Assessing the SMAP Level-4 Carbon Product over the
Arctic and Subarctic Zones

Rémi Madelon, John S Kimball, K. Arthur Endsley, Gabriëlle J M de Lannoy, Oliver Sonnentag, Haley Alcock, Matteo Detto, Anna M Virkkala, Brendan M Rogers, Jennifer D Watts, et al.

► To cite this version:

Rémi Madelon, John S Kimball, K. Arthur Endsley, Gabriëlle J M de Lannoy, Oliver Sonnentag, et al. Assessing the SMAP Level-4 Carbon Product over the Arctic and Subarctic Zones. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18, pp.9850-9864. 10.1109/JSTARS.2025.3555850. hal-05024002

HAL Id: hal-05024002

https://hal.science/hal-05024002v1

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Distributed under a Creative Commons Attribution 4.0 International License
Assessing the SMAP Level-4 Carbon Product over
the Arctic and Subarctic Zones

Rémi Madelon, John S. Kimball Senior, IEEE, K. Arthur Endsley, Gabriëlle J. M. De Lannoy, Oliver Sonntag, Haley Alcock, Matteo Detto, Anna M. Virkkala, Brendan M. Rogers, Jennifer D. Watts, Alex Mavrovic, Scott N. Williamson, Elyn Humphreys, Andreas Colliander Senior, IEEE, Arnaud Mialon, Alexandre Roy.

**Abstract**—The Soil Moisture Active Passive (SMAP) satellite mission distributes a product of CO₂ flux estimates (SPL4CMDL) derived from a terrestrial carbon flux model in which SMAP brightness temperatures are assimilated to update soil moisture (SM) and constrain the carbon cycle modeling. While the SPL4CMDL product has demonstrated promising performance across the continental USA and Australia, a detailed assessment over the Arctic and Subarctic zones (ASZ) is still missing. In this study, SPL4CMDL net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration ($R_E$) are evaluated against measurements from 37 eddy covariance towers deployed over the ASZ, spanning from 2015 to 2022. The assessment indicates that the NEE unbiased root mean square error (ubRMSE) falls within the targeted accuracy of 1.6 gC.m⁻².d⁻¹, as defined for the SPL4CMDL product. However, modeled GPP and $R_E$ are overestimated at the beginning of the growing season

R. Madelon is with the Recherche en modélisation et télédétection des environnements nordiques (ReMoTE-Nord) group, at Université du Québec à Trois-Rivières (UQTR), in Trois-Rivières, QC, Canada as well as the Soil Moisture and Ocean Salinity (SMOS) team, at Centre d'Études Spatiales de la BIOSphère (CESBIO), in Toulouse, France (e-mail: remi.madelon@uqtr.ca)

A. Roy is with the Recherche en modélisation et télédétection des en-
vironnements nordiques (ReMoTE-Nord) group, at Université du Québec
à Trois-Rivières (UQTR), in Trois-Rivières, QC, Canada (e-mail: alexan-
dre.roy@uqtr.ca).

A. Mialon is with Soil Moisture and Ocean Salinity (SMOS) team, at Centre d'Études Spatiales de la BIOSphère (CESBIO), in Toulouse, France (e-mail: arnaud.mialon@cesbio.cnes.fr).

J. S. Kimball and K. A. Endsley are with the Numerical Terradynamic Simulation Group (NTSG), at University of Montana, in Missoula, MT, USA (e-mails: John.Kimball@mso.umt.edu; arthur.endsley@mso.umt.edu).

G. J. M. De Lannoy is with Katholieke Universiteit Leuven (KU Leuven), Department of Earth and Environmental Sciences, Division Soil and Water Management, in Leuven, Belgium (e-mail: gabrielle.delannoy@kuleuven.be).

O. Sonntag and H. Alcock are with Université de Montréal (UdeM), Département de Géographie, in Montréal, QC, Canada (e-mails: oliver.sonntag@umontreal.ca, haley.alcock@umontreal.ca, ghgosselin@gmail.com).

M. Detto is with Princeton University, Department of Ecology and Evolutionary Biology, in Princeton University, NJ, USA (e-mail: teop-araglider@gmail.com).

A. M. Virkkala, B. M. Rogers and J. D. Watts are with Woodwell Climate Research Center, in Falmouth, MA, USA (e-mails: avirkkala@woodwellclimate.org, snatali@woodwellclimate.org, brogers@woodwellclimate.org, jwatts@woodwellclimate.org).

A. Mavrovic is with the Recherche en modélisation et télédétection des environnements nordiques (ReMoTE-Nord) group, at Université du Québec à Trois-Rivières (UQTR), in Trois-Rivières, QC, Canada as well as Cégep de Sherbrooke, Physics Department, in Sherbrooke, QC, Canada (e-mail: alex.mavrovic@uqtr.ca).

S. N. Williamson is with Polar Knowledge Canada, in Cambridge Bay, NU, Canada (e-mail: scott.williamson@polar-polaire.gc.ca).

E. Humphreys is with Carleton University, Geography and Environmental Studies, in Carleton, ON, Canada (e-mail: elyn.humphreys@carleton.ca).

A. Colliander is with Jet Propulsion Laboratory, California Institute of Technology, in Pasadena, CA, USA (e-mail: andreas.colliander@jpl.nasa.gov).

R. Madelon, A. Roy, A. Mavrovic and O. Sonntag are also members of the Centre For Northern Studies (CEN) in Québec, Canada.

over evergreen needleleaf forests and shrublands, while being
underestimated over grasslands. Discrepancies are also found in
the annual net CO₂ budgets. SM appears to have a minimal
influence on the GPP and R<sub>E</sub> modeling, suggesting that ASZ
vegetation is rarely subjected to hydric stress, which contradicts
some recent studies. These results highlight the need for further
carbon cycle process understanding and model refinements to
improve the SPL4CMDL CO₂ flux estimates over the ASZ.

**Index Terms**—SMAP, Arctic and Subarctic zones, carbon cycle, net ecosystem exchange, gross primary production, ecosystem respiration, soil moisture, microwave remote sensing

I. INTRODUCTION

The Arctic and Subarctic zones (ASZ) are the northern-
most regions of the globe and comprise the tundra and the
taiga biomes, respectively. They store approximately 50% of
the global soil organic carbon (SOC) pool (1; 2; 3) and
are sensitive to climate change and warmer conditions. In
particular, air temperatures in the Arctic are rising at a rate
four times faster than the global mean (4), which is expected
to extend the growing season and increase plant productivity
(5; 6; 7; 8; 9; 10). In addition, rising temperatures contribute to
permafrost thawing (11), which allows the decomposition of
long-sequestered SOC. These environmental changes intensify
carbon fluxes over the land surface, disrupting the carbon
balance of ASZ ecosystems. More carbon dioxide (CO₂) may
be absorbed by vegetation through enhanced photosynthesis,
but increased CO₂ release may also occur through autotrophic
and heterotrophic respiration (Rₐ and R<sub>H</sub>, respectively) as a
result of warmer temperatures and extended growing seasons
(12; 13). If emissions increase beyond the rate of carbon
uptake, ASZ ecosystems may become net sources of potent
greenhouse gases, generating a positive feedback within the
climate system by amplifying global warming (14; 15; 16;
17; 18; 19; 20).

Nevertheless, there is disagreement among most terrestrial
carbon flux (TCF) models and ecosystem flux upscaling ap-
proaches regarding both the sign and magnitude of the ASZ
carbon budget, resulting in large uncertainties in carbon flux
estimates (21; 22; 23; 24; 25; 10; 26; 27; 28; 13). Our under-
standing of the carbon cycle in the remote ASZ is constrained
by the complexity and high cost of measuring carbon fluxes.
Additionally, the limited availability of these measurements
in some areas hinders the calibration and evaluation of TCF
models (29; 30; 31; 10).

Microwave remote sensing from space (300 MHz - 100 GHz) has proven to be a promising tool for overcoming some
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challenges related to land surface-atmosphere interactions and carbon cycle monitoring (32; 10; 33). In particular, it enables the retrieval of surface information on environmental variables that partially control carbon cycle processes, such as snow properties (34; 35; 36), aboveground biomass (37; 38; 39), surface freeze/thaw state (40; 41; 42; 43; 44), and surface soil moisture (45; 46).

In 2015, the Soil Moisture Active Passive (SMAP) satellite mission was launched to monitor surface soil moisture (SM) and freeze-thaw (FT) state dynamics on a global scale using the combination of L-band (1.4 GHz) active and passive observations from a radar and a radiometer operating from a shared antenna (47). Unfortunately, the radar ceased operating after less than a year, resulting in data products with coarser spatial resolution than initially expected. Some of the mission's broader objectives included improving the understanding of processes linking terrestrial water, energy, and carbon cycles, as well as quantifying the net carbon budget in the boreal landscape (48). In this context, the SMAP mission currently distributes the SMAP Level-4 Global Daily 9-km EASE-Grid Carbon Net Ecosystem Exchange (hereafter SPL4CMDL) product (49; 50; 51). It is the only operational product of CO₂ fluxes derived from a TCF model in which SMAP L-band brightness temperatures (T<sub>B</sub>) are assimilated to better constrain the carbon cycle modeling through SM. More specifically, the SPL4CMDL product provides estimates of net ecosystem exchange (NEE), gross primary production (GPP) and ecosystem respiration (R<sub>E</sub>). NEE is calculated as the difference between R<sub>E</sub> and GPP, where GPP represents CO₂ uptake by vegetation through photosynthesis and R<sub>E</sub> is the sum of CO₂ emissions from R<sub>A</sub> and R<sub>H</sub>. Although the SPL4CMDL product has shown promising performance with respect to measurements from numerous eddy covariance (EC) towers across the continental USA and Australia (50), its assessment over the ASZ remains limited.

In this study, we aim to evaluate the performance of the SPL4CMDL product over the ASZ. Specifically, SPL4CMDL estimates of NEE, GPP, and R<sub>E</sub> are compared to measurements from 37 EC towers at daily and monthly time scales, spanning from 2015 to 2022. Additionally, we examine the influence of SM on the SPL4CMDL modeling across different vegetation types, distinguishing between peatland and non-peatland areas. Finally, we explore two case studies to investigate how differences between measured and modeled CO<sub>2</sub> fluxes may relate to differences between measured and modeled temperatures. By addressing these objectives, our goal is to assess the ability of the SPL4CMDL product in monitoring carbon dynamics over the ASZ and to identify potential improvements for future modeling efforts.

The paper is structured as follows: Section II presents the study area along with the EC measurements and the SPL4CMDL product. Section III describes the methodology used to evaluate the SPL4CMDL estimates against the EC measurements. Section IV shows the performance of the SPL4CMDL product at daily and monthly time scales. Annual net CO<sub>2</sub> budgets, as well as the influence of SM and temperature on the performance, are also presented. Section V discusses the major discrepancies between the SPL4CMDL

estimates and the EC measurements. Finally, section VI draws
the conclusions of the study.

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II. STUDY AREA AND DATA

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A. *The Arctic and Subarctic zones*

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The Arctic zone is approximately located above 66.55°N latitude (delimitation of the Arctic Circle) and consists of parts of northern Norway, northernmost Sweden, northern Finland, Russia, the United States (Alaska), Canada, Danish Realm (Greenland), and northern Iceland. Arctic vegetation is composed of dwarf shrubs, graminoids, herbs, lichens, and mosses, forming the tundra biome. Temperature usually ranges from -40°C to 18°C. Winters are long, dark, and cold, with mean temperatures below 0°C for 6 to 10 months of the year. Annual precipitation ranges from 150 to 250 mm (52).

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The Subarctic zone is located immediately south of the Arctic Circle and generally falls between 50°N and 70°N latitude, depending on local climates. It includes parts of Canada, Alaska, Iceland, Norway, Sweden, Finland, and Russia. Subarctic vegetation is characteristic of coniferous forests, known as the taiga biome. The latter mostly consists of pines, spruces, and larches. The mean temperature in January, the coldest month, is generally less than -10°C but can reach -50°C in eastern Siberia. The mean temperature of the warmest month, July, is generally between 15°C and 20°C. Annual precipitation ranges from 300 to 500 mm (53).

Continuous and discontinuous permafrost is a defining feature of the ASZ. It typically lies beneath an active layer, which freezes and thaws seasonally, enabling plant growth since roots can only establish in the thawed soil.

B. *The SMAP Level-4 carbon product*

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The SPL4CMDL product version 7 provides global daily estimates of NEE, GPP and R<sub>E</sub> at a spatial resolution of 9 km from March 31, 2015 to present (50; 51). Estimates are derived using a satellite-based TCF model that takes as inputs:

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* static global plant functional type (PFT) classification at 500-m grid resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD12Q1 type 5 product (54).

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* 8-days fraction of photosynthetically active radiation (canopy-intercepted FPAR) data at 500-m grid resolution from the Visible Infrared Imaging Radiometer Suite (VIIRS) VNP15A2H product (55).

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* 3-hours soil temperature (T<sub>S</sub>), surface skin temperature, surface soil moisture (SM<sub>S</sub>) and rootzone soil moisture (SM<sub>RZ</sub>) data at 9-km grid resolution from the SMAP SPL4SMGP product version 7 (56), where SM estimates are obtained by assimilating SMAP L-band T<sub>B</sub> observations into the Goddard Earth Observing System version 5 Catchment Land Surface Model (GEOS-5 CLSM). GEOS-5 CLSM is forced with precipitation observations from the National Oceanic and Atmospheric Administration Climate Prediction Center Unified product, the NASA Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (IMERG) satellite-

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and gauge-based (IMERG-Final) product, or the IMERG
satellite-only product, depending on the time and location
(57; 58; 59).

* 0.25-degree (roughly 25-km grid resolution) pre-processed global, daily averaged meteorology data from the GEOS-5 Forward Processing (FP) system (60), including vapor pressure deficit (VPD) and minimum air temperature (Tmin).

The SPL4CMDL product is posted to a 9-km global grid, but provides mean estimates of NEE, GPP and R<sub>E</sub> for up to eight MODIS MCD12Q1 PFT classes represented within each grid cell. CO<sub>2</sub> fluxes specific to each PFT are initially derived in a 1-km sub-grid and then averaged at 9-km. The four MODIS MCD12Q1 PFT classes used in this study are labelled as follows: evergreen needleleaf forests (ENF); deciduous needleleaf forests (DNF); shrublands (SHR) and grasslands (GRA).

Environmental constraint multipliers (F<sub>ECM</sub>) derived from VPD, T<sub>min</sub>, SM<sub>RZ</sub> and landscape freeze-thaw (FT) status are used to constrain the GPP calculation as:

$$
\begin{equation}
\begin{split}
\text{GPP} = F_{\text{ECM}}(\text{VPD}) \times F_{\text{ECM}}(T_{\text{min}}) \times F_{\text{ECM}}(\text{SM}_{\text{RZ}}) & \\
\times F_{\text{ECM}}(\text{FT}) \times \text{APAR} \times \epsilon_{\text{max}}
\end{split}
\tag{1}
\end{equation}
$$

where APAR is the canopy-absorbed photosynthetically ac-
tive radiation and ϵ<sub>max</sub> is the bulk environmental reduction
to the photosynthetically active radiation (PAR) conversion
efficiency. PAR is estimated as a constant proportion (0.45) of
incident shortwave solar radiation at the surface and is used
with the estimated fraction of incident PAR (FPAR) absorbed
by the vegetation canopy to determine APAR as:

$$
\mathrm{APAR} = \mathrm{PAR} \times \mathrm{FPAR} \tag{2}
$$

FT status was originally defined from the SMAP radar FT
retrievals in the at-launch SPL4CMDL product, while it is
derived from the SPL4SMGP surface skin temperature using
a simple pure water freezing-point threshold (273.15 K) since
the SMAP radar failure. The R<sub>H</sub> calculation is also constrained
by F<sub>ECM</sub> that are derived from SM<sub>S</sub> and T<sub>S</sub> as:

$$
R_H = F_{ECM}(T_S) \times F_{ECM}(SM_S) \times k \times C \quad (3)
$$

where k is the optimal decay rate of the SOC pool and
C is the daily SOC variation. R<sub>A</sub> is defined as a fixed
proportion of GPP and is not subjected to other F<sub>ECM</sub>. F<sub>ECM</sub>
are scaling factors representing ecosystem responses to each
environmental variable mentioned above. Response functions
are defined as linear ramp, exponential or simple switch
functions whose parameters differ according to the PFT class.
Each F<sub>ECM</sub> ranges from 0 to 1, where 0 (1) means that the
environmental variable fully constrains (does not constrain)
the CO<sub>2</sub> fluxes estimation. The SPL4CMDL model logic is
explained in detail in (50; 61).

In version 7, the SPL4CMDL product received a major
update through the inclusion of peatland hydrology in the
GEOS-5 CLSM (62). The PEATCLSM module was specif-
ically introduced to simulate peatland water fluxes using
a microtopography-based approach (63; 64) instead of the
original TOPMODEL approach (65). The improved treatment

of peatlands notably affects 9-km SM<sub>S</sub> SM<sub>RZ</sub> estimates pro-
vided in the SPL4SMGP product. However, F<sub>ECM</sub>(SM<sub>S</sub>) and
F<sub>ECM</sub>(SM<sub>RZ</sub>) response functions do not account for whether or
not the grid cell is considered as peatland. Peatland locations
are determined using the combination of the PEATMAP
database (66), the Harmonized World Soil Database (HWSD;
version 1.21) and the State Soil Geographic (STATSGO2) soil
data (67). Grid cells simulated as peatlands are identifiable
by a soil porosity value of 0.93 m<sup>3</sup>.m<sup>-3</sup> in the land model
constants collection of GEOS-5 CLSM (62; 68). In the context
of the SPL4CMDL and SPL4SMGP products as well as
GEOS-5 CLSM, the term peatland refers to peatland and its
subclasses (such as bogs and fens).

C. Eddy covariance measurements of CO<sub>2</sub> fluxes

The SPL4CMDL estimates were assessed from April 2015
to December 2022 with respect to measurements of NEE,
NEE-derived GPP and R<sub>E</sub> from 37 EC towers deployed both
in tundra and taiga environments (Fig. 1). The in-situ dataset
comprises:

* monthly measurements from 19 EC towers that were retrieved from the ABCflux database (69).

* half-hourly measurements from 11 EC towers in USA that were downloaded from the AmeriFlux Network website (https://ameriflux.lbl.gov).

* half-hourly measurements from 7 EC towers in Canada that were directly provided from the PIs.

EC towers from the ABCflux database needed to provide at least one monthly NEE, GPP or R<sub>E</sub> from April 2015 to December 2022 to be selected. In the case where measurements from the same EC tower were retrievable from different sources, data provided by the PIs were preferred over those from the Ameriflux Network, and the latters were preferred over the monthly data from the ABCflux database.

EC towers measure NEE at a spatial scale up to 1 km or
more. The footprint is complex to determine and notably varies
depending on wind direction, wind strength, and tower height
(70; 71; 72; 73). GPP and R<sub>E</sub> are derived from NEE using
flux partitioning approaches, requiring light and temperature
data (73). The most common methods assume that CO<sub>2</sub>
fluxes measured at nighttime only comprise the R<sub>E</sub> component
(74; 75). Photosynthesis and consequently GPP are consid-
ered negligible because of the absence of light. R<sub>E</sub> is then
empirically modeled using the Arrhenius equation or variants,
considering air or soil temperature as the dominant driver (76).
Air temperature is preferred because it is more representative
of the entire landscape in the vicinity of the EC tower, whereas
soil temperature varies spatially and with depth across the
heterogeneous landscape (75; 77). Once parameterized, the
equation allows to extrapolate R<sub>E</sub> during daytime and to isolate
the GPP contribution from the NEE measurements. Alternative
flux partitioning approaches consist of fitting a light–response
curve model to daytime NEE measurements by taking into
account the effects of PAR and VPD on GPP as well as the
effects of temperature on R<sub>E</sub> (78; 79; 80). Overall, GPP and R<sub>E</sub>
have higher uncertainties than NEE because they are modeled
based on additional data and various assumptions (81). Half-hourly NEE, NEE-derived GPP and R<sub>E</sub> were averaged at daily and monthly time scales without minimal threshold concerning the number of data points available per day or month. A PFT class was also attributed to each EC tower location using the MCD12Q1 product. Among the 37 EC tower sites, 13 were classified as ENF, 3 as DNF, 12 as SHR and 9 as GRA. Overall, ENF and DNF classes are expected to represent Subarctic vegetation while GRA and SHR classes correspond to Arctic vegetation. Information relative to each EC tower site as well as number of data points (NDP) available within the daily and monthly time series are shown in table I. It is noteworthy that the NDP differs from one time series to another and that GPP and R<sub>E</sub> were not systematically derived at each EC tower site. A total of 17 EC towers are located in wetlands and peatlands according to the in-situ description (table I).

### III. METHOD

For each EC tower site, the SPL4CMDL NEE, GPP and R<sub>E</sub> time series were compared to those from the EC measurements at monthly and daily time scales from April 2015 to December 2022, using the nearest neighbour grid cell to the tower. Only the SPL4CMDL time series associated with the PFT classes of the EC tower sites were considered. In the case where several EC towers were located within the same grid cell, the comparison was performed separately for each tower.

In a first step, the monthly time series were used to assess the overall performance of the SPL4CMDL product across the four PFT classes represented among the EC tower sites (ENF, SHR, GRA and DNF). To achieve this, EC measurements and SPL4CMDL estimates were separately grouped according to their PFT class. Within each PFT class, the 25<sup>th</sup>, 50<sup>th</sup> and 75<sup>th</sup> percentiles were computed for each month of the year and each variable (NEE, GPP and R<sub>E</sub>). The approach allowed for the comparison of the monthly median NEE, GPP and R<sub>E</sub> from the SPL4CMDL product and the EC measurements all along the year with a variability interval through the 25<sup>th</sup> and 75<sup>th</sup> percentiles. SPL4CMDL percentiles were computed by considering only months and grid cells for which EC measurements were available. The monthly median NEE were also summed to approximate the net CO<sub>2</sub> budget for each PFT class. Lower and upper bounds were computed by summing the 25<sup>th</sup> and 75<sup>th</sup> NEE percentiles of each month. Note that the approach provides net CO<sub>2</sub> budgets derived from only twelve monthly values. Budgets were not computed specifically for each EC tower site because most of the NEE time series have gaps and unbalanced number of data points during growing season and winter. Years of measurements also differ across EC tower sites (table I).

In a second step, the SPL4CMDL product was evaluated at the EC tower site level by using the daily time series. For each EC tower site, a set of three statistical metrics were derived between measured and modeled NEE, GPP and R<sub>E</sub>: the Pearson correlation coefficient (r), the bias and the unbiased root mean squared error (ubRMSE). The latter is obtained by removing the mean bias error from the traditional RMSE

caused by the mismatch between the spatial representativeness of EC measurements and SPL4CMDL estimates (RMSE<sup>2</sup> = ubRMSE<sup>2</sup> + bias<sup>2</sup>), which improves the evaluation of temporal dynamics (47). In a third step, the influence of SM on the GPP and R<sub>H</sub> calculations within the SPL4CMDL model was explored from June to September, when the permafrost active layer is not frozen. More specifically, the F<sub>ECM</sub>(SM<sub>S</sub>) and F<sub>ECM</sub>(SM<sub>RZ</sub>) distributions were analysed according to the four PFT classes represented among the EC tower sites (ENF, SHR, GRA and DNF), and the peatland classification from GEOS-5 CLSM (section II-B, table I). This analysis was performed by taking into account only grid cells for which EC measurements were available.

Finally, complementary analyses were performed at Bonanza Creek Thermokarst Bog (US-BZB) and Imnavait Creek Heath Tundra (US-ICH) to provide potential reasons for the differences between measured and modeled CO<sub>2</sub> fluxes. Measured NEE is partitioned into R<sub>E</sub> and GPP using measured air temperature (T<sub>A</sub>) at both EC tower sites (82; 83), whereas SPL4CMDL R<sub>E</sub> is estimated using modeled soil temperature (T<sub>S</sub>) at 10 cm deep (50). Hence, GPP and R<sub>E</sub> from the SPL4CMDL product and the EC measurements were compared as a function of T<sub>A</sub>. In addition, differences between T<sub>A</sub> and T<sub>S</sub> were evaluated all along the year.

A schematic overview of the methodology used in this study is shown in Fig. 2.

### IV. RESULTS

#### A. Monthly comparison

For EC tower sites located in ENF (Fig. 3.a-b-c), monthly median NEE is quite similar between the SPL4CMDL product and the EC measurements. The highest median difference is found in July where the SPL4CMDL product underestimates NEE by 0.32 gC.m<sup>-2</sup>.d<sup>-1</sup> (≡ -25%). At the beginning of the growing season (from April to July), both GPP and R<sub>E</sub> are overestimated. Median differences are particularly high in May and June with values of 1.79 and 2.16 gC.m<sup>-2</sup>.d<sup>-1</sup> for GPP (≡ +90%; +51%), and 1.95 and 2.03 gC.m<sup>-2</sup>.d<sup>-1</sup> for R<sub>E</sub> (≡ +143%; +58%), respectively.

The same overall pattern for the three variables (NEE, GPP, R<sub>E</sub>) is observed over SHR (Fig. 3.d-e-f). Modeled NEE is quite consistent with the measured fluxes over the year despite an overestimation in May and July of 0.37 and 0.29 gC.m<sup>-2</sup>.d<sup>-1</sup>, respectively (≡ +382%; +19%). The SPL4CMDL product also overestimates GPP and R<sub>E</sub> at the beginning of the growing season. The highest median differences are obtained in May and June with values of 0.38 and 1.34 gC.m<sup>-2</sup>.d<sup>-1</sup> for GPP (≡ +58%; +68%), and 0.89 and 1.06 gC.m<sup>-2</sup>.d<sup>-1</sup> for R<sub>E</sub>, respectively (≡ +163%; +70%).

For EC tower sites located in GRA (Fig. 3.g-h-i), the SPL4CMDL product overestimates NEE in June, July and August. Median differences of 0.16, 1.18 and 0.74 gC.m<sup>-2</sup>.d<sup>-1</sup> are found for these three months, respectively (≡ +114%; +79%; +81%). During the same period, GPP and R<sub>E</sub> are underestimated. Median differences of 0.43, 0.99 and 1.59 gC.m<sup>-2</sup>.d<sup>-1</sup> are obtained in June, July and August for GPP (≡ -82%; -50%; -75%), whereas values of 0.26, 1.07 and 0.87 are found for R<sub>E</sub>, respectively (≡ -52%; -58%; -59%).
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Due to a lack of EC measurements over DNF (Fig. 4.j), the NEE comparison was incomplete, and no evaluation was performed for GPP and R<sub>E</sub>. It can still be observed that the SPL4CMDL product overestimates NEE in May (+1.21 gC.m<sup>-2</sup>.d<sup>-1</sup>; ≡ +210%), while underestimating it from June to September with a peak in July (-0.97 gC.m<sup>-2</sup>.d<sup>-1</sup>; ≡ -129%).

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## B. Annual net CO₂ budgets comparison

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Modeled and measured monthly NEE percentiles shown in Fig. 3 were summed to provide annual net CO₂ budget approximations over ENF, SHR and GRA (Fig. 4). Due to a lack of EC measurements of NEE over DNF, net CO₂ budgets were not computed for this PFT class.

The lower and upper bounds of the net CO₂ budgets from both the SPL4CMDL product and the EC measurements are respectively negative and positive for all three PFT classes. This indicates that ASZ ecosystems with the same type of vegetation can be either net CO₂ sources or sinks.

Annual measured NEE budgets reveal that ENF are considered as net CO₂ sinks in majority, even though they are close to CO₂ neutral with a median value of -16 gC.m⁻².y⁻¹. The SPL4CMDL product more clearly depicts ENF as net CO₂ sinks with a median value of -45 gC.m⁻².y⁻¹. Measured NEE budgets range from -149 to 92 gC.m⁻².y⁻¹ across EC tower sites and are found to be more variable than those modeled (from -91 to 20 gC.m⁻².y⁻¹).

SHR are also close to CO₂ neutral with a majority of net CO₂ sinks according to the EC measurements. Measured NEE budgets range from -73 to 61 gC.m⁻².y⁻¹ with a median value of -5 gC.m⁻².y⁻¹. In contrast, the SPL4CMDL product predicts SHR as net CO₂ sources and modeled NEE budgets range from -20 to 78 gC.m⁻².y⁻¹ with a median value of 23 gC.m⁻².y⁻¹.

Annual measured NEE budgets reveal that GRA are mainly considered as net CO₂ sinks with a median value of -39 gC.m⁻².y⁻¹ and a dynamic range going from -96 to 37 gC.m⁻².y⁻¹. However, the SPL4CMDL product depicts them close to CO₂ neutral with a majority of net CO₂ sources. Modeled NEE budgets show lower variability than those measured and range from -18 to 24 gC.m⁻².y⁻¹ with a median value of 3 gC.m⁻².y⁻¹.

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## C. Daily time series evaluation

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Overall, results show that the SPL4CMDL daily estimates are well correlated to the EC measurements (Fig. 5.a). Median r of 0.68, 0.86, and 0.78 are obtained for NEE, GPP, and R<sub>E</sub>, respectively. In the case of GPP, 50% of the r reach values of 0.83 to 0.87. Dynamic ranges observed for NEE and R<sub>E</sub> are larger (50% of the values ranging from 0.61 to 0.73 for NEE and from 0.67 to 0.85 for R<sub>E</sub>).

From Fig. 5.b, the lowest ubRMSE are obtained for NEE, where 50% of the values range from 0.61 to 0.89 gC.m⁻².d⁻¹. Intermediate ubRMSE are found for R<sub>E</sub> (50% of the values between 0.67 and 1.47 gC.m⁻².d⁻¹) and the highest ubRMSE are observed for GPP (50% of the values between 1.00 and 1.59 gC.m⁻².d⁻¹). Median ubRMSE are 0.78, 0.99, and 1.30 gC.m⁻².d⁻¹ for NEE, R<sub>E</sub>, and GPP.

Median biases of 0.04, 0.06, and 0.12 gC.m⁻².d⁻¹ are obtained for NEE, GPP, and R<sub>E</sub>, respectively (Fig. 5.b). A higher dispersion is observed for GPP (25<sup>th</sup>; 75<sup>th</sup> percentiles: -0.47; 0.52; gC.m⁻².d⁻¹) and R<sub>E</sub> (25<sup>th</sup>; 75<sup>th</sup> percentiles: -0.27; 0.51; gC.m⁻².d⁻¹), than for NEE (25<sup>th</sup>; 75<sup>th</sup> percentiles: -0.10; 0.11; gC.m⁻².d⁻¹).

In general, higher ubRMSE and biases are found for GPP and R<sub>E</sub>, which is consistent with the overestimations observed when assessing the GPP and R<sub>E</sub> monthly medians in section IV-A (Fig. 3.b,c,e,f).

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## D. Influence of SM on the GPP and R<sub>H</sub> modeling

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F<sub>ECM</sub>(SM<sub>S</sub>) and F<sub>ECM</sub>(SM<sub>RZ</sub>) are derived from SPL4SMGP SM<sub>S</sub> and SM<sub>RZ</sub> estimates using the ecosystem responses shown in Fig. 6. They are then used to constrain the GPP and R<sub>H</sub> calculations within the SPL4CMDL model through equations 1 and 3. Table II shows probabilities of having F<sub>ECM</sub>(SM<sub>S</sub>) and F<sub>ECM</sub>(SM<sub>RZ</sub>) above 0.95 for the four PFT classes represented among the EC tower sites. Probabilities were computed by considering only the months from June to September as well as grid cells for which EC measurements were available.

Over ENF, it appears that F<sub>ECM</sub>(SM<sub>S</sub>) and F<sub>ECM</sub>(SM<sub>RZ</sub>) are above 0.95%, 99.9%, and 99.1% of the time, respectively. Probabilities obtained over DNF are also high with 94.4% for F<sub>ECM</sub>(SM<sub>S</sub>) and 98.5% for F<sub>ECM</sub>(SM<sub>RZ</sub>), and remain similar over peatland and non-peatland grid cells. In the case of SHR, 85.8% and 79.8% of the F<sub>ECM</sub>(SM<sub>S</sub>) and F<sub>ECM</sub>(SM<sub>RZ</sub>) values are above 0.95. However, lower and higher probabilities are obtained for non-peatland grid cells (66.1% and 69.5%) and peatland grid cells (99.9% and 87.1%), respectively. The same overall pattern is observed over GRA. When considering all the grid cells together, 80.6% and 86.7% of the F<sub>ECM</sub>(SM<sub>S</sub>) and F<sub>ECM</sub>(SM<sub>RZ</sub>) values are above 0.95. However, lower probabilities are observed over non-peatland grid cells with 75.1% for F<sub>ECM</sub>(SM<sub>S</sub>) and 85.6% for F<sub>ECM</sub>(SM<sub>RZ</sub>). Higher probabilities are obtained over peatland grid cells with 99.9% for F<sub>ECM</sub>(SM<sub>S</sub>) and 90.5% for F<sub>ECM</sub>(SM<sub>RZ</sub>).

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## E. Specific case studies

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The SPL4CMDL model overestimates GPP and R<sub>E</sub> at both US-BZB and US-ICH at the beginning of the growing season (Fig. 7.b,c and 8.b,c). The overestimation occurs mainly from April to June with strong magnitude variations across years. R<sub>E</sub> and GPP ubRMSE were computed for each year by only taking into account the data from April to June. At US-BZB, R<sub>E</sub> and GPP ubRMSE vary from 4% to 14% and from 5% to 36% across years, respectively. At US-ICH, R<sub>E</sub> and GPP ubRMSE vary from 6% to 56% and from 5% to 26% across years, respectively.

Also from April to June at US-BZB (Fig. 9.a,b), modeled R<sub>E</sub> and GPP over-respond to T<sub>A</sub>, compared to measured R<sub>E</sub> and GPP. The same pattern is found for both R<sub>E</sub> and GPP at US-ICH (Fig. 9c,d).

In summer, T<sub>A</sub> and T<sub>S</sub> are quite consistent with ubRMSE of 1.7°C and 3.1°C at US-BZB (Fig. 10a) and US-ICH (Fig. 10b), respectively. However, T<sub>A</sub> and T<sub>S</sub> strongly deviate from

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each other as they decrease, because of the insulating effect of
snow. At US-BZB, ubRMSE of 5.6, 5.9, and 8.3°C are found
for spring, fall and winter, respectively (Fig. 10a). Values are
comparable at US-ICH with ubRMSE of 5.7°C for spring,
6.5°C for fall and 9.8°C for winter (Fig. 10b).

V. DISCUSSION

A. *Flux amplitude mismatch during the growing season*

The SPL4CMDL product overestimates both GPP and R<sub>E</sub> over ENF and SHR during the beginning of the growing season, compared to the EC measurements (Fig. 3.b,c,e,f). The same overestimation was observed in (84) where GPP estimates from nine TCF models were assessed with respect to EC measurements performed at a variety of locations across the globe. Seven of them overestimate the GPP amplitude during the beginning of the growing season, which highlights that the observed deviation is not specific to the SPL4CMDL product nor to the ASZ. Recently, (49) showed that the R<sub>E</sub> amplitude in regions above 40° of latitude could be reduced by adding in the SPL4CMDL modeling a limit on O<sub>2</sub> diffusion for heterotrophic soils and a seasonally varying litterfall inputs scheme. In addition, the SPL4CMDL model is not vertically stratified: SOC decomposition and R<sub>H</sub> are considered to occur near the surface in a single soil layer of roughly 10 cm depth (50; 85). This could overestimate the diffusion rate of moisture and heat through the subsurface, resulting in an overestimation of R<sub>E</sub> in the early growing season. This also means that deeper SOC pools and R<sub>H</sub> are not taken into account in the modeling, which may not properly reflect carbon processes in the ASZ. According to Fig. 9, the SPL4CMDL model depicts stronger GPP and R<sub>E</sub> responses to T<sub>A</sub> than those observed with the EC measurements. This could partly explain the R<sub>E</sub> and GPP overestimations observed at the beginning of the growing season. In addition, measured NEE is partitioned into R<sub>E</sub> and GPP using T<sub>A</sub> whereas SPL4CMDL R<sub>E</sub> is modeled using T<sub>S</sub>. Hence, differences in temperature could contribute to the disagreement between measured and modeled R<sub>E</sub> at the beginning of the season but also during the shoulder season (freeze-thaw periods) and in the presence of snowpacks.

In contrast to what is observed over ENF and SHR, the SPL4CMDL product underestimates GPP and R<sub>E</sub> over GRA (Fig. 4.h,i). Unfortunately, not enough daily EC measurements of CO<sub>2</sub> fluxes were available to perform site level comparisons against SPL4CMDL estimates for this PFT class. However, the modeled response to temperature for GPP and R<sub>E</sub> might be underestimated over GRA and the mismatch between air and soil temperature might also contribute to differences between measured and modeled R<sub>E</sub>.

Although few monthly EC measurements were retrieved
over DNF, measured and modeled NEE strongly differ during
the growing season (Fig. 4j). Higher NEE differences were
expected for this PFT class owing to the lack of global EC
towers over DNF used for the SPL4CMDL product calibration
(50).

B. *Discrepancies in annual NEE budgets*

Overall, errors in modeled GPP and R<sub>E</sub> appear to partially
compensate each other since lower ubRMSE and biases are

observed for NEE than for GPP and R<sub>E</sub> (Fig. 6b,c). It is actu-
ally noteworthy that NEE ubRMSE are all within the targeted
accuracy of 1.6 gC.m<sup>-2</sup>.d<sup>-1</sup> defined for the SPL4CMDL product
(50). This performance threshold was originally defined on
the basis of reported uncertainty of EC measurements from
the FLUXNET network (86). Nevertheless, errors accumulate
when computing annual NEE budgets, which can lead to
magnitudes and signs different from those obtained with the
EC measurements (section IV-B and Fig. 5).

In general, annual measured NEE budgets reveal a wide variety of net CO₂ sources and sinks across EC tower sites with the same PFT class, and larger than that obtained by the modeled NEE budgets (Fig. 5). Less variability from the SPL4CMDL estimates was expected, compared to that of the EC measurements. Indeed, the SPL4CMDL model makes use of satellite-based data with spatial grid resolutions of 9-km and 25-km (51; 61, section II-B), which are coarser than the spatial footprint of the EC measurements (up to 1 km or more).

According to the EC measurements, ENF and SHR are close to CO2 neutral, raising the question whether or not some ASZ ecosystems will be transitioning from net CO2 sinks to sources in a near future. However, the 37 EC tower sites used in this study are not representative of all the ecosystems present in the ASZ, and most of the EC measurements were performed in Alaska and Canada. Indeed, conclusions concerning the CO2 budget of the tundra differ between studies, and highly depend on the data used and the time window considered. For instance, (23) suggested that the tundra is a net CO2 sink, whereas it is considered as net CO2 source according to (87). (24) also found that the tundra is close to CO2 neutral. These different studies, as ours, highlight the heterogeneity of the ASZ environment and the difficulty to predict properly an ecosystem as net CO2 source or sink. They also underline the interest of developing complementary approaches to improve carbon cycle monitoring, such as combining microwave remote sensed observations with TCF models.

C. *Limited impact of SM on the GPP and R<sub>H</sub> modeling*

Several studies based on ground-based measurements sug-
gested that water availability is an important environmental
constraint on photosynthesis and carbon sequestration in the
northern hemisphere (88; 89; 32) and that droughts can limit
tree growth in boreal forests (90; 91). In addition, (92) recently
showed that reduced SM can limit the peak summer plant
productivity in tundra, and suggested that the role of SM is
generally underestimated when modeling the carbon cycle.

In the same vein, according to results from section IV-D and
table II, the SPL4CMDL model is most of the time uncon-
strained by SM, making it appear that the vegetation is rarely
subjected to hydric stress during the growing season (from
June to September). More specifically, SM barely constrains
GPP and R<sub>E</sub> estimates over EC tower sites located within grid
cells considered as peatlands by GEOS-5 CLSM. SM control
on CO<sub>2</sub> flux estimates is also minimal over EC tower sites
classified as ENF and DNF. GPP and R<sub>E</sub> estimates are more
constrained by SM over GRA and SHR within non-peatland
grid cells, but the SM control still remains low. Our results
contrast with those found in the literature mentioned above.

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It may be that EC tower sites used in our study did not experience significant SM inter-annual variability from 2015 to 2022, whereas time windows used in the other studies reflected stronger SM fluctuations across years and regions. This contrast may also be the result of the spatial scale mismatch between EC measurements and modeled SM as SPL4SMGP SM are produced at 9 km. Though, our results still question the accuracy of the SPL4SMGP SM estimates as well as the way they are taken into account in the SPL4CMDL modeling. Indeed, FECM(SM<sub>S</sub>) and FECM(SMR<sub>Z</sub>) were calibrated per PFT class and constrained by SPL4SMGP SM estimates from older versions that did not account for peatland hydrology processes yet. As a consequence, some discrepancies between measured and modeled NEE could be explained by the lack of SM influence in the SPL4CMDL modeling. Nevertheless, the impact on the SPL4CMDL GPP and R<sub>E</sub> performance should be minimal. At most EC tower sites, measured NEE is partitioned into GPP and R<sub>E</sub> following the approaches described in (74; 93; 75), where only T<sub>A</sub>, VPD, PAR, and incoming shortwave radiation (SW<sub>in</sub>) are used as climatic drivers.

The respective influences of temperature and SM on CO2 fluxes are expected to change with further climate warming. SM control may become increasingly important as cold temperature restrictions relax, especially in the mid-late growing season. In addition, drying out of the soil may lead SM to get lower than the wilting point, hampering the vegetation development and hence reducing GPP and R<sub>E</sub>. In this context, revising the SM influence in the SPL4CMDL modeling may become crucial to accurately monitor the carbon cycle in the ASZ in the near future.

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## D. PFT and peatland misclassification

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Finally, it is noteworthy that some EC towers deployed in bogs and fens are not flagged as peatlands by GEOS-5 CLSM and vice versa (table I). For instance, the three EC towers at Imnavait Creek (US-ICH, US-ICs, US-ICt) are all located in the same grid cell classified as peatland by GEOS-5 CLSM. However, only one of them is described as being in fen. In contrast, the four EC towers in Bonanza Creek (US-BZS, US-BZo, US-BZF, US-BZB) are located in the same non-peatland grid cell while three of them are deployed in bogs and fen according to the in-situ description.

In addition, the SPL4CMDL modeling lumps ASZ vegetation into global PFT for the calibration. Although the latter is weighted more heavily toward northern high latitudes, current PFT may not properly represent the unique ASZ vegetation communities surrounding the EC tower sites. As PFT maps are static in time, misclassification may also occur in regions that recently experienced severe disturbances, such as thermokarst formation at Eight Mile Lake (US-EML) and fires at Poker Flats (US-Rpf).

Overall, PFT and peatland misclassification could contribute to discrepancies between measured and modeled CO2 fluxes. This highlights the potential limitations of using coarse scale SM and CO2 flux estimates to monitor spatially and temporally complex environments.

## VI. CONCLUSION

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## VII. ACKNOWLEDGEMENT

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In this study, modeled estimates of NEE, GPP, and R<sub>E</sub> from the SPL4CMDL product were evaluated against EC tower-based measurements from 2015 to 2022 over the ASZ.

The assessment revealed that errors in modeled NEE fall within the accuracy requirement, but the SPL4CMDL model fails to predict the proper GPP and R<sub>E</sub> amplitude at the beginning of the growing season. In addition, discrepancies regarding annual NEE budgets were found across all PFTs (ENF, SHR, GRA), leading to disagreement regarding their classification as net CO2 sinks or sources.

Although several studies suggest that reduced water availability limits plant productivity in the ASZ (90; 91; 88; 89; 92), SM has minimal impact on the modeling of GPP and R<sub>E</sub>, and does not affect the performance of the SPL4CMDL product. This may underline the need to better understand SM control on CO2 fluxes and to use SM estimates with finer spatial resolution when modeling the carbon cycle.

Finally, the reliance of the SPL4CMDL modeling on soil temperature instead of air temperature may lead to inaccurate estimation of R<sub>E</sub> during freeze-thaw periods and in the presence of snowpacks.

We propose three key aspects to investigate that could improve the capacity of the SPL4CMDL model to monitor carbon cycle over the ASZ: 1) Improving ASZ vegetation communities representation and recalibrating FECM ecosystem responses by taking into account only EC measurements performed in the ASZ; 2) Revising the influence of SM and the inclusion of peatlands in the SPL4CMDL modeling; 3) Representing SOC decomposition and R<sub>H</sub> that occur at a depth greater than 10 cm.

R. Madelon, A. Mialon and A. Roy acknowledge fundings from the Université Toulouse 3 Paul Sabatier in France, and the Université du Québec à Trois-Rivières in Canada through the Universalis Causa, Samuel-de-Champlain, Programme d'Aide à L'Internationalisation de la Recherche (PAIR), Fond Canadien de l'Innovation (FCI) and Conseil de Recherches en Sciences Naturelles et en Génie du Canada (CRSNG) grants.

Andreas Colliander acknowledges contribution from the Jet Propulsion Laboratory (JPL), California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). J.S. Kimball and K.A. Endley also acknowledge fundings from NASA (NX14AI50G, 80NSSC19M0114).

A.M. Virkkala acknowledges the Gordon and Betty Moore foundation and the Audacious funding catalyzed by the TED program (Permafrost Pathways project).

O. Sonnentag wish to acknowledge further financial support through the Canada Research Chair (CRC-2018-00259, awarded to O.S.) and NSERC Discovery Grants programme (DGPIN-2018-05743 awarded to O.S.), ArcticNet, a Network of Centres of Excellence Canada (grant no. P216 awarded to O.S.), the Canada First Research Excellence Fund's Global Water Futures programme (Northern Water Futures), and the Polar Continental Shelf Program.

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This research made use of data from the ABCflux database (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1934) and the AmeriFlux network (https://ameriflux.lbl.gov). Additional data from seven eddy covariance towers were directly provided by the PIs (O. Sonnentag and S. N. Williamson). The SMAP SPL4CMDL Version 7 product can be downloaded from the National Snow and Ice Data Center (NSIDC) website at: https://nsidc.org/data/SPL4CMDL/versions/7. The SMAP SPL4SMGP Version 7 product can be downloaded at: https://nsidc.org/data/SPL4SMGP/versions/7.

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All the authors thank the indigenous and northern communities for agreeing to the installation of eddy covariance towers on their territory.

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Finally, the authors acknowledge constructive reviews from the different referees that resulted in improvement of the clarity of the manuscript.

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**Rémi Madelon** joined the Centre d'Etudes Spatiales de la BIOsphère (CESBIO), in Toulouse, France, within the SMOS (Soil Moisture and Ocean Salinity) team as a research engineer in 2020. Since 2023, he is pursuing the PhD degree in northern remote sensing in the context of a collaboration between CESBIO, Université Toulouse 3 Paul-Sabatier and Université du Québec à Trois-Rivières (UQTR). His research interests include carbon cycle, soil moisture, climate changes and remote sensing.

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**John S. Kimball** received the Ph.D. in Bioresource Engineering from Oregon State University in 1995. He is an IEEE Senior Member and Professor of Systems Ecology at the University of Montana. His research emphasizes the use of remote sensing and computational ecology for monitoring and understanding environmental change.

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**K. Arthur Endsley** received a Ph.D. in Natural Resources and Environment and Scientific Computing from the University of Michigan in 2019. He is currently a Research Scientist in the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana. As a NASA Early Career Investigator, he is investigating the use of microwave remote sensing for constraining ecosystem carbon and water fluxes.

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

**Gabriëlle J. M. De Lannoy** received a PhD in Bioscience Engineering from the Ghent University, Belgium, in 2006. She worked at NASA on the SMAP Level 4 surface and root-zone soil moisture product until 2016. Currently, she is a full professor at the Catholic University of Leuven (KU Leuven, Belgium), where she leads a research group on land surface remote sensing, modeling and data assimilation.

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

**Oliver Sonnentag** is Associate Professor and former Canada Research Chair in Atmospheric Biogeosciences in High Latitudes (2014-2014) at the Université de Montréal. His research aims to understand changes in surface-atmosphere interactions in response to increasing natural and anthropogenic pressures, with a focus on the Northwest Territories. There, Sonnentag uses micrometeorological measurements (e.g., eddy covariance), and remote sensing and modelling techniques to study changes in Arctic-boreal ecosystem composition, structure, function and services. Sonnentag is involved in a range of research initiatives and programs including AmeriFlux/FLUXNET, Can-Peat, NSERC CREATE LEAP and PermafrostNet.

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**Haley Alcock** is a research professional in the Atmosbio lab at the University of Montreal. She holds a master of sciences from McGill University in micrometeorology.

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**Matteo Detto** received a Ph.D. in Environment and Hydraulic Engendering from the Polytechnic of Milan in 2006. He is currently a Research Scholar in the department of Ecology and Evolutionary Biology at the Princeton University, NJ, USA. He has 20 years' experience in installing and maintaining eddy covariance towers over many different ecosystems including Boreal, Artic, Tropical and Mediterranean forests. At Princeton, he collaborates with NOAA Geophysical Dynamics Laboratory (GFDL) to improve the terrestrial component of GFDL's Earth System Models.

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**Anna-Maria Virkkala** received a Ph.D. in physical geography at the University of Helsinki in 2020 and has been working at Woodwell Climate Research Center since then. She is a research scientist in the Permafrost Pathways project and currently leads pan-Arctic carbon flux synthesis and upscaling activities.

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**Brendan M. Rogers** received a Ph.D. in Earth System Science from the University of California, Irvine in 2024, and has worked at the Woodwell Climate Research Center since. Dr. Rogers focuses on how arctic and boreal terrestrial ecosystems are responding to climate change, how this feeds back to climate, and how management and policy can be used for climate mitigation and adaptation. He uses a combination of field measurements, satellite remote sensing, and process-based models.

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**Jennifer D. Watts** received the Ph.D. degree in Systems Ecology from the University of Montana, Missoula in 2017. Her work focuses on improving understanding and quantification of high latitude carbon cycle dynamics through integration of field observations, remote sensing and ecosystem modeling.

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**Alex Mavrovic** obtained his Ph.D. in environmental science from the Université du Québec à Trois-Rivières, Canada, in 2024. He currently holds a researcher and teaching position in the Physics Department of Cégep de Sherbrooke, Canada. His research covers environmental science themes, focusing primarily on the carbon cycle, light pollution, northern regions, and remote sensing applications.

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**Scott N. Williamson** received a PhD in Ecology from the University of Alberta in 2015. He is a research scientist at the Canadian High Arctic Research Station. His research interests include the study of surface and atmosphere interactions that drive non-linear climate change.

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**Elyn Humphreys** is a Professor in the Department of Geography Environmental Studies at Carleton University in Ottawa, Ontario. Her research group employs micrometeorological field methods and terrestrial biosphere models to investigate surface-atmosphere interactions in northern ecosystems across Canada. This work aims to enhance our understanding of energy, water and carbon cycle processes in restored wetlands and pristine tundra and peatland ecosystems impacted by climate and vegetation change.

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**Andreas Colliander** received the M.Sc. (Tech.), Lic.Sc. (Tech.), and D.Sc. (Tech.) degrees in Electrical and Communications Engineering from Aalto University, Espoo, Finland, in 2002, 2005, and 2007, respectively. He is currently a Research Scientist with the Jet Propulsion Laboratory, California Institute of Technology, Pasadena. His research is focused on the development of microwave remote sensing techniques. He is currently leading the calibration and validation of the geophysical retrievals of NASA's SMAP mission and developing multi-frequency retrievals for ice sheets and polar atmosphere.

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**Arnaud Mialon** received a Ph.D. degree in ocean-atmosphere-hydrology from Université Joseph Fourier de Grenoble (France) and in remote sensing from the Université de Sherbrooke, Sherbrooke (Québec, Canada), in 2005. He joined the Centre d'Etudes Spatiales de la Biosphère, Toulouse, France, in 2006. He is part of SMOS (Soil Moisture and Ocean Salinity) satellite mission and especially in the soil moisture and VOD retrieval. He is involved in the CATDS Centre Aval de Traitement des Données SMOS ground segment. His fields of interest are focused on passive microwave remote sensing of continental surfaces and their applications in the water cycle.

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**Alexandre Roy** earned his PhD in Remote Sensing from the Université de Sherbrooke in 2014 studying winter passive microwave emission modeling in boreal forest for snow retrievals. Since 2018, he is professor at Université du Québec à Trois-Rivières and his research interests are remote sensing of snow, vegetation, freeze/thaw cycles and carbon fluxes.
<figure><img src="image_4.png" /></figure>

Fig. 1. Locations of the 37 eddy covariance (EC) tower sites providing measurements of net ecosystem exchange (NEE), NEE-derived gross primary production (GPP) and ecosystem respiration $R_E$ from April 2015 to December 2022. A plant functional type (PFT) class was attributed to each EC tower site using the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD12Q1 type 5 product (54). A total of four classes are represented among all the EC tower sites: evergreen needleleaf forest (ENF), deciduous needleleaf forest (DNF), shrubland (SHR) and grassland (GRA). The High Arctic, Low Arctic and Subarctic zones were delineated following the Conservation of Arctic Flora and Fauna working group of the Arctic Council. The permafrost extent (94) was estimated in percent areal coverage: continuous (>90-100 % areal extent), discontinuous (>50-90 %), sporadic (10-50 %) and isolated patches (<10 %). Due to overlapping, note that a single color dot may represent up to seven EC tower sites on the map. Note also that a couple of EC tower sites are located slightly farther south than the Subarctic zone. The figure was inspired from (32).

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

Fig. 2. Workflow used to assess SPL4CMDL net ecosystem exchange (NEE), gross primary production (GPP), ecosystem respiration ($R_E$) with respect to daily and monthly measurements from a total of 37 eddy covariances (EC) tower sites. Site locations are shown in Fig. 1. PFT stands for plant functional type.
<figure><img src="image_6.png" /><figcaption>Fig. 3. Net ecosystem exchange (NEE), NEE-derived gross primary production (GPP) and ecosystem respiration ($R_E$) monthly median from the eddy covariance (EC) measurements and the SPL4CMDL product across the four plant functional type (PFT) classes (ENF, SHR, GRA and DNF) represented among the 37 EC tower sites (figure 1). PFT classes were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD12Q1 type 5 product (54). ENF, DNF, SHR and GRA stand for evergreen needleleaf forest, deciduous needleleaf forest, shrubland and grassland, respectively. The uppercase letters in the x-axes refer to the twelve months of the year. Below each letter, the number of data points taken into account to compute the monthly median is displayed between parentheses. The shaded uncertainty envelopes from either sides of the medians are delimited by the 25<sup>th</sup> and 75<sup>th</sup> monthly percentiles. As shown in table I, no EC measurements of GPP and $R_E$ were available from EC tower sites located over DNF.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. Eddy covariance (EC) tower sites distribution as net CO₂ sinks or sources according to their plant functional type (PFT) class (ENF, SHR and GRA). PFT classes were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD12Q1 type 5 product (54). ENF, SHR and GRA stand for evergreen needleleaf forest, shrubland and grassland, respectively. Annual cumulative net ecosystem exchange (NEE) were computed by summing the monthly NEE shown in figure 6, after being converted in gC.m⁻².m⁻¹. For each PFT class, three dots are displayed: the centered one corresponds to the median summation, whereas the lower and upper ones correspond to the 25<sup>th</sup> and 75<sup>th</sup> percentile summations, respectively.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 5. Evaluation of the SPL4CMDL net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration ($R_E$) time series with respect to daily measurements from a total of 18 eddy covariance (EC) tower sites. All the EC tower sites are located in Alaska and Canada, among which 10 are located in evergreen needleleaf forest (ENF), 6 in shrubland (SHR) and 2 in grassland (GRA). For each EC tower site and each variable (NEE, GPP, and $R_E$), three metrics were derived: the Pearson correlation coefficient (r), the bias in $gC.m^{-2}.d^{-1}$ and the unbiased root mean square error (ubRMSE) in $gC.m^{-2}.d^{-1}$; and summarized as boxplots. On each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th ($q_{25}$) and 75th ($q_{75}$) percentiles, respectively. Data points are considered as outliers if they are greater than $q_{75} + 1.5 \times (q_{75} - q_{25})$ or less than $q_{25} - 1.5 \times (q_{75} - q_{25})$. The whiskers extend to the most extreme data points not considered as outliers.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 6. Environmental constraint multipliers ($F_{ECM}$) derived from surface and rootzone soil moisture ($SM_S$, $SM_{RZ}$) for four plant functional type (PFT) classes (ENF, SHR, GRA and DNF) as defined in the terrestrial carbon flux (TCF) model used to derive the SPL4CMDL product (50). PFT classes were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD12Q1 type 5 product (54). ENF, DNF, SHR and GRA stand for evergreen needleleaf forest, deciduous needleleaf forest, shrubland and grassland, respectively. This figure is inspired from (61)</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Fig. 7. Daily time series of net ecosystem exchange (NEE), NEE-derived gross primary production (GPP) and ecosystem respiration ($R_E$) measured at the eddy covariance (EC) tower site of Bonanza Creek Thermokarst Bog (US-BZB) in USA (64.70°N, -148.32°E), along with those from the SPL4CMDL product. The plant functional type (PFT) class of the EC tower site is considered as evergreen needleleaf forest (ENF) according to the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD12Q1 type 5 product (54).</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 8. Daily time series of net ecosystem exchange (NEE), NEE-derived gross primary production (GPP) and ecosystem respiration ($R_E$) measured at the eddy covariance (EC) tower site of Imnavait Creek Heath Tundra (US-ICH) in USA (68.61°N, -149.30°E), along with those from the SPL4CMDL product. The plant functional type (PFT) class of the EC tower site is considered as evergreen needleleaf forest (ENF) according to the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD12Q1 type 5 product (54).</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Fig. 9. SPL4CMDL ecosystem respiration ($R_E$) and gross primary production (GPP) along with those from the eddy covariance (EC) measurements as a function of the measured air temperature ($T_A$) at Bonanza Creek Thermokarst Bog (US-BZB) and Imnavait Creek Heath Tundra (US-ICh). Only daily estimates and measurements in April, May and June were considered.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 10. Modeled soil temperature at 10 cm deep ($T_S$) used to constrain SPL4CMDL ecosystem respiration ($R_E$) with respect to measured air temperature ($T_A$) at the eddy covariance (EC) tower sites of Bonanza Creek Thermokarst Bog (US-BZB) and Imnavait Creek Heath Tundra (US-ICh). Spring, summer, fall and winter include March, April, May; June, July, August; September, October, November and December, January, February; respectively.</figcaption></figure>
<caption>TABLE I</caption>

LIST OF THE 37 EDDY COVARIANCE (EC) TOWER SITES USED IN THIS STUDY. COLUMNS 5, 6 AND 7 PROVIDE THE NUMBER OF DATA POINTS (NDP) WITHIN THE NEE, GPP AND R<sub>E</sub> TIME SERIES, RESPECTIVELY. TWO NUMBERS SEPARATED BY A SEMICOLON (;) ARE DISPLAYED: THE FIRST ONE REFERS TO THE NDP WITHIN THE MONTHLY TIME SERIES, WHILE THE SECOND ONE REFERS TO THE NDP WITHIN THE DAILY TIME SERIES. PLANT FUNCTIONAL TYPE (PFT) CLASSES WERE ATTRIBUTED TO EACH EC TOWER SITE USING THE MODERATE RESOLUTION IMAGING SPECTRORADIOMETER (MODIS) MCD12Q1 TYPE 5 PRODUCT (54). COLUMN 10 DISPLAYS WHETHER OR NOT THE EC TOWER IS LOCATED IN A GRID CELL CONSIDERED AS PEATLAND BY THE GODDARD EARTH OBSERVING SYSTEM VERSION 5 CATCHMENT LAND SURFACE MODEL (62, GEOS-5 CLSM).

<table><thead><tr><th>Site name</th><th>Site ID</th><th>Coordinates</th><th>Years</th><th>NDP(NEE)</th><th>NDP(GPP)</th><th>NDP(R<sub>E</sub>)</th><th>In-situ description</th><th>PFT</th><th>Grid cell</th><th>Reference</th></tr></thead><tbody><tr><td>Iqaluktuuttiaq Mesic</td><td>CA-IQm</td><td>69.08°N, -104.58°E</td><td>[2022, 2023]</td><td>7; 130</td><td>7; 134</td><td>7; 131</td><td>Grass, moss tundra</td><td>GRA</td><td>Non-peatland</td><td>Present study</td></tr><tr><td>Iqaluktuuttiaq Wetland</td><td>CA-IQw</td><td>69.08°N, -104.58°E</td><td>[2022, 2023]</td><td>2; 34</td><td>2; 34</td><td>2; 34</td><td>Grass, moss wetland</td><td>GRA</td><td>Non-peatland</td><td>Present study</td></tr><tr><td>Cape Bounty</td><td>CA-CB</td><td>74.91°N, -109.57°E</td><td>[2015, 2016]</td><td>0; 0</td><td>3; 0</td><td>0; 0</td><td>Grass, moss tundra</td><td>GRA</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Daring Lake</td><td>CA-DL1</td><td>64.87°N, -111.59°E</td><td>[2015, 2016]</td><td>4; 0</td><td>4; 0</td><td>4; 0</td><td>Tussock, dwarf-shrub tundra</td><td>SHR</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Havikpak Creek</td><td>CA-HPC</td><td>68.32°N, -133.52°E</td><td>[2015, 2023]</td><td>90; 2721</td><td>53; 1436</td><td>53; 1436</td><td>Boreal woodland</td><td>SHR</td><td>Non-peatland</td><td>(95; 77)</td></tr><tr><td>Scotty Creek Bog</td><td>CA-SCB</td><td>61.31°N, -121.30°E</td><td>[2015, 2023]</td><td>55; 1473</td><td>55; 1473</td><td>55; 1473</td><td>Boreal wetland</td><td>ENF</td><td>Non-peatland</td><td>(96; 77)</td></tr><tr><td>Scotty Creek Landscape</td><td>CA-SCC</td><td>61.31°N, -121.30°E</td><td>[2015, 2023]</td><td>74; 2114</td><td>74; 2114</td><td>74; 2114</td><td>Boreal forest-wetland</td><td>ENF</td><td>Non-peatland</td><td>(97; 77)</td></tr><tr><td>Smith Creek</td><td>CA-SMC</td><td>63.15°N, -123.25°E</td><td>[2017, 2023]</td><td>44; 1091</td><td>44; 1091</td><td>44; 1089</td><td>Boreal forest-peatland</td><td>ENF</td><td>Non-peatland</td><td>(98; 77)</td></tr><tr><td>Trail Valley Creek</td><td>CA-TVC</td><td>68.75°N, -133.50°E</td><td>[2015, 2023]</td><td>90; 2713</td><td>80; 2073</td><td>80; 2073</td><td>Low-shrub tundra</td><td>SHR</td><td>Non-peatland</td><td>(99; 77)</td></tr><tr><td>Hyytiala</td><td>FI-Hyy</td><td>61.85°N, 24.29°E</td><td>[2015, 2019]</td><td>45; 0</td><td>0; 0</td><td>0; 0</td><td>Boreal forest</td><td>ENF</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Siikaneva</td><td>FI-Sii</td><td>61.83°N, 24.19°E</td><td>[2015, 2016]</td><td>6; 0</td><td>6; 0</td><td>6; 0</td><td>Boreal forest-wetland</td><td>ENF</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Varrio</td><td>FI-Var</td><td>67.75°N, 29.69°E</td><td>[2015, 2020]</td><td>57; 0</td><td>57; 0</td><td>57; 0</td><td>Boreal forest</td><td>ENF</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Disko Island</td><td>GL-Dsk</td><td>69.25°N, -53.51°E</td><td>[2015, 2018]</td><td>29; 0</td><td>0; 0</td><td>0; 0</td><td>Barren tundra</td><td>GRA</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Kobbefjord</td><td>GL-NuF</td><td>64.14°N, -51.38°E</td><td>[2015, 2016]</td><td>5; 0</td><td>5; 0</td><td>5; 0</td><td>Sedge, moss, wetland</td><td>GRA</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Zackenberg</td><td>GL-ZaF</td><td>74.48°N, -20.56°E</td><td>[2015, 2019]</td><td>22; 0</td><td>22; 0</td><td>22; 0</td><td>Sedge, moss, wetland</td><td>GRA</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Adventdalen</td><td>NO-Adv</td><td>78.19°N, 15.92°E</td><td>[2015, 2016]</td><td>9; 0</td><td>0; 0</td><td>0; 0</td><td>Shrub, sedge, moss wetland</td><td>GRA</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Cherski Reference</td><td>RU-Ch2</td><td>68.62°N, 161.35°E</td><td>[2015, 2017]</td><td>16; 0</td><td>0; 0</td><td>0; 0</td><td>Shrub, sedge, moss, wetland</td><td>SHR</td><td>Peatland</td><td>(69)</td></tr><tr><td>Chersky Tower 1</td><td>RU-Eusk_cher1</td><td>68.51°N, 161.53°E</td><td>[2015, 2016]</td><td>9; 0</td><td>0; 0</td><td>0; 0</td><td>Larch forest and shrub</td><td>SHR</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Chersky Tower 2</td><td>RU-Eusk_cher2</td><td>68.70°N, 161.54°E</td><td>[2015, 2016]</td><td>8; 0</td><td>0; 0</td><td>0; 0</td><td>Disturbed forest, shrub, grass</td><td>SHR</td><td>Peatland</td><td>(69)</td></tr><tr><td>Igarka</td><td>RU-IG</td><td>67.48°N, 86.44°E</td><td>[2016, 2021]</td><td>19; 0</td><td>0; 0</td><td>0; 0</td><td>Boreal wetland, palsa</td><td>DNF</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Mukhrino Field Station</td><td>RU-Murk</td><td>60.90°N, 68.70°E</td><td>[2015, 2016]</td><td>4; 0</td><td>0; 0</td><td>0; 0</td><td>Pine bog, shrub</td><td>DNF</td><td>Peatland</td><td>(69)</td></tr><tr><td>Samoylov Island</td><td>RU-Sam</td><td>72.37°N, 126.50°E</td><td>[2015, 2018]</td><td>28; 0</td><td>28; 0</td><td>28; 0</td><td>Sedge, moss wetland</td><td>GRA</td><td>Peatland</td><td>(69)</td></tr><tr><td>Tura Nizhnyaya Tunguska River</td><td>RU-Tur</td><td>64.21°N, 100.46°E</td><td>[2016, 2021]</td><td>13; 0</td><td>0; 0</td><td>0; 0</td><td>Larch forest</td><td>DNF</td><td>Non-peatland</td><td>(69)</td></tr><tr><td>Bonanza Creek Black Spruce</td><td>US-BZS</td><td>64.70°N, -148.32°E</td><td>[2015, 2022]</td><td>81; 2467</td><td>61; 1827</td><td>61; 1827</td><td>Black spruce boreal forest</td><td>ENF</td><td>Non-peatland</td><td>(100)</td></tr><tr><td>Bonanza Creek Old Thermokarst Bog</td><td>US-BZo</td><td>64.69°N, -148.33°E</td><td>[2018, 2022]</td><td>45; 1366</td><td>45; 1366</td><td>45; 1366</td><td>Boreal bog</td><td>ENF</td><td>Non-peatland</td><td>(101)</td></tr><tr><td>Bonanza Creek Rich Fen</td><td>US-BZF</td><td>64.70°N, -148.31°E</td><td>[2015, 2022]</td><td>80; 2407</td><td>61; 1827</td><td>61; 1827</td><td>Boreal fen</td><td>ENF</td><td>Non-peatland</td><td>(102)</td></tr><tr><td>Bonanza Creek Thermokarst Bog</td><td>US-BZB</td><td>64.70°N, -148.32°E</td><td>[2016, 2022]</td><td>61; 1827</td><td>61; 1827</td><td>61; 1827</td><td>Boreal bog</td><td>ENF</td><td>Non-peatland</td><td>(103)</td></tr><tr><td>Eight Mile Lake</td><td>US-EML</td><td>63.88°N, -149.25°E</td><td>[2015, 2021]</td><td>61; 1857</td><td>61; 1857</td><td>61; 1857</td><td>Open shrub tundra</td><td>SHR</td><td>Non-peatland</td><td>(104)</td></tr><tr><td>Imnavait Creek Heath Tundra</td><td>US-ICh</td><td>68.61°N, -149.30°E</td><td>[2015, 2022]</td><td>81; 2467</td><td>81; 2467</td><td>81; 2467</td><td>Heath tundra</td><td>SHR</td><td>Peatland</td><td>(105)</td></tr><tr><td>Imnavait Creek Sedge Tundra</td><td>US-ICs</td><td>68.61°N, -149.31°E</td><td>[2015, 2022]</td><td>81; 2450</td><td>81; 2450</td><td>81; 2467</td><td>Sedge fen</td><td>SHR</td><td>Peatland</td><td>(106)</td></tr><tr><td>Imnavait Creek Tussock Tundra</td><td>US-ICt</td><td>68.61°N, -149.30°E</td><td>[2015, 2022]</td><td>81; 2451</td><td>13; 366</td><td>13; 366</td><td>Tussock tundra</td><td>SHR</td><td>Peatland</td><td>(107)</td></tr><tr><td>NGEE Arctic Barrow</td><td>US-NGB</td><td>71.28°N, -156.62°E</td><td>[2017, 2019]</td><td>19; 0</td><td>0; 0</td><td>0; 0</td><td>Snow/ice, sedge, moss wetland</td><td>GRA</td><td>Peatland</td><td>(69)</td></tr><tr><td>NGEE Arctic Council</td><td>US-NGC</td><td>64.85°N, -163.70°E</td><td>[2017, 2019]</td><td>4; 0</td><td>0; 0</td><td>0; 0</td><td>Tussock tundra</td><td>SHR</td><td>Peatland</td><td>(69)</td></tr><tr><td>Poker Flats Black Spruce</td><td>US-Prr</td><td>65.12°N, -147.49°E</td><td>[2015, 2023]</td><td>93; 2832</td><td>93; 2832</td><td>93; 2832</td><td>Black spruce forest</td><td>ENF</td><td>Non-peatland</td><td>(108)</td></tr><tr><td>Poker Flats Fire Scar</td><td>US-Rpf</td><td>65.12°N, -147.43°E</td><td>[2015, 2023]</td><td>93; 2832</td><td>93; 2832</td><td>93; 2832</td><td>Burned black spruce forest</td><td>ENF</td><td>Non-peatland</td><td>(109)</td></tr><tr><td>US KOC Council</td><td>US-KOC</td><td>64.84°N, -163.71°E</td><td>[2015, 2017]</td><td>6; 0</td><td>0; 0</td><td>0; 0</td><td>Tussock, dwarf-shrub tundra</td><td>SHR</td><td>Peatland</td><td>(69)</td></tr><tr><td>University Of Fairbanks</td><td>US-Uaf</td><td>64.87°N, -147.86°E</td><td>[2015, 2023]</td><td>93; 2832</td><td>93; 2832</td><td>93; 2832</td><td>Open black spruce forest</td><td>ENF</td><td>Non-peatland</td><td>(110)</td></tr></tbody></table>
<caption>TABLE II</caption>

PROBABILITIES (P) IN % OF HAVING FECM(SM_S) AND FECM(SMRZ) ABOVE 0.95, AS A FUNCTION OF THE MODERATE RESOLUTION IMAGING SPECTRORADIOMETER (MODIS) MCD12Q1 PLANT FUNCTIONAL TYPE (PFT) CLASS (54). PROBABILITIES WERE COMPUTED BY CONSIDERING ONLY THE MONTHS FROM JUNE TO SEPTEMBER AS WELL AS GRID CELLS FOR WHICH EDDY COVARIANCE (EC) MEASUREMENTS WERE AVAILABLE. FOR EACH PFT, THE ANALYSIS WAS PERFORMED BY TAKING INTO ACCOUNT ALL GRID CELLS TOGETHER, ONLY NON-PEATLAND GRID CELLS AND ONLY PEATLAND GRID CELLS. THE PEATLAND STATUS OF A GRID CELL IS DETERMINED BY THE GODDARD EARTH OBSERVING SYSTEM VERSION 5 CATCHMENT LAND SURFACE MODEL (62, GEOS-5 CLSM). FECM(SM_S) AND FECM(SMRZ) CONSTRAIN THE RH AND GPP CALCULATION WITHIN THE TERRESTRIAL CARBON FLUX (TCF) MODEL USED TO DERIVE THE SPL4CMDL PRODUCT, RESPECTIVELY (EQUATIONS 1 AND 3).

<table><thead><tr><th>PFT</th><th>Grid cell</th><th>P(F<sub>ECM</sub>(SM<sub>S</sub>)) &gt; 0.95</th><th>P(F<sub>ECM</sub>(SM<sub>RZ</sub>)) &gt; 0.95</th></tr></thead><tbody><tr><td rowspan="3">ENF</td><td>Peatland &amp; non-peatland</td><td>99.9%</td><td>99.1%</td></tr><tr><td>Non-peatland</td><td>99.9%</td><td>99.1%</td></tr><tr><td>Peatland</td><td>-</td><td>-</td></tr><tr><td rowspan="3">SHR</td><td>Peatland &amp; non-peatland</td><td>85.8%</td><td>79.8%</td></tr><tr><td>Non-Peatland</td><td>66.1%</td><td>69.5%</td></tr><tr><td>Peatland</td><td>99.9%</td><td>87.1%</td></tr><tr><td rowspan="3">GRA</td><td>Peatland &amp; non-peatland</td><td>80.6%</td><td>86.7%</td></tr><tr><td>Non-Peatland</td><td>75.1%</td><td>85.6%</td></tr><tr><td>Peatland</td><td>99.9%</td><td>90.5%</td></tr><tr><td rowspan="3">DNF</td><td>Peatland &amp; non-peatland</td><td>94.4%</td><td>98.5%</td></tr><tr><td>Non-Peatland</td><td>94.1%</td><td>98.6%</td></tr><tr><td>Peatland</td><td>95.1%</td><td>98.4%</td></tr></tbody></table>

REFERENCES

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