# Contribution of ECOSTRESS thermal imagery to wetland mapping: Application to heathland ecosystems

Liam Loizeau-Woollgar, Sébastien Rapinel, Julien Pellen, Bernard Clément, Laurence Hubert-Moy

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## To cite this version:

Liam Loizeau-Woollgar, Sébastien Rapinel, Julien Pellen, Bernard Clément, Laurence Hubert-Moy. Contribution of ECOSTRESS thermal imagery to wetland mapping: Application to heathland ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 2025, 220, pp.649-660. 10.1016/j.isprsjprs.2025.01.014. halshs-04979987

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# Contribution of ECOSTRESS thermal imagery to wetland mapping: Application to heathland ecosystems

Liam Loizeau-Woollgar<sup>a</sup>, Sébastien Rapinel<sup>a</sup>, Julien Pellen<sup>a</sup>, Bernard Clément<sup>b</sup>, Laurence Hubert-Moy<sup>a, *, c</sup>

## ARTICLE INFO

Keywords:
Satellite
Landsat-TIRS
ASTER-TIR
Sentinel-1/2
DEM
Random forest

## ABSTRACT

While wetlands have been extensively studied using optical and radar satellite imagery, thermal imagery has been used less often due its low spatial – temporal resolutions and challenges for emissivity estimation. Since 2018, spaceborne thermal imagery has gained interest due to the availability of ECOSTRESS data, which are acquired at 70 m spatial resolution and a 3–5 revisit time. This study aimed at comparing the contribution of ECOSTRESS time-series to wetland mapping to that of other thermal time-series (i.e., Landsat-TIRS, ASTER-TIR), Sentinel-1 SAR and Sentinel-2 optical satellite time-series, and topographical variables derived from satellite data. The study was applied to a 209 km² heathland site in north-western France that includes riverine, slope, and flat wetlands. The method used consisted of four steps: (i) four-year time-series (2019–2022) were aggregated into dense annual time-series; (ii) the temporal dimension was reduced using functional principal component analysis (FPCA); (iii) the most discriminating components of the FPCA were selected based on recursive feature elimination; and (iv) the contribution of each sensor time-series to wetland mapping was assessed based on the accuracy of a random forest model trained and tested using reference field data. The results indicated that an ECOSTRESS time-series that combined day and night acquisitions was more accurate (overall F1-score: 0.71) than Landsat-TIRS and ASTER-TIR time-series (overall F1-score: 0.40–0.62). A combination of ECOSTRESS thermal images, Sentinel-2 optical images, Sentinel-1 SAR images, and topographical variables outperformed the sensor-specific accuracies (overall F1-score: 0.87), highlighting the synergy of thermal, optical, and topographical data for wetland mapping.

## 1. Introduction

Wetlands, defined as areas inundated or saturated at a frequency that supports plants adapted to such conditions (Brinson, 1993), are one of the most important ecosystems on Earth, representing ca 41 % of the value of all global ecosystem services (Xu et al., 2020). While these benefits are widely recognized, wetlands remain increasingly threatened by urban and agricultural expansion, as well as climate change (Davidson et al., 2019). Thus, detecting and mapping wetlands is crucial for developing conservation and restoration policies.

Recent progress in mapping wetlands includes high-spatial-resolution optical and SAR satellite data, as well as topographical variables (Adeli et al., 2020a; Demarquet et al., 2023; Guo et al., 2017). For example, wetlands were mapped at 30 m spatial resolution in Africa (Li et al., 2022) and China (Mao et al., 2020) by combining Landsat optical

images with SRTM topographic variables, or at 10 m spatial resolution in Canada (Mahdianpari et al., 2021) by combining Sentinel-2 multispectral instrument (MSI) and Sentinel-1C-SAR data with SRTM topographic variables. However, misclassification of specific wetland types such as swamps and bog slopes has been reported (Mahdianpari et al., 2021; Rapinel et al., 2023).

Thermal infrared (TIR) imagery is an additional source of satellite data that may improve the comprehensiveness of wetland maps (Panigrahy, 2017), as land surface temperature (LST) is sensitive to soil moisture (Petropoulos et al., 2015) and land use (Muro et al., 2018), and reflects the evaporative stress of vegetation as a proxy for soil moisture (Farella et al., 2022). Although satellite TIR data such as Landsat-TIRS or ASTER-TIR images have been useful for classifying wetlands (Mahdianpari et al., 2020; Wei et al., 2008), TIR imagery has been used less due its low spatial and temporal resolutions, as well as challenges for

<footnote><sup>a</sup> Université Rennes 2, LETG UMR 6554 CNRS, Place du Recteur Henri Le Moal 35000, Rennes, France</footnote>

<footnote><sup>b</sup> Université de Rennes, ECOBIO UMR 6553 CNRS, Avenue du Général Leclerc 35000, Rennes, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: laurence.hubert@univ-rennes2.fr (L. Hubert-Moy).</footnote>
emissivity estimation (Neinavaz et al., 2021a), which reduce the ability to distinguish the fine-grained pattern of these ecosystems (Mahdavi et al., 2018). For example, the spatial resolution of LST products derived from Landsat-TIRS imagery at a 16-day revisit time can be improved from 100 to 30 m (Cheng et al., 2021; Vanhellemont, 2020), but the limited number of thermal bands prevents the application of an efficient emissivity estimation approach, such as the temperature-emissivity separation (TES) algorithm (Gillespie et al., 1998).

The launch of the TIR ecosystem spaceborne thermal radiometer experiment on the space station (ECOSTRESS) in 2018 opened new avenues for monitoring moisture in vegetated ecosystems (Neinavaz et al., 2021b). Compared to other spaceborne thermal sensors, ECO-STRESS has the highest spatial resolution (70 m) and the shortest revisit time (ca. 4 days), as it benefits from the high speed of the International Space Station (ISS) (Hulley et al., 2017). In comparison to Landsat TIRS, ECOSTRESS has a sufficient number of thermal bands so that the TES algorithm (Gillespie et al., 1998) can be used to estimate LST with an accuracy of 1.07 K (Hulley et al., 2021). Preliminary studies have highlighted the advantages of day and night – time ECOSTRESS data for describing the full range of LST variability in forests (Hamberg et al., 2022; Liu et al., 2021), savannahs (Johnston et al., 2022), and regularly-flooded wetlands (Desai et al., 2021; Gustine et al., 2023, 2021). However, ECOSTRESS data have not been used to describe, and a *fortiori* map non-flooded wetlands, which comprise the major part of the terrestrial wetland area on the Earth (Tootchi et al., 2019).

This study aimed at comparing the contribution of ECOSTRESS time-series to wetland mapping to that of other thermal time-series (i.e., Landsat-TIRS, ASTER-TIR), Sentinel-1 SAR and Sentinel-2 MSI satellite

time-series, and topographical variables derived from satellite data. Specifically, wetlands were mapped using the hydrogeomorphic classification system (HGM), which groups wetlands into six main types (depressional, fluvial, flat, estuarine, lacustrine, and slope) based on their topographic location and hydrodynamics (Brinson, 1993; Semeniuk and Semeniuk, 2016). The HGM classification system is suitable for wetland management because it can be used to distinguish units within a wetland that contribute to specific hydrological, biogeochemical, or ecological functions (Maltby and Barker, 2009). The study's method was applied to a 209 km² heathland and peatland-complex ecosystem in north-western France that includes riverine, slope, and flat wetlands.

## 2. Materials and methods

### 2.1. Study site

The 209 km² study site is located in the Monts d'Arrée in Brittany, north-western France (Fig. 1). This windswept, low-elevation mountain range (maximum elevation 385 m) is a remnant of Hercynian orogeny, whose bedrock is dominated by granite, shale, Armorican sandstone, and quartzite (Guilcher, 1949). This oligotrophic and acidic substrate promoted the formation of heathland and peatland-complex ecosystems. The humid oceanic climate (mean annual temperature 10.6 °C, precipitation ± 1500 mm / year) combined with shale-dominated impermeable bedrock created three HGM wetland types (Semeniuk and Semeniuk, 2016): riverine wetlands; slope wetlands; and flat wetlands. The geomorphic, hydrologic, and vegetation characteristics of each HGM are listed in the Table 1. The wetlands of the Monts d'Arrées are

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** (a) Location of the study site in western Europe (red star); (b) Distribution of reference samples extracted from a field vegetation map (Durfort and Gendre, 2002), a hydrological network (BD TOPAGE®, IGN), and the elevation (RGE ALTI® IGN) of the study site; (c) Google Earth® images, Photographs of a (d) slope, (e) flat, and (f) riverine wetland. Credit for photographs: the authors.</figcaption></figure>
<caption>**Table 1**</caption>

Geomorphic, hydrologic, and vegetation characteristics of each hydrogeomorphic type (HGM). Vegetation types and plant species are listed by dominance order.

<table><thead><tr><th>HGM<br>Class</th><th>Geomorphology<br>Geomorphological type</th><th>Hydrology<br>Dominant water source</th><th>Flooding duration<br>(month)</th><th>Vegetation<br>Dominant vegetation types</th><th>Dominant plant species</th><th>Typical height<br>(cm)</th></tr></thead><tbody><tr><td>Non-wet</td><td>Various</td><td>Precipitation</td><td>0</td><td>Heath<br>Grassland</td><td>Erica ciliaris<br>Ulex gallii<br>Agrostis curtisii</td><td>30 – 60</td></tr><tr><td>Wet-slope</td><td>Steep terrain</td><td>Return flow from<br>groundwater</td><td>0 – 5</td><td>Bog<br>Heath</td><td>Narthecium ossifragum<br>Eriophorum<br>angustifolium<br>Sphagnum ssp</td><td>30 – 60</td></tr><tr><td>Wet-flat</td><td>Plateau</td><td>Precipitation</td><td>0</td><td>Heath<br>Bog</td><td>Erica tetralix<br>Trichophorum<br>caespitosum<br>Molinia caerulea</td><td>20 – 40</td></tr><tr><td>Wet-riverine</td><td>Valley bottoms</td><td>Runoff from upland</td><td>0 – 5</td><td>Grassland<br>Bog<br>Forest</td><td>Molinia caerulea<br>Juncus acutiflorus<br>Sphagnum ssp<br>Salix aurita<br>Myrica gale</td><td>50 – 250</td></tr></tbody></table>

waterlogged at the subsurface level (0–50 cm) for 5 to 10 months a year, resulting in the formation of hydromorphic soils (redoxysol, reductisol, or histosol) and the development of hyrophilous plant species such as *Erica tetralix*, *Molinia caerulea*, or *Sphagnum ssp*. Although most of these wetlands are rarely flooded, some of them can be inundated for a period of 3 to 5 months in the winter and early-spring (December – April). An artificial lake at the site, located in a valley bottom, was formed by damming the *Ellez* River. This area of heathland and peatland-complex ecosystems, which is part of the Armorique Regional Natural Park and includes a protected European Union Natura 2000 site (FR5300013), experiences wildfires, such as during the summer of 2022 (Clément, 2022). A detailed analysis of the composition of vegetation types per HGM is provided in the fig. S1 and table S1 in the supplementary materials.

## 2.2. Satellite images

Seven satellite time-series from January 2019 to December 2022 were created using TIR, optical, and SAR data to consider inter-annual climate variations affecting wetland hydrodynamics and vegetation phenology. Landsat-TIRS and ASTER-TIR data were used to compare the contribution of other satellite thermal data with a spatial resolution ≤ 100 m to wetland mapping to that of ECOSTRESS data. Sentinel-1&2 satellite time-series were also used to assess the utility of combining thermal data with optical and SAR imagery. Five satellite time-series were used (Table 2). For optical and thermal imagery, only cloud-free images were selected, based on using cloud-cover filters followed by visual inspection. The seasonal distribution of the number of images acquired for each satellite time series is shown in Table S3 in the supplementary materials.

### 2.2.1. Thermal imagery

#### 2.2.1.1. ECOSTRESS.
Day and night ECOSTRESS level-2 LST and emissivity products (ECO2LSTE v001) were downloaded from the LP DAAC database using NASA’s Earthdata search platform (https://search.earthdata.nasa.gov/search), along with the corresponding level 1B geolocation files (ECO1BGEO). Cloudy images were discarded based on quality flags (QAPercentCloudCover < 50) and visual inspection. ECOSTRESS LST products were generated using the TES algorithm (Gillespie et al., 1998). These products, provided in swath format, were converted into geolocated gridded geoTIFF files in the UTM 30 N projection system (EPSG:32630) using the swath2grid python algorithm provided by LP DAAC Data User Resources. A total of 50 images were selected (i.e., 21 day and 29 night).

ECOSTRESS is a 5-band radiometer that acquires TIR imagery from the ISS at the highest spatial resolution currently available from space (70 m). The ISS’s orbit enables ECOSTRESS to operate at a different temporal scale than that of sun-synchronous orbiting Earth observation satellites such as Landsat, with a shorter revisit time (ca. 4 days) and acquisitions obtained at varying times throughout the day and night (Hulley et al., 2017). Because the ISS’s orbit follows non-repetitive paths and can have low location accuracy, ECOSTRESS geolocation error can vary from ± 50 m to 7 km (Soszynska et al., 2023). Furthermore, although ECOSTRESS’s TIR sensor functions as a push-whisk scanner, its wide angle decreases the true spatial resolution of LST measurements from 70 × 70 m at nadir to 90 × 90 m at the edge of the swath, which causes slight geometric deformations (Logan et al., 2018). We performed automated image co-registration based on the Fourier transform using AROSICS software (Scheffler et al., 2017) to geometrically correct ECOSTRESS images. Co-registration was performed separately for the day and night time-series, using the ECOSTRESS image whose geometry had the best visual fit with that of the high-spatial-resolution Google

<caption>**Table 2**</caption>

Description of the five satellite time-series used. The acquisition dates for each time series are detailed as a timeline and table in Fig. S2 and Table S2 in the Supplementary Materials.

<table><thead><tr><th>Satellite and sensor</th><th>Product name</th><th>Product level</th><th>Spectral range (µm)</th><th>Spatial resolution (m)</th><th>No. of acquisitions (2019–2022)</th></tr></thead><tbody><tr><td>ISS-ECOSTRESS</td><td>ECOSTRESS Land Surface Temperature and Emissivity Daily L2 Global 70 m V001</td><td>Level 2</td><td>8.29–12.09</td><td>70</td><td>50</td></tr><tr><td>Landsat-TIRS</td><td>Landsat-8 LST Landsat-9 LST</td><td>Collection 2 T1 L2</td><td>10.6–12.5</td><td>100</td><td>11</td></tr><tr><td>ASTER-TIR</td><td>AST_08 Surface Kinetic Temperature</td><td>Level 2</td><td>8.47–11.65</td><td>90</td><td>3</td></tr><tr><td>Sentinel-1C-SAR</td><td>Interferometric wide swath mode ground range detected Sentinel-1</td><td>Level 1</td><td>1000–180 000</td><td>10</td><td>176</td></tr><tr><td>Sentinel-2 MSI</td><td>Sentinel-2 Surface Reflectance</td><td>Collection 2 Level 2A</td><td>0.49–2.19</td><td>10</td><td>54</td></tr></tbody></table>
Earth ® images used as a reference. The accuracy of the co-registered images was estimated using the structural similarity (SSIM) index. The mean SSIM index ranged from 0.36 to 0.50 for day and night images, indicating respectively satisfactory and good performance (Gyasi-Agyei, 2022).

2.2.1.2. ASTER-TIR. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) surface kinetic temperature products (AST_08) acquired by the TIR sensor were downloaded from the LP DAAC database through NASA’s Earthdata search platform. These products were generated using the TES algorithm (Gillespie et al., 1998). The absolute geolocation error of the 90 m spatial resolution products is ≤ 50 m (Abrams and Hook, 2002). All available cloudless images over the study area were downloaded, which yielded three night images over the four years. AST_08 data, expressed in °K, were converted to °C before analysis.

2.2.1.3. Landsat-TIRS. A combined Landsat-8 and Landsat-9 LST time-series was acquired from the Google Earth Engine (GEE) platform. As a Collection 2 Level 2 Science Product, band 10 images acquired by TIR sensors (TIRS) at 100 m spatial resolution were available as orthorectified (geolocation error < 7 m) LSTs expressed in °K. Specifically, Landsat LSTs were retrieved from the USGS using ancillary data such as the ASTER Global Emissivity Database and the ASTER normalized difference vegetation index (Cook et al., 2014). All cloud-free images were selected, which yielded 11 images over the four years. Thermal band 10 was converted to °C and downloaded from the GEE platform before analysis.

2.2.2. Sentinel-1C-SAR

Interferometric wide swath mode ground range detected Sentinel-1 products were acquired from the GEE platform at 10 m spatial resolution in ascending orbit for exhaustive repeated coverage of the study area, which wasn’t consistent in descending orbit causing many scenes to be split in half. VV and VH dual-polarization modes were selected, because with C-band polarimetry, the combined use of VV and VH polarizations were shown suitable for estimation of soil moisture (El Hajj et al., 2017). Specifically, 176 Sentinel-1 images were pre-processed in five steps: (i) thermal noise removal, (ii) radiometric calibration, (iii) terrain correction, (iv) noise reduction using a 3 × 3 mean filter, and (v) calculation of a VV/VH ratio by combining backscattering information from both polarizations (Slagter et al., 2020).

2.2.3. Sentinel-2 MSI

A time-series of the normalized difference moisture index (NDMI) at 10 m spatial resolution was derived from 54 Sentinel-2 MSI cloud-free images (Collection 2, Level 2A) available from the GEE platform. NDMI, an index commonly used to estimate the moisture content of vegetation (Sankey et al., 2021), was calculated per image as follows (Eq. (1)):

$$
\text{NDMI} = \frac{(\text{NIR} - \text{SWIR})}{(\text{NIR} + \text{SWIR})} \qquad (1)
$$

where NIR and SWIR correspond respectively to the near-infrared (band
8) and shortwave infrared (band 11) spectra.

2.3. Topographical variables

Four topographical variables relevant for wetland mapping (Rapinel
et al., 2023; Xiang et al., 2023) were selected among global 90 m open-
access products. Specifically, the slope, maximum multiscale deviation
from mean elevation (DEV) (i.e., the position of a pixel relative to its
neighbourhood at multiple spatial scales (Lindsay et al., 2015)), and
compound topographic index (CTI) (i.e., potential soil wetness as a
function of the contributing area and local slope (Beven and Kirkby,
1979)), were extracted from the Geomorpho90m dataset (Amatulli

et al., 2020). The fourth variable, height above the nearest drainage
(HAND) (i.e., elevation normalized by the nearest drainage point), was
obtained from Donchyts et al. (2016).

2.4. Reference data

Reference samples were collected in two steps. As the wet and non-
wet areas of this study site sometimes have quite similar vegetation
types (Fig. S1 and Table S1 in the Supplementary Materials), it would
have been difficult to collect reference samples based on visual inter-
pretation of recent images at very high spatial resolution. We selected
reference samples from a field vegetation map, because vegetation is a
bioindicator of soil moisture conditions (Maltby, 2009). First, wet and
non-wet samples were identified using the most recent field vegetation
map at 1:5000 scale available in GIS polygon format (Durfort and
Gendre, 2002). To avoid bias in the accuracy assessment due to differ-
ences in the spatial resolution of the images used, sampling points were
placed in the centre of vegetation patches (i.e., polygons) that covered
an area of at least 150 × 150 m to exclude mixed-class reference pixels,
especially for images with coarser spatial resolution. Reference samples
were located and classified based on the presence of vegetation types
that clearly indicated wet (e.g. sedge meadows and watersides, wet
heathlands, megaphorbs, wet rush meadows, slope bogs) or non-wet (e.
g. mesophilic heathland, dry heathland) conditions. Although the
vegetation map was created more than 20 years ago, and the site has
experienced regular wildfires since then, plant and soil surveys per-
formed at the site on 21 April 2023 confirmed the wet and non-wet
classifications of the reference samples. This is due, on the one hand,
to the low vegetation dynamics in relation to the harsh environmental
conditions (poor soil and strong winds) and, on the other hand, to the
low agricultural pressure experienced on this protected area, which is
part of the Regional Nature Park since 1969 and of the protected Eu-
ropean Union Natura 2000 network since 2007.

In the second step, wet samples were re-classified as slope, flat, or
riverine wetlands based on analysing the vegetation map when the
vegetation classes were explicitly labelled (e.g. “slope bogs”). When they
were not, the geomorphological forms that Jasiewicz and Stepinski
(2013) described in the “geomorphons” raster layer of the Geo-
morpho90m database (Amatulli et al., 2020) were inspected. Peak,
ridge, shoulder, and flat forms were classified as flat wetlands; spur,
slope, and hollow forms were classified as slope wetlands; and valley
forms were classified as riverine wetlands. The reference data consisted
of 218 sampling points (76 non-wet, 47 slope, 23 flat, and 72 riverine)
that reflected the proportion of each class’s area in the study site (Fig. 1).

3. Methods

3.1. Methodological framework

The data were compared in four steps: (i) the four-year time-series (2019–2022) were aggregated into dense annual time-series; (ii) the temporal dimension was reduced using functional principal component analysis (FPCA); (iii) the most discriminating components of the FPCA were selected based on recursive feature elimination (RFE); and (iv) the contribution of each sensor time-series to wetland mapping was assessed based on the accuracy of a random forest (RF) model trained and tested using reference field data. Data were analysed using open-source Python (Srinath, 2017) and R (R Core Team, 2019) programming languages.

3.2. Time-series analysis

3.2.1. Generation of dense time-series

Dense time-series were generated for each satellite time-series by
sorting images from the four-year period by their day of the year, which
yielded a single pseudo-year dense time-series. Images were masked
using the study area boundary, co-registered using cubic interpolation,
and then stacked for analysis. Values of the raster stack were mean-centred to minimize noise (e.g. shadows, aberrant values) but were not scaled in order to retain the amplitude information.

### 3.2.2. Functional principal component analysis

FPCA is an approach for reducing temporal dimensionality based on functional data analysis. FPCA is similar to principal component analysis except that it analyses and generates functions instead of vectors (Shang, 2014). The principal components of FPCA are functions that describe the main behaviours of data observed over time: the first few components describe the main temporal trends, while the last few components usually represent noise, but also subtle temporal variations.

FPCA was applied to each dense time-series using a b-spline function. Components were selected to capture > 90 % of the total variance which was considered as an appropriate threshold in the FPCA (Ségalas et al., 2024), resulting in 2 to 10 components according to the time-series (Table 3). All rasterized component scores were then extracted at each sampling point to generate a labelled reference dataset to select and classify the variables. The spatio-temporal model of seasonal spectral variations per component of the FPCA based on each time series is shown in Figs. S6 – 12 in the supplementary materials.

## 3.3. Random forest modelling

RF is a machine-learning supervised model (Breiman, 2001) that has been extensively used to map wetlands (Mahdavi et al., 2018) and shown to do so more reliably than other algorithms (Lang et al., 2015). In this study, RF modelling was based on the following five steps.

### 3.3.1. Variable selection

For each time series, RFE was used to select the most discriminating FPCA components for wetland mapping. RFE is a backscatter variable-selection method that uses the importance ranking of variables of a wrapped model (here, RF) to identify the most informative variables of a dataset in an iterative approach (Santos et al., 2023). Results were plotted as a curve, and the number of variables that yielded the highest overall accuracy were selected for each time series (Fig. S5 in the Supplementary material).

### 3.3.2. Scenario building

Nine RF modelling scenarios were built to compare the contribution of ECOSTRESS time-series for wetland mapping to that of the other satellite time-series (Table 3). Scenarios 1–5 were used to assess and compare TIR satellite imagery, including the potentially large differences in LST between day and night ECOSTRESS acquisitions. Scenario 6 and 7 were used to assess respectively Sentinel-2 MSI and Sentinel-1C-SAR imagery. Scenario 8 was used to assess the performance of topographical variables alone, while scenario 9 assessed the synergy of multi-source data (i.e., “fusion”).

### 3.3.3. Model training and testing

To limit model overfitting and a biased estimate of accuracy, reference samples were split into spatially independent training and testing datasets using a spatial hold-out separation procedure (Karasiak et al., 2021) (see Fig. S3 and S4 in the supplementary material for details). A distance of ca. 1200 m was used to generate spatial blocks that split the reference samples, while maintaining class proportions, into training (80 %) and testing (20 %) datasets (Valavi et al., 2019). The RF model was fit using the training dataset with the following parameters: 200 trees (ntree) and a number of variables at each node of a tree (mtry) equal to the square root of the number of variables in the model (Belgiu and Drăguț, 2016). Individual classification trees were derived from a bootstrapped sample of the training dataset, in which ca. 66 % of the samples were used for training, and the rest were used for internal model validation (Breiman, 2001). The class assigned to each sample was the one with the most votes across the classification trees. Performance of RF modelling was assessed using the testing dataset by calculating the class-wise and weighted F1-score (Tharwat, 2020). This model training and testing procedure was repeated 10 times to estimate the variability in accuracy.

### 3.3.4. Scenario comparison

To identify significant differences in accuracy between modelling scenarios, one-way permutation analysis of variance of the 90 (9 scenarios × 10 replicates) weighted F1-scores was performed, followed by a non-parametric post-hoc Wilcoxon signed-rank test, a p-value over 0.05 indicating similarity between the pair of compared modelling scenarios (Wilcoxon, 1945). In addition, a pairwise comparison of modelling scenarios based on their spatial patterns was performed using the global difference metric (Pontius Jr and Santacruz, 2014).

### 3.3.5. Spatial prediction

To consider model variability among the 10 replicates, a crisp classification map was derived for each scenario by (i) predicting a suitability map based on the number of votes across the classification trees per class for each replicate, (ii) calculating the mean suitability map among the replicates per class, and then (iii) stacking the mean suitability maps of each class and selecting for each pixel the class with the highest suitability.

## 3.4. Linear discriminant analysis

Linear discriminant analysis (LDA) was applied to the reference samples to determine the contribution and complementarity of variables derived from different data sources (i.e., fusion scenario) to the discrimination of wetland types. LDA identifies the linear combinations of variables that maximize the discrimination of classes and that form a set of discriminating components, reducing the data to a lower-dimensional subspace (Izenman, 2013). The resulting LDA model contained three linear components (i.e., number of classes minus one).

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

The nine scenarios (S) used for random forest modelling. FPCA: functional principal component analysis.

<table><thead><tr><th>S</th><th>Data type</th><th>Variables used</th><th>No. of variables</th><th>Rationale</th></tr></thead><tbody><tr><td>1</td><td>ECOSTRESS (day)</td><td>Selected FPCA components</td><td>4</td><td>Evaluate ECOSTRESS images acquired during the day</td></tr><tr><td>2</td><td>ECOSTRESS (night)</td><td>Selected FPCA components</td><td>4</td><td>Evaluate ECOSTRESS images acquired during the night</td></tr><tr><td>3</td><td>ECOSTRESS (day + night)</td><td>Selected FPCA components</td><td>8</td><td>Evaluate the synergy of ECOSTRESS images acquired during the day and the night</td></tr><tr><td>4</td><td>Landsat-TIRS</td><td>Selected FPCA components</td><td>3</td><td>Evaluate the Landsat thermal images</td></tr><tr><td>5</td><td>ASTER-TIR</td><td>Selected FPCA components</td><td>2</td><td>Evaluate the ASTER thermal images</td></tr><tr><td>6</td><td>Sentinel-2</td><td>Selected FPCA components</td><td>5</td><td>Compare widely used optical images with thermal imagery</td></tr><tr><td>7</td><td>Sentinel-1</td><td>Selected FPCA components</td><td>10</td><td>Compare widely used SAR images with thermal imagery</td></tr><tr><td>8</td><td>Topographical</td><td>Topographical variables</td><td>4</td><td>Compare global topographical data with thermal imagery</td></tr><tr><td>9</td><td>Fusion</td><td>Topographical variables + selected ECOSTRESS, Sentinel-2, and Sentinel-1 FPCA components</td><td>35</td><td>Evaluate the synergy between thermal, optical, SAR, and topographical data</td></tr></tbody></table>
<table><caption>**Table 4**

Characteristics of the five products used for comparison with this study map.</caption><thead><tr><th>Acronym</th><th>Description</th><th>Retained wetland classes</th><th>Spatial resolution</th><th>Coverage</th><th>Reference</th></tr></thead><tbody><tr><td>CW_TCI</td><td>Composite global wetland map (topography-climate wetness index)</td><td>All positive classes</td><td>500 m</td><td>World</td><td>(Tootchi et al., 2019)</td></tr><tr><td>CLC</td><td>Copernicus Corine Land Cover (2018)</td><td>Rice fields, Inland marshes, Peat bogs, Salt marshes, Salines, Intertidal flats, Coastal lagoons, Estuaries, Water bodies</td><td>100 m</td><td>Europe</td><td>(Büttner et al., 2016)</td></tr><tr><td>ESA_WC</td><td>European Space Agency World Cover (V2 2021)</td><td>Herbaceous wetland, Mangrove, Permanent Water bodies</td><td>10 m</td><td>World</td><td>(Zanaga et al., 2022)</td></tr><tr><td>ESRI_GLC</td><td>ESRI Global Land Cover (2022)</td><td>Flooded vegetation, Water</td><td>10 m</td><td>World</td><td>(Karra et al., 2021)</td></tr><tr><td>DW</td><td>Dynamic World (2019–2022)</td><td>Flooded vegetation, Water</td><td>10 m</td><td>World</td><td>(Brown et al., 2022)</td></tr></tbody></table>

### 3.5. Comparison with existing wetland maps

The scenario with the highest classification accuracy was compared to five global or pan-European products that include the wetland class (Table 4). The classes of the HGM classification and each of these products were assigned into a binary (wet/non-wet) classification system. The Dynamic World map was generated using the time period considered for this study. The accuracy (weighted average F1 score) of each binary map was calculated by HGM type using the test dataset.

## 4. Results

### 4.1. Statistical accuracy

Overall modelling accuracy differed greatly among scenarios (Fig. 2). Accuracy was highest for the fusion scenario (0.87), followed by the Sentinel-2 scenario (0.81). Accuracy was the same and satisfactory for the scenarios based on combined ECOSTRESS thermal day + night images or the topographical variables (0.71). Accuracy was more moderate for the scenarios based on ECOSTRESS thermal day or night images or Landsat-TIRS images (0.59–0.62). Conversely, accuracy was low for the scenarios based on Sentinel-1 SAR images (0.42) or ASTER-TIR thermal images (0.40).

When predicting wetland types (HGM), only the fusion scenario had very high accuracy (> 0.80) for all types of HGM (Table 5). Conversely, accuracy was low for scenarios based on thermal ASTER-TIR or Sentinel-1 SAR images for all types of HGM. Specifically, for non-wet areas, accuracy was high for the fusion scenario; satisfactory for the scenarios based on Sentinel-2 MSI images, topographical variables, or ECOSTRESS thermal day images; and moderate for the scenarios based on ECOSTRESS thermal day + night images or Landsat-TIRS images. Accuracy was low for the other scenarios. For slope wetlands, accuracy was high using the fusion data or Sentinel-2 MSI images, moderate using ECOSTRESS thermal day + night images or topographical variables, and low for the other data. For flat wetlands, accuracy was very high using ECOSTRESS thermal night images, the fusion data, or topographical variables; moderate using ECOSTRESS thermal day + night images; and low to null for all other data. Interestingly, for riverine wetlands, accuracy was higher (> 0.80) using thermal (ECOSTRESS or Landsat-TIRS), Sentinel-2 MSI, or the fusion data than topographical variables (0.67).

A comparison of the map of the scenario 9 (fusion) with the five global products showed that the accuracy of the former was significantly higher (Table 6). The CW_TCI map mostly showed higher accuracy than land cover products, especially in riparian wetlands. The accuracy of the other four land use products was zero to very low in slope, plateau, and riparian wetlands and very high in non-wetlands.

### 4.2. Mapping accuracy

When comparing maps of wetland types derived from RF modelling

among scenarios, the map derived from ECOSTRESS thermal day images overestimated slope wetlands and underestimated flat wetlands (Fig. 3). The map derived from ECOSTRESS thermal night images overestimated all wetland types and underestimated non-wet areas. In contrast, the combination of thermal ECOSTRESS day + night images increased mapping accuracy greatly, with a consistent spatial pattern, even though the low spatial resolution (70 m) of the images prevented precise delineation of narrow wetlands. The map derived from Landsat-TIRS thermal images underestimated non-wet areas and overestimated slope wetlands. The maps derived from ASTER-TIR and Sentinel-1 SAR images yielded an inconsistent spatial pattern, except for the lake, which was correctly recognized as riverine wetlands from Sentinel-1 images. In contrast, the map derived from Sentinel-2 MSI images yielded a more consistent, fine-grained spatial pattern, even though it clearly underestimated slope wetlands and non-wet areas and overestimated flat wetlands. The map derived from the topographical variables correctly delineated non-wet areas, riverine wetlands (including the lake), and flat wetlands, but incorrectly considered all slopes as wet. The map derived from the fusion data was the most accurate: it yielded a consistent fine-grained pattern, with patches of riverine wetlands located correctly in valley bottoms and around the lake, isolated patches of flat wetlands consistently located between two ridgelines, and well-identified slope wetlands on the northern slopes of the study site. All classification maps and reference samples are available in GIS format at Doi: 10.5281/zenodo.13336446. Pairwise global difference between spatial pattern of the nine modelling scenarios is detailed in Table S4 in the supplementary materials.

The binary wetland map resulting from our study showed the finest and most comprehensive patterns for wetlands compared with the binary wetland maps derived from global or pan-European products (Fig. 4). Despite its coarser resolution, the CW_TCI product showed a consistent distribution of wetlands among the five global products. In contrast, none wetlands, except the lake, was identified using the ESA_WC, ESRI_GLC, and DW products. The lake and adjacent bog were both identified using CLC product.

### 4.3. Synergy of remote sensing data for discriminating wetland types

The LDA highlighted the synergy among topographic, ECOSTRESS thermal, and Sentinel-2 MSI variables for discriminating wetland types (Fig. 5). Axis 1, which was positively correlated with CTI and component 4 of the FPCA in the ECOSTRESS images, and negatively correlated with the slope, DEV, and HAND, identified riverine wetlands. Axis 2, which was positively correlated with component 1 of the ECOSTRESS FPCA and component 9 of the Sentinel-2 FPCA, and negatively correlated with component 2 of the Sentinel-2 FPCA, discriminated slope wetlands. Axis 3, which was positively correlated with the slope, discriminated flat wetlands from non-wet areas. The contribution of Sentinel-1 SAR images to all three LDA components was low (cos² < 0.3).
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Comparison of overall random forest modelling accuracy of the scenarios across 10 replicates. Different letters indicate significant differences among scenarios according to the Wilcoxon rank-sum test (p < 0.05). Whiskers represent 1.5 times the interquartile range.</figcaption></figure>

**Table 5**

Median (and interquartile range) F1-scores of random forest models of wetlands across 10 replicates as a function of hydrogeomorphic (HGM) type and scenario. F1-scores higher than 0.8 are in bold font. Different letters indicate significant differences among HGM according to the Wilcoxon rank-sum test (p < 0.05).

<table><thead><tr><th rowspan="2">Scenario</th><th colspan="4">HGM</th></tr><tr><th>Non-wet</th><th>Wet (Slope)</th><th>Wet (Flat)</th><th>Wet (Riverine)</th></tr></thead><tbody><tr><td>ECOSTRESS (day)</td><td>0.70 (0.02) d</td><td>0.51 (0.02) e</td><td>0.00 (0.00) de</td><td><strong>0.80</strong> (0.00) c</td></tr><tr><td>ECOSTRESS (night)</td><td>0.52 (0.02) g</td><td>0.33 (0.00) g</td><td><strong>1.00</strong> (0.11) a</td><td><strong>0.81</strong> (0.04) c</td></tr><tr><td>ECOSTRESS (day + night)</td><td>0.65 (0.02) e</td><td>0.67 (0.00) c</td><td>0.67 (0.00) c</td><td><strong>0.84</strong> (0.06) bc</td></tr><tr><td>Landsat-TIRS</td><td>0.66 (0.05) e</td><td>0.48 (0.04) ef</td><td>0.00 (0.00) e</td><td><strong>0.81</strong> (0.03) bc</td></tr><tr><td>ASTER-TIR</td><td>0.58 (0.01) f</td><td>0.19 (0.00) h</td><td>0.29 (0.00) d</td><td>0.45 (0.05) e</td></tr><tr><td>Sentinel-2</td><td>0.77 (0.04) b</td><td><strong>0.85</strong> (0.00) b</td><td>0.50 (0.05) c</td><td><strong>0.90</strong> (0.00) a</td></tr><tr><td>Sentinel-1</td><td>0.45 (0.04) h</td><td>0.47 (0.04) f</td><td>0.00 (0.00) e</td><td>0.45 (0.03) e</td></tr><tr><td>Topographical</td><td>0.74 (0.02) c</td><td>0.65 (0.00) d</td><td><strong>0.86</strong> (0.11) ab</td><td>0.67 (0.00) d</td></tr><tr><td>Fusion</td><td><strong>0.84</strong> (0.03) a</td><td><strong>0.90</strong> (0.00) a</td><td><strong>0.86</strong> (0.00) b</td><td><strong>0.85</strong> (0.03) ab</td></tr></tbody></table>

**Table 6**

Comparison of the classification accuracy per HGM (expressed as weighted average F1 score) obtained in this study in the two-class system classification (wetlands and non-wetlands) with those of the five existing global products. See Table 4 for definitions of product's acronyms.

<table><thead><tr><th></th><th>Non-wet</th><th>Wet-slope</th><th>Wet-flat</th><th>Wet-Riverine</th><th>All</th></tr></thead><tbody><tr><td>This study</td><td>0.81</td><td>0.79</td><td>1.00</td><td>0.93</td><td>0.86</td></tr><tr><td>CW_TCI</td><td>0.69</td><td>0.50</td><td>0.00</td><td>0.67</td><td>0.57</td></tr><tr><td>CLC</td><td>1.00</td><td>0.00</td><td>0.00</td><td>0.13</td><td>0.37</td></tr><tr><td>ESA_WC</td><td>1.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.33</td></tr><tr><td>ESRI_GLC</td><td>1.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.33</td></tr><tr><td>DW</td><td>1.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.33</td></tr></tbody></table>

**5. Discussion**

To our knowledge, this is the first study to highlight the advantages of using ECOSTRESS thermal imagery for spatial modelling of wetlands. The wetlands in the study site have waterlogged soil and are rarely flooded, except for the lake and its edges. Consequently, the temperatures estimated using ECOSTRESS imagery were those of the vegetation surface (Neinavaz et al., 2021b) and not those of the water, as observed in previous wetland studies (Desai et al., 2021; Gustine et al., 2023, 2021).

Although ECOSTRESS has advantages over other thermal satellite images, geometric correction of images is challenging due to deformations at the edges of the swath and potentially high location error (Hulley et al., 2017; Logan et al., 2018). This has major implications for monitoring ecosystems such as wet heathlands and peatlands, some of whose vegetation patches have a size similar to the spatial resolution of ECOSTRESS images. However, this issue has received little attention in studies on ecosystem monitoring, even though ca. 60 % of ECOSTRESS

images released by NASA have a geometric offset of more than 2 km (Soszynska et al., 2023). The default solution is to select ECOSTRESS images with the correct geometric quality based on the QA metadata flag (Desai et al., 2021; Johnston et al., 2022; Liu et al., 2021), but our analysis showed that several remaining images had large (2 km) geometric offsets, particularly at night. One solution would have been to correct each ECOSTRESS image by manually selecting reference points, as Hamberg et al. (2022) did, but this would have been time consuming given the large number of images. Another solution would have been to discard images that had a large geometric offset, such as Desai et al. (2021) or Gustine et al. (2021) did, but this would have drastically decreased the number of images. Instead, we followed the solution of Soszynska et al. (2023) and used Fourier-shift-based co-registration. Although some images still had small geometric shifts, which resulted in noise, especially in narrow wetlands, the results were largely acceptable, which enabled retaining a large number of ECOSTRESS images while decreasing the geometric offset greatly. This was possible mainly due to the large lake at the study site, which provided a good reference point for thermal imagery, especially at night (Soszynska et al., 2023). In addition to geometric deformations, ECOSTRESS LST product was affected in our study by radiometric biases due to two main reasons: first, it should be noted that a cold bias of 0.75 K was found in the ECOSTRESS LST product when the temperature was below 295 K values (Hulley et al., 2021); second, the ECOSTRESS LST product was affected by the large amount of water vapor in the atmosphere (Neinavaz et al., 2021a), which is common at this site with an oceanic climate. These geometric and radiometric biases may explain some errors in modelling scenarios based on the ECOSTRESS images.

The high temporal resolution of ECOSTRESS imagery generates many images, which makes spatial modelling a challenge (Maxwell
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Comparison of maps of wetland types derived from random forest modelling per scenario. For clarity, one sample is displayed per 300 × 300 m grid.</figcaption></figure>

et al., 2018). To address this issue, we applied FPCA, which reduced the entire temporal profile (i.e., 50 ECOSTRESS images) to only 10 FPCA components while still providing an ecological interpretation based on the spatio-temporal pattern of mean seasonal spectral variations of each component (Pesaresi et al., 2022). For example, the spatio-temporal pattern of the mean seasonal spectral variations of components 1 and 4 (Fig. S8 in Supplementary material) indicated that slope wetlands had different surface temperatures than other types of wetlands and non-wet areas in autumn, winter, and spring (October-June) but similar surface temperatures in summer (June-September). In contrast, riverine wetlands had higher surface temperatures than the rest of the ecosystem in winter and early spring (January-April) but lower surface temperatures in mid-spring and summer (May-August). The temperature differences between wet and non-wet areas can be explained not only by differences in soil moisture, but also by the physiology of the plant species (Table 1). Hygrophilous plant species common to this study site, such as purple moor grass (Molina caerulea) or cross-leaved heath (Erica tetralix) have inefficient stomatal control of transpiration (Wheeler, 2005), which affects surface temperature (Farella et al., 2022). For example, in our study site, the early morning temperatures measured above the sphagnum mosses in summer were close to 0 °C (B. Clément, personal communication, July 2024). ECOSTRESS imagery can be used to measure temperature variability at the ecosystem surface, which is high (e.g. hourly, annually). In this study, we considered a four-year period to

include climatic variability and events such as the wildfire of the summer of 2022, which was clearly visible in the spatio-temporal pattern of mean seasonal spectral variations of component 3 (i.e., patches of positive values to the west of the lake, corresponding to much higher temperatures than those for the rest of the ecosystem in summer (July-September)).

Modelling accuracy based on thermal sensors was highest when combining ECOSTRESS day + night images, indicating that it can operate over a flexible time scale, which highlights an advantage of using ECOSTRESS images instead of thermal ASTER-TIR and Landsat-TIRS images. However, it is difficult to explain whether the higher modelling accuracy based on ECOSTRESS images is due to the day/night complementarity, as previously reported (Desai et al., 2021; Johnston et al., 2022; Liu et al., 2021), or rather to seasonal complementarity, as the monthly distribution of ECOSTRESS acquisitions was different for day and night (Table S3 in the Supplementary Material). It is noteworthy that the accuracy of the model based on ECOSTRESS night images is significantly lower than that of the model based on ECOSTRESS day images, despite the larger number of night acquisitions. This could be explained by two main reasons: (i) the quality of night images, which are noisier than day images due to more diffuse thermal radiation and higher atmospheric moisture; (ii) the seasonal acquisition dates of night images, few ECOSTRESS night images being acquired in early-winter and spring (Table S3 in the Supplementary Material), whereas the
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Comparison of the wetland map produced in this study to five wetland maps derived from global or pan-European products. See Table 4 for definitions of product's acronyms.</figcaption></figure>

thermal characteristics of some wetland types can be highly discrimi-
nated during these seasons (Fig. S8 in the Supplementary Material).
Moreover, this study pointed out that the model based on the five
Landsat images outperformed the model based on the three ASTER-TIR
images, suggesting that the expected accuracy of the LST values tends to
be a less important factor than the number of acquisitions.

Compared to models using non-thermal data, the accuracy of the
model based on ECOSTRESS day + night imagery was lower than the
accuracy of the model based on Sentinel-2 images, similar to the accu-
racy of the model based on topographic data, and significantly higher
than the accuracy of the model based on Sentinel-1 SAR images. The
advantage of the model using Sentinel-2 images can be explained by the
NDWI's ability to reflect the moisture content of vegetation, but also by

the extensive seasonal coverage of these images (Table S3 in the sup-
plementary materials), which enables discriminating wet from dry
heathlands (Hubert-Moy et al., 2022). Interestingly, although HGMs are
all characterized by the presence of heathland (Fig. S1 in the supple-
mentary materials), LDA analysis (Fig. 5) highlighted the contribution of
Sentinel-2 imagery in distinguishing wet-slopes and, to a lesser extent,
wet-flats from the other HGMs. Analysis of the proportion of vegetation
types per HGM (Fig. S1 in the supplementary materials) and the Bray-
Curtis dissimilarity index matrix (Table S1 in the supplementary mate-
rials) showed that these two HGMs were characterized by a higher
proportion of bog, supporting the value of optical data for distinguishing
natural vegetation types in wetlands (Mahdavi et al., 2018). The
modelling accuracy of scenarios based on topographic variables was
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Linear discriminant analysis (LDA) of wetland types (HGM) for axes 1–2 (top) and 2–3 (bottom) based on topographic, thermal, multispectral, and SAR images included in the fusion scenario. For clarity, only the variables that contributed the most (cos² > 0.5) are shown. FPCA: function principal component analysis, HAND: height above nearest drainage, DEV: maximum multiscale deviation from mean elevation.</figcaption></figure>

similar to that of scenarios based on ECOSTRESS day + night imagery. However, the value of the global difference metric (Table S4 in the supplementary materials) and the comparison of wetland maps reveal quite different spatial patterns: the topographic variables accurately discriminated riverine wetlands where water accumulates (Higginbottom et al., 2018) from non-wet areas, but they were less accurate than ECOSTRESS images at detecting wetlands on slopes, as slopes are not necessarily wet (Rapinel et al., 2023). Conversely, Sentinel-1 SAR images contributed the least to the discrimination of wetland types. Sentinel-1 SAR images are sensitive to flood (Cazals et al., 2016), soil moisture (Maleki et al., 2020), but even more so to vegetation structure (Schulte to Bühne and Pettorelli, 2018), which was dense throughout the study site and prevented the SAR signal from reaching the ground, except on flooded surfaces on the lake and its edges, which were accurately detected. The contribution of Sentinel-1 SAR imagery would likely have been higher for mapping wetlands characterized by a high flood frequency, or vertical structure and roughness of vegetation typical of wetlands (Adeli et al., 2020b). This study also demonstrates

that the fusion of ECOSTRESS thermal images, Sentinel-2 MSI images, and topographic variables was the only scenario with very high modelling accuracy, regardless of wetland type. These results confirm the complementarity of thermal imagery with widely used environmental and multispectral data for discriminating natural ecosystems (Anand et al., 2021; Farella et al., 2022).

Class imbalance in machine-learning datasets needs to be considered, as it can influence the quality of predictions greatly (Millard and Richardson, 2015). Due to the low resolution of thermal satellite imagery and the relative rareness and small size of flat wetlands, few reference samples were available for this class (e.g. only four flat wetlands in the testing dataset), which reduced the robustness of its modelling accuracy. These limitations are inherent to thermal imagery and will be reduced by the upcoming TRISHNA mission, which will combine high spatio-temporal resolution and high geometric accuracy for both day and night measurements.

The binary wetland map produced in this study is significantly more accurate than the five wetland maps derived from global or pan-
European products. Besides the synergy of the satellite data used, the high availability of the reference samples, and the small size of the site area, the superiority of our results can also be explained by the definition of wetlands. In the wetland map produced in this study, as well as in the CW_TCI product, we observe that most of the correctly mapped wetlands corresponded to non-flooded wet heaths, bogs or wet grasslands, whereas global LULC products mostly define wetlands as permanently or temporarily flooded areas, preventing the identification of these non-flooded wetlands, which are common in western France.

## 6. Conclusion

ECOSTRESS thermal satellite imagery is a useful data source for wetland mapping in heathland and peatland-complex ecosystem in combination with multispectral and topographical data, which can better detect flat and slope wetlands, which were previously subject to high misclassification error. The contribution of using ECOSTRESS to wetland mapping is its ability to provide LSTs over a flexible, previously unexplored temporal scale, with both day and night measurements at non-repeating points of the diurnal cycle. While this study application was limited to a 209 km2 heathland site in France, further studies should be conducted to assess the broader applicability of our findings to other regions with different landscape characteristics worldwide. Future research could also explore the contribution of thermal data to wetland mapping for other wetland types, with other classification systems, but also provide in-depth sensitivity analysis of the effect of season and time of image acquisition on modelling accuracy.

### CRediT authorship contribution statement

**Liam Loizeau-Woolgar:** Writing – original draft, Formal analysis, Data curation. **Sébastien Rapinel:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Conceptualization. **Julien Pellen:** Resources, Data curation. **Bernard Clément:** Validation, Resources, Investigation. **Laurence Hubert-Moy:** Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

### Funding

This study was funded by the University Rennes 2.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

Satellite images were freely provided by NASA (ISS/ECOSTRESS, Terra/ASTER), the USGS (Landsat-8/TIRS, Landsat-9/TIRS), and the Copernicus European Program (Sentinel-1, Sentinel-2). Topographic variables were provided by the French Institut Géographique National, and geological maps were provided by the French Bureau de Recherches Géologiques et Minières. The authors are especially thankful to José Durfort, Blandine Lemercier, Adrien Jacotot, and Quentin Demarquet for their assistance in the field and to Léa Panhelleux for her help with data analysis. This study is a preliminary work on the use of thermal data for the national wetland mapping programme of the French Ministry of Ecology.

## Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.isprsjprs.2025.01.014.

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