# High-resolution mapping of peatland CO2 fluxes using drone multispectral images

Romain Walcker, C. Le Lay, Laure Gandois, A. Elger, V.E.J. Jassey

► To cite this version:

Romain Walcker, C. Le Lay, Laure Gandois, A. Elger, V.E.J. Jassey. High-resolution mapping of peatland CO2 fluxes using drone multispectral images. Ecological Informatics, 2025, 86, pp.103060. 10.1016/j.ecoinf.2025.103060. hal-05033536

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# High-resolution mapping of peatland CO₂ fluxes using drone multispectral images

R. Walcker*, C. Le Lay, L. Gandois, A. Elger, V.E.J. Jassey

Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 – Paul Sabatier (UT3), Toulouse, France

## ARTICLE INFO

**Keywords:**

Drone

Carbon Flux

Peatlands

Multispectral images

## ABSTRACT

Large-scale mapping of ecosystem CO₂ fluxes between land and atmosphere is a challenging task. Typically, it is based on multispectral satellite images with low (~250 m) to moderate ground pixel resolution (~20 m). However, in small, fine-scale ecosystems such as peatlands, the representation of CO₂ fluxes heterogeneity across microhabitats is very limited by the ground pixel resolution of satellites. In this context, high ground pixel resolution of drone imagery might prove useful, alone or in synergy with large-scale satellite data, to better support field investigations and carry out rapid carbon assessment at a relatively low cost. Here, we carried out a survey of CO₂ exchanges over 4 ha of peatland during the growing season using chamber measurements, as well as simultaneous aerial multispectral orthophotographs acquired by drone. To assess the ability of drone multispectral images at providing relevant information for the prediction of CO₂ fluxes, we developed robust linear regression models and used drone imagery to map net ecosystem exchange, ecosystem respiration and gross ecosystem productivity at a very fine scale (~5 cm). Our predictions were in the range of those found in the satellite remote sensing literature with errors lesser than 0.5 gCO₂.m⁻².h⁻¹. Our study offers new opportunities to refine large scale satellite assessment of CO₂ fluxes on small, valuable peatland areas that can be easily flown over by drone. Moreover, we believe that our results will be of interest for the scientific community as well as environmental managers wishing to carry out rapid carbon assessments of peatlands.

## 1. Introduction

Peatlands are one of the densest carbon (C) repository on land. They contain about 30 % of the global soil C stock while covering only 3 % of the Earth's land area (Yu et al., 2010). Most of extensive peatland areas are located in the northern hemisphere, typically in Siberia, Canada and northern Europe (Greifswald Mire Centre, 2022; Melton et al., 2022; Tanneberger et al., 2017; Xu et al., 2018). However, a significant proportion of the world's peatland extent is also occupied by numerous small patches of peatland area (less than 5 ha), especially in mountains and southern Europe, where environmental and historical conditions were favourable to their establishment and preservation (Fig. 1a). Although peatlands are still currently a net C sink, climate change together with anthropogenic degradations such as wild fires and land use changes are threatening this C pool (Dise, 2009; Hopple et al., 2020; Loisel et al., 2021). Thus, the monitoring of peatlands from a regional to a local scale is of primary importance in the context of carbon emissions reduction programs (Humpenöder et al., 2020).

On a local scale, peatlands are composed of heterogeneous micro-habitats of some square centimetres ranging from elevated moist hummocks and lawns, to wet hollows and open water pools (Harris et al., 2015; Rydin et al., 2013). This heterogeneity results from microtopographic irregularities and water table level, together controlling the composition of vegetation and microbial communities and thus fine scale variations in biogeochemical fluxes (Arsenault et al., 2018; Hamard et al., 2021; Mitchell et al., 2000; Siewert and Olofsson, 2020). Consequently, fine scale observations of CO₂ fluxes in peatland micro-habitats are needed for better monitoring the C balance and managing this ecosystem on a local scale (Waddington and Roulet, 1996). Moreover, large-scale assessments of peatland global carbon balance as well as local restoration initiatives as part of CO₂ emission reduction programs could greatly benefit from considering the fine scale heterogeneity of peatland CO₂ fluxes (Humpenöder et al., 2020; Riquelme del Río et al., 2024).

Current field-based technics for measuring in situ CO₂ exchanges between peatlands and the atmosphere include eddy covariance (EC)

<footnote>* Corresponding author.
E-mail address: romain.walcker@univ-tlse3.fr (R. Walcker).</footnote>
towers and flux chambers. EC towers allow good analysis of CO₂ tem-
poral variations (Baldocchi, 2003; Baldocchi, 2020; Lafleur et al., 2003;
Pocztá et al., 2023; Rebmann et al., 2018). CO₂ concentration and air
turbulence are measured by a tower in areas spanning dozens of square
meters to square kilometres at high temporal frequency and over rela-
tively extended periods of time. Some limitations arise from the high
degree of spatial heterogeneity of peatland microhabitats, meaning that
a single EC tower cannot be assumed to be a good proxy of every patch.
The second technique for measuring in situ CO₂ exchanges between
peatlands and the atmosphere is the closed chamber technique
(Hutchinson and Livingston, 1993; Rochette and Hutchinson, 2005;
Marushchak et al., 2013; Dabrowska-Zielinska et al., 2016b; Korrensalo
et al., 2020). A closed chamber is connected to a gas analyser which
measures CO₂ fluxes over few square centimetres of soil and low-lying
vegetation. The closed chamber technique is more adapted than EC
towers to investigate the variability of carbon fluxes over peatland mi-
crohabitats. Still, these in situ measurements are very restricted in space
coverage, time consuming and therefore are not well suited to monitor
heterogeneity of CO₂ exchanges at the ecosystem scale (Ingle et al.,
2023).

One way to map CO₂ fluxes over a whole peatland area with enough
spatial details is to infer CO₂ fluxes from remote sensing images. Ac-
quired images are integrated into models based on the light use effi-
ciency concept (LUE). LUE concept states that a relatively constant
relationship exists between CO₂ fluxes and light radiations receipt by
vegetation (Monteith, 1977). This type of model has been extensively
used to predict plant primary productivity in peatlands (see for review
Lees et al., 2018). Predictions are obtained by multiplying the photo-
synthetically active radiation absorbed by vegetation (APAR) by a
photosynthetic light use efficiency parameter (E) which varies according
to plant type. APAR is obtained by multiplying the sun emitted photo-
synthetically active radiation (PAR) by the fraction of PAR absorbed by
vegetation (fPAR). In empirical modelling based on remote sensing data,
fPAR is usually modelled as a linear function of a vegetation index (VI)
and E is a regression coefficient that scales the VI (Cai et al., 2021; Harris
and Dash, 2011; Kross et al., 2013; Schubert et al., 2010). Thus,
empirical LUE models generally combine PAR measurements with one
VI, often the normalized difference vegetation index (NDVI; Tucker,
1979), the enhance vegetation index (EVI; Huete, 1988) or the leaf area
index (LAI; see for example Myneni et al., 2002; Garisoain et al., 2023).
Besides CO₂ uptake by peatland vegetation, models which aimed at
estimating the CO₂ released by ecosystem respiration, and particularly
those which seek at estimating heterotrophic respiration, are much
scarcer (Lees et al., 2018). Moreover, CO₂ uptake and release by

peatland ecosystems are known to be highly dependent to environ-
mental conditions. Studies on this topic showed that soil water avail-
ability, vapor pressure deficit (VPD) and air or soil temperature are
essential covariables for prediction of CO₂ fluxes in empirical LUE
models (Lees et al., 2018). Besides, empirical LUE models are generally
based on satellite images whose pixel sizes are too coarse to account for
microhabitat heterogeneity. Typically, satellite optical imagers provide
multispectral (MS) reflectance data in pixels of size ranging from ~1.5-5
m (e.g. Pléiades, Ikonos, PlanetScope) to ~20-30 m (e.g. Sentinel-2,
Landsat) and 250-500 m (e.g. MODIS). Thus, fine resolution images
acquired by drones can provide a complementary data source for map-
ping peatland local heterogeneity.

Drones equipped with MS sensors have rapidly become market
available during the past few years, together with computer vision and
algorithms able to produce image mosaics geometrically and radio-
metrically corrected (Zhang and Zhu, 2023). MS drones are particularly
used in the agriculture sector, for instance to monitor plant growth and
health (Phang et al., 2023). Still, a very limited amount of studies
employed MS drone imagery to map CO₂ fluxes in peatlands. Recently,
Kelly et al. (2021) used a thermal infrared drone sensor to extrapolate a
peatland respiration model. Pajula and Purre (2021) studied the relation
between NDVI and net ecosystem exchange during the growing season
in an Estonian peatland. Besides these couple of studies, estimation of
CO₂ exchanges in peatlands using MS drone remains very scarce. In a
highly heterogeneous Arctic tundra environment situated in the vicinity
of Abisko (Sweden), Siewert and Olofsson (2020) have shown the ability
of drone MS imagery in reducing bias of primary productivity estimates
compared with satellite imagery.

In this study, we aimed to map the fine scale heterogeneity of peat-
land CO₂ fluxes using drone MS imagery. To achieve this goal, we ac-
quired field measurements of CO₂ fluxes in several microhabitats using
the closed chamber technique simultaneously with in situ meteorolog-
ical data and drone MS imagery in May, June, July and October 2023
over a small peatland site of the French Pyrenees. We developed robust
statistical models and show their performance at mapping CO₂ fluxes.

2. Material and methods

2.1. *Study site*

The study was conducted in a Pyrenean peatland of about 4 ha
located in the south of France (Fig. 1; Bernadouze hamlet, 42.80°N,
1.42°E, alt. 1343 m). The site is characterized by the presence of scat-
tered dwarf pines and ashes in the driest patches, while in the wetter,

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Location of the study site. **a)** Distribution of peatland surfaces expressed in hectares per square kilometre (Melton et al., 2022). The black dot indicates location of the study site situated in the French Pyrenees at 1300 m altitude (42.80°N, 1.42°E). **b)** Aerial view of the study site (Esri, Maxar, Earthstar Geographics, and the GIS User Community). The peatland area was about 4 ha in area and was surrounded by a beech forest. **c)** Field photography taken from the south-western part of the site in June the 15th 2023 at 11: 53 AM (UTC + 2) and looking eastward. This part of the peatland is an active ombrotrophic bog mainly occupied by *Sphagnum palustre* and *Carex* spp. which can be seen in the foreground.</figcaption></figure>
lower-lying areas pioneer willows, dwarf juniper and thorn bushes occur. Low lying vegetation communities are structured into micro-habitats over two main areas. The south-western part is an active ombrotrophic bog dominated by *Sphagnum S. capillifolium*, *Calluna vulgaris* and *Vaccinium myrtillus* whereas the north-eastern part is a transition between a minerotrophic fen dominated by *Sphagnum palustre* and a wet-grassland, occupied by herbaceous species such as *Carex* spp. and *Equisetum fluviatile*. This site exhibits a vegetation that is comparable to what can be found in European *Sphagnum*-dominated peatlands (Robroek et al., 2017). In this peatland, peat accumulated in the last 10,000 years reaching nowadays an average depth of 2 m, 10 m at its deepest (Jalut et al., 1982). A meteorological station was installed near the study site in 2013 (Gascoin and Fanise, 2018) collecting data on air temperature (°C) at 2 m above ground, precipitation (mm) and downward shortwave radiation (DSR; Watt.m⁻²) every 30 min. During the 2015–2023 period, mean diurnal temperature was 10.0 °C, the highest temperatures occurring in July and August with a maximum of 33.2 °C, and lower temperatures in January reaching a minimum of -13.3 °C. Mean annually cumulated rainfall was 1530 mm, ranging between a minimum of 222 mm and a maximum of 2151 mm in 2017 and 2018, respectively. The average annual vegetation period (daily mean air temperature > 5 °C) was 229 days, generally between April and late October. The snow period was generally intermittent between November and April. In 2023, the site was covered by snow until late April. The mean water table depth was generally -0.21 m below the soil surface with summer depth ranging between -0.28 to -0.32 m (Garisoain et al., 2023).

## 2.2. Field measurement of CO₂ fluxes

Field work was conducted during the growing season 2023, from May to October. A total of four field campaigns were conducted: May the 17th, June the 15th, July the 10th and October the 5th. Each campaign event included ground data acquisition at 9 permanent sampling points (Fig. 2). The number of sampling points was determined to be able to

proceed all flux measurements under favourable light and temperature conditions, typically between 10 AM and 12 AM to avoid summer heat, and allow comparison between sampling dates. The location of plots was determined to cover the diversity of microhabitats in the site (Fig. 3). CO₂ fluxes were measured using a portable infrared gas analyser IRGA Targas-1 (PP System, Amesbury, USA) supplied with the CPY-5 Canopy Assimilation Chamber (see for similar approaches Korrensalo et al., 2020; Barel et al., 2021; Gavazov et al., 2022; Garisoain et al., 2023). We created an airtight seal within the chamber by attaching it to a custom-made PVC collar (15 cm diameter, 5 cm height) with a plastic skirt pressed tight onto the surrounding vegetation. In each plot, net ecosystem CO₂ exchange (NEE) was measured using the transparent chamber. NEE is commonly defined as the difference between CO₂ release through ecosystem respiration and CO₂ uptake through photosynthesis by plants. Following the NEE measurement, the chamber was flushed with ambient air until the ambient CO₂ concentration was reached. Then, it was replaced on the collar with an opaque lid to measure ecosystem respiration (ER) under dark conditions. Dark conditions hamper CO₂ uptake through photosynthesis while measuring only respiration. Each measurement lasted 90 s to avoid the build-up of heat and condensation inside the chamber. We calculated NEE and ER flux rates as a linear change in CO₂ concentration (ppm) over the measurement period using the R package gasfluxes (Fuss et al., 2020). We considered ambient atmospheric pressure, soil temperature, the volume and surface of the CPY-5 chamber, and light intensity to calculate CO₂ flux rates. Gross ecosystem production (GEP), i.e. CO₂ uptake as a result of photosynthesis, was posteriori calculated as the difference between NEE and ER. All fluxes were expressed following the atmospheric sign convention in which positive fluxes represent emission to the atmosphere.

## 2.3. Drone reflectance data

Each campaign event included the collection of geo-localised

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Aerial view of the study site in the peak season showing microhabitat heterogeneity. This orthomosaic has been acquired at sun zenith in June the 15th by the Phantom 4 multispectral drone (DJI, Shenzhen, China). Ground pixel size is approximatively 5 cm. Images are displayed as true colour composites and histograms are stretched to improve colour display. CO₂ sampling points are shown as green circles labelled from P1 to P9. The central image is on the scale of 1:2000 and all zooms are on the scale of 1:1000. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Microhabitats sampled in this study. These photographs were captured in June the 15th with a standard smartphone camera vertically oriented to the North and looking towards the ground. Each photograph covers a plot of 40*40 cm at ground and its centre coordinates are expressed in decimal degrees. Each corresponding part of the multispectral orthomosaic acquired the same day by the drone is displayed in true colours in the top right corner. For each plot, mean value in each spectral band was calculated and included in the predictive dataset to derived several vegetation indices. Permanent sampling PVC collars for close chamber measurements were installed few centimetres next to these emplacements.</figcaption></figure>

multispectral aerial images using a Phantom 4 multispectral drone (P4M; DJI, Shenzhen, China). The onboard camera array had 5 CMOS sensors delivering 1600*1300-pixel images and covering the blue (B; 450 ± 16 nm), green (G; 560 ± 16 nm), red (R; 650 ± 16 nm), red edge (RE; 730 ± 16 nm) and near infrared bands (NIR; 840 ± 26 nm). During each campaign, the drone followed exactly the same flight path and parameters, i.e. automatic flight mode and automatic exposure time setting in clear sky conditions, making time series images comparable. The camera was equipped with an integrated spectral sunlight sensor on top of the drone to capture solar irradiance and to correct for the influence of external light changes on images during flight. The drone was connected to a D-RTK 2 Mobile Station (DJI, Shenzhen, China) providing

real-time differential corrections that generated centimetre-level positioning data for the drone in flight. Each flight lasted about 18 min and was processed at sun zenith, i.e. between 13:00 and 14:00 (UTC + 2). Before each flight, a vertical image of a MAPIR Camera Target V2 (San Diego, California, USA) was acquired at 3 m above ground to calibrate pixel values to reflectance values in each band. Five positioning targets were placed in the middle and at the 4 site corners and were geolocalised using a Multi-band RTK GNSS receiver Reach RS2 (Emlid, Budapest, Hungary) to obtain ground control points with about 10 cm planimetric accuracy. During the flight, the drone was at an altitude of 95 m above ground, moving at 6.5 m per second, the camera was looking at nadir, capture interval time was set to 2 s and image overlapping was
set to 80 % laterally and longitudinally. Resulting ground pixel size was ~5 cm. The photogrammetric processing of the drone imagery was carried out using the Pix4D Mapper software (Pix4D, Lausanne, Switzerland). This photogrammetry software allows the P4M camera model to extract automatically relevant information about camera, calibration panels and sun sensors directly from EXIF files, where all image metadata are stored. The application of structure-from-motion algorithms enabled computation of dense 3D point clouds to generate digital elevation models and multispectral orthomosaics with the five radiometric bands calibrated in reflectance. At each field campaign, 146 multiband aerial images were thus collected and processed.

## 2.4. Meteorological data

We retrieved meteorological data from the meteorological station dataset spanning the period 2015–2023 (Gascoin and Fanise, 2018). It consisted in air temperature at 2 m above ground (TEMP) expressed in degree Celsius (°C) and downward shortwave radiation (DSR) expressed in Watt.m² collected over 30 min. DSR was converted to PAR expressed in µmole.m².s⁻¹ using a 0.45 ratio as proposed by Spitters et al. (1986) and an approximation of 4.6 µmole per Watt. The day of the year (DOY), i.e. a number between 1 and 365, was added to the dataset.

## 2.5. Prediction of CO₂ fluxes

We employed multiple linear regression to calculate CO₂ fluxes from P4M data. A total of 36 observations constituted the original dataset used to fit a predictive model of NEE, ER and GEP, i.e. 9 points sampled during 4 field campaigns. At the geographic location of each observation, we calculated mean reflectance value of each spectral band within areas of 40*40 cm situated few centimetres apart permanent sampling points (Fig. 3). Using these values, we calculated 19 VIs (Table 1). We added to this remote sensing dataset meteorological variables, this accounted for 27 explanatory variables, i.e. 5 spectral bands, 19 VIs and 3 environmental variables. In order to avoid misleading statistical results due to collinearity issues between explanatory variables (Dormann et al., 2013), as well as under and overfitting to due to an inappropriate level of model complexity (Aliferis and Simon, 2024), we adopted the following pipeline (Fig. 4). First, collinearity between explanatory variables was detected using an iterative variation inflation factor (VIF) analysis (Booth et al., 1994). In detail, a stepwise approach was adopted to delete from the remaining list of explanatory variables those with VIF was greater than 5, a common method and conservative threshold found in the literature (Dormann et al., 2013). Using the non-colinear variables finally retained, each unique combination of them was identified using the ‘combinations’ function from the Python `itertools` standard library module (Van Rossum and Drake Jr, 1995). Second, the 36 observations were randomly split 100 times with replacement into training and testing samples, i.e. bootstrap sampling (Davison and Hinkley, 1997), accounting respectively for about 60 % and 40 % of the dataset. Training samples were employed to fit linear models for the prediction of NEE, ER and GEP using ordinary least square regression, while testing samples were leaved out to evaluate model performance. Model performances were evaluated by comparing predicted values and observed values of testing samples. For each model, mean Pearson product-moment correlation coefficient (r) between predicted and test values as well as root mean square error (RMSE) were calculated over the 100 iterations. The importance of variables in models was assessed using their absolute coefficient after prior variable standardization, i.e. after subtracting from each value the mean of its series and dividing by its standard deviation.

## 2.6. Mapping ecosystem metabolism

Using the ArcGIS software (ESRI, Redlands, USA), we first removed image pixels with no vegetation (e.g. open water, rocks, bare ground),

<table><caption>**Table 1**
Formulas of vegetation indices used in this study.</caption><thead><tr><th>Full Names</th><th>Formulas</th><th>References</th></tr></thead><tbody><tr><td>Blue Chromatic<br>Coordinates (BCC)</td><td>B / (R + G + B)</td><td>Gillespie et al.<br>(1987)</td></tr><tr><td>Browning Reflectance<br>Index (BRI)</td><td>((1/G) - (1/RE)) / NIR</td><td>Merzlyak et al.<br>(2003)</td></tr><tr><td>Blue-Wide Dynamic<br>Range Vegetation Index<br>(BWDRVI)</td><td>((0.1*NIR) - B) / ((0.1*NIR) + B)</td><td>Hancock and<br>Dougherty<br>(2007)</td></tr><tr><td>Chlorophyll Index (CI)</td><td>(NIR/G) -1</td><td>Gitelson et al.<br>(2003)</td></tr><tr><td>Chlorophyll Vegetation<br>Index (CVI)</td><td>NIR*(R/(G*G))</td><td>Vincini et al.<br>(2008)</td></tr><tr><td>Enhanced Vegetation<br>Index (EVI)</td><td>2.5 * ((NIR - R) / ((NIR + 6 * R -<br>7.5 * B) + 1))</td><td>Huete et al.<br>(1994)</td></tr><tr><td>Enhanced Vegetation<br>Index 2 (EVI2)</td><td>2.5*((NIR-R) / (NIR + (2.4*R) +1))</td><td>Jiang et al.<br>(2008)</td></tr><tr><td>Green Atmospherically<br>Resistant Vegetation<br>Index (GARI)</td><td>(NIR - (G - (B-R))) / (NIR - (G + (B-<br>R)))</td><td>Gitelson et al.<br>(1996)</td></tr><tr><td>Green Chromatic<br>Coordinates (GCC)</td><td>G / (R + G + B)</td><td>Gillespie et al.<br>(1987)</td></tr><tr><td>Green Leaf Index (GLI)</td><td>((2*G) - R - B) / ((2*G) + R + B)</td><td>Gobron et al.<br>(2000)</td></tr><tr><td>Green Normalized<br>Difference Vegetation<br>Index (GNDVI)</td><td>(NIR - G) / (NIR+ G)</td><td>Buschmann and<br>Nagel (1993)</td></tr><tr><td>Leaf Chlorophyll Index<br>(LCI)</td><td>(NIR - RE) / (NIR + R)</td><td>Bisun (1999)</td></tr><tr><td>Normalized Difference<br>Red Edge Index (NDRE)</td><td>(NIR - RE) / (NIR + RE)</td><td>Barnes et al.<br>(2000)</td></tr><tr><td>Normalized Difference<br>Vegetation Index<br>(NDVI)</td><td>(NIR - R) / (NIR+ R)</td><td>Tucker (1979)</td></tr><tr><td>Optimized Soil Adjusted<br>Vegetation Index<br>(OSAVI)</td><td>(1 + 0.16) * (NIR - R) / (NIR + R +<br>0.16)</td><td>Rondeaux et al.<br>(1996)</td></tr><tr><td>Red Chromatic<br>Coordinates (RCC)</td><td>R / (R + G + B)</td><td>Gillespie et al.<br>(1987)</td></tr><tr><td>Simple Ratio Vegetation<br>Index (RVI)</td><td>NIR / R</td><td>Pearson and<br>Miller (1972)</td></tr><tr><td>Structure Intensive<br>Pigment Index (SIPI)</td><td>(NIR - B) / (NIR - R)</td><td>Penuelas et al.<br>(1995)</td></tr><tr><td>Transformed Vegetation<br>Index (TVI)</td><td>√((NIR - R)/(NIR + R)) + 0.5</td><td>Bannari et al.<br>(1995)</td></tr></tbody></table>

shadows and trees situated at the edge of the site. After visual inspection of raw images, no vegetation pixels were excluded from analysis by computing and thresholding the Normalized Difference Vegetation Index (NDVI). Pixels with NDVI value lower than 0.4 were considered as non-vegetation pixels. Shadows were excluded by thresholding the NIR band at 0.1, pixels lower than this value were excluded as shadow pixels. We excluded the surrounding forest by drawing a geometrical mask delineating the peatland. Finally, we used the Python's rasterio library (https://github.com/rasterio/rasterio) to produce predictive maps of ER, GEP and NEE using selected models.

## 2.7. Variability analysis of measurements over space and time

We analysed variability of CO₂ fluxes and P4M spectral bands over months and sampling stations using linear mixed-effects models (LME) from LME4 R package (Bates et al., 2015). Models were fitted with variable types (type of CO₂ flux or type of spectral bands), months, and stations (point for CO₂ fluxes or plots for spectral bands) as fixed effects and with stations as a random effect on the intercept to account for potential pseudoreplication resulting from repeated measurements over time in same station (Pinheiro and Bates, 2000). Tukey multiple comparison tests were used for post hoc analyses of differences among levels of fixed effects in every LME model when necessary. Normality and variance homogeneity assumptions of model residuals were assessed using a Shapiro test and diagnostic plots.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Pipeline developed for best model selection. **a)** The original dataset was composed of 5 spectral images acquired Phantom 4 Multispectral (B: blue, G: green, R: red, RE: red edge and NIR: near infrared), 19 vegetation indices (VIs) and 3 variables acquired by a meteorological measuring station. **b)** Iterative Variance Inflation Factor (VIF) analysis was conducted on the original dataset (36 observations, 27 variables) to reduce it to a new set of non-colinear predictive variables. **c)** 127 unique combinations of non-colinear variables were identified and **d)** used in ordinary least square regression models over 100 randomly selected subsamples (i. e. train/test splits) to predict and evaluate predictions of Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER) and Gross Ecosystem Productivity (GEP). Mean and standard deviation to the mean (Std) of Pearson correlation coefficient (r) and Root Mean Square Error (RMSE) were calculated to **e)** finally select best models to apply to images. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

# 3. Results

## 3.1. CO₂ fluxes measured by closed-chambers

Overall, CO₂ fluxes measured by closed-chambers varied across space and time (Fig. 5; Table 2). Regarding NEE (Fig. 5b), flux rates ranged between -1.17 and 0.29 gCO₂.m⁻².h⁻¹ (mean = -0.34 ± 0.25 (SD)). Field measurements among the 9 sampled points were relatively more variable in June (-0.69 ± 0.20 gCO₂.m⁻².h⁻¹) and July (-0.17 ± 0.23 gCO₂.m⁻².h⁻¹) than in May (-0.34 ± 0.16 gCO₂.m⁻².h⁻¹) and October (-0.16 ± 0.15 gCO₂.m⁻².h⁻¹). Mean NEE increased between May and June (P = 0.03), decreased between June and July (P < 0.001), and stabilized between July and October (P = 0.99). ER and GEP both

ranged between ~0.2 and ~ 2.7 gCO₂. m⁻².h⁻¹ (absolute values; Fig. 5c-d). ER showed similar temporal patterns than NEE, with a peak in July (P < 0.001). GEP showed large interquartile ranges compared to NEE and ER (Fig. 5d). GEP was relatively stable between May and July (P = 0.75), with mean rates around -1.6 ± 0.4 gCO₂.m⁻².h⁻¹ and then decreased to -0.61 ± 0.2 gCO₂.m⁻².h⁻¹ in October.

## 3.2. Reflectance data in sampling plots

Overall, reflectance data in sampled plots were characteristic of plant spectra, exhibiting high absorption in the visible (reflectance ~ < 0.1 between ~400 to ~650 nm) and high reflectance in the red-edge and near-infrared wavelengths (reflectance ~ < 0.3 between ~700 and ~
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Measured CO₂ fluxes in sampling points. **a)** Averaged balance of CO₂ flux among sampling points. Green bars indicate gross ecosystem production (GEP) rates, light blue bars indicate ecosystem respiration (ER) rates and red solid line indicates net ecosystem exchange (NEE). Error bars show the standard deviation to the temporal mean. Detailed temporal dynamics for May, June, July and October for **b)** NEE, **c)** ER and **d)** GEP. Median is shown as red line in boxplots and the mean as a green dashed line. The whiskers extend from the box to the farthest data point lying within 1.5× the inter-quartile range from the box and outliers are shown as black circles. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<table><caption>**Table 2**

Summary of the linear mixed effects models testing the effects of Month and Plot (fixed effects) on CO₂ flux rates. Values in bold indicate a significant effect at *P* < 0.05.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Flux type (F)</th><th colspan="2">Month (M)</th><th colspan="2">Plot (P)</th><th colspan="2">M x P</th><th colspan="2">F x M x P</th></tr><tr><th>F</th><th>P</th><th>F</th><th>P</th><th>F</th><th>P</th><th>F</th><th>P</th><th>F</th><th>P</th></tr></thead><tbody><tr><td>Full model</td><td>498.1</td><td>&lt; 0.001</td><td>7.6</td><td>0.0012</td><td>0.09</td><td>0.78</td><td>1.7</td><td>0.21</td><td>2.5</td><td>0.034</td></tr><tr><td>NEE</td><td>-</td><td>-</td><td>10.9</td><td>&lt; 0.001</td><td>0.09</td><td>0.77</td><td>2.4</td><td>0.1</td><td>-</td><td>-</td></tr><tr><td>ER</td><td>-</td><td>-</td><td>38.1</td><td>&lt; 0.001</td><td>3.2</td><td>0.12</td><td>1.8</td><td>0.17</td><td>-</td><td>-</td></tr><tr><td>GEP</td><td>-</td><td>-</td><td>14.7</td><td>&lt; 0.001</td><td>2.5</td><td>0.16</td><td>2.9</td><td>0.06</td><td>-</td><td>-</td></tr></tbody></table>

900 nm; Fig. 6). Plot reflectance mainly overlapped, except for P9 in October which was in the shadow of a tree (Fig. 6d). At the individual band level, we found that visible bands (i.e. blue, green and red) significantly varied over months (*P* < 0.05 for the three bands; Table 3), with less absorption in May (early green-up) and October (early dormancy) than in June and July (peak season). Furthermore, our analyses revealed that the red-edge and NIR bands significantly varied across plots (*P* < 0.01 for the two bands), with lower reflectance in the ombrotrophic part of the peatland compared to the rest (i.e. P8 and P9).

### 3.3. Selected models

We detected a high-level of collinearity in the predictive dataset (Fig. 7). Thus, over 27 potential explanatory variables, 7 variables were retained as non-collinear by the iterative VIF analysis. Retained variables were the blue-wide dynamic range vegetation index (BWDRVI), the chlorophyll vegetation index (CVI), TEMP, the normalized difference red edge index (NDRE), the NIR band, the PAR, and the browning reflectance index (BRI). Altogether, they accounted for 127 combinations to be tested for predicting CO₂ fluxes, i.e. 7 models with 1 explanatory variable, 21 models with 2 explanatory variables, 35 models with 3 explanatory variables, 35 models with 4 explanatory variables.
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Mean reflectance spectra collected at sampling plots. Spectra were acquired by the Phantom 4 multispectral drone (P4M) in a) May, b) June, c) July and d) October. Reflectance are unitless. Plot 9 in October was in the tree shadow during the drone flight.</figcaption></figure>

<caption>**Table 3**</caption>

Summary of the linear mixed effects models testing the effects of Month and Plot (fixed effects) on reflectance bands. Values in bold indicate a significant effect at P < 0.05.

<table><thead><tr><th rowspan="2"></th><th colspan="2">Band type (F)</th><th colspan="2">Month (M)</th><th colspan="2">Plot (P)</th><th colspan="2">M x P</th><th colspan="2">F x M x P</th></tr><tr><th>F</th><th>P</th><th>F</th><th>P</th><th>F</th><th>P</th><th>F</th><th>P</th><th>F</th><th>P</th></tr></thead><tbody><tr><td>Full model</td><td>635.4</td><td>&lt; 0.001</td><td>8</td><td>&lt;0.001</td><td>27.7</td><td>&lt;0.001</td><td>12.5</td><td>&lt;0.001</td><td>2.5</td><td>0.004</td></tr><tr><td>B</td><td>-</td><td>-</td><td>10.3</td><td>&lt; 0.001</td><td>3.9</td><td>0.06</td><td>2.2</td><td>0.11</td><td>-</td><td>-</td></tr><tr><td>G</td><td>-</td><td>-</td><td>4.3</td><td>0.04</td><td>4.2</td><td>0.01</td><td>2.6</td><td>0.07</td><td>-</td><td>-</td></tr><tr><td>R</td><td>-</td><td>-</td><td>11.8</td><td>&lt; 0.001</td><td>8.3</td><td>0.007</td><td>1.45</td><td>0.25</td><td>-</td><td>-</td></tr><tr><td>RE</td><td>-</td><td>-</td><td>2.4</td><td>0.08</td><td>11.8</td><td>0.002</td><td>4.1</td><td>0.02</td><td>-</td><td>-</td></tr><tr><td>NIR</td><td>-</td><td>-</td><td>1.2</td><td>0.33</td><td>8.2</td><td>0.008</td><td>5.1</td><td>0.006</td><td>-</td><td>-</td></tr></tbody></table>

variables, 21 models with 5 explanatory variables, 7 models with 6 explanatory variables and 1 model with 7 explanatory variables. We tested each of the 127 combinations of variables over 100 randomly selected train/test splits and found that model performances varied depending on the number and the combination of variables. Typically, model performance increased with the number of variables until an optimum number was reached, then falls again due to overfitting (Fig. 8a, d, g). We found that NEE was better predicted by the linear combination of 3 variables (red dot; Fig. 8a). Variable coefficient analysis showed that PAR and CVI were less important than BWDRVI, NDRE, TEMP and NIR even if the differences are not very pronounced (Fig. 8b).

The most performant NEE model employed BWDRVI, NDRE and TEMP (Fig. 8c; r = 0.59 and RMSE = 0.27 g.CO2.m⁻².h⁻¹). Predicted NEE values where not clearly temporally structured although predicted values in July and October appeared higher than May and June values, and close to 0 as we observed with closed-chambers (Fig. 5b). The linear combination of 4 variables slightly better predicted ER than models with 2 and 3 variables (Fig. 8d). PAR and TEMP were clearly identified as the most important variables (Fig. 8e). The linear combination of PAR, BWDRVI, TEMP and NIR showed best performances (Fig. 8f; r = 0.75 and a RMSE = 0.35 g.CO2.m⁻².h⁻¹). Monthly variations of ER were successfully predicted as predictions were quite well aligned to the 1:1
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Correlation matrix of explanatory variables in the Phantom 4 multispectral dataset (P4M). Matrix labels are absolute values of the Pearson product-moment correlation coefficient (r). Collinearity increases from light yellow to dark blue. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

correlation line. The model highlighted a clear decrease of ER between May and June, a strong increase between June and July, and a very strong decrease between July and October as highlighted by closed-chamber data (Fig. 5c). For the prediction of GEP, we found that 3 variables provided slightly better predictions (Fig. 8g). As for ER, PAR appeared as very important feature. NRDE and CVI were more important than BWDRVI and NIR (Fig. 8h). The best model combined PAR, NDRE and TEMP (Fig. 8i). As observed in the field (Fig. 5d), our predictions did not evidence clear monthly variations in GEP, at the exception of October (Fig. 8i; r = 0.70 and RMSE = 0.44 g.CO2.m⁻².h⁻¹).

## 3.4. Maps of CO₂ fluxes

Predictive maps of CO₂ fluxes were consistent with field measurements over months and revealed fine scale spatial variability over the peatland (Fig. 9). First, temporal dynamics in mean CO₂ fluxes followed main temporal patterns than measured in situ (Fig. 5). Second, spatial variability in CO₂ fluxes highlighted contrast between the two main parts of the peatland: the north-eastern, fen/grassland part, exhibited lower NEE in May, June and October (Fig. 9a, b, d), lower ER (Fig. 9e, f, h) and higher GEP values (Fig. 9i, j, l), and the south-western, ombrotrophic part, characterized by higher NEE values throughout months

(Fig. 9a-d). It should be noted that most non-vegetation surface masked between May and June (white pixels) corresponded to water surfaces, but in October many masked pixels appeared corresponding to cow dungs as the peatland was temporarily pastured during summer. At the peatland scale (4 ha in area), we found that the ecosystem was a daily net sink of C storing nearly 20 kgC.d⁻¹ in the growing season (Fig. 10). GEP varied between -10 and -40 kgC.d⁻¹ whereas ER varied between 10 and 30 kgC.d⁻¹.

# 4. Discussion

## 4.1. Essential proxies for mapping CO₂ fluxes with P4M data

CO₂ fluxes in peatlands are driven by many factors including air, soil and surface temperatures and moistures, plant productivity, litter quality, soil organic matter quality and microbial community composition (Kelly, 2021). Predicting CO₂ fluxes using models combining remote sensing data with few meteorological constants is thus a complex and ambitious exercise. Moreover, VIs extracted from a single multispectral remote sensing image are known to suffer from multicollinearity problems, leading to bias in model predictions, inflating variance and inducing uncertainty and instability in regression
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Selected models for the prediction of CO₂ fluxes using the P4M dataset. Best models (red dots) were selected among multiple predictive variable combinations (blue dots) based on higher averaged Pearson product-moment correlation coefficient (r) tested 100 times on randomly selected samples for a) Net Ecosystem Exchange (NEE), d) Ecosystem Respiration (ER) and g) Gross Ecosystem Productivity (GEP). Box horizontal bars represent the median, box edges represent interquartile ranges and whiskers represent maximums and minimums. Variable importance was analysed and plotted using their absolute coefficients for b) NEE, e) ER and h) GEP. Blue dots represent the absolute coefficient of a variable found in one of the 100 iterations of a model. Modelled against observed values are presented for selected models of c) Net Ecosystem Exchange (NEE), f) Ecosystem Respiration (ER) and i) Gross Ecosystem Production (GEP). Predictive accuracy was measured by the root mean square error (RMSE). Colours represent monthly field campaigns. Predictive variables and their coefficient indicated in plot titles are blue-wide dynamic range vegetation index (BWDRVI), chlorophyll vegetation index (CVI), normalized difference red edge index (NDRE), near-infrared band (NIR), photo-synthetically absorbed radiation (PAR) and mean air temperature (TEMP). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

coefficients (Aliferis and Simon, 2024; Dormann et al., 2013; Kim, 2019). This is consistent with our findings in which strong multicollinearity was detected in the dataset. To overcome this problem, variable selection analysis has been employed. There are many approaches for selecting a subset of best variables, i.e. forward selection,

backward elimination, hierarchical clustering, some of which employ machine learning algorithms like Boruta (Kursa et al., 2010). Here we employed a stepwise selection approach based on a very conservative VIF threshold. This procedure retained an essential subset of 4 VIs (BWDRVI, NDRE, CVI and BRI), 1 spectral band (NIR) and 2
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Anomalies of net ecosystem exchange (δNEE), ecosystem respiration (δER) and gross ecosystem productivity (δGEP). Anomalies were calculated for May (a, e, i), June (b, f, j), July (c, g, k) and October (d, h, l), respectively. Anomalies were calculated as the difference to the map average indicated on top left-hand corners and expressed in g.CO₂·m⁻²·h⁻¹. Linear stretch was applied on colour scale using one standard deviation to emphasize how much feature values vary from the mean value.</figcaption></figure>

meteorological variables (PAR and TEMP) from the 27 explanatory variables of the original dataset. Their importance has been evaluated based on their coefficients found in the 12,700 tested models. This resulted in the identification of 3 more important proxies for mapping CO₂ fluxes with the drone, i.e. BWDRVI, NDRE and NIR. These remote sensing variables have been shown to be good proxies of plant photosynthesis, leaf chlorophyll content and plant nitrogen concentration (de Lima et al., 2021; Jorge et al., 2019; Vincini and Frazzi, 2011). But the main advantage of these variables used together in a multiple linear model is their independence from one another, enabling them to cover the different aspects of peatland vegetation phenology and physiology without compromising model validity. While selected by the variable

selection procedure, BRI did not appear sufficiently important for prediction of CO₂ fluxes. BRI captures the phenomenon of vegetation browning which reflects plant senescence and we might have expected this index to be significant to predict evolution of *sphagnum* phenology from the early to the end of the growing season. This result is not in line with Bartold and Kluczek (2023) who used machine learning based on satellite data to predict chlorophyll fluorescence in a wetland of Poland between April and October and found that the BRI was a significant variable. Our variable importance analysis also revealed that PAR and TEMP were very important constants to account with. This is consistent with many studies which demonstrated the necessity to integrate environmental variables in models, such as solar irradiance, soil/air
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Daily cumulated carbon (C) balance for the entire peatland in mid-summer. Considered fluxes are net ecosystem exchange (NEE), ecosystem respiration (ER) and gross ecosystem productivity (GEP). Hourly CO₂ fluxes have been converted to daily cumulated C fluxes using the CO₂ to C conversion factor of 0.2727 and 10 h of sunlight per day.</figcaption></figure>

temperature and moisture (Cai et al., 2021; Harris and Dash, 2011; Kross et al., 2013; Schubert et al., 2010). PAR and TEMP were measured by a meteorological station but their values could have been retrieved from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) products (Myneni et al., 2015).

During the model selection procedure, single-variable models appeared largely underfitted for predicting CO₂ fluxes and models with more than 4 variables revealed progressively overfitted. This shows that predicting CO₂ fluxes with a single vegetation index is not sufficient because combining several non-collinear VIs together in a multiple linear model increases prediction accuracy through the likelihood to capture the spatial variations of different aspects of plant physiology and phenology. Our findings suggest that additional data from thermal infrared sensors (TIR) might certainly improve predictions, in particular for predicting ER know to be highly dependent of temperature (Kelly et al., 2021). We think that integration of thermal and multispectral data in modelling peatland CO₂ fluxes is a promising line of research which will gain momentum with the launch of the TRISHNA satellite in 2025 (Lagouarde et al., 2019). Moreover, we deplore absence of shortwave infrared sensors (SWIR) mounted on commercial drone platforms, a spectrum interval known to be good proxy of surface moisture that modulates biogeochemical processes (Garisoain et al., 2023).

## 4.2. Unprecedented levels of detail in CO₂ flux mapping

We mapped CO₂ fluxes at approximatively 5 cm at ground. This pixel size is well adapted in peatlands, where the dimension of microhabitats is about few square centimetres at the surface. This ultra-high-resolution (i.e. pixel <10 cm) can be useful for environment managers and planners who need to report a carbon balance at low cost and identify areas that need to be monitored particularly, for instance for peatland restoration purpose in areas recognized under the Ramsar Convention (Dinesen et al., 2021; Loisel and Gallego-Sala, 2022). Among the most popular available maps of CO₂ fluxes, those produced by MODIS deliver 8-days cumulated vegetation gross primary productivity (GPP) and net photosynthesis maps (NPP, i.e. GPP minus the vegetation respiration) at pixel resolution of approximatively 250 m at best (Running and Zhao, 2021).

In our study case, and for the study of small-scale ecosystems, such pixel size is far too coarse, mixing signals from surrounding vegetation types and water surfaces. That is the reason why some studies aimed at obtaining more accurate GPP maps using Sentinel-2 multispectral data (Bartold and Kluczek, 2023; Cai et al., 2021; Junttila et al., 2021). Here, we went one step further in obtaining maps of CO₂ fluxes four hundred times more accurate for better monitoring the productivity of small-scale ecosystems.

## 4.3. Applicability and limitation issues

Our field measurements and predictions fall in the same range than values generally reported in northern peatlands (Table 4) and our accuracies were broadly consistent with accuracies obtained from satellite data in other northern unforested peatlands (Table 5). This suggests that our models can be applied to other northern unforested peatlands although extrapolation to daily, monthly and yearly C balance must still be carried out with caution. Indeed, our models are based on sampling plots punctually measured at midday and thus cannot account for daytime fluctuation patterns of CO₂ fluxes, nor the night-time flux component specifically concerning ER (Järveoja et al., 2020). This would have required EC towers or permanent automatic flux chambers which were not possible to set up for this study. Our maps revealed that in October many areas of the peatland were in near equilibrium, and some as net CO₂ sources which is in line with Poczta et al. (2023) who reported that peatlands are overall net CO₂ sinks during summer and usually net source of CO₂ outside of the growing season. However, more data collected between October and April would have been needed to confirm better this pattern but during this period, the peatland is generally covered by snow, limiting the use of drone to monitor C fluxes.

While we showed that drone remote sensing can provide highly detailed maps of CO₂ fluxes with errors lesser than 0.5 gCO₂·m⁻²·h⁻¹, which is approximatively less than 25 % of flux ranges, several aspects affecting confidence in quality and limiting reproducibility require special attention. One key aspect is the signal-to-noise ratio, which can vary depending on environmental conditions like lighting, sensor quality and flight altitude. Low signal-to-noise ratio can reduce data accuracy, particularly under poor lighting, leading to unreliable results. In our study, the P4M limited this problem through the use of the irradiance sensor mounted on top of the drone platform, capable of measuring global incident irradiance in synchrony with the vegetation radiance captured by the camera. A second key aspect is that drone reflectance data may not be easily comparable from one time series to another due to variations in factors such as sensor calibration, weather conditions (e.g. cloud cover or sun angle) and drone altitude or speed during different flights. Although the reference panels supplied with the

<table><caption>**Table 4**
Comparison of some field measurements of Net Ecosystem Exchange (NEE) in northern unforested peatlands.</caption><thead><tr><th>Regions</th><th>Mean NEE (gCO2·m<sup>-2</sup>·day<sup>-1</sup>)</th><th>References</th></tr></thead><tbody><tr><td>North America and Europe</td><td>-4.4 ± 3.2</td><td>Lund et al. (2010)</td></tr><tr><td>Canada</td><td>-4.3 ± 7.1</td><td>Webster et al. (2018)</td></tr><tr><td>France</td><td>-3.0 ± 3.0*</td><td>This study</td></tr><tr><td>Russia</td><td>-2.4 ± 1.8*</td><td>Ivanov et al. (2021)</td></tr><tr><td>Tibetan Plateau</td><td>-1.4 ± 1.5</td><td>Liu et al. (2019)</td></tr><tr><td>Finland</td><td>-1.3 ± 0.1</td><td>Aurela et al. (2009)</td></tr><tr><td>Sweden</td><td>-1.1 ± 0.2</td><td>Christensen et al. (2012)</td></tr><tr><td>Sweden</td><td>-0.7 ± 0.2</td><td>Peichl et al. (2014)</td></tr><tr><td>Canada</td><td>-0.6 ± 0.2</td><td>Roulet et al. (2007)</td></tr><tr><td>Scotland</td><td>-0.6 ± 0.2</td><td>Helfter et al. (2015)</td></tr><tr><td>Canada</td><td>-0.1 ± 0.2</td><td>Strilesky and Humphreys (2011)</td></tr></tbody></table>

<footnote>* Growing season daily fluxes were obtained by multiplying hourly fluxes by 10.</footnote>
<caption>**Table 5**</caption>

Comparison of some remote sensing sensing-based model accuracies for other northern unforested peatlands. Accuracies for Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER) and Gross Ecosystem Productivity (GEP) are presented as mean Root Mean Square Errors (RMSE). Sw = Sweden, Fr = France, Fi = Finland, Ru = Russia, Gr = Greenland, Al = Alaska, De = Denmark, Ca = Canada, Po = Poland.

<table><thead><tr><th>Regions</th><th>Flux</th><th>RMSE (gCO<sub>2</sub>.m<sup>-2</sup>.day<sup>-1</sup>)</th><th>Sensors</th><th>References</th></tr></thead><tbody><tr><td rowspan="3">Sw</td><td>NEE</td><td>0.8</td><td rowspan="3">MODIS</td><td rowspan="3">Schubert et al. (2010)</td></tr><tr><td>ER</td><td>1.1</td></tr><tr><td>NEE</td><td>2.7</td></tr><tr><td rowspan="3">Fr</td><td>ER</td><td>3.5</td><td rowspan="3">Drone P4M</td><td rowspan="3">This study</td></tr><tr><td>GEP</td><td>4.5</td></tr><tr><td>NEE</td><td>4.7</td></tr><tr><td rowspan="2">Fi Ru Ge<br/>Sw Al</td><td>ER</td><td>3.2</td><td rowspan="2">MODIS<br/>MERRA</td><td rowspan="2">Watts et al. (2014)</td></tr><tr><td>GEP</td><td>3.6</td></tr><tr><td>De Fi Sw</td><td>GEP</td><td>4.9</td><td>Sentinel-2<br/>MODIS</td><td>Cai et al. (2021)</td></tr><tr><td rowspan="3">Sw Fi</td><td>NEE</td><td>13 % (normalized)</td><td rowspan="3">Sentinel-2<br/>MODIS</td><td rowspan="3">Junttila et al. (2021)</td></tr><tr><td>ER</td><td>15 % (normalized)</td></tr><tr><td>GEP</td><td>14 % (normalized)</td></tr><tr><td rowspan="3">Ca Fi</td><td>NEE</td><td>3.6</td><td rowspan="3">MODIS</td><td rowspan="3">Kross et al. (2013)</td></tr><tr><td>GEP</td><td>6.3</td></tr><tr><td></td><td></td></tr><tr><td>Po</td><td>GEP</td><td>6.7</td><td>Sentinel-2&amp;3<br/>MODIS</td><td>Dąbrowska-Zielińska<br/>et al. (2022)</td></tr></tbody></table>

P4M is a robust calibration technique which normalize reflectance values, inconsistent flight parameters or environmental conditions can still introduce variability. This can make it difficult to compare data between different points in time, unless strict protocols are followed for each flight. Di Gennaro et al. (2022) showed that different exposure settings for the P4M provided results comparable to those obtained with the automatic exposure time setting, in which, thanks to the sun sensor, the multispectral camera can normalize spectral data according to light variations that may occur during flight. This demonstrates the high efficiency of the system, which considerably simplifies flight planning and sensor configuration, by setting the automatic exposure mode and thus limiting problems of overexposure or underexposure. In addition to the automatic flight programming software, which sets the speed, height and position of camera shots between flights, the efficiency of the automatic exposure mode makes the time series highly comparable for scientific applications, as long as the flight is operated in similar and stable weather conditions.

## 4.4. Perspectives for monitoring broader spatial scales

While we have shown that the P4M can achieve high levels of detail in mapping CO₂ fluxes over small areas, one of the key challenges ahead is to use it to monitor fluxes over larger areas of peatland. To achieve this challenge, we identify two main research avenue. The first one is the use of drone remote sensing in synergy with satellite remote sensing. Several strategies coexist, depending on the purpose of the research. One of them, model calibration, could prove useful in our case. By instance, CO₂ flux models based on P4M data could be simply applied to satellite multispectral data after reflectance adjustment (Zhang et al., 2019). Beyond, radiometric data obtained by the P4M could be used directly to calibrate spectral unmixing models based on satellite data (Alvarez-Vanhard et al., 2020). An important point to consider is that moderate-resolution satellite data exhibit a high degree of sub-pixel signal heterogeneity due to the fine scale peatland microhabitat heterogeneity (Leitão et al., 2018; Siewert and Olofsson, 2020). As a result, a significant portion of the uncertainty in satellite-based models is actually rooted in the integration of field data collected in highly fragmented microhabitats (Harris and Bryant, 2009; Kalacska et al., 2015; Krankina et al., 2008). Hence, drone data can provide an intermediate dataset between the field sampling and the large-scale mapping (Alvarez-

Vanhard et al., 2021; Lees et al., 2021; Padró et al., 2018; Bhatnagar et al., 2021; Ghazaryan et al., 2024). Data fusion is another promising strategy for use of drone remote sensing in synergy with satellite remote sensing (Alvarez-Vanhard et al., 2021). One of the best-known fusion techniques is pan-sharpening of satellite (drone) bands by drone (satellite) bands to improve the spatial (spectral) resolution (Jenerowicz et al., 2017; Yue et al., 2016). Data fusion integrating together drone with high- or medium-resolution satellite data in machine/deep learning algorithms appears to be another promising strategy of synergy (Alvarez-Vanhard et al., 2021).

The second, and complementary, avenue of research for using drone data to monitor CO₂ on larger peatlands is to jointly accumulate drone imagery to gradually complete the coverage over larger areas. This involves setting up drone data repositories, freely accessible to online calculation infrastructures such as Open Data Cube (Lewis et al., 2017), Google Earth Engine (Gorelick et al., 2017), JEODPP (Soille et al., 2018) or Rasdaman (Baumann et al., 2019). Current initiatives like the Open Aerial Map (OpenAerialMap, 2024), the GEOSS platform (Geoss portal, 2024)), or the spatial data infrastructure Indigeo (Indigeo, 2024) contribute to this challenge. National programs for mapping and monitoring wetlands could greatly benefit from such solutions such as the POLWET system (Dabrowska-Zielinska et al., 2016a).

## 5. Conclusion

We conducted a study to evaluate the value of drone multispectral imagery for mapping CO₂ fluxes at high pixel resolution. We used empirical modelling based on in situ closed chamber measurements and found that hourly NEE, ER and GEP at midday can be mapped at ±5 cm pixel size with a mean accuracy of ±0.27, ±0.35 and ± 0.45 gCO₂.m⁻². h⁻¹, respectively. To obtain these results, the drone platform was equipped with red, green, blue, red edge and near-infrared sensors to derive the BWDRI and the NDRE indices. It is also necessary to obtain PAR and mean air temperature at ground during flight. Our findings suggest that SWIR and TIR sensors might be crucial to improve model prediction. Thanks to the very high ground pixel resolution of drone imagery, we highlighted significant spatial variability in CO² fluxes that would not have been detected using coarser satellite imagery. We think our study will raise the attention of peatland managers and planners who are interested in monitoring ecosystem carbon balance rapidly and at low cost. Further calibration and validation are needed in other peatlands distributed across a broad climate gradient before to establish general equations for unforested peatlands.

## Authorship contribution statement

VEJJ and RW conceived the study and designed methodology; RW, CLL and VEJJ collected the data; RW, CLL, LG and VEJJ analysed the data; RW led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

## CRediT authorship contribution statement

R. Walcker: Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. C. Le Lay: Writing – review & editing, Investigation, Formal analysis, Data curation. L. Gandois: Writing – review & editing, Writing – original draft, Funding acquisition, Formal analysis. A. Elger: Writing – review & editing, Writing – original draft, Funding acquisition. V.E.J. Jassey: Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
Declaration of competing interest

All authors on the manuscript have seen and approved the submitted version of the manuscript. We declare that all authors have substantially contributed to the work, and that all persons entitled to co-authorship have been included.

Acknowledgements

This project was supported by the Service National d'Observation Tourbières (SNO—French Peatland Observatory), part of the research infrastructure OZCAR, accredited by the INSU/CNRS. We are grateful to the Occitanie / Pyrénées-Méditerranée Region for funding the drone used in the present study, as part of the DIAGNOSE project. We acknowledge the technical unit “Observation and Instrumentation” part of the « Centre de Recherche sur la Biodiversité et Environnement » (CRBE—Center of Reasearch in Biodiversity and Environment) for their assistance.

Data availability

The data supporting the findings of this study are publicly available to ensure transparency and reproducibility of the research. Imagery is made available on request to the corresponding author. The dataset and Python's scripts employed in generating figures and tables are accessible at https://github.com/WalckR/EcoInf2025.

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