# Assessment of L-band passive microwave soil moisture retrievals against in situ measurements in the North American boreal biome

Azza Gorrab, Oliver Sonnentag, Arnaud Mialon, Chelene C Hanes, Juliette Ortet, Aaron A Berg, Muhammad Umair, Alexandre Roy

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

## To cite this version:

Azza Gorrab, Oliver Sonnentag, Arnaud Mialon, Chelene C Hanes, Juliette Ortet, et al., Assessment of L-band passive microwave soil moisture retrievals against in situ measurements in the North American boreal biome. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, In press, 18, pp.12713-12727. 10.1109/JSTARS.2025.3569063. hal-05064908

HAL Id: hal-05064908

https://hal.science/hal-05064908v1

Submitted on 13 May 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

# Assessment of L-band passive microwave soil moisture retrievals against in situ measurements in the North American boreal biome

Azza Gorrab, Oliver Sonnentag, Arnaud Mialon, Chelene Hanes, Juliette Ortet, Aaron Berg, Muhammad Umair, Alexandre Roy

**Abstract**—Climate change greatly impacts forest carbon and water dynamics in the boreal biome. To understand the vulnerability of the North American boreal biome to climate change disturbances, including droughts and temperature extremes, a biome-wide characterization of near-surface soil moisture (SM) is essential. Soil moisture is an important control on photosynthetic carbon dioxide uptake and the associated evaporative loss of water through transpiration. Two missions with passive microwave radiometers operating at L-band (1–2 GHz) were launched to estimate SM at a global scale: Soil Moisture and Ocean Salinity (SMOS) launched in 2009 and Soil Moisture Active Passive (SMAP) launched in 2015. In this study, we analyzed the relationships between various microwave observations (brightness temperature [TB], soil reflectivity [r], and soil moisture [SM]) derived from SMOS and SMAP with in situ SM measurements (2016–2021) over 16 sites across the boreal biome of North America. Our results show good agreement between SMAP and SMOS in terms of relationships between TB and in situ SM measurements (with a coefficient of determination R² > 0.7). Analysis comparing microwave variables (TB, r and SM) and in situ SM data demonstrated considerable differences across sites and years. We observed moderate to strong correlation coefficients (R_TB > 0.5) between the TB and in situ SM data, generally when a seasonal soil drying trend emerges over the summer. However, soils with high water content and frequent precipitation input showed weaker correlations between TB and in situ SM. By analyzing the correlations between r and in situ SM data, we demonstrated that the soil effective temperature partly affects performances in terms of R_TB. Comparisons with other SMAP SM retrievals (MT-DCA and MCCA) revealed similar results in correlations and showed high spatio-temporal differences across all SM retrievals and study sites. Our findings highlight the complexity of the interactions between microwave observations and SM dynamics across the boreal biome, thus challenging L-band passive microwave SM retrievals.

**Index Terms**—Boreal biome, soil moisture, SMOS, SMAP brightness temperature, in situ measurement

This work was supported by the Canadian Space Agency (CSA), the Fonds de recherche du Québec – Nature et technologies (FRQNT), and the Natural Sciences and Engineering Research Council of Canada (CRSNG).

Azza Gorrab, Alexandre Roy and Juliette Ortet are from the Département des sciences de l'environnement, Université du Québec à Trois-Rivières, Trois-Rivières, QC, G9A 5H7, Canada and from the Centre d'études nordiques, Québec, QC, G1V 0A6, Canada (e-mail: azza.gorrab.ep.el.khedhri@uqtr.ca; alexandre.roy@uqtr.ca; juliette.ortet@uqtr.ca)

Oliver Sonnentag and Muhammad Umair are from the Département de géographie, Université de Montréal, Montréal, Canada. Oliver Sonnentag is with the Centre d'études nordiques, Québec, QC, G1V 0A6, Canada (e-mail: oliver.sonnentag@umontreal.ca; muhammad.umair.1@umontreal.ca)

Arnaud Mialon and Juliette Ortet are from the Centre d'Etudes Spatiales de la BIOSphère, CESBIO (CNES/CNRS/INRAE/IRD/UT3), 31401 Toulouse, France (e-mail: arnaud.mialon@univ-tlse3.fr)

Chelene C. Hanes is from the Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, 1219 Queen Street East, Sault Ste. Marie, ON, P6A 2E5, Canada (e-mail: chelene.hanes@nrcan-rncan.gc.ca)

Aaron A. Berg is from the Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON, N1G 2W1, Canada (e-mail: aberg@uoguelph.ca).

## I. INTRODUCTION

THE boreal biome counts among the largest forests in the world, covering a vast area of approximately 1.89 billion hectares in the northern hemisphere, with 28% in Canada [1]–[3]. The boreal biome plays a crucial role in regulating climate by contributing to the exchange of energy, water, and carbon between the soil and the atmosphere [2], [4]. The boreal biome might strongly impact the climate system, due to its important carbon stock, [1], [2], [5], with 28 Pg carbon in biomass, dead organic matter, and soil pools [6].

More frequent and intense disturbances including wildfires, droughts, and water limitations increasingly threaten the North American boreal biome [3], [5]. Soil moisture (SM) must be accurately estimated in the boreal forest to quantify biome-wide vulnerability to climate change regarding the close link between SM and the carbon cycle [4]–[6]. Soil moisture controls processes involved in evapotranspiration and photosynthesis [7]. Trees need to remain well hydrated to sustain their physiological activities when they open their stomata to satisfy the photosynthetic demand for atmospheric carbon dioxide (CO₂) [3], [7]. Under low SM availability, trees close leaf stomata, and reduce their root water uptake and the use of CO₂ for photosynthesis to reduce transpiration and water loss [3], [6]–[9]. Therefore, SM plays a crucial role in understanding water cycle impacts on boreal biome productivity [7], [9]. Soil moisture strongly varies in space and time, and measurements at the point scale (e.g., gravimetrically or with time-domain reflectometry) are not enough to describe its variation across the full extent of the North American boreal biome [10].

Several studies demonstrated the effective use of active and passive microwave remote sensing data to retrieve SM at global, regional, and local scales [9]–[15]. Such data offer both a high temporal resolution (high revisit time of 1 to 3 days) and a global coverage [15]. Satellite microwave radiometers measure the emitted radiation from the land surface which is expressed in Kelvin (K) as brightness temperature (TB).
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

Brightness temperature largely depends on near-surface soil (e.g., moisture, roughness, and texture) and vegetation characteristics (e.g., biomass, water content, and height). Brightness temperature appears to be sensitive to SM (e.g., expressed as volumetric water content) [9], [13], [16]–[18], with wetter soils causing TB to decrease [19], [20]. In vegetated areas, vegetation properties and associated scattering effects within the canopy attenuate the microwave emission from the soil, often impeding the SM retrieval below the canopy [14], [15], [19], [21]–[23].

Two operational L-band radiometers estimate SM: the Soil Moisture and Ocean Salinity (SMOS) launched in November 2009 by the European Space Agency (ESA) [16], and the Soil Moisture Active Passive (SMAP) launched in January 2015 by the National Aeronautics and Space Administration (NASA) [24]. Various SMOS and SMAP SM retrievals were assessed at the global scale and for “low-biomass” vegetation ecosystems [12], [13], [17], [18], [24]. The accuracy of SMOS and SMAP SM depends on the land cover type and generally meets the mission requirements (0.04 m³ m⁻³) over areas with low vegetation water content (< 5 kg m²) [13], [17], [18], [23]–[26]. However, only a few studies evaluated SMOS and SMAP SM over forested regions due to various challenges related to vegetation density, canopy structure, remoteness, and sparse in situ data. For example, Kerr et al. [17] found that SMOS SM retrieval results in Europe are satisfying over arid areas but poor over forested regions. In Africa, lower correlations were observed between in situ SM measurements against SMOS and SMAP SM over savannas and forests than over shrub and grassland ecosystems [27]. Over a few forest sites in the United States (US), Rahmoune et al. [28] SMOS SM retrievals yielded a low accuracy with root mean square error (RMSE) values between 0.07 and 0.12 m³ m⁻³. Also, in the US, the SMAP accuracy was poor (Pearson's correlation coefficient [R] about 0.1) in certain deciduous and evergreen forests [25]. However, under temperate forest canopies, Colliander et al. [22] showed that SMAP TB appears to be sensitive to SM but more evaluation efforts are needed. To our knowledge and after more than 15 years of L-band passive microwave acquisitions, few studies have evaluated SMOS and SMAP SM retrievals in high latitudes including the boreal biome [29], and its different forest, wetland, and lake ecosystems. The boreal biome has a high density of open and abundant small water bodies [1], [2], [5], [6]. Monitoring SM in these regions remains a challenge because of multiple factors like permafrost, ice, snow, water bodies besides a high degree of ecosystem heterogeneity [22], [29], [30].

Our study aims to evaluate SMOS and SMAP SM against in situ SM across the North American boreal biome. Our objectives are thus to 1) compare SMOS and SMAP SM with in situ SM at 16 sites for different sensor configurations (orbits, polarizations, and spatial resolutions), 2) identify the spatial and temporal variability of the retrievals among sites, 3) analyze how microwave-derived variables are sensitive over time with in situ SM to understand their temporal relationships, and 4) identify the main challenges and limitations of SMOS

and SMAP SM in the North American boreal biome.

## II. METHODOLOGY

### A. Study area

The study was conducted at 16 boreal biome research sites across North America representing an informal network of in situ SM observations across a diverse range of ecoregions and permafrost conditions (see Fig. 1 and Table I). The sites included Baie-James (BJ)—Le Moyne and Kuujjuarapik (KJ) in Québec [31]. Both sites are typical for the Canadian taiga ecoregion dominated by black spruce (*Picea mariana*) and are in the discontinuous (KJ) and sporadic permafrost zone (BJ). The BJ and KJ sites have relatively well-drained surfaces, with a shallow organic layer (few cm) above mineral soil. Another site in eastern Québec, Forêt Montmorency (FMJ), is dominated by balsam fir (*Abies balsamea*) with interspersed white birch (*Betula papyrifera*), white spruce (*Picea glauca*) and black spruce (*Picea mariana*) [32].

We also included four sites of Hanes et al. [33]: two in Alberta near Edson (Ed) and Red Earth Creek (REC), and two in Ontario near Dryden (Dr) and Chapleau (Ch) (see Fig. 1). All four sites are in the Northern Forest ecoregion. We used three Chapleau [Ch] sites with different forest types: jack pine (*Pinus banksiana*, ChP), mixed-wood (ChM) and black spruce (ChSb). Both the Ed and Dr sites are mixed-wood forests while, the REC site is an Aspen plot, part of the Boreal Plains.

Data from three Boreal Ecosystem Research and Monitoring Sites (BERMS) where permafrost is absent were analyzed in this study. The three sites are in the western Boreal Plain, in Saskatchewan [34]. The Old Black Spruce (OBS) site is dominated by black spruce, the Mature Jack Pine (OJP) is dominated by jack pine and the Fen site (Fen) is a minerotrophic patterned fen dominated by herbaceous plants and several sedge species.

We also included four more northern sites (> 59° N) where permafrost occurs: the Scotty Creek Landscape (SCC) and the Smith Creek (SMC) sites in the Northwest Territories; Key Pile Camp (KPC) in northeastern British Columbia and one site in the US at the University of Alaska, Fairbanks site (Uaf). They are all within the taiga and are dominated by an open canopy of black spruce [35], [36].

In situ SM datasets were obtained from various SM probes installed at the topsoil layer between 5 and 10 cm (see Table I). For each probe, SM values were recorded at 1h intervals and the mean daily SM values were calculated for each study site. Note that most probes were calibrated based on soil samples from the sites.

Land cover information was derived from the classification dataset at 300 m for 2015 and provided by the European Space Agency Climate Change Initiative (ESA CCI: see section II-B.3) (Table I). We focused on the “forest”, “wetland” and “water bodies” categories, because of their potential important impact on SMOS and SMAP observations [16], [19], [20]. The three land categories were extracted within SMOS and SMAP pixels (Table I).
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

<caption>TABLE I</caption>

GEOGRAPHIC COORDINATES AND MAIN CHARACTERISTICS OF THE 16 STUDY SITES. ESA CCI LAND COVER CATEGORIES: 2. FOREST AND 4. WETLAND AND CLASS VALUE 210 (WATER BODIES) WERE EXTRACTED FOR THE DIFFERENT PIXELS (SMOS 25 KM, SMAP 9 KM, SMAP 36 KM). THE MAIN FOREST COVER CLASSES WERE ALSO IDENTIFIED [TREE COVER EVERGREEN NEEDLELEAF (EN), TREE COVER MIXED LEAF TYPE (Mx)].

<table><thead><tr><th>Site ID</th><th>Lat (DD)/Long (DD)</th><th>Soil composition</th><th>Permafrost extent</th><th>SM Probe type</th><th>Dominant ESA LC_CCI</th><th>Water bodies (%)</th><th>Wetlands (%)</th><th>Forests (%)</th><th>Key reference</th></tr></thead><tbody><tr><td>FMJ</td><td>47.28/-71.16</td><td>Sandy loam</td><td>Absent</td><td>CS650</td><td>Mx</td><td>2.9/1.9/1.9</td><td>0.0/0.0/0.0</td><td>94.3/95.1/97.6</td><td>[32]</td></tr><tr><td>ChSb</td><td>47.55/-83.55</td><td>Sandy loam</td><td>Absent</td><td>CS616</td><td>Mx</td><td>6.0/9.2/3.6</td><td>1.2/1.6/0</td><td>89.8/86.4/91</td><td>[33]</td></tr><tr><td>ChP</td><td>47.67/-83.48</td><td>Sandy loam, some clay</td><td>Absent</td><td>CS616</td><td>Mx</td><td>8.8/9.2/10</td><td>2.7/1.6/1.8</td><td>85.5/86.4/84.4</td><td>[33]</td></tr><tr><td>ChM</td><td>47.91/-83.44</td><td>Sandy loam</td><td>Absent</td><td>CS616</td><td>Mx</td><td>9.5/9.2/3.9</td><td>0.2/1.6/0.2</td><td>89.1/86.4/93</td><td>[33]</td></tr><tr><td>Dr</td><td>49.86/-92.60</td><td>Sandy clay</td><td>Absent</td><td>CS616</td><td>Mx</td><td>19.7/17.7/31.5</td><td>0.9/0.6/0.2</td><td>70.0/78.9/68.2</td><td>[33]</td></tr><tr><td>BJ</td><td>53.40/-75.01</td><td>Silt loam to sandy loam</td><td>Sporadic</td><td>Teros 12</td><td>EN closed to open (> 15%)</td><td>18.3/21.6/7.4</td><td>2.4/1.8/3.5</td><td>59.9/57.9/68.7</td><td>[31]</td></tr><tr><td>Ed</td><td>53.63/-116.60</td><td>Silty clay</td><td>Absent</td><td>CS616</td><td>EN closed (> 40%)</td><td>0.7/0.9/0.1</td><td>0.1/0.2/0.1</td><td>87.8/93.4/97</td><td>[33]</td></tr><tr><td>Fen</td><td>53.80/-104.61</td><td>Peat</td><td>Absent</td><td>CS616</td><td>EN closed (> 40%)</td><td>1.1/1.5/0.8</td><td>1.6/1.7/3.1</td><td>91.9/92.2/86.8</td><td>[34]</td></tr><tr><td>OJP</td><td>53.92/-104.69</td><td>Sandy soil</td><td>Absent</td><td>CS616</td><td>EN closed (> 40%)</td><td>1.1/1.5/0</td><td>1.6/1.7/0.4</td><td>91.9/92.2/91.1</td><td>[34]</td></tr><tr><td>OBS</td><td>53.98/-105.12</td><td>Sandy loam to sand</td><td>Absent</td><td>CS616</td><td>EN closed (> 40%)</td><td>13.8/14.3/5.7</td><td>1.0/0.8/1.3</td><td>84.5/84.3/93</td><td>[34]</td></tr><tr><td>KJ</td><td>54.97/-76.31</td><td>Loamy sand</td><td>Discontinuous</td><td>Teros 12</td><td>EN closed to open (> 15%)</td><td>14.2/9.9/9.2</td><td>0.5/0.6/1.3</td><td>70.0/66.1/66.4</td><td>[31]</td></tr><tr><td>REC</td><td>56.69/-115.28</td><td>Silty Clay</td><td>Absent</td><td>CS625</td><td>EN closed (> 40%)</td><td>1.4/0.3/0</td><td>14.4/21.4/10.2</td><td>79.8/77.1/89</td><td>[33]</td></tr><tr><td>KPC</td><td>59.57/-120.69</td><td>-</td><td>Isolated</td><td>CS616</td><td>EN closed to open (> 15%)</td><td>1.3/2.3/0.3</td><td>4.2/4.6/2.5</td><td>92.1/89/95.2</td><td>-</td></tr><tr><td>SCC</td><td>61.30/-121.29</td><td>Peat underlain by lacustrine silt and clay</td><td>Isolated</td><td>CS616</td><td>EN closed (> 40%)</td><td>4.1/1.3/2.9</td><td>0.1/0.2/0.6</td><td>89.4/92.2/89.7</td><td>[35]</td></tr><tr><td>SMC</td><td>63.15/-123.25</td><td>Silty clay to silty clay loam</td><td>Sporadic</td><td>CS616</td><td>EN closed (> 40%)</td><td>5.2/5.2/18.7</td><td>0.1/0.3/0.1</td><td>87.6/90.8/80.3</td><td>[36]</td></tr><tr><td>Uaf</td><td>64.86/-147.85</td><td>Silt loam, peatland</td><td>Discontinuous</td><td>CS616</td><td>EN closed (> 40%)</td><td>2.0/1.9/1.3</td><td>5.6/16.8/9.1</td><td>82.9/63.8/72.3</td><td>[35]</td></tr></tbody></table>
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Locations of the 16 study sites and ecoregions in the North American boreal biome and permafrost zones [49] of North America. United States Environmental Protection Agency (US EPA) Level I Ecoregions of North America [50]. Permafrost is isolated (≤ 10% in areal extent), sporadic (> 10%–50%), discontinuous (> 50%–90%), and continuous (> 90%). Sites acronyms are: Baie-James (BJ), Kuujjuarapik (KJ), Forêt Montmorency (FMJ), Chapleau Jack pine (ChP), Chapleau Mixedwood (ChM), Chapleau Black spruce (ChSb), Dryden (Dr), Edson (Ed), Red Earth Creek (REC), Old Jack Pine (OJP), Old Black Spruce (OBS), Fen site (Fen), Scotty Creek Landscape (SCC), Smith Creek (SMC), Key Pile Camp (KPC), and the University of Alaska, Fairbanks (Uaf).</figcaption></figure>

All study sites are classified as “forest” dominated by evergreen needleleaf trees, with different fractions of “wetland” and “water bodies”. Discrepancies between the land categories at the site can be high between SMOS and SMAP pixels. For example, for both the BJ and Dr sites, waterbodies showed a difference of 11% between SMOS 40 km and SMAP 9 km pixels, while the OBS site varies by approximately 8%.

## B. Satellite data

SMOS and SMAP observations between 2016 and 2021 were collected. Only the thawed (growing season) period, which covers the unfrozen soil water, was considered to avoid areas covered with snow, ice, or frozen soil (see section II-B.3). We used Level 3 (L3) SMOS and SMAP products at different configurations: SMOS (25 km), SMAP (9 km and 36 km), horizontal (H) and vertical (V) polarizations and two orbits (A: Ascending and D: Descending). Table II provides the key characteristics of the two instruments.

### 1) SMOS data

The main objective of SMOS mission is to retrieve near-

surface SM at a global scale [16]. The Centre Aval de Traitement des Données SMOS (CATDS) L3 TB and SM products at the global cylindrical Equal-Area Scalable Earth Grid V2 (EASE2GRID) with a spatial resolution of 25 km were downloaded from http://www.catds.com. The SMOS CATDS L3 TB datasets are available at dual polarization and several bins of incidence angles. We used only the TB at the bin 40°+/-1, which corresponds to the SMAP incidence angle. The Radio Frequency Interference (RFI) is filtered out from TB L3 observations [37].

The L3SM CATDS retrievals were processed from ESA L1B [16] multi-angular TB and need to be filtered to remove RFI-contaminated TB. We applied the following criteria based on information provided in the LS3SM product: SMOS SM values are kept when the Chi_2 probability > 0.05 and the RFI probability < 0.2. The Chi_2 measures the goodness of the fit modeled TB from L-MEB versus SMOS TB, while the RFI probability is the number of RFI detected on 12 days divided by the number of TB measurements acquired on the same period.
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

<table><caption>TABLE II
SMOS AND SMAP MAIN CHARACTERISTICS AND USED PRODUCTS</caption><thead><tr><th></th><th>SMOS</th><th>SMAP</th></tr></thead><tbody><tr><td>Dates of operation</td><td>Since 11/2009</td><td>Since 01/2015</td></tr><tr><td>Sensor mission</td><td>Microwave Imaging Radiometer using Aperture Synthesis (MIRAS)</td><td>SMAP</td></tr><tr><td>Frequency (GHz)</td><td>1.42</td><td>1.43</td></tr><tr><td>Repeat cycle over the study sites</td><td>1 to 3 days</td><td>1 to 3 days</td></tr><tr><td>Native spatial resolution (km) on average of footprint size</td><td>≈ 40</td><td>≈ 40</td></tr><tr><td>Polarization</td><td>Dual (H, V)</td><td>Dual (H, V)</td></tr><tr><td>Incidence angle (°)</td><td>0–55</td><td>40</td></tr><tr><td>Radiometer</td><td>Interferometric sensor</td><td>Conical scan antenna</td></tr><tr><td>Sensitivity over land surfaces</td><td>≈ 3 K</td><td>less than 1 K</td></tr><tr><td>Sampling grid</td><td>25 km</td><td>36 km and 9 km</td></tr><tr><td>Orbit at local solar time (A: ascending, D: descending)</td><td>A orbit at ≈ 6:00 a.m.<br>D orbit at ≈ 6:00 p.m.</td><td>A orbit at ≈ 6:00 p.m.<br>D orbit at ≈ 6:00 a.m.</td></tr><tr><td>Water TB correction</td><td>Is not performed</td><td>Is performed</td></tr><tr><td>Used Products: Level (L)/Version (V)</td><td>CATDS L3/V330</td><td>NSIDC L3/V5 (R18290)</td></tr><tr><td></td><td colspan="2">- L3 TB (CDF3TA/CDF3TD)<br>- SM (CLF31A/CLF31D)</td></tr><tr><td>Used effective soil temperature</td><td>soil temperature L1 (ECMWF products)</td><td>provided with the SMAP products (from the Global Modeling and Assimilation Office, GMAO)</td></tr><tr><td>Key references</td><td>[16], [38]</td><td>[19], [23]</td></tr></tbody></table>

The SM products are retrieved using the L-band Microwave Emission of the Biosphere (L-MEB) inversion model [16], [20]. For each site-year at the different polarizations and orbits, we calculated the soil reflectivity (r) as in (1), where e is the microwave emission, TB is the microwave brightness temperature (in K) and T is the effective physical temperature of the surface (in K).

$$r = 1 - e = 1 - \frac{TB}{T} \qquad (1)$$

For SMOS, we considered T equal to the soil temperature L1 at the top 0–7 cm soil layer derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) products [12].

## 2) SMAP data

SMAP satellite was launched to provide microwave SM estimations at a global scale [23]. The native spatial resolution of the radiometer footprint is approximately 40 km. SMAP data are interpolated to 36 and 9 km EASE-Grid 2.0 equal-area projection [19]. In this study, we used the latest SMAP version: the SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture, Version 5 (SPL3SMP_E) and the SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 8 (SPL3SMP). They are available on the National Snow and Ice Data Center (NSIDC) and the RFI is filtered out from TB data [23]. SM L3 retrievals are included with (SPL3SMP_E) data. They are estimated from SMAP TB using the radiative transfer equation with the (L-MEB) model [24]. We removed the SMAP SM flagged as “not recommended” quality.

The r was also computed for SMAP products (1). We used the soil effective temperature data provided with the SMAP products, corresponding to the average of the surface temperature and soil temperature at 10 cm provided in the SM L3 product [19].

Other SMAP SM retrievals were also compared against in situ SM: the Multi-Temporal Dual Channel Algorithm (MT-DCA) by Konings et al. [8, 51] and the recently introduced Multi-Channel Collaborative Algorithm (MCCA) by Peng et al. [52]. The MT-DCA incorporates a temporal regularization approach that stabilizes the simultaneous retrieval of SM and VOD by assuming slower changes in VOD compared to SM across consecutive SMAP overpasses. In contrast, the MCCA retrieves a new SM product and polarization-dependent VOD from H- and V-polarized TB. Unlike other SMAP algorithms, the MCCA does not require ancillary data for calibrating vegetation and soil parameters or constraining the retrieval process.

## 3) Ancillary data

a) The Interactive Multisensor Snow and Ice Mapping System (IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km resolution, Version 1 [G02156]) products allowed identifying the snow and ice fractions covering the SMOS (25 km) and SMAP (36 and 9 km) pixels corresponding to the sites [39]. Only the pixels with 0% of snow and ice coverage were retained to define the thawed period. IMS data were retrieved from https://doi.org/10.7265/N52R3PMC.

b) The ESA CCI dataset at 300 m includes several land
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

cover classes that represent different types of land cover and land use [37]. The “forest,” “wetland,” and “water bodies” land categories were extracted for SMOS and SMAP nodes on their EASE2 Global grid (Table I). For SMOS observations, the different land cover class fractions are calculated within a circular buffer area of 40 km around the closest SMOS node coordinates to the site. This circular buffer area illustrates the real radiometer footprint well. The land cover information was extracted with a grid cell area of 9 × 9 km², and 36 × 36 km² for the gridded SMAP (9 km and 36 km) products, respectively. This representation aligns with the pixel format of the global EASEv2 Grids 9 and 36 km considered for the two SMAP products (more details in [18]).

### C. Data analysis

This study describes relationships between in situ SM measurements and SMOS and SMAP TB, r and SM for 16 sites for the period 2016–2021. From these relationships, the Pearson’s correlation coefficient (R) was computed for each variable (see Appendix A). Pearson’s correlation coefficients are not additive and should not be directly averaged between years. Therefore, for multiple-year comparisons of R, the Alexander [40] approach allowed computing averages of R for different years (see Appendix B).

To evaluate SM retrievals against in situ SM the bias (2), the root mean square error (RMSE) (3), and the unbiased root mean square error (ubRMSE) (4) were computed between the SM derived from satellite and site for each site–year. The significance level for the correlation analysis was α = 0.05.

$$Bias = \overline{SM_{sat} - SM_{site}} \qquad (2)$$

$$RMSE = \sqrt{(SM_{sat} - SM_{site})^2} \qquad (3)$$

$$ubRMSE = \sqrt{RMSE^2 - Bias^2} \qquad (4)$$

For our data analysis, we considered the thawing period calculated on each site–year. To define the first day of the thawing period, we considered IMS data that indicated no snow and ice cover for three consecutive days. The end of the thawing period was identified when air temperature from meteorological stations remained below 0 °C for three consecutive days.

## III. RESULTS

### A. SMOS and SMAP TB relationships with in situ SM for different satellite configurations

The performances of SMAP and SMOS observations were analyzed for different sensor configurations: spatial resolution (9 km, 25 km and 36 km), polarizations (H and V) and orbits (A and D). We compared the SMOS and SMAP correlations

based on the R_TB obtained between the microwave TB observations and in situ SM for all sites and years (Table III and Fig. 2). A good agreement of R_TB correlations appears between SMAP (9 and 36 km) and SMOS (25 km) with a strong linear relationship (Fig. 2a and b). However, the R_TB were generally lower for SMOS than for SMAP (9 km and 36 km) for most sites. Regarding the sensor configuration (Table III and Fig. 2), slightly better R_TB were observed with Orbit D and polarization H than Orbit A and polarization V between SMOS (25 km) and SMAP (9 km and 36 km), respectively. However, similar correlations were found between SMAP (9 km versus 36 km) despite the difference in spatial resolution and polarizations (Fig. 2c). The following section only presents the results of the SMOS and SMAP (9 km) L3 products for Orbit D and polarization H.

### B. Inter-site SMOS and SMAP products analysis

Average annual correlations for SMOS and SMAP variables (R_TB, R_r and R_SM) against in situ SM were summarized for each site (Fig. 3, Appendix C). The highest mean annual correlations (R_TB, R_r and R_SM) were obtained with SMAP products compared to SMOS products for most sites–years. Relationships between TB and in situ SM varied greatly among the 16 sites ranging from R_TB (SMAP) > -0.6 for SCC, OBS, OJP, Fen, ChM, ChP and ChSb, to low R_TB (SMAP) < -0.4 for BJ, KJ, REC, or non-significant positive R_TB for SMC and Uaf. Note that Fen and OJP were in the same SMOS pixel and are close to OBS (distance between 15 km to 40 km) leading to similar average R_TB (SMAP) (Fig.1). Similarly, the three Ch sites are spatially close (distance between 13 km and 38 km), providing similar average R_TB.

When comparing the standard deviation (STD) of the annual R_TB among sites, a notable variability appears through the years (see Appendix C). For example, Ed, KJ, FMJ and REC yield a high STD (above 20% of difference in R_TB), showing the strong interannual variability of the relationship between TB and in situ SM. When the effect of the soil effective temperature was removed by computing r, the correlation R_r was weaker than R_TB for both SMOS and SMAP. For SM products, R_SM values were very low (with R_SM < 0.3) for most sites and moderate (R_SM about 0.5) for OJP, SCC and Ed (see Appendix C). Also, the R_TB were almost always higher than the R_SM for all the sites and for both SMOS and SMAP. This points to remaining uncertainties in SM retrievals due to limitations in the retrieval algorithms.

### C. Other SMAP SM retrievals

We compared SM retrievals from SMOS, SMAP, MT-DCA [51] and MCCA [52] against in situ SM for all study sites (Figure 4). Overall, no correlations (R_SM ≈ 0 or negative) are observed between in situ SM and any of the SM retrievals. Overall, all SM retrievals overestimate SM. The bias values showed slight differences, ranging from 4% for MT-DCA to 14% for SMOS. However similar ubRMSE of approximately 0.3 m3 m-3 were found across all SM retrievals.
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Comparing Pearson's correlation coefficients (R\_TB) computed with in situ soil moisture, SM, and brightness temperature, TB, products from (a) SMOS (25 km) and SMAP (9 km) at two orbits (A: Ascending and D: Descending), (b) SMOS (25 km) and SMAP (36 km) at two polarizations H and V and (c) SMAP (9 km and 36 km) at two polarizations (H: horizontal and V: vertical). Each data point corresponds to the R\_TB obtained from each year and site.</figcaption></figure>

<caption>TABLE III</caption>

THE MEAN PEARSON'S CORRELATION COEFFICIENT BETWEEN TB AND IN SITU SM (R\_TB) FOR ALL SITES, CALCULATED FOR THE DIFFERENT SMOS (25 KM) AND SMAP CONFIGURATIONS (9 KM AND 36 KM): TWO POLARIZATIONS (HORIZONTAL [H] AND VERTICAL [V]) AND TWO ORBITS (A: ASCENDING AND D: DESCENDING). THE STANDARD DEVIATION (STD) IS COMPUTED BETWEEN R\_TB OF ALL SITES.

<table><thead><tr><th rowspan="3">Sensors<br>Configuration</th><th colspan="4">SMOS (25 km)</th><th colspan="4">SMAP (9 km)</th><th colspan="4">SMAP (36 km)</th></tr><tr><th colspan="2">Orbit D</th><th colspan="2">Orbit A</th><th colspan="2">Orbit D</th><th colspan="2">Orbit A</th><th colspan="2">Orbit D</th><th colspan="2">Orbit A</th></tr><tr><th>H</th><th>V</th><th>H</th><th>V</th><th>H</th><th>V</th><th>H</th><th>V</th><th>H</th><th>V</th><th>H</th><th>V</th></tr></thead><tbody><tr><td>R\_TB (all sites)</td><td>-0.46</td><td>-0.44</td><td>-0.44</td><td>-0.42</td><td>-0.56</td><td>-0.55</td><td>-0.53</td><td>-0.52</td><td>-0.57</td><td>-0.56</td><td>-0.53</td><td>-0.53</td></tr><tr><td>STD (all sites)</td><td>0.28</td><td>0.27</td><td>0.28</td><td>0.28</td><td>0.30</td><td>0.29</td><td>0.33</td><td>0.32</td><td>0.27</td><td>0.26</td><td>0.29</td><td>0.28</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Comparison of mean Pearson's correlation coefficients (R\_TB, R\_r and R\_SM) calculated between in situ SM and TB, r and retrieved SM for SMOS (a) and SMAP (b) at each site (detailed in Figure 1).</figcaption></figure>

Across the 16 study sites, large differences in R\_SM, bias and ubRMSE were observed (Appendix D). For example, the highest R\_SM was observed at SCC for SMAP (0.6), followed by MT-DCA and SMOS at about 0.5, while MCCA shows a much lower R\_SM of -0.19 at the same site. The weakest correlations (R\_SM<-0.2) were noted at Fen for MCCA and Uaf for SMOS. Bias values ranged from approximately -0.6 m³

m⁻³ for all SM retrievals at Fen to 0.16 for SMOS at OJP and up to 0.3 for SMAP and MT-DCA at OBS, as well as for MCCA at KJ. Among all SM retrievals, the lowest ubRMSE was found at KJ for SMOS, SMAP, MT-DCA and MCCA (0.03 m³m⁻³). In contrast, the highest ubRMSE was observed at Uaf, reaching up to 0.2 m³m⁻³ across all retrievals.
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Comparison between in situ SM and SM retrievals from SMOS, SMAP, MT-DCA [51] and MCCA [52] for all study sites. Mean Pearson's correlation coefficients (R_SM), Bias and unbiased Root mean Square error (ubRMSE) are provided.</figcaption></figure>

### D. Temporal evolution analysis over illustrative examples

Examples illustrate high (e.g., OJP), moderate (e.g., ChM), and weak correlations (e.g., BJ) based on the R_TB between TB against in situ SM. At OJP, the dynamic range of approximately 35 K in TB was observed with a decrease for in situ SM from 12% to 4.5% as the season progresses (Fig. 5a). OJP yields a strong negative correlation with an R_TB of -0.87 in 2017 (Fig. 5d). The seasonal SM variations seems to drive the TB trends at OJP leading to good TB sensitivity to SM. To determine whether the high performances observed with TB during summer with soil drying trends (a general SM decrease throughout the thawing period) are related to surface temperature, we compared soil reflectivity with in situ SM. At OJP, r varied throughout the thawing period (Fig. 5b), closely following the temporal dynamics of in situ SM. The r displayed a less variable behavior with a strong correlation (R_r) of 0.83, indicating a good consistency between satellite observation and SM dynamics at OJP (Fig. 5b). Regarding SM retrievals, similar trends were observed between SMAP SM and in situ SM at OJP (Fig. 5c) with strong correlations (R_SM of 0.81). However, high bias and RMSE were observed (not exceed 25% [Fig. 5e]).

For ChM, the temporal evolution of SMOS TB was more variable due to the stronger fluctuations in SM (Fig. 6a) as indicated by a moderately negative correlation with R_TB of -0.64 (Fig. 6d). The TB increased by approximately 30 K when SM decreased from 0.54 m³ m⁻³ to 0.22 m³ m⁻³ during the thawed period, followed by a decrease in TB of around 25 K as in situ SM increases from 0.2 to 0.4 m³ m⁻³. Following consecutive rain events, TB generally decreased considerably. We obtained a weak correlation between r and in situ SM with R_r about 0.26 (Fig. 6b), suggesting that the correlation between TB and in situ SM is partly related to surface

temperature variation. Moderate correlations were obtained between SMOS SM and in situ SM (Fig. 6c) with bias equal to 1% and RMSE about 10% (Fig. 6e).

For BJ, we observed low amplitude variations in TB of approximately 15 K, corresponding to minor fluctuations in situ SM of about 0.05 m³ m⁻³ during the thawed period (Fig. 7a). Weak correlations (R_TB) were noted, not exceeding -0.20 (Fig. 7d). Similarly, a weak correlation (R_r equal to 0.13) was obtained between r and in situ SM (Fig. 7b). Regarding SMAP SM, weak agreement was observed against in situ SM (with R_SM < 0.3) whereas low discrepancies were observed between them with Bias of 2% and RMSE about 4% at BJ.

For all illustrative examples (OJP, ChM and BJ), we noted that both SMAP SM and SMOS SM were overestimating in situ SM with positive bias and RMSE. We also noted that TB signals could not always capture spikes in situ SM following rainfall events (Fig. 7). The seasonal soil r and retrieved SM trends have almost no response to rain pulses.

The time series of SMOS SM and SMAP SM were compared to in situ SM for BJ and OBS to highlight temporal variations through years at the site scale (Fig. 8). Both SMOS SM and SMAP SM differ significantly and in terms of in situ SM. The correlations (R_SM) and bias vary through the years at the same location and between the two sensors. SMAP SM consistently overestimated the in situ SM for both sites. The BJ has a much higher in situ SM at the near-surface (around 0.4 m³ m⁻³) and is wetter than the OBS. The performance on BJ varies but is generally poor over 2017–2021. For OBS, the SMOS SM and SMAP SM patterns sometimes capture the summer seasonal drying trend and showed agreements over the six years. Although in some years (2019 and 2021), the performances are much lower despite the visible drying patterns. The high temporal variability appearing between years at BJ and OBS could stem from differences in meteorological conditions like temperature, precipitation, and thawing dates.

## IV. DISCUSSION

### A. Comparison of SMOS SM and SMAP SM

We found a similar correlation between TB and in situ SM for SMOS and SMAP, likely due to their comparable sensor characteristics (frequency, native spatial resolution, revisit, and acquisition time). However, R_TB for SMOS was generally lower than SMAP for most site-years. The difference could stem from SMAP's higher radiometric sensitivity (less than 1 K) compared to SMOS (more than 3 K) [17], [23]. SMAP also incorporates a water bodies correction to TB, which likely contributes to better R_TB correlations compared to SMOS. SMAP efficiently filters the RFI that degrades the quality of TB, whereas RFI appears to significantly contaminate SMOS data [17], [18], [23], [41]. Consistent with previous studies (e.g., [29], [41]), slight differences in results appear when comparing Orbit D and Orbit A, and when comparing polarization H and polarization V.
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Examples for high correlations based on the R_TB between SMAP TB against in situ SM over OJP in 2017. Our data analysis was considered during the thawed period and is represented by the vertical dashed black lines. Blue bars represent the precipitation. (n) is the number of available samples. Correlations that pass the significance test of p-value < 0.05 are considered.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Examples for moderate correlations based on the R_TB between SMAP TB against in situ SM over ChM in 2020. Our data analysis was considered during the thawed period and is represented by the vertical dashed black lines. Blue bars represent the precipitation. (n) is the number of available samples. Correlations that pass the significance test of p-value < 0.05 are considered.</figcaption></figure>
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Examples for low correlations based on the R_TB between SMAP TB against in situ SM over the BJ site in 2021. Our data analysis was considered during the thawed period and is represented by the vertical dashed black lines. Blue bars represent the precipitation. (n) is the number of available samples. Correlations that pass the significance test of p-value < 0.05 are considered.</figcaption></figure>

While Chan et al. [18] and Wrona et al. [43] found that polarization V leads generally to better SMAP SM estimations than polarization H. When comparing SMAP observations at different spatial resolutions (9 km and 36 km), the performances were consistent (Fig. 2c) which aligns with the findings of [26].

The differences between SMOS, SMAP, MT-DCA and MCCA SM retrievals (Fig.4, Table V) might partly stem from their different retrieval algorithms and processing techniques [16], [23], [52]. The SMOS and SMAP inversion algorithms rely on static maps for land use and soil texture, which do not fully capture the dynamic variability of near-surface characteristics such as roughness, cover fractions, vegetation structure, organic matter, and ground cover (e.g., bare ground, lichens, mosses). Different approaches to assess the contribution of vegetation to the recorded SMOS and SMAP signals appear to impact the accuracy of SM retrievals [17]. In contrast to SMAP, which

uses ancillary optical vegetation indices (e.g., normalized difference vegetation Index: NDVI), SMOS uses multi-angular information. Unlike other SMAP-based algorithms, the MCCA approach does not rely on ancillary data to retrieve SM, minimizing dependence on external datasets and reducing the risk of influencing the independence of the simultaneously derived vegetation optical depth (VOD) and SM. Results from [18] and [27] show that SM retrievals from both SMAP and SMOS exhibit considerable differences over forested areas. Also, [18] found that the SMOS products show higher bias and uRMSD than the SMAP algorithm. Differences in subpixel land cover (forest, wetland, and water bodies) obtained within the SMOS and SMAP footprint (Table I) may affect the accuracy of microwave observations and retrievals, particularly in the context of the boreal biome. However, we did not find any significant relationship between the SM retrievals and the water bodies in the SMOS or SMAP pixels.
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

<figure><img src="image_10.png" /></figure>

<table><thead><tr><th rowspan="2">Sensor</th><th colspan="2">2017</th><th colspan="2">2018</th><th colspan="2">2019</th><th colspan="2">2020</th><th colspan="2">2021</th></tr><tr><th>SMAP</th><th>SMOS</th><th>SMAP</th><th>SMOS</th><th>SMAP</th><th>SMOS</th><th>SMAP</th><th>SMOS</th><th>SMAP</th><th>SMOS</th></tr></thead><tbody><tr><td>R SM</td><td>0.1</td><td>0.11</td><td>-0.26</td><td>-0.12</td><td>-0.49</td><td>-0.25</td><td>0.36</td><td>-0.21</td><td>0.26</td><td>-0.2</td></tr><tr><td>Bias</td><td>0.03</td><td>-0.24</td><td>0.03</td><td>-0.24</td><td>0.02</td><td>-0.26</td><td>0.04</td><td>-0.23</td><td>0.02</td><td>-0.26</td></tr></tbody></table>

<figure><img src="image_11.png" /></figure>

<table><caption>**Fig. 8.** Time series of in situ SM against SMOS SM and SMAP SM for (a) BJ (2017–2021) and (b) OBS (2016–2021).</caption><thead><tr><th rowspan="2">Sensor</th><th colspan="2">2016</th><th colspan="2">2017</th><th colspan="2">2018</th><th colspan="2">2019</th><th colspan="2">2020</th><th colspan="2">2021</th></tr><tr><th>SMAP</th><th>SMOS</th><th>SMAP</th><th>SMOS</th><th>SMAP</th><th>SMOS</th><th>SMAP</th><th>SMOS</th><th>SMAP</th><th>SMOS</th><th>SMAP</th><th>SMOS</th></tr></thead><tbody><tr><td>R SM</td><td>0.31</td><td>0.14</td><td>0.59</td><td>0.49</td><td>-0.1</td><td>0.02</td><td>-0.3</td><td>-0.18</td><td>0.35</td><td>0.39</td><td>-0.22</td><td>-0.03</td></tr><tr><td>Bias</td><td>0.31</td><td>0.06</td><td>0.33</td><td>0.06</td><td>0.27</td><td>0.02</td><td>0.3</td><td>0.03</td><td>0.32</td><td>0.06</td><td>0.29</td><td>0.01</td></tr></tbody></table>

## B. Impact of boreal biome SM heterogeneity on SMOS and SMAP performances

Different behaviors of microwave variables (TB, r and SM) as a function of in situ SM were shown between sites and seasons (Table II and Fig. 3). For sites exhibiting strong seasonal SM decreases during the thawed period, TB tends to exhibit strong correlations with the in situ SM (Fig. 5). For OJP and BJ, both SMAP and SMOS SM overestimated the in situ SM with positive bias and RMSE, indicating some limits in the inversion models for the boreal biome. Konings et al. [8] and Peng et al. [52] observed that MT-DCA and MCCA tend to produce “wetter” SM retrievals in boreal regions compared to other SMAP products. By analyzing the relationships between soil reflectivity and in situ SM (Fig. 5b and 6b), we demonstrated that the seasonality of the surface temperature

could affect the variations between TB and in situ SM [42]. Hence, we demonstrated that part of the correlation between TB and in situ SM is related to the seasonal surface temperature variation (Fig. 6b).

During heavy rain events, SMOS and SMAP yield different responses across study sites. The timing of the satellite passes regarding the rain events, along with the duration and intensity of the rain even likely contributed to the heterogeneity within the radiometer footprint (e.g., [12], [18], [26], [42], [44]). Moreover, our analyses revealed that the thawed period must be identified for each site (represented with vertical dashed black lines in Fig. 5 and 6) to avoid the strong TB signal related to the soil freeze/thaw cycles. While freeze/thaw products can provide valuable insights into frozen SM dynamics, we excluded them from this analysis due to persistent challenges in detecting
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

liquid water content during freeze/thaw transitions in boreal
forests (Prince et al., 2019; Roy et al., 2020).

Soil conditions (e.g., moisture, roughness, and texture) and vegetation (e.g., biomass, water content, structure) are key drivers of microwave signal variations and must be considered in SM retrievals ([12], [17], [20]). The attenuation and scattering effects in areas with vegetation pose challenges for SMOS and SMAP SM ([9], [12], [21], [22], [28]). SMOS and SMAP retrieval algorithms use a two-parameter optimization (SM-vegetation optical depth (VOD) approach [20]). In such approaches, changes of VOD in the inversion can compensate TB variations that could be related to SM. Moreover, the boreal biome is composed of a complex multi-layer soil-vegetation-atmosphere continuum with trees, understory, ground cover (e.g., bare ground, lichen, *Sphagnum* mosses), etc. and the simple two-layer soil-vegetation radiative transfer model might underestimate the complexity of the microwave interaction in such structurally complex environments [14], [21]. The complex soil and vegetation conditions of the boreal biome warrant refinement in SM retrievals [45]. Konings et al. [8] and Peng et al. [52] emphasized the critical role of vegetation parametrization in improving the accuracy of SM retrievals in forested areas.

C. Challenges of SM retrievals in boreal regions

SMAP and SMOS inversion algorithms use the commonly
known tau-omega model based on radiative transfer equations
to retrieve simultaneously SM and VOD from TB [15], [20].
This microwave emission model relates TB observations
(SMAP or SMOS L1) to SM or VOD retrievals through
ancillary information (soil texture, soil temperature, surface
roughness, and vegetation water content) and a soil dielectric
model [19]. Our study demonstrated that R_TB values are
generally higher than the R_SM for all sites and for both sensors
(SMOS and SMAP), suggesting that current retrieval
algorithms introduce large uncertainties in SM retrievals.
Comparisons of MT-DCA and MCCA SM retrievals with in
situ measurements led to similar results, with low correlations
R_SM, consistent with those obtained from SMAP and SMOS.
We emphasize the need to improve SMOS and SMAP radiative
transfer models in boreal regions by optimizing radiative
transfer model key parameters such as the roughness and
vegetation attenuation related parameters. The SMOS and
SMAP algorithms consider generalized scattering coefficients
(ω) for vegetation, with values such as 0.06-0.08 for forests
[19], [38]. Boreal-specific (ω) values could enhance the
accuracy in capturing the unique canopy structure and
scattering characteristics of this biome. Also, considering the
effect of the litter and organic layers in the soil dielectric
models, which is currently excluded, might allow improving
SM retrievals [19], [45]. Della Vecchia et al. [54] demonstrated
that including the litter as a dielectric layer in a radiative
transfer model significantly influenced the overall emissivity of
coniferous forest at L-band. Chen et al. [42] and Ambadan et al.
[29] emphasized the importance of reliable ancillary data,
particularly effective soil or vegetation temperatures in retrieval

algorithms. Ambadan et al. [29] could improve the SM
accuracy in southern boreal plain by optimizing vegetation and
soil parameters.

One of the difficulties in estimating the sources of
uncertainties of SMOS, SMAP, MT-DCA and MCCA SM
inversion algorithms regards the spatial resolution (about 40 km
on average), associated with the presence of mixed land and
open water pixels [12], [17], [46]. In L-band radiometer
inversion algorithms, considering the contribution of the
dominant land use within the corresponding footprint can lead
to high discrepancies in the retrievals [14], [21]. Effectively
managing heterogeneous pixels, especially in high-latitude
regions, is challenging in the retrieval process [16], [17], [21].
The boreal biome has extensive coverage of open and abundant
small water bodies [1], [2], [5], [6]. As demonstrated by Du et
al. [47], the use of the NASA Terra Moderate Resolution
Imaging Spectroradiometer (MODIS) Land Water Mask
(MOD44W) improved SMAP SM in high latitudes in the
Northern Hemisphere. Like SMAP, TB SMOS data must
undergo a water body correction to enhance the SM retrieval
accuracy in boreal regions. Note that SMOS SM/VOD
retrievals include water contributions in terms of emissions,
using the water dielectric constant and water temperatures from
ECMWF [38]. Yang et al. [53] simulated the TB of water
bodies using a radiative transfer model to correct water
contamination in SMAP products over areas with dense lake
coverage. This approach improved SMAP SM retrievals in the
Qinghai-Tibet Plateau in China, a region with 1,268 lakes larger
than 1 km², and could provide valuable insights for addressing
similar challenges in boreal regions.

The distortion of the global projection of the EASE v2 grid at high latitudes is one of the issues of SMAP SM in boreal regions [23] and [43]. In the Canadian Arctic tundra, Wrona et al. [43] showed a much stronger relationship between the in situ SM and TB SMAP L2 (36 km) data interpolated to the north polar grid. However, in the southern Canadian boreal forest, Ambadan et al. [29] concluded that no significant difference exists between the global and the polar grid on the optimization or the SM retrieval at the site (latitude of 53.94 °).

D. Limitations of the study

As noted by Colliander et al. [22] and Wrona et al. [43], using
a single-point SM measurement location within the SMOS or
SMAP footprint can result in errors and a lack of
representativeness. They added that reliable ground SM
measurements are necessary for the calibration and the
validation of SM inversion algorithms from radiometers. Given
the coarse radiometer resolution, about twenty SM
measurement locations were suggested to adequately capture
SM variability [12], [23], [46].

Different types of SM probes (Table I) and installation
methods are used for the studied sites which can generate
differences or biases in the in situ SM measurements. For
example, the CS616 probes used in the ChSb, ChP and ChM
sites were installed from the near-surface at an angle so that the
length of the probe is integrated in its measurement over a given
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

depth (10 or 18 cm). Consequently, SMOS and SMAP performances obtained in our studied sites might be impacted by the SM probes and the calibration approaches. Additionally, spatial representativeness of SM probes within the large footprint of SMOS and SMAP observations can introduce uncertainties in SM evaluation. The layer of the soil profile in which the probes are placed, whether it is the organic or the mineral layer, can also impact the accuracy of SM measurements. This is particularly important in the boreal biome where the organic matter when the thickness of a soil varies [1], [6], [45]. However, Ambadan et al. [29] found that the apparent emissivity has a stronger correlation coefficient with the in situ SM measurements in the mineral layer (about -0.6) than in the organic layer (about -0.4) over one site in the Canadian boreal forest. They suggest that the majority of the SMAP TB signals are associated with the mineral layer, rather than the organic layer. However, the organic layers in their study were rather shallow (10 cm or less).

V. CONCLUSION

We analyzed the sensitivity of SMOS and SMAP TB, r, and SM against in situ SM from across 16 study sites in the North American boreal biome. We demonstrated strong spatial and temporal variations in correlations (R_TB, R_r, and R_SM) for the 16 study sites. The best performance in terms of R_TB was obtained with sites with high seasonal soil drying trends. We also showed considerable discrepancies in R_SM performances between the sites and the two radiometers (SMOS and SMAP). Further work should explore how the boreal biome's different factors (water bodies, vegetation density, soil type, etc.) affect the SMOS and SMAP observations. We could not identify precisely what limits the SM retrievals in the boreal context.

Further analyses and sensitivity tests should be conducted in the boreal biome considering, for example, soil roughness, organic matter, vegetation attenuation, lichens, and moss effects to have better insight into the reasons behind the observed findings. Also, to improve SM retrievals in boreal biomes we recommend:

1) to expand SM observations and make data available in the global SM network [48]. This will help extend the validity domain of the passive microwave retrieval algorithms over the boreal biome;

2) to improve the radiative transfer model or develop an algorithm for the boreal biome to resolve the issues of mixed land and open water cover;

3) to synergize passive and active microwave observations. Also, improved spatial resolution of L-Band radiometers

could help disentangle the contribution of forests, wetlands and waterbodies and help improve the inversion in these heterogeneous ecosystems.

Future studies should explore the relationships between VOD products as a function of tree hydraulics.

APPENDIX A

The statistical Pearson's Correlation Coefficient (R) was computed for each variable as

$$
R_x = \sum \frac{[(x_{sat} - \bar{x}_{sat}) * (SM_{site} - \bar{S}_{site})]}{\sigma_{sat} \sigma_{site}} \quad (1)
$$

with x corresponding to the variables: TB, r and SM; x̄ is the mean of x variable, σ is the variance of the variable (x). For each site, the nearest neighbor method allowed determining the closest SMAP and SMOS pixel.

APPENDIX B

According to Alexander [40], the R coefficients are not
additive and should not be averaged directly. Instead, he
recommends computing the average using the following
formula:

- Transform R_x to Fisher's z-score (z):

$$
z = 0.5 \log \left( \frac{1 + R_x}{1 - R_x} \right) = \tanh^{-1} (R_x) \quad (2)
$$

- Compute $\bar{z}$:

$$
\bar{z} = \frac{\sum (n - 3)z}{\sum n - 3k} \quad (3)
$$

with n the number of samples used to compute (R_x) and k the
number of (R_x) to average

- Transform $\bar{z}$ to $\bar{R}_x$:

$$
\bar{R}_x = \frac{e^{2\bar{z}} - 1}{e^{2\bar{z}} + 1} = \tanh(\bar{z}) \quad (4)
$$

APPENDIX C

Table IV summarizes the average annual correlations for microwave-derived variables (R_TB, R_r and R_SM) against in situ SM for SMOS and SMAP, respectively, at each site.

<caption>TABLE IV</caption>

PEARSON'S CORRELATION COEFFICIENTS (R_TB, R_r and R_SM) OBTAINED BETWEEN IN SITU SM AND TB, R AND RETRIEVED SM FOR SMOS AND SMAP, FOR ALL THE STUDIED SITES. THE VALUES IN BRACKETS ARE THE STANDARD DEVIATION (STD) OF R_TB, R_r and R_SM BETWEEN THE YEARS PER SITE. THE SIGNIFICANT VALUES IN TERMS OF P > 0.05 APPEAR IN BOLD. THE NUMBER OF YEARS PER SITE (NB_Y) APPEARS IN BRACKETS.

<table>
  <thead>
    <tr>
      <th rowspan="2">Sites (Nb_y)</th>
      <th colspan="3">SMOS</th>
      <th colspan="3">SMAP</th>
    </tr>
    <tr>
      <th>R_TB</th>
      <th>R_r</th>
      <th>R_SM</th>
      <th>R_TB</th>
      <th>R_r</th>
      <th>R_SM</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>BJ (5)</td>
      <td>-0.20 (0.15)</td>
      <td>-0.15 (0.13)</td>
      <td>-0.01 (0.15)</td>
      <td>-0.35 (0.16)</td>
      <td>0.05 (0.23)</td>
      <td>-0.02 (0.32)</td>
    </tr>
  </tbody>
</table>
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

<table><tr><td>Fen (6)</td><td><b>-0.62 (0.22)</b></td><td>0.13 (0.29)</td><td>-0.04 (0.11)</td><td><b>-0.68 (0.19)</b></td><td>0.05 (0.32)</td><td>-0.04 (0.31)</td></tr><tr><td>FMJ (4)</td><td>-0.38 (0.21)</td><td>0.14 (0.08)</td><td>0.15 (0.07)</td><td><b>-0.64 (0.23)</b></td><td><b>0.47 (0.15)</b></td><td>0.31 (0.20)</td></tr><tr><td>KJ (3)</td><td>-0.37 (0.12)</td><td>-0.10 (0.14)</td><td>0.03 (0.21)</td><td>-0.34 (0.23)</td><td>0.09 (0.24)</td><td>0.30 (0.12)</td></tr><tr><td>OBS (6)</td><td>-0.51 (0.22)</td><td>0.19 (0.33)</td><td>0.16 (0.24)</td><td><b>-0.65 (0.18)</b></td><td>0.40 (0.32)</td><td>-0.04 (0.25)</td></tr><tr><td>OJP (6)</td><td><b>-0.59 (0.20)</b></td><td><b>0.48 (0.22)</b></td><td>0.37 (0.23)</td><td><b>-0.69 (0.21)</b></td><td><b>0.60 (0.21)</b></td><td><b>0.52 (0.23)</b></td></tr><tr><td>SCC (6)</td><td><b>-0.71 (0.08)</b></td><td>0.61 (0.27)</td><td><b>0.52 (0.30)</b></td><td><b>-0.78 (0.13)</b></td><td><b>0.64 (0.35)</b></td><td><b>0.60 (0.34)</b></td></tr><tr><td>Uaf (4)</td><td>0.08 (0.20)</td><td>-0.09 (0.15)</td><td>-0.23 (0.21)</td><td>0.08 (0.25)</td><td>-0.02 (0.20)</td><td>0.11 (0.23)</td></tr><tr><td>SMC (3)</td><td>0.07 (0.10)</td><td>0.02 (0.19)</td><td>-0.06 (0.21)</td><td>0.06 (0.05)</td><td>0.24 (0.12)</td><td><b>0.35 (0.04)</b></td></tr><tr><td>KPC (4)</td><td><b>-0.64 (0.15)</b></td><td><b>0.48 (0.17)</b></td><td>-0.00 (0.05)</td><td><b>-0.73 (0.13)</b></td><td><b>0.59 (0.22)</b></td><td>0.32 (0.38)</td></tr><tr><td>ChM (5)</td><td><b>-0.64 (0.07)</b></td><td>0.30 (0.11)</td><td>0.26 (0.23)</td><td><b>-0.74 (0.07)</b></td><td><b>0.42 (0.05)</b></td><td>-0.14 (0.17)</td></tr><tr><td>ChP (5)</td><td><b>-0.68 (0.13)</b></td><td>0.33 (0.14)</td><td>0.22 (0.12)</td><td><b>-0.77 (0.12)</b></td><td><b>0.56 (0.09)</b></td><td>-0.19 (0.17)</td></tr><tr><td>ChS (5)</td><td><b>-0.62 (0.10)</b></td><td>0.29 (0.11)</td><td>0.31 (0.16)</td><td><b>-0.76 (0.11)</b></td><td><b>0.59 (0.13)</b></td><td>-0.04 (0.12)</td></tr><tr><td>REC (3)</td><td><b>-0.28 (0.09)</b></td><td>0.24 (0.14)</td><td>0.26 (0.19)</td><td><b>-0.41 (0.19)</b></td><td>0.33 (0.17)</td><td>0.23 (0.25)</td></tr><tr><td>Dr (3)</td><td><b>-0.63 (0.16)</b></td><td><b>0.39 (0.16)</b></td><td><b>0.34 (0.10)</b></td><td><b>-0.71 (0.10)</b></td><td><b>0.50 (0.23)</b></td><td>-0.03 (0.07)</td></tr><tr><td>Ed (3)</td><td><b>-0.17 (0.36)</b></td><td><b>0.37 (0.19)</b></td><td><b>0.38 (0.10)</b></td><td><b>-0.27 (0.40)</b></td><td><b>0.61 (0.10)</b></td><td><b>0.56 (0.07)</b></td></tr></table>

APPENDIX D

Table V presentes the statistics (R_SM, Bias and ubRMSE) obtained between in situ SM against different SM retrievals derived from (SMOS, SMAP, MT-DCA and MCCA), respectively, at each site.

<table><caption>TABLE V
PEARSON'S CORRELATION COEFFICIENTS (R_SM), BIAS AND UNBIASED ROOT MEAN SQUARE ERROR (UBRMSE) COMPUTED BETWEEN IN SITU SM AND RETRIEVED SM FOR SMOS, SMAP, MT-DCA AND MCCA AT EACH STUDIED SITE. THE NUMBER OF YEARS PER SITE (NB_Y) APPEARS IN BRACKETS.</caption><thead><tr><th rowspan="2">Sites (Nb_y)</th><th colspan="4">R_SM</th><th colspan="4">Bias (m³ m⁻³)</th><th colspan="4">ubRMSE(m³ m⁻³)</th></tr><tr><th>SMOS</th><th>SMAP</th><th>MT-DCA</th><th>MCCA</th><th>SMOS</th><th>SMAP</th><th>MT-DCA</th><th>MCCA</th><th>SMOS</th><th>SMAP</th><th>MT-DCA</th><th>MCCA</th></tr></thead><tbody><tr><td>BJ (5)</td><td>-0.01</td><td>-0.02</td><td>0.02</td><td>-0.06</td><td>-0.24</td><td>0.03</td><td>-0.14</td><td>-0.06</td><td>0.05</td><td>0.04</td><td>0.04</td><td>0.04</td></tr><tr><td>Fen (6)</td><td>-0.04</td><td>-0.04</td><td>-0.03</td><td>-0.28</td><td>-0.61</td><td>-0.59</td><td>-0.56</td><td>-0.61</td><td>0.15</td><td>0.08</td><td>0.08</td><td>0.05</td></tr><tr><td>FMJ (4)</td><td>0.15</td><td>0.31</td><td>0.15</td><td>0.18</td><td>0.06</td><td>0.13</td><td>0.17</td><td>0.02</td><td>0.11</td><td>0.08</td><td>0.10</td><td>0.07</td></tr><tr><td>KJ (3)</td><td>0.03</td><td>0.30</td><td>0.29</td><td>0.24</td><td>0.08</td><td>0.24</td><td>0.11</td><td>0.29</td><td>0.06</td><td>0.03</td><td>0.03</td><td>0.03</td></tr><tr><td>OBS (6)</td><td>0.16</td><td>-0.04</td><td>-0.11</td><td>-0.02</td><td>0.04</td><td>0.29</td><td>0.30</td><td>0.14</td><td>0.10</td><td>0.08</td><td>0.08</td><td>0.05</td></tr><tr><td>OJP (6)</td><td>0.37</td><td>0.52</td><td>0.18</td><td>0.12</td><td>0.16</td><td>0.23</td><td>0.29</td><td>0.18</td><td>0.11</td><td>0.07</td><td>0.07</td><td>0.04</td></tr><tr><td>SCC (6)</td><td>0.52</td><td>0.60</td><td>0.53</td><td>-0.19</td><td>0.02</td><td>0.00</td><td>-0.08</td><td>-0.01</td><td>0.12</td><td>0.08</td><td>0.09</td><td>0.09</td></tr><tr><td>Uaf (4)</td><td>-0.23</td><td>0.11</td><td>0.13</td><td>0.12</td><td>-0.29</td><td>-0.34</td><td>-0.19</td><td>-0.29</td><td>0.21</td><td>0.24</td><td>0.24</td><td>0.24</td></tr><tr><td>SMC (3)</td><td>-0.06</td><td>0.35</td><td>0.41</td><td>0.24</td><td>-0.36</td><td>-0.46</td><td>-0.41</td><td>-0.33</td><td>0.10</td><td>0.10</td><td>0.10</td><td>0.11</td></tr><tr><td>KPC (4)</td><td>0.00</td><td>0.32</td><td>0.18</td><td>-0.16</td><td>-0.06</td><td>0.14</td><td>0.11</td><td>0.12</td><td>0.07</td><td>0.06</td><td>0.05</td><td>0.04</td></tr><tr><td>ChM (5)</td><td>0.26</td><td>-0.14</td><td>0.07</td><td>0.00</td><td>-0.01</td><td>0.17</td><td>0.08</td><td>0.04</td><td>0.10</td><td>0.07</td><td>0.09</td><td>0.07</td></tr><tr><td>ChP (5)</td><td>0.22</td><td>-0.19</td><td>0.15</td><td>0.03</td><td>0.04</td><td>0.27</td><td>0.13</td><td>0.11</td><td>0.11</td><td>0.06</td><td>0.08</td><td>0.07</td></tr><tr><td>ChS (5)</td><td>0.31</td><td>-0.04</td><td>0.15</td><td>0.04</td><td>-0.01</td><td>0.23</td><td>0.08</td><td>0.05</td><td>0.10</td><td>0.07</td><td>0.08</td><td>0.07</td></tr><tr><td>REC (3)</td><td>0.26</td><td>0.23</td><td>0.07</td><td>-0.06</td><td>-0.01</td><td>0.00</td><td>0.13</td><td>-0.02</td><td>0.11</td><td>0.08</td><td>0.08</td><td>0.06</td></tr><tr><td>Dr (3)</td><td>0.34</td><td>-0.03</td><td>0.09</td><td>-0.07</td><td>-0.09</td><td>0.17</td><td>0.11</td><td>0.09</td><td>0.12</td><td>0.09</td><td>0.10</td><td>0.11</td></tr><tr><td>Ed (3)</td><td>0.38</td><td>0.56</td><td>0.39</td><td>0.37</td><td>-0.17</td><td>-0.15</td><td>-0.07</td><td>-0.18</td><td>0.10</td><td>0.06</td><td>0.08</td><td>0.06</td></tr></tbody></table>
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

ACKNOWLEDGMENTS

The authors would like to thank Warren Helgason and Johnson Bruce for providing the Boreal Ecosystem Research and Monitoring Sites (BERMS) data. Part of in situ SM and meteorological data presented in this study are available in https://zenodo.org/records/10064750 SMOS data was obtained from the “Centre Aval de Traitement des Données SMOS” (CATDS), operated for the CNES (France) by IFREMER (Brest, France). The CATDS-PDC L3TB—Global polarised brightness temperature product from SMOS satellite. CATDS (CNES, IFREMER, CESBIO) http://dx.doi.org/10.12770/6294e08c-baec-4282-a251-33fee22ec67f. The SM SMOS products were available from: CATDS-PDC L3SM Filtered—1-day global map of soil moisture values from SMOS satellite. CATDS (CNES, IFREMER, CESBIO) http://dx.doi.org/10.12770/9cef422f-ed3f-4090-9556-b2e895ba2ca8 SMAP L3 TB and SM products were downloaded from the National Snow and Ice Data Centre (NSIDC). The data was freely available at https://nsidc.org/data/SMAP/SMAP-data.html

REFERENCES

[1] J. P. Brandt, M. D. Flannigan, D. G. Maynard, I. D. Thompson and W. J. A. Volney, "An introduction to Canada's boreal zone: Ecosystem processes, health, sustainability, and environmental issues1," *Environ. Rev.* vol.21, pp. 207-226, Sept. 2013, doi.org/10.1139/er-2013-0040

[2] S. Gauthier, P. Bernier, T. Kuuluvainen, A. Z. Shvidenko and D. G. Schepaschenko, "Boreal forest health and global change," Science, vol. 349, no. 6250, Aug. 2015, pp. 819-822, doi.org/10.1126/science.aaa9092.

[3] J. Wang, A.R. Taylor and L. D'Orangeville, "Warming-induced tree growth may help offset increasing disturbance across the Canadian boreal forest," *Proc. Nat. Acad. Sci.*, vol. 120, no.2, pp. 1-10, Jan 2023, doi:10.1073/pnas.2212780120

[4] G.B. Bonan. "Forests and climate change: forcings, feedbacks, and the climate benefits of forests," *Science*, vol.320, no.5882, Jun. 2008 pp. 1444-1449. doi.org/10.1126/science.1155121

[5] A.C. Foster et al., "Disturbances in North American boreal forest and Arctic tundra: impacts, interactions, and responses," *Environ. Res. Lett.*, vol., 17, no 11, Oct 2022, doi:10.1088/1748-9326/ac98d7

[6] W.A. Kurz, C.H. Shaw, C. Boisvenue, G. Stinson, J. Metsaranta, D. Leckie, A. Dyk, C. Smyth, E.T. Neilson, "Carbon in Canada's boreal forest—a synthesis," *Environ. Rev.*, vol. 21, no. 4, pp. 260-292, 2013, doi: org/10.1139/er-2013-0041

[7] G. G. Katul, R. Oren, S. Manzoni, C. Higgins, and M. B. Parlange, "Evapotranspiration: a process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system," *Rev. Geophys.* vol. 50, no. 3, pp. 1-25, Jul. 2012, doi:10.1029/2011RG000366

[8] A.G. Konings, M. Piles, N. Das, and D. Entekhabi, "L-band vegetation optical depth and effective scattering albedo estimation from SMAP," *Remote Sens. Environ.* Sept. 2017, pp. 460-70. doi.org/10.1016/j.rse.2017.06.037

[9] A.G. Konings, K. Rao, and S.C. Steele-Dunne, "Macro to micro: microwave remote sensing of plant water content for physiology and ecology," *New Phytol*, 223: 1166-1172. 2019, doi.org/10.1111/nph.15808

[10] A. Colliander et al., "Validation of SMAP surface soil moisture products with core validation sites," *Remote Sens. Environ.* Mar. 2017 15; 191:215-31. doi.org/10.1016/j.rse.2017.01.021

[11] A. Gorrab, M. Zribi, N. Baghdadi, B.Mougenot, P. Fanise, and Z. Lili Chabaane, "Retrieval of Both Soil Moisture and Texture Using TerraSAR-X Images," *Remote Sens.*, 2015, vol. 7, no. 8, pp 10098-10116. doi.org/10.3390/rs70810098

[12] R. Magagi, Y. Kerr, J.-P. Wigneron, "Estimation of soil Water conditions using passive microwave remote sensing," in: *Land Surface Remote Sensing in Continental Hydrology*. Elsevier, pp.41-78. 2016, doi.org/10.1016/B978-1-78548-104-8.50002-4

[13] Y.H. Kerr et al., "Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation," Remote Sens. Environ. 2016 Jul 1;180: pp 40-63. doi.org/10.1016/j.rse.2016.02.042

[14] F., Frappart et al., "Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review," Remote Sens. 2020, 12, 2915. doi.org/10.3390/rs12182915

[15] A. Mavrovic, O. Sonnentag, J. Lemmetyinen, J.L. Baltzer, C. Kinnard and A.Roy. "Reviews and syntheses: Recent advances in microwave remote sensing in support of terrestrial carbon cycle science in Arctic-boreal regions," *Biogeosciences*. vol. 20, no. 14, Jul. 21, 2023, pp. 2941-2970, doi.org/10.5194/bg-20-2941-2023

[16] Y.H. Kerr, R. P. Waldteufel, J.P.Wigneron, P. Ferrazzoli, A. Mahmoodi, A. Al Bitar, F. Cabot, C. Gruhier, SE. Juglea, D. Leroux, A. Mialon, and S. Delwart. "The SMOS soil moisture retrieval algorithm," *IEEE Trans.Geosci. Remote Sens.*, Feb. 2012 29:50(5):1384-403. doi 10.1109/TGRS.2012.2184548

[17] Y. H. Kerr, J.P. Wigneron, A. Al Bitar, A. Mialon and P. K. Srivastava, "Soil Moisture from Space. Satellite Soil Moisture Retrieval," pp. 3-27. 2016. doi:10.1016/b978-0-12-803388-3.00001-2

[18] S. K. Chan et al., "Assessment of the SMAP Passive Soil Moisture Product," *IEEE GRSL*, vol. 54, no. 8, pp. 4994-5007, Aug. 2016, doi: 10.1109/TGRS.2016.2561938

[19] P. E. O'Neill, S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, and J. Chaubell, "SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 4," Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. 2020, doi.org/10.5067/NJ34TQ2LFE90 [01-07-2023].

[20] J.P. Wigneron et al., "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 Sens. Environ.* 2017 Apr 1; 192:238-62. doi.org/10.1016/j.rse.2017.01.024

[21] A.G. Konings, S.S. Saatchi, C. Frankenberg, M. Keller, V. Leshyk, Anderegg WR, Humphrey V, Matheny AM, Trugman A, Sack L, Agee E, "Detecting forest response to droughts with global observations of vegetation water content," *Glob. Change Biol*. Dec. 2021 ;27(23):6005-24. doi.org/10.1111/gcb.15872

[22] A. Colliander, M.H. Cosh, V.R. Kelly, S. Kraatz, L. Bourgeau-Chavez, P. Siqueira, A. Roy, A.G. Konings, N.Holtzman, Misra S, Entekhabi D, "SMAP detects soil moisture under temperate forest canopies," *Geophys. Res. Lett*. Oct. 2020 16:47(19). doi: org/10.1029/2020GL089697

[23] A. Colliander, R. Reichle, W. Crow, M. Cosh, F. Chen, et al., "Validation of Soil Moisture Data Products From the NASA SMAP Mission," *IEEE JSTARS*, Jan. 2022, 15, pp. 364-92, doi: 10.1109/JSTARS.2021.3124743

[24] T.J. Jackson, M.H. Cosh, R. Bindlish, P.J. Starks, D.D.Bosch, M. Seyfried, D.C.Goodrich, M.S. Moran, J.Y. Du, "Validation of advanced microwave scanning radiometer soil moisture products," *IEEE Trans. Geosci. Remote Sens.*, 2010, 48, pp 4256-4272. doi:10.1109/tgrs.2010.2051035

[25] X. Zhang, T. Zhang, P. Zhou, Y. Shao, S. Gao, "Validation analysis of SMAP and AMSR2 soil moisture products over the United States using ground-based measurements," *Remote Sen*. Jan. 2017 25:9(2):104. doi.org/10.3390/rs9020104

[26] G. Portal, T. Jagdhuber, M. Vall-llossera, A. Camps, M. Pablos, D. Entekhabi, M. Piles, "Assessment of multi-scale SMOS and SMAP soil moisture products across the Iberian Peninsula," *Remote Sens*. Feb.2020 8:12(3):570. doi.org/10.3390/rs12030570

[27] B. G. Mousa and H. Shu, "Spatial evaluation and assimilation of SMAP, SMOS, and ASCAT satellite soil moisture products over Africa using statistical techniques," *Earth and Space Science*,7, 2020, doi: org/10.1029/2019EA000841

[28] R. Rahmoune, P. Ferrazzoli, Y.K. Singh, Y. H.Kerr, P. Richaume and A. Al Bitar, "SMOS Retrieval Results Over Forests: Comparisons With Independent Measurements," *IEEE JSTARS*, 2014. 7(9), 3858-3866. doi:10.1109/jstars.2014.2321027

[29] J.T. Ambadan, H.C.MacRae, A. Colliander, E. Tetlock, W.Helgason, Z.E. Gedalof, A.A. Berg, Evaluation of SMAP Soil Moisture Retrieval Accuracy Over a Boreal Forest Region. *IEEE Trans.Geosci. REMOTE SENS*. Oct. 2022; 60:1-1.

[30] A. Mialon, N.J. Rodríguez-Fernández, M. Santoro, S. Saatchi, S. Mermoz, E. Bousquet, Y.H.Kerr, "Evaluation of the Sensitivity of SMOS L-VOD to Forest Above-Ground Biomass at Global Scale," Remote Sens. 2020; 12(9):1450. doi.org/10.3390/rs12091450

[31] M. Prince, A. Roy, A. Royer, A. Langlois, Timing and spatial variability of fall soil freezing in boreal forest and its effect on SMAP L-band
> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

radiometer measurements. *Remote Sens. Environ.* Sep.2019 15; 231:111230. doi.org/10.1016/j.rse.2019.111230

[32] B. Hadiwijaya, S.Pepin, P.-E.Isabelle, D.F. Nadeau, "The Dynamics of Transpiration to Evapotranspiration Ratio under Wet and Dry Canopy Conditions in a Humid Boreal Forest," *Forests*, 2020, 11, 237. doi.org/10.3390/f11020237

[33] C.C. Hanes, M. Wotton, L. Bourgeau-Chavez, D.G. Woolford, S. Bélair, D. Martell, M.D. Flannigan, "Evaluation of new methods for drought estimation in the Canadian Forest Fire Danger Rating System," *IJWF*. 6 Apr.2023 doi.org/10.1071/WF22112

[34] A.G. Barr, G. Van der Kamp, T.A. Black, J.H. McCaughey, Z. Nesic, "Energy balance closure at the BERMS flux towers in relation to the water balance of the White Gull Creek watershed 1999–2009," *Agric. For. Meteorol.* Feb 2012; vol. 153:pp.3-13. doi.org/10.1016/j.agrformet.2011.05.017

[35] M. El-Amine, A. Roy, F. Koebsch, Baltzer JL, A. Barr, A. Black, Ikawa H, Iwata H, Kobayashi H, Ueyama M, Sonnentag O, "What explains the year-to-year variation in growing season timing of boreal black spruce forests?" *Agric. For. Meteorol.* 2022 Sep 15; 324:109113. doi.org/10.1016/j.agrformet.2022.109113

[36] Qu. Bo, A. Roy, J.R. Melton, T.A. Black, B. Amiro, E.S. Euskirchen, M. Ueyama, H. Kobayashi, C. Schulze, G.H. Gosselin, A.J. Cannon, "A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC)," *Environ. Res Lett.* 2023 Jul 18;18(8):085002. doi 10.1088/1748-9326/ace376

[37] Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi: 10.24381/cds.006f2c9a (Accessed on 01-07-2023).

[38] A. Al Bitar, A. Mialon, Y.H. Kerr, F. Cabot, P. Richaume, E. Jacquette, A. Quesney, A. Mahmoodi, S. Tarot, M. Parrens, A. Al-Yaari, "The global SMOS Level 3 daily soil moisture and brightness temperature maps," *Earth Syst. Sci. Data*. 2017 Jun 6;9(1):293-315. doi.org/10.5194/essd-9-293-2017

[39] U.S. National Ice Center. 2008, updated daily. IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. doi.org/10.7265/N52R3PMC. [29-03-2023].

[40] R. A. Alexander, "A note on averaging correlations," Bulletin of the Psychonomic Society. Oct. 1990; vol. 28, no., 4, pp. 335-6, doi.org/10.3758/BF03334037

[41] R. Oliva, E. Daganzo, P. Richaume, Y. Kerr, F. Cabot, Y. Soldo, E Anterrieu, N Reul, A Gutierrez, J Barbosa, G Lopes, "Status of Radio Frequency Interference (RFI) in the 1400–1427 MHz passive band based on six years of SMOS mission," *Remote Sens. Environ.* 2016 Jul 1; 180:64-75. doi.org/10.1016/j.rse.2016.01.013

[42] Q. Chen, J Zeng, C Cui, Z Li, KS Chen, Bai X, Xu J. "Soil Moisture Retrieval From SMAP: A Validation and Error Analysis Study Using Ground-Based Observations Over the Little Washita Watershed," *IEEE Trans.Geosci. Remote Sens.*, vol. 56, no. 3, pp. 1394-1408, March 2018, doi: 10.1109/TGRS.2017.2762462

[43] E. Wrona, T. L. Rowlandson, M. Nambiar, A. A. Berg, A. Colliander, and P. Marsh, "Validation of the Soil Moisture Active Passive (SMAP) satellite soil moisture retrieval in an Arctic tundra environment," *Geophys. Res. Lett.*, 44, 4152–4158, 2017, doi:10.1002/2017GL072946

[44] A. Colliander et al., "Effect of rainfall events on SMAP radiometer-based soil moisture accuracy using core validation sites," *JHM*. Feb. 2020, 21no. 2, pp. 255-64. doi.org/10.1175/JHM-D-19-0122.1

[45] A. Colliander, M.H. Cosh, A. Berg, S. Misra, J. Thomas, Bourgeau-Chavez L, Kelly V, Kraatz S, Siqueira P, Roy A, Helgason W, "Development of SMAP Retrievals for Forested Regions: SMAPVEX19-22 and SMAPVEX22-Boreal," in *Proc. IGARSS, IEEE International Geosc.and Remote Sens.* Symposium pp. 4228-4231. 2022 Jul 17.

[46] J. S. Famiglietti, D. Ryu, A. A. Berg, M. Rodell, and T. J. Jackson, "Field observations of soil moisture variability across scales," *Water Resour. Res.*, 44, W01423, 2008, doi:10.1029/2006WR005804

[47] J. Du et al., "Assessment of Surface Fractional Water Impacts on SMAP Soil Moisture Retrieval," in *IEEE JSTARS*, vol. 16, pp. 4871-4881, 2023, doi: 10.1109/JSTARS.2023.3278686

[48] W. Dorigo et al., "The International Soil Moisture Network: serving Earth system science for over a decade," *Hydrol. Earth Syst. Sci.*, 25, 5749–5804, 2021, doi.org/10.5194/hess-25-5749-2021.

[49] S. Gruber, "Derivation and analysis of a high-resolution estimate of global permafrost zonation," *The Cryosphere*, 6 pp 221–33, 2012. doi.org/10.5194/tc-6-221-2012

[50] Ed. Wiken, F. J. Nava, and G. Griffith, "North American Terrestrial Ecoregions—Level III," *Commission for Environmental Cooperation, Montreal, Canada*, May 2011, 149p.

[51] Feldman A., Konings A., Piles M., and Entekhabi D. "The Multi-Temporal Dual Channel Algorithm (MT-DCA) (Version 5)". Zenodo, oct.19, 2021 doi.org/10.5281/zenodo.5619583

[52] Peng, Z., Zhao, T., Shi, J., Hu, L., Rodríguez-Fernández, N. J., Wigneron, J. P., and Wei, Z. "First mapping of polarization-dependent vegetation optical depth and soil moisture from SMAP L-band radiometry". *Remote Sens. Environ.*, 302, 113970, 2024. doi.org/10.1016/j.rse.2023.113970

[53] Yang, Q., Zhao, T., Shi, J., Peng, Z., & Ye, C. "A simulation-based approach for removing the effect of water contamination on SMAP soil moisture retrieval over the Qinghai-Tibet Plateau". *Remote Sens. Lett.*, 12(8), 757–767, 2021. doi.org/10.1080/2150704X.2021.1936266

[54] Della Vecchia, A., P. Ferrazzoli, J. P. Wigneron, and J. P. Grant. "Modeling forest emissivity at L-band and a comparison with multitemporal measurements". *IEEE Geosci. Remote Sens. Lett.*, 4(4), 508–512, 2007. doi.org/10.1002/2015JG002952