasonal scattering effects be considered in
45 the passive microwave VOD retrieval algorithms. These findings can contribute to the improvement of
46 VOD retrieval algorithms and help with the development of their ecological applications over Amazon
47 dense forests.

48 **Key words:** vegetation optical depth, vegetation water content, seasonality, Amazon forest,
49 ORCHIDEE-CAN-NHA land surface model, SMOS-IC, ASCAT-IB, AMSR2, VODCA

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## 1. Introduction

52 Amazon forests have been listed as an important tipping element of the Earth system (Lenton et al.,
53 2008), representing a major component of the global and regional carbon and hydrological cycles
54 (Restrepo-Coupe et al., 2013; Fassoni-Andrade et al., 2021). Amazon forests cover 5.3 million km²,
55 representing 40% of the global tropical area (Marengo et al., 2018). The forests hold a large carbon
56 stock but show a long-term decreasing trend of carbon accumulation (Brienen et al., 2015) due to
57 climate change, degradation and deforestation (Brando et al., 2014; Trondoli Matricardi et al., 2020;
58 Barlow et al., 2016). However, some research argues that Amazon forests could be more resilient to
59 disturbance than expected. For example, dry seasonal greening up has been observed in Amazon region,
60 shifting the canopy composition toward younger leaves with higher light use efficiency (Wu et al., 2016;
61 Green et al., 2020; Doughty et al., 2019). An accurate understanding of vegetation dynamics in the
62 Amazon basin is extremely important for global and regional carbon budgets, as well as for the
63 hydrology and energy exchange (Phillips et al., 2009).
Remote sensing data provide widespread observations for monitoring vegetation dynamics over dense forests, including both optical (e.g. Sun-Induced Chlorophyll Fluorescence (SIF), Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) and microwave domain indices (Vegetation Optical Depth (VOD)). Optical vegetation indices usually have high spatial resolution and a long period of observations, and thus can provide long-term monitoring of vegetation dynamics of the top canopy (Brando et al., 2010; Wu et al., 2016; Doughty et al., 2019; Green et al., 2020). In the microwave domain, VOD is a promising ecological indicator with coarse spatial resolution and high temporal resolution, derived from microwave observations at different frequencies with varying extinction effects and penetration ability. The multi-frequency VOD indices broaden the study domain of diverse canopy layers, facilitating the monitoring of the vegetation dynamics (Frappart et al., 2020). Some high frequency VOD products are sensitive to vegetation features of the top of the canopy, including Ku- and X-VOD, which have been used to monitor phenology (Tian et al., 2018; Jones et al., 2011), isohydricity patterns (Konings and Gentine, 2017), and drought resilience (Boulton et al., 2022) over Amazon regions. More recently, low frequency VOD at L-band has been reported as a useful indicator for monitoring Amazon forests biomass dynamics (Fan et al., 2019; Qin et al., 2021).

VOD retrieved from passive or active microwave observations is used to parameterize vegetation microwave extinction effects (including absorption and scattering effects) (Karthikeyan et al., 2019; Frappart et al., 2020). Owing to the higher penetrability of microwave radiations within vegetation, there are less saturation effects over dense forests in VOD retrievals (VODs) than in optical remote sensing vegetation indices (Brandt et al., 2018a; 2018b). In addition, microwaves can penetrate clouds, particularly for long wavelength, which avoids the biases caused by incomplete measurements in only cloud-free periods when using optical-based indices (Konings and Gentine, 2017). Thus, microwave
observations can be made regardless of solar illumination and atmosphere aerosol contamination effects,
an advantage over optical and infrared spectrum products (Baur et al., 2019; Li et al., 2021). For these
reasons, VOD products have been widely used for ecological applications in tropical regions in the field
of phenology (Tian et al., 2018; Tong et al., 2019), carbon cycle science (Fan et al., 2019; Wigneron et
al., 2020) and vegetation response to drought (Liu et al., 2021a; Boulton et al., 2022).

However, VOD and soil moisture (SM) retrievals are complex in dense forests, due to the intensity
of the absorption and multiple scattering microwave effects, the complexity of the effects of the
vegetation structure, the litter effects affecting the soil surface reflectivity, dew and precipitation
interception (Grant et al., 2016; Xu et al., 2021). In the active domain, microwave VOD is usually
retrieved by inverting the semi-empirical Water Cloud Model (WCM) (Ulaby et al., 1978) to simulate
the radar backscatter signal from vegetation and bare soil land surfaces (Vreugdenhil et al., 2016, 2017).
In WCM, the total backscatter signal is the sum of vegetation contribution, soil contribution attenuated
by the vegetation canopy and the double-bounce backscatter between the vegetation canopy and bare
soil surface (Liu et al., 2021b). However, WCM does not consider high order scattering mechanisms like
double scattering from ground-vegetation interactions (El Hajj et al., 2019). In the passive domain, VOD
is typically retrieved by inverting models simulating brightness temperature (TB) measured by
radiometers. Modelling TB is more challenging for dense forest canopies than for moderate vegetation
cover (Wigneron et al., 2017). Generally, models are based on the 0th-order Tau-Omega (τ-ω) radiative
transfer equations which account for several soil and vegetation parameters, e.g., vegetation absorption
and scattering, surface temperature and roughness (Baur et al., 2019; Frappart et al., 2020). However,
the Tau-Omega model does not simulate volume scattering caused by branches with varying sizes and
orientation and multiple reflections between the soil surface and the vegetation elements, inaccurately
representing the coherent effects and multiple scattering in dense forests (Feldman et al., 2018a;
Wigneron et al., 2017). The scattering effects of vegetation in the Tau-Omega model are characterized by a parameter called the effective scattering albedo (Kurum, 2013), usually assumed to be constant for a certain plant functional type as defined by the International Geosphere-Biosphere Programme (IGBP) scheme (Fernandez-Moran et al., 2017; see Table S1 in Li et al. 2022a). Moreover, previous studies have shown that the accuracy of the SM retrievals may decline with increasing vegetation density due to the decreasing sensitivity of TB to SM (Kurum et al., 2011; Wigneron et al., 2021). The negative impact on SM caused by high vegetation density in turn may also lead to error propagation to VOD retrievals as both SM and VOD are usually retrieved simultaneously (Wigneron et al., 1995; 2000; 2017; Konings et al., 2016; Feldman et al., 2021; Li et al., 2022a).

An in-depth evaluation of both active and passive microwave VOD performance is strongly needed over dense forests, especially in tropical wet regions that are a key focus of VOD applications to infer long term biomass changes (Wigneron et al., 2021). VOD has been found to be linearly related to vegetation water content (VWC) (kg/m²) (Jackson and Schmugge, 1991; Momen et al., 2017; Santi et al., 2017). However, evaluations based on field data are rare due to the unavailability of large-scale field observation of VWC data, especially over forests (Li et al., 2021). Currently, most VOD seasonal evaluations have been based on proxies of vegetation greenness (Du et al., 2017; Karthikeyan et al., 2019; Tong et al., 2019), e.g., NDVI, EVI and LAI, which implicitly suppose that these indices capture seasonal changes in the ecosystem total water mass. Although this assumption is reasonable for short vegetation, it is unlikely to hold true over dense forests where optical vegetation indices saturate with canopy mass (Jones et al., 2011; Tian et al., 2016) and where seasonal decoupling occurs between wood, root and canopy (foliage) mass changes (Doughty et al., 2015). Particularly, long wavelength VOD is sensitive to the seasonal changes in the whole canopy mass including both the foliar and woody
components (Jones et al., 2011), which makes optical vegetation indices unsuitable for the assessment of
VOD over dense forests, especially for low frequency VOD as L-VOD (Li et al., 2021).

Process-based terrestrial biosphere models may be a new tool for simulating seasonal changes in
the ecosystem water storage that are not observable at large scale, and thus to evaluate VOD (Xu et al.,
2016; 2021). Yet, few biosphere models can simulate VWC as a state variable across large spatial scales,
a process which requires a detailed numerical representation of plant hydraulic architecture, including
water transport and storage. Until now, most models calibrated at the site level have been used to
simulate diurnal and seasonal VWC variations (Powell et al., 2018; Xu et al., 2021). In the present study,
we take advantage of the representation of VWC in the ORCHIDEE-CAN-NHA global model, which is
a new hydraulic architecture module in the ORCHIDEE-CAN model (Yao et al., 2021) and simulates
hourly VWC variations in roots, stems and leaves over the entire Amazon at a spatial resolution of 1°.
This model was calibrated at the Caxiuanã rainforest site in Brazil where detailed measurements were
collected both for normal and drought conditions (Yao et al., 2021). Additionally, we also take
advantage of radar backscatter observations which are sensitive to VWC changes (Konings et al., 2017)
for the evaluation of the seasonality of VWC-related variables.

Notably, VOD represents the total mass of water in the vegetation layer which is the product of
plant mass multiplied by plant relative water content per unit of mass (RWC), the latter reflecting the
temporal dynamics of leaf and xylem water potential responding to hydro-meteorological conditions
(Konings et al., 2017, 2019; Momen et al., 2017; Zhang et al., 2019). The VOD dynamics during SM
dry-down periods (Feldman et al., 2018b) and the relationships between VOD changes and subsequent
tree mortality (Rao et al., 2019) both indicate that VOD is significantly sensitive to relative water
content (RWC) (Konings et al., 2021a). In Amazon intact forests, AGB does not have a strong
seasonality and it is close to constant throughout the year, especially when compared with its mean AGB
level (Doughty et al., 2015). Although remote-sensing observations suggest that central Amazon forests with yearly precipitation > 2000 mm increase their photosynthetic capacity during dry seasons (Guan et al., 2015; Saleska et al. 2016), camera observations and Lidar data show that the total LAI during dry seasons is nearly constant (with slightly increase) and the seasonality of total LAI is limited due to coordinated leaf loss and new leaf production (Nunes et al., 2022; Wu et al., 2016). The variation of the photosynthetic seasonality can be explained by the new leaf growth with old leaves shedding, shifting the canopy composition toward younger and more light-use efficient leaves during dry seasons (Wu et al., 2016). Thus, we hypothesize that: (H1) SM and VOD should be seasonally positively correlated because RWC is monotonically related to root-depth SM (Konings et al., 2021a), assuming limited seasonal biomass dynamics over the Amazon intact forests; (H2) the seasonal correlations between SM and VOD should be higher in regions where the dry season is more pronounced.

This study aims to comprehensively evaluate the seasonal dynamics of eight active or passive microwave VOD products (Table 1) over Amazon intact forests. Specifically, we: 1) evaluated the seasonal dynamics of ORCHIDEE-CAN-NHA model-simulated leaf and stem RWC and VWC against soil water availability (a proxy of SM here); 2) assessed ASCAT backscatter seasonality relative to SM changes during SM dry-down events; 3) inter-compared the seasonality among VODs, model-simulated VWC, backscatter and SM to evaluate the accuracy of VOD retrievals; and 4) analyzed the potential reasons for VOD retrieval errors from the perspective of retrieval algorithms. Notably, step 1 and step 2 can help to validate the two hypotheses (H1-2), based on which we conduct the evaluation of VOD seasonal variations. We used the SM-VOD seasonal correlation as a main metric to evaluate the accuracy of the VOD seasonality, rather than directly compared the model-simulated VWC with VOD. This is because the model does not separate top canopy and middle canopy which could cause some uncertainties in the evaluation of high frequency VOD products, e.g., Ku-, X-, and C-band VODs. In
addition, there is still a need to improve the model and we illustrate the model-related uncertainty in the discussion part. To enhance the robustness of the evaluation results, we also evaluated LAI/SIF-VOD correlations to indicate the impact of canopy biomass on the seasonality of vegetation water content.

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## 2. Materials

### 2.1 VOD products and remote sensing observations

Eight VOD datasets are evaluated in this study during 2013-2016 with all evaluated VOD datasets available, including passive microwave products (e.g., SMOS-IC, VODCA and AMSR-2 VODs), and the active microwave ASCAT-IB VOD products (Table 1). All the passive microwave VOD products are retrieved from the inversion of the 0th order Tau-Omega radiative transfer model simulating the observed brightness temperature (TB) and considering soil and vegetation temperature, surface roughness, effective scattering albedo and soil dielectric mixing models as inputs (Owe et al., 2008; Wigneron et al., 2017; Konings et al., 2016; Li et al., 2021; Wang et al., 2021). All the VOD products evaluated in this study provides daily retrievals with a spatial resolution of 0.25°, except for SMOS-IC L-VOD (25 km). Most VOD products provide daytime and nighttime retrievals, except for reprocessed VODCA products with only nighttime retrievals for descending orbits available with different overpass local times from diverse sensors, including Ku-, X- and C-band VODs. Because the local overpass time of the diverse VOD products are different, we indicate observations at 6:00 am (SMOS-IC L-VOD) and 9:30 am (ASCAT-IB C-VOD) as morning retrievals, 1:30 am (AMSR2 VODs) as midnight retrievals, 6:00 pm (SMOS-IC L-VOD) and 9:30 pm (ASCAT-IB C-VOD) as evening retrievals and 1:30 pm (AMSR2 VODs) as midday retrievals.

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<table><caption>Table 1 Overview of the VOD products evaluated in this study</caption><thead><tr><th>VOD name</th><th>Time period</th><th>Frequency</th><th>Dataset/Sensor</th><th>Algorithm</th><th>Reference</th></tr></thead><tbody><tr><td>VODCA Ku-VOD</td><td>1987–2017</td><td>18.7 GHz, 19.35GHz</td><td>WindSat, AMSR-E, AMSR2, SSM/I and TMI</td><td></td><td></td></tr><tr><td>VODCA X-VOD</td><td>1997–2018</td><td>10.65 GHz, 10.7 GHz</td><td>WindSat, AMSR-E, AMSR2 and TMI</td><td>LPRM V6</td><td>Moesinger et al. (2020)</td></tr><tr><td>VODCA C-VOD</td><td>2002–2018</td><td>6.93 GHz, 7.3 GHz, 6.8GHz</td><td>WindSat, AMSR-E and AMSR2</td><td></td><td></td></tr><tr><td>AMSR2 X-VOD</td><td>2012-2022</td><td>10.7 GHz</td><td>AMSR2</td><td></td><td></td></tr><tr><td>AMSR2 C1- VOD</td><td>2012-2022</td><td>6.9 GHz</td><td>AMSR2</td><td>LPRM V5</td><td>Owe et al. (2008)</td></tr><tr><td>AMSR2 C2- VOD</td><td>2012-2022</td><td>7.3 GHz</td><td>AMSR2</td><td></td><td></td></tr><tr><td>SMOS-IC L-VOD</td><td>2010-2021</td><td>1.4 GHz</td><td>SMOS</td><td>SMOS-IC V2</td><td>Wigneron et al. (2021)</td></tr><tr><td>ASCAT-IB C-VOD</td><td>2008-2020</td><td>5.25GHz</td><td>ASCAT</td><td>ASCAT-IB</td><td>Liu et al. (2021c)</td></tr></tbody></table>

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Note: Only ASCAT-IB C-VOD is an active microwave VOD product among all the eight VOD products.

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### 2.1.1 SMOS-IC

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The SMOS satellite is the first mission equipped with an L-band (1.4 GHz) microwave radiometer for global SM and VOD mapping. The satellite transits the Earth twice each day, at 6:00 am and 6:00 pm local time in ascending and descending orbits, respectively. The SMOS-IC algorithm improved the

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original algorithm proposed for the SMOS mission (Wigneron et al., 2000), and achieved maximal independence of auxiliary data without requiring modeled SM and optical vegetation indices as inputs for the retrievals (Wigneron et al., 2021). The SMOS-IC L-VOD product has been found to have an almost linear relationship with AGB across large spatial gradients of AGB and presents low saturation signs even at high biomass levels (Brandt et al., 2018a; Fan et al., 2019). The SMOS-IC L-VOD data in version 2 used in this study can be downloaded at https://ibremote-sensing.inrae.fr/.

## 2.1.2 VOD Climate Archive (VODCA)

The TU Wien's VODCA products (Moesinger et al., 2020) integrated and reprocessed VOD retrievals from Advanced Microwave Scanning Radiometer 2 (AMSR2), WindSat, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Special Sensor Microwave Imager (SSM/I) and the Microwave Imager on board the Tropical Rainfall Measuring Mission (TMI) into long-term VOD datasets at Ku-band (1987-2017), X-band (1997-2018) and C-band (2002-2018). The retrieval algorithm is the Land Parameter Retrieval Model (LPRM) Version 6 (Moesinger et al., 2020), where both SM and VOD are obtained simultaneously from the Microwave Polarization Difference Index (Meesters et al., 2005). The VODCA LPRM V6 product (nighttime retrievals) is available at https://doi.org/10.5281/zenodo.2575599.

## 2.1.3 AMSR2 VOD products

AMSR2 is an improved successor of AMSR-E onboard the Global Change Observation Mission-Water1 satellite (GCOM-W1), with local overpass times of 1:30 am and 1:30 pm for descending and ascending orbits, respectively (Imaoka et al., 2012). It has a second C-band channel (C2-band, 7.3 GHz), which can be used to cover areas where radio frequency interference (RFI) exists in the main C1 channel
231 (6.9 GHz). The AMSR2 VOD products used in this study were retrieved from the LPRM version 5
232 algorithm (Owe et al., 2008). The data can be downloaded from https://hydro1.gesdisc.eosdis.nasa.gov.

233 2.1.4 ASCAT backscatter and ASCAT-IB VOD

234 The Advanced Scatterometer (ASCAT) is an active microwave sensor which provides long-term C-
235 band (5.25 GHz) backscatter data at Vertical-Vertical (VV) polarization (Figa-Saldana et al., 2002;
236 Wagner et al., 2013). ASCAT is operated by the Meteorological Operational Satellite Program of
237 Europe (MetOp) series of satellites, composed of MetOp-A, MetOp-B and MetOp-C, and launched on
238 October 19, 2006, September 17, 2012 and November 7, 2018, respectively. The satellites overpass the
239 Earth twice a day at the local time of 9:30 am (descending orbit) and 9:30 pm (ascending orbit). Four
240 years (2013-2016) of MetOp-A backscatter data normalized at an incidence angle of 40° were used in
241 this evaluation work. Low quality data were masked using the quality flags (Lievens et al., 2017).

242 ASCAT-IB VOD was retrieved by inverting the Water Cloud Model (for the vegetation scattering
243 components) coupled with the Ulaby linear model (for soil backscattering), with ASCAT backscatter,
244 modelled SM and soil property data as inputs (Liu et al., 2021b). ASCAT-IB VOD has three main
245 features: 1) it takes the fifth generation European Centre for Medium-Range Weather Forecasts
246 (ECMWF) atmospheric reanalysis dataset (ERA5)-Land SM as an auxiliary input rather than retrieving
247 SM and VOD simultaneously as for most passive microwave VOD retrievals; 2) it uses pixel-level soil
248 parameters to improve the VOD retrieval accuracy; 3) it calibrates daily the vegetation model parameter.
249 ASCAT-IB VOD can be downloaded from https://ibremote-sensing.inrae.fr/ (Liu et al., 2021b).

250 2.2 Vegetation water storage and relative water content from ORCHIDEE-CAN-NHA model

251 To simulate VWC and RWC of leaves and stems (VWC indicates total vegetation water content
252 (kg/m²) and RWC indicates the ratio of vegetation water content and biomass (kg/gC), we used the
ORCHIDEE-CAN-NHA land surface model including a mechanistic plant hydraulic module that simulates the water transport along the continuum from soil to atmosphere at a cohort level, developed by Yao et al. (2021). This model has been validated against observations of the seasonal variations in stand-scale sap flow and soil moisture dynamics at the Caxiuanã throughfall exclusion field experiment in eastern Amazonia (Yao et al., 2021). This model simulates plant vertical water flow and water charge/discharge from/to organ-specific water storage regulated by water capacitances and dynamic water potential on hourly time steps. The leaf mass and wood mass (aboveground sapwood and heartwood) simulated by the model were also used in the present study to enable the calculation of RWC (the ratio of organ-based water storage and its biomass). The details of the simulation methods are given in Yao et al. (2021). We calculated daytime and nighttime monthly VWC and RWC of leaves and stems, as well as root-depth SM over the Amazon intact forests for the period of 2013-2016, with a spatial resolution of 1°. The ORCHIDEE-CAN-NHA model provides separate outputs for canopy leaves and stems. We assumed that simulated variables for leaves can represent vegetation features from the top of the canopy and can be used for comparisons with high frequency VODs (e.g., Ku-, X-, and C-band) while the simulated parameters for stems can be used to assess low frequency VODs (L-VOD, which is predominantly sensitive to stem VWC in forests).

## 2.3 Auxiliary datasets

The MODIS-based global land cover map, Global Forest Watch (GFW) forest cover product and water fraction data were applied to define the study area boundaries. The MODIS-based land cover map is produced by combining the 0.5 km MODIS product (MCD12Q1) in the IGBP scheme (Broxton et al., 2014). GFW provides a tree cover map in 2000, gross loss of annual tree cover in 2001-2019, and gross gain of tree cover in 2000-2012 at a spatial resolution of 30 meters (Hansen et al., 2013). The GFW forest cover data were used to keep only undisturbed forests. We used water fraction data from Du et al.
(2017) to mask the potentially spurious VOD retrievals caused by the water bodies. Additionally, the 276 1km resolution digital elevation model (DEM) from Global Multi-resolution Terrain Elevation Data 277 (GMTED) (Danielson and Gesch, 2011) was used to infer soil surface roughness (defined as ln(standard 278 deviation of DEM)) as a disturbing factor for VOD retrievals, as done in several studies assessing the 279 quality of remotely sensed soil moisture retrievals (Kim et al., 2015; Ma et al., 2019; Liu et al., 2021b; 280 Li et al., 2022b). The reason why we applied DEM as a measure of soil surface roughness is that the 281 volumetric fraction of vegetation is extremely small in a vegetation layer (<1/100), so diffraction effects 282 at the vegetation/atmosphere interface are generally neglected, as well as roughness effects caused by 283 vegetation at this interface (Wigneron et al., 1993). Here, we analyzed the correlations between SM and 284 VOD under different soil surface roughness conditions to indicate the impact of surface roughness on 285 VOD retrievals and we quantified the impact by using the GeoDetector method. 286

The ERA5 hourly SM (m³ m⁻³) at 0.25° spatial resolution was applied to evaluate the VOD 287 sensitivity to soil water availability. ERA5 provides SM layers at four soil depths, including the 0-7 cm, 288 7-28 cm, 28-100 cm and 100-289 cm. In this study, the soil moisture data of the four layers were 289 averaged to represent root-depth SM conditions over the Amazon basin based on Fan et al. (2017). The 290 24-hour SM values within a day were averaged to daily values for the evaluation, given that in situ and 291 model results indicate that there is generally no or small changes in soil moisture between morning and 292 evening (Rowlandson et al., 2012). Similarly, ERA5 hourly topsoil temperature data (0-7 cm) with a 293 spatial resolution of 0.25° were averaged to daily values and used to evaluate the potential impact of soil 294 temperature on the accuracy of the VOD retrievals. The ERA5 climatic data can be downloaded from 295 https://cds.climate.copernicus.eu. TRMM (Tropical Rainfall Measuring Mission satellite) daily 296 precipitation data (mm) (0.25° spatial resolution) were used in this study to calculate rainfall seasonality 297 for sub-regions identification, which can be downloaded from https://disc.gsfc.nasa.gov/. We used ESA-298
299 CCI LAI to indicate the seasonal dynamics of canopy biomass. Additionally, considering the saturation
effect of optical LAI over tropical regions, we also used SIF as a proxy of canopy biomass dynamics.
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301 SIF is mechanistically linked to photosynthesis and does not saturate in the tropics (Green et al., 2020).
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303 The CSIF datasets (Zhang et al., 2018) with a spatial resolution of 0.05° and temporal resolution of 4
days have been used in this study to make the evaluation results robust. The LAI/SIF-VOD correlations
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305 are used to evaluate whether biomass variations confound the SM-VOD correlations. The period of
these climatic factors LAI, and SIF is 2013-2016, corresponding to the evaluation period of VOD
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products.

307 **3. Methods**

308 **3.1 Study boundary selection**

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To minimize the influence of deforestation and low-quality observations on the evaluation of VOD
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seasonal dynamics, we restricted our analysis to only include forests identified as ‘intact’, and the grid
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cells are selected by: 1) evergreen broadleaf plant functional type, defined by IGBP; 2) regions with a
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forest cover higher than 99%, and without deforestation based on the GFW forest cover data; and 3)
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regions with a low fraction (<0.02) of surface water body to filter out low-quality VOD values. To
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explore the regional response of VWC to soil water availability, we determined four distinct climatic
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regions by clustering the rainfall seasonality index (Ciemer et al., 2019) using a K-means clustering
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algorithm. The rainfall seasonality index was calculated from the average monthly and annual
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precipitation value, in which mean monthly rainfall values were viewed as vector quantities with both
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magnitude and direction (Markham, 1970; Ciemer et al., 2019). A high Markham seasonality index
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means a strong precipitation seasonality and usually a low annual precipitation. The seasonality index
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was negatively correlated to the annual rainfall amount (Fig. 1a) in space (R~ -0.75, p<0.05), and
generally increased along a northwestern-southeastern diagonal, with a more pronounced seasonality in
the drier southern and eastern parts of the Amazon intact forests (Fig. 1b). Based on this index we
defined four main sub-regions (Fig. 1c). From Region 1 to Region 4, a higher rainfall seasonal index
value exemplified an increasing seasonal water stress (Fig. 1b) and a deeper, steeper and longer SM
seasonal dry-down (Fig. 1d).

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

Fig. 1 Rainfall and soil moisture conditions over the study region. a) Spatial distribution of mean annual rainfall (mm) (2013-2016); b) Markham rainfall seasonality index with higher values indicating a higher seasonality; c) four sub-regions defined by the K-means clustering method using rainfall seasonality index as input and d) average seasonal dynamics of root-depth ERA5 soil moisture over each sub-region (2013-2016).
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## 3.2 Data preprocessing

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The accuracy of the VOD retrievals is influenced by RFI, indicating microwave radiations emitted by artificial devices. Daily VODs affected by RFI were filtered out by using corresponding flags as recommended by each data producer. We used 10 days smoothing window to produce daily VOD values in order to decrease the noise signal and reduce the potential impact of precipitation interception on VOD retrievals. Because of the diverse spatial resolution of the VOD datasets, all VOD data were resampled into a spatial resolution of 0.25° for calculation and comparison using the nearest-neighbor interpolation method, which has been extensively applied in evaluation studies of SM (Al-Yaari et al., 2014; Ma et al., 2021). Furthermore, to ensure a fair and reliable evaluation of the VOD products, the assessment was conducted over the same period for all VOD products, namely January 2013 to December 2016. To evaluate whether the VOD seasonality follows the seasonality of the soil water availability and to reduce the impact of the noise (due to the VOD retrieval algorithm) on the assessment of the VOD seasonality, the four-year values were averaged into a mean climatology for both VOD products and SM. Here, we used Pearson correlation coefficients to indicate the seasonal and spatial correlations between VWC-related indices and SM. A level of probability value (p) < 0.05 was used as a measure of significant statistics.

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## 3.3 Seasonal consistency between variables

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Nash-Sutcliffe efficiency (NSE) is a nonparametric index that quantifies the misfit between two time series, which has originally been used to quantitatively assess the accuracy of hydrological model outputs (Nash and Sutcliffe, 1970). NSE is sensitive to both absolute values and seasonal variations (Wu and Johnston, 2007), and it can also be used to measure the similarity of seasonality between two variables (Liu et al., 2021c). To remove the impacts of diverse seasonal amplitudes across variables and make datasets comparable, we first computed the Z-score transformed variables (Eq. 1),
$$X_{\text{anomaly}} = \frac{X_t - \bar{X}_t}{\sigma(X_t)} \qquad (1)$$

where $X_t$ is the original variable at day t, $X_{anomaly}$ is the standardized anomaly of $X_t$, $\bar{X}_t$ and $\sigma(X_t)$ are the mean and standard deviation of $X_t$ for the time-averaged values during 2013-2016.

Next, using the Z-score transformed variables, we calculated the NSE values between two variables (Eq. 2),

$$NSE = 1 - \frac{\sum_{t=1}^{T} (Var1_t - Var2_t)^2}{\sum_{t=1}^{T} (Var2_t - \overline{Var2})^2} \qquad (2)$$

where $Var1_t$ is the normalized SM or model-simulated VWC and $Var2_t$ is the normalized VOD product at day t. $\overline{Var2}$ is the mean value of Var2. The range of NSE is (-∞, 1]. A NSE value of 1 means that the two variables are exactly similar with each other, and a NSE value of 0 means that $Var1$ equivalently performs to the mean value of $Var2$, respectively. A negative NSE value indicates that the $Var1$ is a poor predictor of $Var2$ in seasonality (Chen et al., 2021).

## 3.4 Quantitative attribution analysis of disturbing factors on VOD retrievals

We used the GeoDetector method to quantify the impact of potential factors on the VOD retrieval accuracy. The GeoDetector method is a measure of the spatially stratified heterogeneity, which can identify possible causal relationships between two variables by examining the coupling consistency of their spatial distribution (Wang et al., 2010; 2016). The basic assumption is that if a factor X is relevant to a factor Y, the spatial distribution of X should be similar with that of Y. The strength of association between potential influencing factor X and variable Y can be calculated using Eq. 3,
$$q = 1 - \frac{1}{n\sigma^2} \sum_{h=1}^{L} n_h \sigma_h^2 \quad (3)$$

# 4. Results

## 4.1 ORCHIDEE-CAN-NHA simulations of the vegetation water content and relative water content of leaf and stem

ORCHIDEE-CAN-NHA simulations of both RWC and VWC in leaves and stems show similar seasonal dynamics with ORCHIDEE-SM (root-depth soil moisture simulated by ORCHIDEE-CAN-NHA) for most pixels (Fig. S1a-d), with median values of correlations higher than 0.5 over most of the sub-regions (Fig. 2a). The spatial pattern of positive correlations across most pixels is robust when using ERA5-SM (root depth) as a substitute of ORCHIDEE-SM (Fig. S1e-h), and the NSE values between model-simulated VWC and ERA5-SM are higher than 0.77 across Amazon and each of the four sub-regions (Table S1). The seasonal dynamics of ORCHIDEE-SM are consistent with ERA5-SM with median seasonal correlation of 0.68 ($p < 0.05$) (Fig. S2), even though SM in ORCHIDEE-CAN-NHA is
simulated by resolving the soil hydrology processes through the Richards equation that characterizes the
vertical water transfer including infiltration and diffusion and does not assimilate satellite data. For
leaves, the correlation between VWC and SM is higher than that between RWC and SM across the
whole Amazon intact forests (Fig. 2a). However, for stems, the correlation between RWC and SM is
slightly higher than that between VWC and SM. Both RWC and VWC in stems are more positively
correlated with SM than for leaves, indicating a stronger sensitivity of stem water content to soil water
availability (Fig. 2a). The temporal correlations between monthly ORCHIDEE simulations (RWC and
VWC) and SM are higher in the midday than in the midnight during 2013-2016, meaning a stronger
water stress occurs at midday (Fig. 2b, Table S2).

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

<figure><img src="image_5.png" /></figure>
Fig. 2 Box plot of temporal correlations between model-simulated monthly relative water content/vegetation water content and soil moisture during 2013-2016. a) Correlations between monthly relative water content/vegetation water content and soil moisture in Amazon (the whole selected pixels) and its sub-regions; b) midnight (1:00 am) and midday (1:00 pm) correlations between relative water content/vegetation water content and soil moisture.

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## 4.2 Inter-comparison between C-band ASCAT backscatter and ERA5-SM

In this section, we assessed the sensitivity of ASCAT backscatter to soil water availability by examining seasonal correlations between ERA5-SM (SM means ERA5-SM in the following manuscript) and ASCAT backscatter, and ASCAT backscatter changes during SM dry-down events. Results show that the ASCAT backscatter is highly correlated to SM with median seasonal correlations of 0.73 and 0.85 for morning and evening observations over the whole study region, respectively (Fig. 3a-b, Fig. 4a). However, the seasonal correlation between photosynthetic seasonality (a proxy of SIF here) or LAI and backscatter is negative across most pixels (Fig. S3-4). The spatial pattern of seasonal correlations between backscatter and SM is heterogeneous across pixels, in which relatively small correlations can be found in wetter northwestern Amazon (Fig. 3a-b), especially for morning observations. Additionally, we found higher seasonal correlations between backscatter and SM along the rainfall seasonality gradient from Region 1 to Region 4 (Fig. 3a-b, 4a), indicating that the ASCAT backscatter is more related to SM for increasing water-stressed conditions. Backscatter also shows a high similarity of seasonality with model-simulated VWC over Amazon intact forests with NSE values of 0.77 and 0.89 for morning and evening observations, respectively (Table S3).

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We also evaluated pixel-based backscatter magnitude changes during SM dry-down events, in which the dry-down period was defined by the maximum (starting day when SM begins to decrease) and the minimum (ending day, maximum water stress) SM values during May-September (Fig. 3c-d). Results show that the ASCAT backscatter declines during SM dry-down events across the whole Amazon basin; and a smaller drop of morning backscatter than evening backscatter is found (Fig. 3c-d, Fig. 4b). The smaller drop of backscatter in the morning can be explained by the water-refilling process during nighttime when transpiration shuts off: the xylem-refilling process contributes to the equilibrium reached between pre-down leaf water potential and soil water potential (Konings and Gentine, 2017). With increasing precipitation seasonality from Region 1 to Region 4, the magnitude of the backscatter drop during the SM dry-down events increases for both morning and evening observations (Fig. 4b).
<figure><img src="image_6.png" /><figcaption>Fig. 3 Evaluation of the relationships between morning (Morn)/evening (Even) backscatter and soil moisture. a-b) Spatial pattern of seasonal correlations between time-averaged backscatter and soil moisture during 2013-2016; c-d) backscatter magnitude changes during soil moisture dry-down events (ending day –starting day of water stress). Note: Pixels with non-significant correlations (p > 0.05) were masked in Fig. 3a-b.</figcaption></figure>
<figure><img src="image_7.png" /></figure>

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

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441 Fig. 4 Statistics of backscatter sensitivity to soil moisture in the study region based on the results of Fig.

442 3. a) Seasonal correlations between backscatter and soil moisture; b) backscatter magnitude changes in soil moisture dry-down events.
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444 **4.3 Seasonality of vegetation water content-related variables and ERA5 soil moisture**

445 Here, we inter-compared the seasonality of model-simulated VWC, VOD products, backscatter and SM (Fig. 5, Table S1, 3). The VOD products show different seasonal dynamics (Fig. 5), even for VOD products retrieved at a similar frequency, e.g., large differences can be found in C-band VODs. The passive microwave VODCA C-VOD and active microwave ASCAT C-VOD decrease in the dry season

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(Fig. 5). This decrease is expected from that water loss through leaf, leading to leaf and xylem water potential declines under high atmospheric water demand or severe dry soil water conditions (Konings et al., 2021b) and relative water content decreases driven by very negative pressure (large tension) in the xylem (Tyree and Sperry, 1989). However, AMSR2 C1-VOD shows a seasonal increase when SM drops, and a similar seasonality also occurs for SMOS-IC L-VOD (Fig. 5).

We also compared the seasonality of morning or midnight VODs at different frequencies but obtained from the same algorithm. For VODCA, Ku-, X- and C-VOD show similar seasonal dynamics with an increase in the wet season and a decrease in the dry season, but there is a time lag between these VODs and SM (Fig. 5). Notably, a similar time lag effect between plant water storage and SM has also been found in ASCAT backscatter, ASCAT C-VOD and AMSR2 X-VOD. The NSE values between SM and VODCA VODs (between model-simulated VWC and VODs) for the whole Amazon intact forests decline with decreasing frequency of VODs, with values of 0.07 (0.19), -0.85 (-0.53), and -1.17 (-0.89) for Ku-, X- and C-band VOD, respectively (Table S1, 3). For AMSR2, X-, C1- and C2-VODs show extremely different seasonality. For example, AMSR2 X-VOD decreases during the dry season and increases during January-May and thus follows the SM changes, while C2-VOD shows seasonal fluctuating variations. Generally, evening or midday VODs have higher NSE values with SM than morning or midnight VODs, except for AMSR2 C1-VOD, which shows opposite results (Table S1).

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<figure><img src="image_9.png" /><figcaption>Fig. 5 Time-averaged (2013-2016) seasonal dynamics of ERA5 soil moisture, model-simulated VWC, backscatter and VODs over the Amazon intact forests (all values are given in terms of Z-scores calculated from spatial averaged values). Note: The shaded area indicates the periods of dry season (as defined by Marengo et al., 2011).</figcaption></figure>
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## 4.4 VODs-SM seasonal correlations and the impacts of disturbing factors

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Fig. 6 shows contrasted spatial patterns of seasonal correlations between SM and different morning or midnight VODs, which are mostly used for ecological applications (Fan et al., 2019; Qin et al., 2021). For VODCA, Ku-, X-, and C-VODs positively correlate to SM across most pixels but with low median correlations of 0.08, 0.16 and 0.08, respectively, and negative correlations are mainly concentrated in the southwestern Amazon (Fig. 6a-c). For AMSR2, X-VOD positively correlates to SM across most pixels (median R~0.26) (Fig. 6d), but C1- and C2-VODs show the opposite results with median correlations of -0.20 and -0.18 (Fig. 6e-f), respectively. The correlations between SMOS-IC L-VOD and SM are negative across most pixels (Fig. 6g). In contrast, ASCAT C-VOD correlates positively to SM (median R~0.50) with a few pixels showing negative correlations (Fig. 6h). Generally, evening or midday VOD retrievals are more correlated to SM than morning or midnight VOD retrievals (Fig. 6, Fig. S5).
<figure><img src="image_10.png" /><figcaption>Fig. 6 Seasonal correlations (time-averaged during 2013-2016) between VODs and soil moisture over the study region. Note: Pixels with non-significant correlations (*p* > 0.05) were masked.</figcaption></figure>

The seasonal correlations between morning or midnight VODs and SM under various layers of disturbing factors are exhibited in Fig. 7, and the non-linear impact of disturbing factors can be identified. As shown in Table S4, all pixels were classified into five layers/categories based on the
magnitude of factors. For soil temperature, the correlations become higher with increasing temperature (from Layer 2 to Layer 5) for SMOS-IC L-, VODCA C- and VODCA X-VODs (Fig. 7a). For precipitation, higher correlations between VODs and SM occur in areas with lower precipitation for AMSR2 C1-, AMSR2 C2- and ASCAT C-VODs, and results are opposite for AMSR2 X-, SMOS-IC L- and VODCA X-VODs (Fig. 7b). For surface roughness, the highest median value of correlations between VODs and SM are generally obtained for pixels with moderate surface roughness, like layer 3 (Fig. 7c).

<figure><img src="image_11.png" /><figcaption>Fig. 7 Box plot of seasonal correlations between VODs and soil moisture corresponding to diverse layers of disturbing factors. a) Soil temperature; b) precipitation; c) surface roughness. The values of the</figcaption></figure>
disturbing factors become higher from layer 1 to layer 5, e.g., layer 5 for soil temperature suggests highest soil temperature (the classification principle for each factor is given in Table S4).

Then, we quantified the non-linear impact of disturbing factors on the accuracy of VOD retrievals by using the GeoDetector method. Generally, evening or midday VOD retrievals are more sensitive to climatic disturbing factors (soil temperature and precipitation) with relatively higher *q* values than morning or midnight VODs (Table 2). Notably, compared with high frequency VODs, SMOS-IC L-VOD is less influenced by the disturbing factors, with relatively smaller *q* values for surface roughness, soil temperature and precipitation, especially for morning VOD retrievals. Generally, the impact of potential disturbing factors is smaller for passive microwave VODs than for the active microwave ASCAT-IB C-VOD (Table 2).

<table><caption>Table 2 The strength of association (*q* value) between disturbing factors and VOD-SM correlations</caption><thead><tr><th>VOD indices</th><th>Local time</th><th>Soil temperature</th><th>Precipitation</th><th>Roughness</th></tr></thead><tbody><tr><td>VODCA-Ku</td><td>midnight</td><td>0.09</td><td>0.04</td><td>0.08</td></tr><tr><td>VODCA-X</td><td>midnight</td><td>0.05</td><td>0.05</td><td>0.08</td></tr><tr><td>VODCA-C</td><td>midnight</td><td>0.06</td><td>0.13</td><td>0.02</td></tr><tr><td rowspan="2">AMSR2-X</td><td>1:30 am</td><td>0.08</td><td>0.04</td><td>0.08</td></tr><tr><td>1:30 pm</td><td>0.04</td><td>0.06</td><td>0.14</td></tr><tr><td rowspan="2">AMSR2-C1</td><td>1:30 am</td><td>0.03</td><td>0.11</td><td>0.12</td></tr><tr><td>1:30 pm</td><td>0.12</td><td>0.13</td><td>0.06</td></tr><tr><td rowspan="2">AMSR2-C2</td><td>1:30 am</td><td>0.04</td><td>0.10</td><td>0.10</td></tr><tr><td>1:30 pm</td><td>0.05</td><td>0.09</td><td>0.04</td></tr><tr><td rowspan="2">SMOS-IC L</td><td>6:00 am</td><td>0.02</td><td>0.04</td><td>0.02</td></tr><tr><td>6:00 pm</td><td>0.03</td><td>0.08</td><td>0.07</td></tr><tr><td rowspan="2">ASCAT-IB C</td><td>9:30 am</td><td>0.10</td><td>0.02</td><td>0.14</td></tr><tr><td>9:30 pm</td><td>0.24</td><td>0.15</td><td>0.14</td></tr></tbody></table>

Note: All the *q* values are significant (*p*<0.01) and a higher *q* value indicates a stronger impact.
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## 4.5 Seasonality of the spatial correlations between VODs/backscatter and SM

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The seasonality of the spatial correlations between daily SM anomalies and daily VOD anomalies (the anomalies were calculated from Eq. 1) are displayed in Fig. 8. For VODCA, Ku-VOD holds positive correlations in July-September (Fig. 8a) and X-VOD shows the highest spatial correlations during August (Fig. 8b). However, for VODCA C-VOD, the spatial correlations are generally negative with limited days showing positive correlations (Fig. 8c). For AMSR2 X-VOD, the spatial correlations are positive during June-September for midnight VOD retrievals (Fig. 8d); the spatial correlations are always negative for AMSR2 C1-VOD (Fig. 8e); and positive correlations can only be observed in a few days throughout the year for AMSR2 C2-VOD (Fig. 8f). The spatial correlations between SMOS-IC L-VOD and SM are also generally negative throughout the year (Fig. 8g). For ASCAT-IB C-VOD and ASCAT backscatter, the correlations are almost always positive and peaks during June-September (Fig. 8h-i). Generally, evening or midday VOD/backscatter has a higher spatial correlation with SM than morning or midnight VOD/backscatter, except for AMSR2 C1-VOD and SMOS-IC L-VOD (Fig. 8).
<figure><img src="image_12.png" /><figcaption>Fig. 8 Seasonality of the spatial correlations between morning or midnight VODs/backscatter anomalies and soil moisture anomalies over the Amazon study area. Note: Non-sig indicates non-significant ($p > 0.05$) and the green line means seasonal dynamics of SM Z-score values.</figcaption></figure>

# 5. Discussion

## 5.1 Possible improvement methods for VOD retrievals

### 5.1.1 Uncertainties associated with the retrieval algorithms
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The present study identified the opposite seasonality between some passive microwave VOD products and SM. However, a higher SM usually leads to a higher RWC (Konings et al., 2021a), which would lead to a higher VOD value under conditions of limited seasonal variations of biomass, especially for L-VOD. Although the seasonal correlation between L-VOD and SIF is positive across some regions (Fig. S5g), the seasonality of total canopy LAI is non-significant (Wu et al., 2016) and limited leaf mass changes should not dominate VWC changes at L-band, as L-band observations are more sensitive to woody components, e.g., branches and trunks (Tian et al., 2018). Additionally, woody growth occurs at a low rate in the dry season (Doughty et al., 2015; Girardin et al., 2016). Thus, the unreasonable seasonality of some passive microwave VODs when compared to the SM seasonality, e.g., SMOS-IC L-VOD, may be caused by algorithm issues in the VOD retrievals.

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There are two possible reasons accounting for the unexpected opposite seasonality between some passive microwave VODs (especially for L-VOD) and SM from the perspective of retrieval algorithm. The first one is the simplified scattering process considered in the retrieval algorithms, and the other one is the parameter setting of time-invariant scattering effects. The Tau-Omega model on which most passive retrieval algorithms are based does not account for the volume scattering effects across dense forests (Feldman et al., 2018a; Wigneron et al., 2017). To solve this problem, a higher order radiative transfer model, the Two-Stream emission model (Schwank et al., 2018), which holds a stronger physical background with consideration of multiple scattering and reflection, has been applied to SMOS TB to retrieve SM and VOD (Li et al., 2020a). However, the temporal correlations between VODs retrieved from the Tau-Omega model and the Two-Stream model are higher than 0.8, indicating a similar seasonality of VOD retrievals for both models (Li et al., 2020a). Thus, the simplified representation of scattering effects in the Tau-Omega model is probably not the main reason for the unreasonable seasonality of VODs we found in this study.
Another possible explanation is that the effective scattering albedo (omega), a calibration parameter, is commonly assumed to be time-invariant in the retrieval algorithms. This assumption could be explained by the fact that omega is usually viewed to be saturated and becomes relatively stable, due to the overwhelming multiple scattering effects, especially in very dense forests (Wigneron et al., 2017). Similarly, Baur et al. (2021) found that omega was almost constant throughout the year over the Amazon region by retrieving both VOD and omega in two separate Tau-Omega model frameworks using known soil moisture as inputs. However, some evidences show that omega is observed to vary with spatiotemporal land cover dynamics (Konings et al., 2016). Scattering effects depend on the total path of the electromagnetic waves that cross the canopy (Baur et al., 2019), and are a function of plant geometry and of vegetation moisture content, which exhibit considerable variability in time (Owe et al., 2008). For example, time dependence of omega is significant over crop fields (Wigneron et al., 2004) and omega may range from 0 to 0.2 with total water content varying from 0 to 120 tons/ha (Ferrazzoli et al., 2002). Additionally, omega is shown to be SM dependent and increases monotonically with increasing soil moisture (Kurum, 2013). In this study, the backscatter observed by ASCAT also demonstrates time-varying scattering effects explaining the backscatter decreases in the dry season, which is in line with this previous study in Prigent et al. (2022). Thus, we deduce that in the Amazon intact forests, omega is not constant over time. On the contrary, it varies in time and should decrease during the dry season. To test the sensitivity of VOD to changes in omega values, we run the L-band VOD retrieval algorithm from SMOS-IC over the study region with an omega value of 0.05 as input for the scattering effects, a value slightly lower than its original value for evergreen forests (0.06). The results show a dramatic decrease in VOD magnitude when varying omega values from 0.06 to 0.05, with the magnitude of decrease even exceeding its original seasonal amplitude (Fig. 9). Thus, neglecting the possible decrease of the omega value during the dry season in the retrieval algorithms may lead to a
large overestimation of L-VOD in the dry season. Thus, we concluded that the unreasonable increase of SMOS-IC L-VOD in the dry season may be caused by the time-constant omega parameter used in the retrieval algorithms. We therefore recommend that seasonal scattering effects should be considered in passive VOD retrieval algorithms over Amazon dense forests due to the strong seasonality in the moisture content of the vegetation scattering elements (Kurum et al., 2012), e.g., leaves, branches and trunks.

<figure><img src="image_13.png" /></figure>

Fig. 9 The seasonality of SMOS-IC L-VOD retrievals for zonal-mean values of the study region with two different omega values as inputs: omega=0.05 and omega=0.06 (a fixed standard value in the SMOS-IC algorithm for evergreen forests). Note: The middle solid line indicates the time-averaged values and shaded areas indicate one standard deviation during 2013-2016.

### 5.1.2 Disturbing factors for retrieval errors related to observations

Our results indicate that midnight/morning passive microwave VOD is less influenced by soil temperature with smaller *q* values than midday/evening VOD, except for AMSR2 X-VOD (Table 2).
This conclusion is in line with previous studies indicating that midnight/morning TB is less affected by temperature-related errors because soil and vegetation are closer to temperature equilibrium at night (Frappart et al., 2020); and daytime emitting layer temperature is often more difficult to estimate due to more intense surface heating (Owe, et al., 2008). We also found a low but significant ($p<0.05$) impact of precipitation on correlations between VODs and SM for both active and passive microwave VOD products, with a mean $q$ value of 0.08, ranging from 0.02 to 0.15 (Table 2). The influence of precipitation may be explained by the fact that precipitation has an impact on leaf surface water. For example, Hornbuckle et al. (2006) found that water on plant surfaces may have a moderate effect on decreasing TB at 1.4 GHz varying by a few Kelvin. Xu et al. (2021) indicated that leaf surface water (including dew and precipitation) drove diurnal X-VOD variations over tropical forests.

Soil surface roughness, defined by DEM which is used to identify the complexity of topography, contributes to observations of the soil microwave reflectivity (Ma et al., 2019) through changing the local incidence angle and shadowing effects caused by topography and mixed pixels (Talone et al., 2007; Monerris et al., 2008). Generally, surface roughness effects on retrieval errors are found to be low for passive microwave VODs, with a mean $q$ value of 0.07 (ranging from 0.02 to 0.14) (Table 2), which may be caused by the masking effects of dense vegetation covers (Parrens et al., 2016). Terrain was found to influence more the active microwave backscatter observations (Mladenova and Lakshmi, 2009). This is consistent with our study, as we found that surface roughness can explain 14% ($q$ value~14%) of the spatial distribution of the correlations between SM and ASCAT-IB C-VODs for both morning and evening retrievals (Table 2). The influence of terrain on active microwave observations and VOD retrievals has also been found in previous publications. For example, Mladenova and Lakshmi (2009) found a strong backscatter dependence on the local incidence angle caused by changes in slope over the Northern American Monsoon Experiment region characterized by complex topographic terrain. Liu et al.
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(2021b) found terrain strongly contributes to the soil parameters C (importance weight of 30.42%) and D (importance weight of 16.46%) in active passive VOD retrievals based on the Water Cloud model.

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## 5.2 ASCAT C-band backscatter as an indicator of the forest water content

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Backscatter observed from microwave radars is sensitive to canopy structure, biomass and water content (Santos et al., 2014; Schroeder et al., 2016; Steele-Dunne et al., 2012; 2017; van Emmerik et al., 2015; van Emmerik et al., 2017; Petchiappan et al., 2022). In this study, we found that ASCAT C-VOD and ASCAT C-band backscatter have comparable capabilities for monitoring the seasonality of SM, and therefore the time variations in the ASCAT backscatter may mostly be due to changes in the vegetation water content and not in soil moisture, especially considering the low penetration depth at C-band in dense forests. The ASCAT backscatter was very sensitive to the soil water availability in the Amazon intact forests in space and time especially for water-limited regions (Region 4) (Fig. 3, Fig. 4a, Fig. 8). Additionally, evening backscatter is more correlated to SM than morning backscatter (Fig. 3a-b, Fig. 4a), which is consistent with the performance of other VODs and model-simulated VWC in following the SM seasonality (Fig. 2b, Fig. 6, Fig. S5). This is contrary to the seasonal variations of leaf water potential during midday and midnight. The midnight leaf water potential should be more correlated to soil water potential because an equilibrium between leaf water potential and soil water potential is reached at predawn (Richter, 1997; Konings and Gentine, 2017), while Amazon forests tend to keep a relatively constant midday leaf water potential during soil moisture dry-downs because of isohydric regulation (Konings and Gentine, 2017; Zhang et al., 2019).

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There are several reasons why vegetation water content is less correlated with SM during midnight/morning retrievals. First, soil moisture in volumetric unit is not perfectly coupled with soil water potential, a threshold of soil moisture exists in the relationship between soil moisture and soil water tension (negatively to soil water potential) (Lee and Kim, 2019). Thus, in the very wet Amazon
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643 the relatively small variation in the soil water potential and midnight VOD (Konings and Gentine, 2017).
644 Additionally, vegetation water content does not only scale with leaf water potential, and the regulation
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646 (Meinzer et al., 2003). However, the main driving force of water movement through the plant organs is
647 transpiration. The water supply for transpiration is composed by vertical water flow (absorbed from soil)
648 and water charge by water storage pool of each organ regulated by capacitance (Yao et al., 2021). The
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650 (Nagasuga, 2004). Thus, the higher stress from vapor pressure deficit and transpiration during
651 midday/evening leads to a higher correlation between VOD and SM.

652 **5.3 Limitations and future perspectives**

653 While offering a comprehensive evaluation of VODs over Amazon intact forests based on process-
654 based ORCHIDEE-CAN-NHA land surface model and remote sensing observations from active
655 microwave sensor ASCAT radar backscatter, this study still has some limitations. First, the
656 ORCHIDEE-CAN-NHA model is a simplified process-based vegetation hydraulic model, which cannot
657 completely capture the complex vegetation response to soil water availability in regions with high
658 rainfall seasonality. For example, the correlations between backscatter and soil moisture showed an
659 increasing trend with rainfall seasonality increasing from Region 1 to Region 4 (Fig. 4a). However, the
660 ORCHIDEE-CAN-NHA model cannot capture the highest correlation between SM and VWC over the
661 driest region (Region 4) (Fig. 2a, Table S2). Additionally, a time lag effect between plant water storage
662 and terrestrial groundwater storage is expected (Tian et al., 2018), but the ORCHIDEE model shows
663 limited capability to capture the lagged response of vegetation water storage to soil moisture changes.
664 Nevertheless, although there is still a need of improvement of the ORCHIDEE-CAN-NHA model to
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accurately capture the response of vegetation moisture variations to soil water availability in regions
with high rainfall seasonality, the results confirmed that the main driver of the VWC seasonality is the
seasonal variations of soil water availability.

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Temporal or spatial correlations between VODs and SM cannot be viewed as an ‘absolute’ criterion
of accuracy for evaluating the VOD retrievals. First, uncertainty exists in the accuracy of the ERA5 SM
data in tropical rainforests where SM simulations are largely unvalidated, even though ERA5 SM shows
high accuracy at the scale of the international soil moisture network (Li et al., 2020b) and a high
temporal correlation (median correlation ~ 0.68) is found between ERA5-SM and ORCHIDEE-CAN-
NHA model-simulated SM over Amazon intact forests (Fig. S2). Second, high correlations between SM
and VOD does not absolutely mean high accuracy, because the low correlations may be caused by the
lagged response of vegetation to water stress and the plant adaptations to the reduced soil water
availability (Oliveira et al., 2005). Trees have strong stomatal control strategies and deep roots to adapt
to water stress, dampening short-term plant responses to available SM, thus a lagged VWC response
exists. Third, a nonlinear relationship between plant and soil water content emerges with threshold
behavior depending on SM (Feldman et al., 2018b). Additionally, SM and VPD can be decoupled in the
midday/afternoon when VPD and evaporation have larger effects on VOD, which could lead to a lower
correlation between SM and VODs. Furthermore, the magnitude of correlations between SM and VODs
could also be influenced by seasonal variations in the canopy biomass over different canopy layers, e.g.,
upper canopy and understory.

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While the total canopy LAI shows non-significant seasonality (Wu et al., 2016; Tian et al., 2018),
there are some limitations to consider when interpreting the time variations in high frequency VOD
products because of the complex canopy structure and diverse leaf mass seasonality across different
canopy depths. The high frequency VOD products (Ku- and X-VOD) are sensitive to top canopy, where

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old leaves will shed during dry seasons (Nunes et al., 2022), thus both LAI and RWC should follow the seasonality of SM. A positive correlation between SM and Ku-, X-VOD products can be observed across most pixels (Fig. 6a,b,d), while these VOD products are less sensitive to photosynthetic seasonality (Fig. S6a,b,d) or LAI (Fig. S7a,b,d). Similarly, the active microwave C-band backscatter and VOD retrievals show a strong correlation with SM and a negative correlation with SIF/LAI across most pixels (Fig. 3a-b, Fig. 6g, Fig. S3a-b, Fig. S4a-b, Fig. S6h, Fig. S7h). However, passive microwave C-band VOD products show a large difference of seasonality. The VODCA C-band VOD is more correlated to SM, but AMSR2 C1- and C2-VOD are more correlated to SIF/LAI (Fig. 6c,e,f, Fig. S6c,e,f, Fig. S7c,e,f). Because the sensing depth of passive microwave C-band is largely unknown, there is still a need for a further evaluation of C-band passive microwave VOD products over the Amazon rainforests. For instance, triple collocation method may be used to estimate the correlation metric of independent C-band VOD products. Notably, simultaneously retrieving VOD and SM can cause larger errors than regularized retrieval, and the errors tend to disproportionately propagate into VOD instead of soil moisture in the simultaneous retrieval algorithms (Feldman et al., 2021). Thus, independent C-band and L-band VOD retrievals considering SM as input, rather than simultaneously retrieving SM and VOD, may provide references for further evaluation studies.

This study indicated that an unexpected negative correlation between SM and some passive VODs may be caused by a problem in the retrieval algorithms that do not take into account the scattering seasonality thus leading to wrong seasonal cycles of VODs. The impact of scattering effects is more obvious for low frequency VODs because scatterers are large with respect to the wavelength (Kurum, 2011) and large scatterers could result in a strong scattering effect, e.g., the single scattering albedo values at L-band are higher than those at C-band across several diverse land use types (Mo et al., 1982). Although SMOS-IC L-VOD retrievals are impacted by the seasonal scattering effects on the seasonal
timescale, for the annual VOD retrievals, annual mean SMOS-IC L-VOD holds the best spatial correlation with AGB in comparison to high frequency VODs (Fig. S8, detailed analysis about VOD-AGB relationships can be seen in Appendix. A). Notably, the strong consistency between SMOS-IC L-VOD and AGB/forest-cover in multi-annual dynamics over dense forests has been found in previous publications (Fan et al., 2019; Wigneron et al., 2020; Qin et al., 2021). Thus, we recommend that the SMOS-IC L-VOD can still be used for analyzing multi-year changes in biomass despite the fact a correction is required in the algorithm to account for the seasonal scattering effects.

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## 6. Conclusion

This study evaluated eight recently developed and reprocessed passive and active microwave VOD products (from L- to Ku-band) against the simulated vegetation water content based on process-based ORCHIDEE-CAN-NHA land surface model and direct remote sensing observations of ASCAT backscatter (C-band) over Amazon intact forests. Our results can contribute to better target suitable ecological applications of VOD products and improve passive microwave VOD retrieval algorithms over Amazon dense forests. The primary conclusions are summarized as follows.

1) Seasonal variations in the vegetation water content over the Amazon intact forests are mainly dominated by soil water availability. Evening or midday VODs are more sensitive to the vegetation water stress with higher correlations with soil moisture than morning or midnight VODs.

2) ASCAT backscatter (C-band) is highly sensitive to the impact of soil moisture stress on vegetation in space and time over the Amazon intact forests. ASCAT C-band backscatter and ASCAT-IB C-VOD can be applied for drought-related applications, e.g., drought center identification and plants response to water stress.
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3) Implausible seasonality, especially unreasonable increases of several passive microwave VOD retrievals (e.g., SMOS-IC L-VOD) when soil moisture decreases during the dry season may be caused by the time-invariant scattering effects considered in the algorithm. The scattering effects should theoretically decrease during the dry season and lower scattering effects lead to lower VOD values. We recommend that the seasonal scattering effects should be considered accurately in the passive retrieval algorithms to acquire accurate passive microwave VOD seasonal dynamics over Amazon dense forests, and backscatter from active microwave observations may provide a scientific reference for evaluating the seasonal variations in the scattering effects.

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4) In the passive microwave domain, morning or midnight VOD retrieval is less influenced by soil temperature than evening or midday VOD retrieval. Additionally, SMOS-IC L-VOD is less influenced by disturbing factors than high frequency VODs, including surface roughness, soil temperature and precipitation.

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Author contribution

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Huan Wang, Jean-Pierre Wigneron and Philippe Ciais conceived and designed the study. Huan Wang did all calculations and data analysis. Yitong Yao simulated vegetation water content, soil moisture and biomass based on ORCHIDEE-CAN-NHA model. Xiangzhuo Liu provided ASCAT C-band backscatter and VOD datasets. Xiaojun Li provided SMOS-IC L-VOD products. Huan Wang wrote the first draft of the manuscript. All authors contributed to the discussion and edited the manuscript.
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Acknowledgments

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This work was supported by CNES, France (Centre National d'Etudes Spatiales), by the RECCAP2 project which is part of the ESA Climate Change Initiative (contract no. 4000123002/18/I-NB) and by the China Scholarship Council (202106010124).

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