# Performance of the Surface Water and Ocean Topography (SWOT) Mission for Monitoring Small Lakes in West Africa

Félix Girard, Laurent Kergoat, Ibrahim Mainassara, Maxime Wubda, Edwige Dayangnéwendé Nikièma, Amadou Abdourhamane Touré, Julien Renou, Maxime Vayre, Nicolas Taburet, Nicolas Picot, et al.

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## To cite this version:

Félix Girard, Laurent Kergoat, Ibrahim Mainassara, Maxime Wubda, Edwige Dayangnéwendé Nikièma, et al. Performance of the Surface Water and Ocean Topography (SWOT) Mission for Monitoring Small Lakes in West Africa. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18, pp.14855 - 14869. 10.1109/jstars.2025.3570859. hal-05387573

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# Performance of the Surface Water and Ocean Topography (SWOT) Mission for Monitoring Small Lakes in West Africa

Félix Girard<sup>1D</sup>, Laurent Kergoat<sup>1D</sup>, Ibrahim Mainassara<sup>1D</sup>, Maxime Wubda, Edwige Dayangnéwendé Nikièma, Amadou Abdourhamane Touré<sup>1D</sup>, Julien Renou, Maxime Vayre, Nicolas Taburet<sup>1D</sup>, Nicolas Picot<sup>1D</sup>, and Manuela Grippa<sup>1D</sup>

**Abstract**—The volume variability and the hydrological functioning of the thousands of lakes in the Sahelian region are poorly known. The recently launched Surface Water and Ocean Topography (SWOT) mission carrying a wide-swath Ka-band radar interferometer offers new opportunities for large-scale monitoring of lake dynamics, and overcomes the spatial coverage limitations of the nadir altimeters. Here, we evaluate the performance of two SWOT data products, namely the PIXel Cloud (PIXC) and the lake single pass (LakeSP), over 16 small and medium-sized lakes in the Central Sahel. For both products, water surface elevations (WSEs) are in excellent agreement with in situ data on two lakes, with 1-σ errors (68th percentile of absolute errors) between 0.06 and 0.11 m, consistently with the mission science requirements. When compared to Sentinel-3 WSE, the PIXC product shows better results than LakeSP, with 1-σ differences of 0.16 m and 0.32 m, respectively. SWOT LakeSP water surface area (WSA) estimates show a general overestimation with a median bias of 17.2% compared to Sentinel-2 measurements. SWOT pixel classification errors are

Received 22 November 2024; revised 21 February 2025; accepted 4 May 2025. Date of publication 16 May 2025; date of current version 23 June 2025. This work was supported in part by the CNES, focused on the Surface Water and Ocean Topography (SWOT) mission, in part by the Occitanie Region and Collecte Localisation Satellites (CLS), and in part by the Félix Girard's Ph.D. thesis. (Corresponding author: Félix Girard.)

Félix Girard is with the Géosciences Environnement Toulouse (Université de Toulouse, CNRS, IRD, CNES), 31400 Toulouse, France, and also with Collecte Localisation Satellites (CLS), 31520 Ramonville-Saint-Agne, France (e-mail: felix.girard@get.omp.eu).

Laurent Kergoat and Manuela Grippa are with the Géosciences Environnement Toulouse (Université de Toulouse, CNRS, IRD, CNES), 31400 Toulouse, France (e-mail: laurent.kergoat@get.omp.eu; manuela.grippa@get.omp.eu).

Ibrahim Mainassara is with HydroSciences Montpellier (HSM), Univ Montpellier, CNRS, IRD, 34090 Montpellier, France (e-mail: ibrahim.mainassara@ird.fr).

Maxime Wubda is with the Université Joseph Ki-Zerbo, UFR-SVT/DST, Ouagadougou 09 BP 293, Burkina Faso (e-mail: mwubda@gmail.com).

Edwige Dayangnéwendé Nikièma is with the LERMIT, Université Joseph Ki-Zerbo, Ouagadougou 09 BP 293, Burkina Faso (e-mail: hedwigen@gmail.com).

Amadou Abdourhamane Touré is with the Faculté des Sciences et Techniques, Département de Géologie, Université Abdou Moumouni, Niamey 10896, Niger (e-mail: amadou.abdourhamanetoure@uam.edu.nep).

Julien Renou, Maxime Vayre, and Nicolas Taburet are with the Faculté des Sciences et Techniques, Département de Géologie, Faculté des Sciences et Techniques, Département de Géologie, Collecte Localisation Satellites (CLS), 31520 Ramonville-Saint-Agne, France (e-mail: jrenou@groupcls.com; mvayre@groupcls.com; ntaburet@groupcls.com).

Nicolas Picot is with the Centre National d'Etudes Spatiales, 31400 Toulouse, France (e-mail: nicolas.picot@cnes.fr).

This article has supplementary downloadable material available at https://doi.org/10.1109/JSTARS.2025.3570859, provided by the authors.

Digital Object Identifier 10.1109/JSTARS.2025.3570859

found to affect both products, especially for LakeSP. Elevation-area relationships derived from combined SWOT PIXC and Sentinel-2 compare well with in situ (root mean square errors below 0.28 m), highlighting the capabilities of SWOT for monitoring lake volume changes once complemented by external water masks. Finally, annual water level amplitude estimates of more than 600 lakes of different sizes are derived using SWOT PIXC, emphasizing the spatial coverage potential of SWOT.

**Index Terms**—Africa, altimetry, lakes, reservoirs, surface water and ocean topography (SWOT), satellite applications, synthetic aperture radar, water surface area (WSA), water surface elevation (WSE), water volume.

## I. INTRODUCTION

CLIMATE and anthropogenic changes are putting increasing pressure on the world's fresh surface water, making it a critical resource. This is particularly true in the semiarid regions of West Africa where the rainy season is relatively short [1], and where lakes, as provider of directly accessible water, are associated with essential uses such as irrigation, fishing, and human and livestock consumption [2]. Several recent global studies have reported large-scale trends in lake water volumes [3], [4], [5], [6]. However, few lakes in West Africa have been included in these global studies, and more local studies have covered either only a few lakes [7], [8], [9] or a larger number but at a specific date [10], [11], [12]. Moreover, volume variability, water uses and hydrological functioning of the thousand small lakes in this region remain largely unknown [13]. It is, therefore, necessary to complete the monitoring of lake volume changes in West Africa.

As it provides repeated measurements of water surface elevation (WSE) and area, remote sensing is a relevant tool for calculating lake volume changes [14]. For this purpose, radar and lidar altimetry missions combined with optical or radar imagery have been widely used at the global scale [15], [16], [17], [18], [19], [20]. In the study region, Fleury et al. [21] used Sentinel-2 optical imagery and Sentinel-3 altimetry data to characterize seasonal and interannual water level variations in 42 lakes between Mali, Niger, and Burkina Faso at high temporal resolution. In addition, Girard et al. [22] focused on 16 lakes in the same area and showed that the combination of Sentinel-3 radar data and Ice, Cloud and land Elevation Satellite-2 (ICESat-2) lidar data with Sentinel-2 imagery provides an accurate solution for estimating volume
changes. However, the observational capabilities of nadir altimeters are limited to the few lakes located under the satellite tracks and lidar altimeters have low temporal revisit. The Surface Water and Ocean Topography (SWOT) mission, launched in December 2022, is a breakthrough in radar remote sensing as it carries the KaRIn instrument, a Ka-band interferometric radar with a 10 m baseline [23]. With its wide swathes covering angles of incidence between 0.6° and 3.9° on either side of the satellite's nadir [24], KaRIn provides simultaneous WSE and area measurements globally. With its high spatial coverage (+90% of inland areas, [25] and submonthly temporal revisit (21-day cycle), SWOT offers new opportunities for large-scale monitoring of seasonal and interannual lake volume changes. In particular, the mission science requirements [26] specify that SWOT should be able to inventory all terrestrial water bodies larger than (0.25 km)², with (0.1 km)² as a goal.

Few evaluations of SWOT capabilities have been carried out so far. Among these, the work of Fu et al. [27] on the Yukon River delta in Alaska showed that the SWOT lake single pass (LakeSP) vector product could provide spatially consistent water level measurements of a thousand lakes, with some as small as (0.1 km)². In addition, they reported water extent observations in good agreement with high-resolution optical imagery. Hamoudzadeh et al. [28] also highlighted the promising capabilities of SWOT LakeSP-derived WSEs on six global lakes (none in West Africa) ranging in size from 35 to 19 000 km². They reported a mean root mean square error (RMSE) of 0.35 m and a mean accuracy of 0.22 m after rejecting outliers. A good comparison of SWOT LakeSP estimates with those of the altimetry data platforms Hydroweb [29] and G-REALM [19] was obtained globally by Yu et al. [30], with a mean absolute error (MAE) of less than 0.5 m and 0.3 m, respectively. However, this study does not use in situ data for validation and includes very few data on the African continent. Therefore, evaluation of the performance of SWOT on West African lakes, particularly small ones, is still to be done.

It has been shown that the contrast between water and soil in nadir Ka-band backscattering measurements can sometimes be low and depends on soil type, soil moisture, and wind conditions [31], [32]. Therefore, water level dynamics and surrounding soil properties can be challenging for water detection in this area.

This study provides answers to the following questions: what is the performance of SWOT data in estimating WSE, area and water volume in shallow West African lakes (most < 5 m deep) of different size (some < 1 km²) and spectral signature (open water, turbid water, aquatic vegetation)? Is the SWOT measurement error consistent with scientific requirements? Which SWOT product, either the standard LakeSP product or the more upstream pixel cloud (PIXC) product, is the most appropriate? What are the observational capabilities provided by more than one year of SWOT data regarding large-scale measurements of seasonal lake dynamics? To address these questions, this work analyzes 16 lakes already studied in Girard et al. [22], located in Mali, Niger, and Burkina Faso. The evaluation of SWOT performance is made using in situ data, Sentinel-3 WSE

data, Sentinel-2 water areas, and elevation-area relationships extracted from Girard et al. [22]. Finally, the annual water level amplitude is calculated for over 600 lakes in the study region using one year of SWOT measurements.

## II. MATERIALS AND METHODS

### A. Study Area and Regions of Interest

1) **Study Area and Lakes:** The study area is located in West Africa. It encompasses part of Mali, Niger, and Burkina Faso (Central Sahel, Fig. 1), between 10.8 and 15.5°N, and -4.7 and 2.3°E. The climate is semiarid to subhumid from north to south, with rainfall ranging from 290 to 1015 mm/yr [33], concentrated during the rainy season from June to October.

A total of 16 small (< 1 km²) and medium-sized (1–100 km²) water bodies were selected based on Girard et al. [22], of which ten are reservoirs and the other six are natural lakes. Their average surface area ranges from 0.09 km² (North Tanvi) to 14.8 km² (Kokorou) and they are all located between 190 m (Bangou Kirey) and 460 m (Toussiana) above sea level. They have different water spectral signatures, ranging from clear to turbid and from open to vegetated water. All the 16 study lakes and reservoirs, are covered by the SWOT nominal orbit, seven (Arzuma, Babou, Bam, Manga, North Tanvi, South Tanvi, and Tibin) by the Calval Phase orbit dedicated to mission calibration and validation, and 12 by the Sentinel-3 orbits.

2) **SWOT Prior Lake Database (PLD):** The SWOT mission PLD (see [34]) is a global static water mask containing locations and benchmark extents of nearly six million lakes and reservoirs larger than 0.01 km², dedicated to supporting SWOT observations analysis. The PLD facilitates the linkage between SWOT measurements and observed lakes by assigning an ID to each lake in the database.

### B. In Situ WSE and Hypsometry Data

1) **WSE Data From Pressure Transducer:** Two lakes, the Arzuma reservoir and the Bangou Kirey lake are equipped with a Pressure Transducers (PTs) providing water level measurements with an accuracy of 0.5 cm every 30 min, since March 2023 and July 2022, respectively. As the absolute leveling of these datasets was not feasible in the field, they were referenced to the Earth gravitational model (EGM) 2008 geoid [35] using synchronous WSE measurements from the ICESat-2 mission. Either the ICESat-2 ATL08 (land and vegetation height [36]) or ATL13 (inland water surface height [37]) products were used, depending on their availability. Absolute leveling was performed by adding the median difference between the two datasets to the in situ data. For this, either one (Arzuma) or two (Bangou Kirey) matches with ICESat-2 were used.

2) **Elevation-Area Relationships:** Elevation-volume relationships have been established by Girard et al. [22] as briefly summarized below. For Bam, Seguenega, and Seytenga reservoirs, they have been provided by the Institut International d'Ingénierie de l'Eau et de l'Environnement (2IE) du Burkina Faso and come from direct leveling topographic surveys
<figure><img src="image_3.png" /><figcaption>Fig. 1. Study area and lakes analyzed in this study.</figcaption></figure>

<table><caption>TABLE I
REMOTE SENSING DATA AND CORRESPONDING MISSIONS USED IN THIS STUDY</caption><thead><tr><th>Mission</th><th>SWOT</th><th>SWOT</th><th>Sentinel-3</th><th>Sentinel-2</th></tr></thead><tbody><tr><td>Launch date</td><td>December 2022</td><td>December 2022</td><td>February 2016 (3A), April 2018 (3B)</td><td>June 2015 (2A), March 2017 (2B)</td></tr><tr><td>Instrument</td><td>KaRIn</td><td>KaRIn</td><td>SRAL</td><td>MSI</td></tr><tr><td>Parameter</td><td>WSE</td><td>WSE</td><td>WSE</td><td>WSA</td></tr><tr><td>Product</td><td>Level-2 HR PIXC</td><td>Level-2 HR LakeSP</td><td>Level-1B radar waveform</td><td>Level-2A surface reflectance</td></tr><tr><td>Product type</td><td>2-D point cloud</td><td>polygons (shapefile)</td><td>1-D vector</td><td>2-D image</td></tr><tr><td>Orbital cycle</td><td>1-21 days (Calval-Science phase)</td><td>1-21 days (Calval-Science phase)</td><td>27 days</td><td>5 days</td></tr><tr><td>Posting rate</td><td>22 m (along-track), 10-60 m (cross-track)</td><td></td><td>300 m along-track</td><td>20 m × 20 m</td></tr></tbody></table>

performed before impoundment by the Direction Générale des Ressources en Eau of Burkina Faso. The elevation-area relationship of the Kokorou lake is extracted from Baba et al. [38] and derived from limnimetry for the elevations and a combination of topographic map digitization, GPS field survey and Landsat-based water contouring for the areas. Finally, the elevation-volume-area relationship of the Toussiana reservoir is derived from Sanogo and Dezetter [39].

To generate elevation-area relationships, in situ WSE data over the Arzuma reservoir, the Agoufou, and the Bangou Kirey lakes were combined with synchronous Sentinel-2 derived water surface areas (WSAs). For Agoufou, the WSE measurements have been collected between 2015 and 2019 by the AMMA-CATCH observatory (see [8]) and are based on limnimetric scale readings with a weekly or monthly frequency and an accuracy of 0.5 cm. For the Bam, Seguenega, and Seytenga reservoirs, the

elevation-volume relationships were converted to elevation-area relationships by calculating the area as the derivative of volume with respect to elevation [22].

### C. Remote Sensing Derived WSE and Hypsometry Data

1) *SWOT WSE and Area Data:* The SWOT mission provides two-dimensional surface elevation measurements globally (see Table I). For calibration and validation purposes (Calval phase), the SWOT satellite flew in a specific orbit from early April 2023 to July 2023, with a 1-day revisit time. The satellite was then moved to its nominal orbit (Science phase) at an altitude of 890 km with a 21-day revisit time.

Both SWOT_L2_HR_PIXC and SWOT_L2_HR_LakeSP products are evaluated here. Data from the version C of
these two products are extracted from the Hydroweb.next platform [40].

The LakeSP product [41] is the standard SWOT user vector product for lake and wetland analysis. It takes the PIXC product as input and provides lake-aggregated WSE and WSA measurements for both left and right 64 km × 64 km swaths [42, p. 13], over an entire satellite pass (from pole to pole). In this work, we use the LakeSP Prior product, which provides one WSE and WSA record for each lake identified in the PLD. WSE includes geophysical corrections and is referenced to the EGM2008 geoid. In addition, we discard the observations with off-nominal measurements (quality_f equals to zero, [42, p. 44]) and bad crossover calibration quality (xovr_cal_q equals to zero or one, [42, 43, p. 44]) using the dedicated flags in the product. These filters remove large outliers and impact the amount of remaining data: 29% of data are excluded in total, the majority (25%) being due to off-nominal measurements.

The PIXC product [44] provides the location, elevation, surface classification, and geophysical corrections for each pixel in the form of a geolocated point cloud for each 64 km × 64 km tile (see Figure S1 of Supplementary Material). The point ground spacing is 22 m in the along-track direction and varies from 60 to 10 m, from the near-range to the far-range, in the cross-track direction [44]. In this work, we first select pixels located between 10 and 60 km from nadir, as specified by the mission science requirements. To maximize data quality, we use the quality flags included in the PIXC product to retain only pixels with “good” quality of geolocation, classification, and interferogram-related variables (i.e., value equal to zero for the geolocation quali, classification quali, and interferogram quali flags [45, p. 100], [46, p. 65]). In addition, we perform a restrictive spatial selection by selecting the pixels included in the lake PLD polygon to limit the impact of classification errors. As the PLD polygon of the Kokorou lake is not accurate enough, we use the 1984–2021 maximum extent polygon from the global surface water occurrence (GSWO, [47]) dataset for this lake. To mitigate unwanted land pixels when the lake water extent is smaller than that of the PLD polygon (or the GSWO maximum extent for Kokorou lake), we only consider the pixels classified as “open water” in the SWOT product classification. The PIXC product provides WSE data referenced to the ellipsoid (World Geodetic System 1984) and corrected for the atmospheric corrections (ionospheric, wet tropospheric, and dry tropospheric corrections) and the crossover calibration. Therefore, we applied the additional corrections related to the pole, solid Earth, and load tides, which are included in the product, and eventually subtracted the EGM2008 geoid. The resulting lake WSE is calculated as the median height of the remaining pixels. For lakes straddling two different tiles on the same SWOT track (e.g., the Tibin reservoir during the Calval period), we combine the two median height estimates of each tile as the mean weighted by the respective pixel counts in each tile. Calval and Science phases combined, a total of 605 WSE estimates were generated over the 16 study lakes from the 989 different estimates derived using only the pixels initially included in the 10–60 km swath segment and the PLD polygons, and classified as “open water.” This resulted in the rejection of 31% of the acquisitions after the

application of the quality flags (see Figure S2 of Supplementary Material).

2) Sentinel-3 WSE Data: The SAR Radar ALtimeter (SRAL) on board the Sentinel-3 A and Sentinel-3B satellites is a globally operating delay/Doppler altimeter, which provides along-track surface elevation data every 300 m approximately, with a revisit time of 27 days for each satellite (see Table I). Synthetic aperture radar waveforms are extracted from the Level-1B Sentinel-3 Surface Topography Mission (STM) Hydrology Thematic Product [48] along with the different geophysical corrections mentioned in Section II-C1 and provided in the Level-2 Sentinel-3 STM Hydrology Thematic Product. Since the lake water surface is mainly specular, a Sinc² model-based numerical tracking algorithm [49], [50] was used to retrieve the WSE from the radar waveforms. After applying the geophysical corrections and referencing them to the geoid, the Sentinel-3 WSE data are selected within the GSWO maximum extent polygon of the lake. In line with classical filterings used for inland water surfaces [21], we discard measurements with σ₀ lower than a certain threshold (40 dB in this case). This filtering removes 39% of the individual measurements but significantly reduces the variability associated with off-water measurements, for example. The WSE over the transect is then calculated as the median value of the associated measurement points. As the location of the satellite’s ground track can vary by +/- 1 km from its theoretical position [51], the temporal revisit of Sentinel-3 measurements over some of the study lakes may vary, depending on the location of the theoretical ground track.

3) Sentinel-2 WSA Data: The multispectral instrument (MSI) carried by the Sentinel-2 A and -2B satellites provides optical images of the Earth’s surface every 5 days with 13 spectral bands, ranging from blue to short-wave infrared (SWIR), and with spatial resolutions from 10 to 60 m (see Table I). We use the Google Earth Engine online platform [52] to download cloud-free Level-2 A surface reflectance (SR) images from the “COPERNICUS/S2_SR_HARMONIZED” collection, corrected for atmospheric effects using the Sen2Cor algorithm [53]. Prior to download, the images are downscaled (nearest neighbor) to 20 m pixel size in order to match the spatial resolution of the SWIR bands. The WSA is calculated using a modified normalized difference water index [54] thresholding approach. The MNDWI is a spectral index based on the green and SWIR bands, as follows:

$$MNDWI = \frac{\text{green} - \text{SWIR}}{\text{green} + \text{SWIR}} \quad (1)$$

Lake-specific MNDWI thresholds are determined ad hoc, according to Girard et al. [22], and the number of pixels with an MNDWI value greater than the threshold is multiplied by the pixel area to compute the WSA.

4) SWOT/Sentinel-2 Elevation-Area Relationships: Synchro- nous SWOT PIXC WSE and Sentinel-2 WSA are com- bined within a time tolerance of 3 days [22] to generate SWOT/Sentinel-2 elevation-area relationships. WSA is linearly interpolated to the SWOT acquisition date if the time difference between the matching elevation and area is not zero.
5) *Elevation-Area Relationships From Combination of Other Remote Sensing Data:* Remote sensing-based elevation-area relationships of the study lakes are derived from a fusion of different methods, which are described in Girard et al. [22]. These methods are based on open access data such as Sentinel-2 WSA, Sentinel-3 WSE, and ICESat-2 ground elevation measurements, and perform either synchronous matching (+/- 3 days) or geometric intersection between the elevation and area data to compute the lake isobaths. This dataset will be referred to as the “combined open-source” dataset [55].

### D. Methodology for Evaluating SWOT Performance

In line with the SWOT science requirements, the 1-σ error, which is the 68th percentile of the absolute errors, is used to compare SWOT WSE to in situ. The 1-σ error is referred to as the 1-σ difference when comparing SWOT WSE to Sentinel-3 data. We also use the 50th percentile, or median, of the differences to compare SWOT WSA to Sentinel-2 data. In addition, these differences are divided by the mean Sentinel-2 WSA recorded during the study time period, since study lakes may have significantly different areas.

As the in situ WSE data related to the Agoufou lake and the elevation-area relationships of the Bam, Kokorou, Seguenega, Seytenga, and Toussiana are not absolutely leveled, an elevation bias removal is required prior to performing a comparison. For this purpose, the “combined open-source” method is used as a reference and, as described in Girard et al. [22], the elevation bias is computed as the mean of the elevation differences and eventually subtracted from the in situ data. The accuracy of the SWOT/Sentinel-2 elevation-area relationships is then evaluated using the WSE RMSE and the WSE normalized root mean square error (NRMSE) with respect to in situ. The NRMSE is calculated for each lake as the RMSE divided by the in situ WSE amplitude (maximum–minimum of the lake time series). We also calculate the WSE root mean square difference (RMSD) and the normalized root mean square difference (NRMSD) to compare SWOT/Sentinel-2 relationships to the combined open-source method. As we do not have in situ data over half of the lakes, the NRMSD here is calculated as the RMSD divided by the combined open-source WSE amplitude (maximum–minimum).

To evaluate SWOT's capabilities for large-scale water level seasonal dynamics retrieval, we derive the annual water level amplitude of each lake in the study region using science phase data and calculating the difference between the maximum and minimum of the PIXC WSE time series. Prior to this calculation, a first outlier removal is applied to the data by discarding all those outside the mean +/- 3 standard deviation (STD) interval of the WSE time series. Then, seasonal criteria are applied to maximize the chances of capturing the maximum and minimum water levels. Accordingly to the rainy season timing (see Section II-A1) and the hydrological cycle of the lakes [21] in the study area, we keep only lakes whose WSE time series contain at least one measurement between the beginning of October and the end of December (end of the wet season), and at least two measurements between the beginning of January and the end of May (dry season).

## III. RESULTS

### A. SWOT WSE Performance

1) *SWOT WSE Accuracy:* SWOT PIXC and LakeSP products show good accuracy for both lakes, which have PT water level data (Arzuma reservoir and Bangou Kirey lake) as they compare well with in situ data [see Fig. 2(a)]. For the Arzuma reservoir, whose area varies between 0.1 and 4 km² during the study time period, the 1-σ error of the PIXC product is 0.08 m for the full time series [see Fig. 2(b)], with values of 0.08 m and 0.04 m for the Calval and Science phases, respectively. A 1-σ error of 0.09 m is obtained for the LakeSP product during the Calval phase, and no SWOT observations are synchronous with in situ data during the science phase. For the Bangou Kirey lake, whose area ranges from 0.15 to 0.45 km² during the study time period, the PIXC and LakeSP products achieve 1-σ errors of 0.11 m and 0.06 m, respectively. These results outperform the SWOT requirements for WSE, which specify 1-σ errors of 0.10 m and 0.25 m for lakes larger than 1 km² and lakes between (0.250 km)² and 1 km², respectively. In addition, for each of these two lakes, we do not observe an evolution of SWOT accuracy as a function of lake area (see Figure S3 of Supplementary Material), suggesting the ability of SWOT to provide reliable WSE estimates for lakes of various sizes.

2) *Comparison With Sentinel-3:* Fig. 3(a) shows that the SWOT PIXC product compares well with Sentinel-3 data, both during the Calval and science phases. Except for one lake (North Tanvi) where the number of matches is insufficient (only one matchup available), we find a good agreement, particularly during the dry season, from September to May, when both products record a similar trend in WSE. Moreover, the decrease in lake water area during the dry season does not affect the agreement between SWOT PIXC and Sentinel-3 data.

The SWOT LakeSP data show a reasonable agreement with Sentinel-3 [see Fig. 3(a)] but display significantly more variability and outliers, particularly for four lakes (Bam, North Tanvi, South Tanvi, and Tibin). Outliers can be caused by classification errors due to a bright land issue [see Fig. 4(b)], where land pixels such as bare soil have a relatively high σ₀ (e.g., more than 10 dB above surrounding ground σ₀) and are subsequently detected as water. Depending on the ratio of bright land pixels to water pixels, this contamination can have a significant impact (several meters) on WSE. Indeed, the impact of bright land pixels is greater at the end of the dry season, when the lakes dry up significantly, or even completely in the case of north and south Tanvi. The spatial restriction to the PLD polygon helps to prevent the PIXC-derived WSE from being affected by the bright land pixels but does not solve this issue completely.

The higher variability of the LakeSP product compared to PIXC is highlighted in Fig. 3(b) with a 1-σ difference of 0.32 m compared to 0.16 m for the PIXC data. Similar results are obtained regardless of whether the metrics are computed on the full set of Sentinel-3 matchups or only on the shared Sentinel-3 matchups between PIXC and LakeSP. In addition, the filtering of the LakeSP data based on the two main quality flags included in the product has a significant impact on the remaining number of data [see Fig. 3(a), see number of LakeSP data per lake].
<figure><img src="image_4.png" /><figcaption>Fig. 2. (a) Comparison of SWOT PIXC and LakeSP WSE data with in situ and Sentinel-3 on the Arzuma reservoir (left) and the Bangou Kirey lake (right). (b) Histograms of absolute WSE difference between SWOT and in situ on the Arzuma reservoir for the PIXC (left) and the LakeSP (right) products. CDF stands for cumulative distribution function.</figcaption></figure>

This higher variability and poorer number of data limit the capability of the LakeSP product to capture the full seasonal WSE dynamics for half of the study lakes.

## B. Comparison of SWOT WSA With Sentinel-2

Except for four lakes (Bangou Kirey, Kokorou, Seguenega, and Toussiana) where the SWOT derived WSA dynamics are in reasonable agreement with Sentinel-2 [see Fig. 5(a)], all lakes show poor comparisons [see Fig. 5(b)]. Even for lakes with good agreement between SWOT and Sentinel-2 estimates, we observe significant SWOT variability (Seguenega, Toussiana) and outliers (Kokorou). Furthermore, SWOT LakeSP product largely overestimates the WSA of half of the lakes (Arzuma, Bam, Bangui Mallam, Djibo, Manga, Seytenga, South Tanvi, and Tibin) and shows inconsistent dynamics in terms of dry season lake shrinkage. This overestimation is highlighted in Fig. 5(b) where the difference in WSA between SWOT LakeSP and Sentinel-2 reveals a positive bias of 17.2%.

The reasons for such overestimation of the WSA are discussed below. For lakes such as Arzuma, Seytenga, and South Tanvi, this is due to misclassification when low signal returns to the radar.

In this case, a prior occurrence water mask (GSWO dataset) is used to complement the scene classification. As shown in Fig. 6(a) and (b), using the Seytenga reservoir as an example, a significant fraction of the emerging banks, which are close to water have $\sigma_0$ values close to zero. The algorithm integrated in the SWOT LakeSP processing chain will, therefore, try to complement the observation with the iso-occurrence water mask that best fits the footprint of the pixels detected as water and which is, in this case, much larger than the actual water extent. The accuracy of this procedure strongly depends on errors in the prior water mask or on the presence of low $\sigma_0$ values on the real water surface, particularly near the shores, as it is the case in Fig. 6(b). If the iso-occurrence contour retrieval is erroneous, additional land pixels included in the iso-occurrence mask can be flagged as dark water and taken into account in the WSA calculation, as specified in the algorithm baseline. In the example shown, this results in an overestimation of the WSA by a factor of 3.

A variable containing the fraction of pixels classified as dark water within the resulting polygon is included in the LakeSP product and may be a relevant parameter to limit this overestimation issue. However, defining an appropriate threshold for
<figure><img src="image_5.png" /><figcaption>Fig. 3. (a) Comparison of SWOT PIXC and LakeSP WSE time series with Sentinel-3. (b) Histograms of absolute WSE difference between SWOT and Sentinel-3 for PIXC (left) and LakeSP (right).</figcaption></figure>

the dark water fraction would be challenging and would also have a significant impact on the quantity of remaining LakeSP WSA estimates, in addition to the impact of the quality flags. Furthermore, Fig. 3(a) shows that the corresponding LakeSP WSE estimates are not affected by this classification issue as the WSE is computed using the pixels flagged as open water only.

For lakes such as Djibo or Tibin, the overestimation of WSA by the LakeSP product is also due to the bright land contamination, described previously. These classification errors, namely the bright land issue and the dark water flagging error are documented in the known issues related to SWOT measurements [56].

### C. SWOT Elevation-Area Relationships

Given the poor performance of the SWOT WSA estimates, the accuracy of the relationships based on both SWOT WSE and WSA has not been evaluated, and in the following, we focus only on the SWOT/Sentinel-2 elevation-area relationships. Except for Bangou Kirey, we find a generally good agreement of the SWOT/Sentinel-2 elevation-area curves with those obtained using in situ data and combined open-source method (see Fig. 7). Although the SWOT/Sentinel-2 curves, based on one year of SWOT data only, are sometimes incomplete, missing the minimum, maximum, or intermediate water levels in some cases, they still generally capture most of the dynamics. Besides,
<figure><img src="image_6.png" /><figcaption>Fig. 4. SWOT PIXC σ₀, classification and WSE, and closest cloud-free Sentinel-2 optical image for the Tibin reservoir on (a) 19 January 2024 and (b) 23 May 2024. Classification values stand for 1 = land, 2 = land near water, 3 = water near land, 4 = open water, 5 = dark water, 6 = low coherence water near land, and 7 = open low coherence water.</figcaption></figure>

<table><caption>TABLE II
ACCURACY STATISTICS OF THE SWOT/SENTINEL-2 ELEVATION-AREA RELATIONSHIPS</caption><thead><tr><th>Lake</th><th>In-situ RMSE (m) / NRMSE (%)</th><th>Combined open source RMSD (m) / NRMSD (%)</th></tr></thead><tbody><tr><td>Agoufou</td><td>0.09 / 8.4</td><td>0.09 / 6.3</td></tr><tr><td>Arzuma</td><td>0.28 / 8.9</td><td>0.31 / 8.3</td></tr><tr><td>Babou</td><td></td><td>0.27 / 13.6</td></tr><tr><td>Bam</td><td>0.28 / 5.7</td><td>0.16 / 5.3</td></tr><tr><td>Bangou Kirey</td><td>0.25 / 10.9</td><td>0.19 / 44.9</td></tr><tr><td>Bangui Mallam</td><td></td><td>0.18 / 8.4</td></tr><tr><td>Djibo</td><td></td><td>0.24 / 9.1</td></tr><tr><td>Inbanta</td><td></td><td>0.20 / 12.6</td></tr><tr><td>Kokorou</td><td>0.10 / 3.9</td><td>0.10 / 3.6</td></tr><tr><td>Manga</td><td></td><td>0.24 / 14.6</td></tr><tr><td>North Tanvi</td><td></td><td>0.25 / 9.9</td></tr><tr><td>Seguenega</td><td>0.22 / 12.2</td><td>0.22 / 12.9</td></tr><tr><td>Seytenga</td><td>0.17 / 4.3</td><td>0.08 / 5.1</td></tr><tr><td>South Tanvi</td><td></td><td>0.20 / 12.1</td></tr><tr><td>Tibin</td><td></td><td>0.26 / 8.7</td></tr><tr><td>Toussiana</td><td>0.28 / 3.3</td><td>0.28 / 7.9</td></tr></tbody></table>

The statistics are in meters as they represent differences of elevation between points of equal area.

their slope remains consistent with that of the other methods. In addition, we report no significant bias compared to the combined open-source method, for which no elevation offset removal was performed.

In terms of accuracy (see Table II), the SWOT/Sentinel-2 elevation-area relationships show a good performance with RMSEs between 0.09 m (Agoufou) and 0.28 m (Arzuma, Bam, Toussiana), and NRMSEs between 3.25% (Toussiana) and 12.16% (Seguenega). When comparing to the combined open-source data, the results are also satisfactory as, without Bangou Kirey,

which is discussed next, we report RMSDs between 0.08 m (Seytenga) and 0.31 m (Arzuma), and NRMSDs between 3.60% (Kokorou) and 14.57% (Manga).

For Bangou Kirey, the ICESat-2 lidar profiles used to generate the combined open-source curve are almost tangential to the Sentinel-2 derived lake contours. In such a situation water detection errors become critical as they may lead to surface misclassification of the lidar data. Consequently, lidar measurement points acquired on the water instead of the ground happened to be used for the calculation of the intersections with the water contours. The combined open source derived elevation-area relationship is, therefore, flat and inconsistent with the SWOT/Sentinel-2 and in situ relationships.

## D. Water Level Amplitude

After the filters applied to the SWOT PIXC WSE time series (see Section II-C1), we are able to produce estimates of annual water level amplitude for more than 609 PLD lakes in the study region. Most of the lakes are small: the median PLD polygon area is 0.27 km² and the 80th percentile of the distribution corresponds approximately to 1 km². The average number of measurements per time series is 17 and the median STD of the elevation of the pixels selected to compute the WSE estimates is 0.18 m. The water level amplitude map shown in Fig. 8 provides large-scale information on the seasonal dynamics of the lakes. It also provides large-scale information on the depth of the lakes, as some of them dry out completely during the dry season.
<figure><img src="image_7.png" /><figcaption>Fig. 5. (a) Comparison of SWOT LakeSP WSA time series with Sentinel-2 and (b) histogram of the differences between SWOT and Sentinel-2. The x-axis has been limited to [−150%, +150%].</figcaption></figure>

We note that a large fraction of the lakes have a relatively small water level amplitude, as the median of the distribution is 2.1 m and the third quartile is 2.7 m. A few lakes have a significantly larger water level amplitude (> 5 m) and are either reservoirs (e.g., Toussiana) that are actively used, or lakes located in the inner Niger Delta and connected to the river network.

Overall, SWOT is able to retrieve the dynamics of shallow lakes such as the Feildegasse reservoir (1.5 m amplitude, Fig. 9 left), as well as larger dynamics such as that of the Bagre reservoir (8 m amplitude, Fig. 9 right), on a large scale and with good precision.

IV. DISCUSSION

A. Comparison With the Literature

1) Water Surface Elevation Performance: Comparing SWOT LakeSP WSE data with Hydroweb, Yu et al. [30] reported MAEs and RMSEs of 1–6 m and up to 12 m, respectively, on two lakes in West Africa, and less than 0.5 m and 1.5 m when averaging the metrics globally. This is an order of magnitude higher than what we obtained in terms of 1-σ error when comparing SWOT LakeSP WSE with in situ and Sentinel-3. Comparing SWOT to Hydroweb, DAHITI [57], and
<figure><img src="image_8.png" /><figcaption>Fig. 6. SWOT PIXC σ₀ and classification, and closest cloud-free Sentinel-2 optical image, for the Seytenga reservoir on (a) 18 December 2023 and (b) 1 January 2024.</figcaption></figure>

gauge measurements over six medium-sized and large global lakes, Hamoudzadeh et al. [28] reported MAEs and RMSEs of 0.04–0.40 m (average of 0.26 m) and 0.06–0.52 m (average of 0.35 m), respectively, which is more consistent with our results. As the data version has been updated several times since the version 2 used in these two publications, the SWOT performance may have changed accordingly.

In terms of precision, the median STD of 0.18 m that we obtain for the SWOT PIXC WSE estimates (after rejecting the large outliers using the mean +/- 3 STD criterion) is better than the value of 0.33 m reported by Hamoudzadeh et al. [28] for the SWOT LakeSP product. In addition, our result compares well with the median STD of 0.17 m reported by Taburet et al. [58] using former Land Sentinel-3 delay/Doppler products and the offset centre of gravity retracking, although new Hydrology Thematic Sentinel-3 products show better precision [48].

2) *Elevation-Area Relationship Performance:* The RMSE between 0.09 m and 0.28 m and the NRMSE between 3.25% and 12.16% are slightly larger but comparable to the results of 0.05–0.25 m and 1.7%–8.9% obtained with the combined open-source method in Girard et al. [22] whose capability to derive

elevation-area relationships was close to that of high-resolution Pleiades digital surface models.

3) *Large-Scale Water Level Amplitudes:* Although the data used in this article cover only one year and are not synchronous with those used in other publications, we can compare the order of magnitude of the water level amplitudes to assess their consistency. Using ICESat-2 ATL08 data between October 2018 and July 2020, Cooley et al. [59] measured median and third quartile of water level variability of about 1.5 m and 3 m, respectively, in the Niger hydrological basin. This is consistent with the values of 2.1 m and 2.7 m we obtain in the same area. As water level variability is calculated in Cooley et al. [59] as soon as lakes are observed by lidar only twice, part of the difference between the median values may be due to the scarcity of ICESat-2 data, which does not capture the actual water level maximum or minimum. de Fleury et al. [21] estimated the 2016–2021 averaged water level variation of 42 lakes and reservoirs in Mali, Niger, and Burkina Faso using Sentinel-3 data. A good agreement is achieved, as the mean difference between water level variations in their study and the variations calculated in this study is only 0.4 m for the common lakes. Only three lakes show larger discrepancies (> 1 m).
<figure><img src="image_9.png" /><figcaption>Fig. 7. Comparison of the SWOT/Sentinel-2 elevation-area relationships with those derived from in situ and combined open-source methods, (a) for lakes with in situ data and (b) for the other lakes.</figcaption></figure>

## B. Volume Change Estimation Capabilities

Both accurate WSE and WSA monitoring capabilities are required to estimate lake volume changes. The analysis of the SWOT PIXC and LakeSP WSE time series, as well as the comparison with Sentinel-3 WSE (see Section III-A2), highlights the benefits of using additional quality flags and spatially restrictive polygons to optimize data quality. Indeed, we show that using such polygons mitigates the impact on WSE of bright land pixels. Specular ringing, a contamination caused by near-nadir specular

features, which can propagate far from nadir [56], is also well filtered by the quality flags used here. In addition, the reduction in the number of pixels induced by such filtering does not reduce the general WSE retrieval performance. As we reported no significant correlation between SWOT WSE retrieval and lake size, our results suggest that even a small number of pixels may be sufficient to derive robust WSE estimates. Overall, with 69% of the PIXC data remaining after applying the quality flags, the filtering did not limit the ability of SWOT to retrieve the seasonal WSE variability of the study lakes.

The comparison between SWOT LakeSP and Sentinel-2 suggest that SWOT capability to measure WSA dynamics is limited and sensitive to soil and water surface properties. The SWOT LakeSP product includes a variable representing the fraction of the measured area covered by dark water. This attribute, employed together with an appropriate threshold, could be used to filter out the erroneous WSA estimates caused by dark water classification issues such as described in Section III-B. However, the SWOT LakeSP product does not include a bright land fraction variable to address the bright land issues that cause the WSA to be overestimated (see Section III-A2). To obtain this information, it is necessary to go back to the SWOT PIXC product and investigate the accuracy of the associated bright land flag. Again, using this flag as a filtering criterion would require defining an appropriate threshold for the bright land pixel fraction. Once supplemented with external WSA data such as those from Sentinel-2, the accuracy of the SWOT/Sentinel-2 elevation-area relationships shows that SWOT data are well-suited to monitor lake volume changes.

## C. Spatial Coverage

Cooley et al. [59] were able to produce water level variability for 179 water bodies within the entire Niger hydrological basin. In addition, de Fleury et al. [21] were able to produce WSE time series for 42 water bodies within a region in Central Sahel close to the bounding box of the 16 lakes studied in this work. The 609 lakes and reservoirs covered here, 202 of which are located in the subregion of the Niger hydrological basin within the study region, therefore, indicate that SWOT is a major step forward in terms of spatial coverage of space-based altimetry.

In addition, the water level amplitude map may provide large-scale information on the lake water use. The statistics indicate that for at least 25% of the study lakes, water level depletion is greater than the average evaporation-induced water loss [21], suggesting anthropogenic water withdrawal.

## V. CONCLUSION

WSE time series of more than 600 West African lakes and reservoirs included in the SWOT PLD were derived using SWOT PIXC data. The accuracy of SWOT PIXC and LakeSP WSE estimates was evaluated against in situ data from two lakes. Excellent agreement was found for both products with 1-σ errors between 0.06 and 0.11 m, in line with SWOT science requirements. SWOT WSE time series were also compared with Sentinel-3 data over 12 lakes and an overall good agreement between the different sensors was reported. A better comparison was achieved with SWOT PIXC data, with a 1-σ difference of 0.16 m compared to 0.32 m for the SWOT LakeSP product.
<figure><img src="image_10.png" /><figcaption>Fig. 8. Map (left) and distribution (right) of the SWOT PIXC derived annual water level amplitude over the lakes located in the study area. The size of the points is the logarithm of the lake PLD polygon size. The upper limit of the color is exceeded by only one lake, in dark red, with an amplitude of 8 m.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 9. SWOT PIXC derived WSE time series of the Feildegasse (left) and Bagre (right) reservoirs.</figcaption></figure>

The effects causing this discrepancy were investigated and it was found that classification errors caused by bright land pixels could have a significant impact on the SWOT LakeSP WSE estimates. The use of an external water mask, such as the PLD, together with the product quality flags can mitigate the impact of classification errors on the WSE and therefore make the PIXC estimate more robust.

SWOT WSA retrieval performance was also evaluated by comparing SWOT LakeSP WSA estimates with Sentinel-2 over 16 lakes. It was shown that SWOT generally overestimated the WSA, with a median bias of 17.2% relative to Sentinel-2. The causes of such overestimation and the presence of significant outliers were again found to be classification errors, either related to bright land pixels or land pixels misclassified as dark water. The latter issue is more likely to occur during the drying out of the lake as the fraction of exposed smooth soil increases.

Comparison with in situ and combined open-source elevation-area relationships showed a good accuracy below 0.28 m and 12.16% and a difference below 0.31 m and 14.57%, respectively. In addition, a large-scale water level amplitude map derived from the PIXC time series and covering 609 water bodies, 80% of which are smaller than 1 km², could be produced. Given that WSA estimation using SWOT remains challenging, complementing SWOT WSE with an external source of WSA such as Sentinel-2 will provide excellent capabilities for monitoring

water volume changes of small, medium, and large lakes in the study region. Such capabilities are of particular interest for large-scale analysis of lake water balance and hydrological functioning in West Africa [21] and for multiple applications [60].

## ACKNOWLEDGMENT

The authors would like to thank Roger Fjortoft, Damien Desroches, Nicolas Gasnier, and Claire Pottier for their expertise and the valuable discussions on the SWOT data. They also would like to thank Roland Yonaba and Tazen Fowé from the International d'Ingénierie de l'Eau et de l'Environnement (2IE) for sharing volume-elevation relationships on three reservoirs in Burkina Faso and the anonymous reviewers for their comments and thorough review of the previous version of this article.

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<figure><img src="image_12.png" /></figure>

Félix Girard received the Engineering Degree in applied physics from the Institut National des Sciences Appliquées de Toulouse, France, in 2021, after completing a one-year apprenticeship in Radar Altimetry for Hydrology with Collecte Localisation Satellites, Ramonville-Saint-Agne, France. He, then, worked for one year as a Research Engineer in Remote Sensing Hydrology at Géosciences Environnement, Toulouse, France, and is currently a third-year Ph.D. student with Géosciences Environnement and Collecte Localisation Satellites. His research focuses on synergistic

multisensor approaches, including the recently launched Surface Water and Ocean Topography mission, to study the hydrological functioning and volume changes of lakes, reservoirs and wetlands in West Africa.

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

Laurent Kergoat received the Diploma from Ecole Centrale de Paris, Paris, France, in 1988 and the Ph.D. degree in carbon cycle modeling from Université de Toulouse, Toulouse, France, in 1995.

He joined CNRS in 1997 (Laboratoire d'Ecologie Végétale, Toulouse, France), spent 1 year with NCAR Boulder, Boulder, CO, USA, in 1999, and joined CESBIO, Toulouse, France, in 2003, focusing on boreal zone and tropical eco-hydrology and optical remote sensing. He is currently the head of the Hydrobiogeochemical Cycle Group, Géosciences Environnement Toulouse, Toulouse, France. His research focused on vegetation

observation and modeling at the global scale.

<figure><img src="image_14.png" /></figure>

Ibrahim Mainassara received the Engineering degree in hydraulics/hydrology from the Ecole Nationale Supérieure d'Hydraulique, Blida, Algeria, in 2006 and the master's degree in integrated water resources management from Institut International d'Ingénierie de l'Eau et de l'Environnement, Ouagadougou, Burkina, in 2010.

Since 2011, he has been a Field Engineer with Hydrosciences Montpellier, Montpellier, France. At IRD Representation in Niger as local technical responsible of AMMA-CATCH Observatory, site of

Niger, he contributes in the monitoring of the network of stations deployed in the field (rain, flux tower, surface water, and groundwater survey), installation of new instruments, collection, processing and analysis of data destined for AMMA-CATCH database.

<figure><img src="image_15.png" /></figure>

Maxime Wubda received the master's degree in geophysics from Pierre et Marie Curie University, Paris, France, in 1998, and the Ph.D. degree from the International Chair in Physics, Mathematics and Applications, Abomey-Calavi University, Littoral, Benin, in 2017.

He has been a Lecturer-Researcher with Joseph KI-ZERBO University, Ouagadougou, Burkina Faso, since 2021. At Joseph KI-ZERBO University, he teaches geophysics and geotechnics in connection with integrated water resource management: research

into the water cycle, prospecting for the construction and maintenance of hydraulic structures. Before joining the University of Ouagadougou, he was posted to the Institut de Recherche pour le Développement (IRD), Cotonou, Benin, in 2006 as a coordinator of the research activities of the hydrometeorological observatory of the AMMA-CATCH programme.

<figure><img src="image_16.png" /></figure>

Edwige Dayangnéwendé Nikiéma received the Ph.D. degree in health geography from the University of Paris Créteil, Créteil, France, in 2008.

She is currently a Research Professor with Joseph KI-ZERBO University, Ouagadougou, Burkina Faso. Her research focuses on the relationship between environmental and geographic conditions and population health in Burkina Faso. In recent years, she has worked on a number of population health vulnerabilities in urban and rural areas, including HIV infection, diarrheal diseases, sanitation, and hygiene.

In addition, she is collaborating with Géosciences Environnement Toulouse, Toulouse, France, on issues related to surface water monitoring in Burkina Faso using remote sensing and field measurements.
<figure><img src="image_17.png" /></figure>

**Amadou Abdourhamane Touré** received the Ph.D degree in soil erosion dynamics from the University of Earth Sciences of Dijon, France, in 2011. He is currently a Full Professor of Earth Sciences and Environment and a Researcher with the Faculty of Science and Technology, Abdou Moumouni University, Niamey, Niger. He is also the Deputy Coordinator of the SFR RACINES, Paris, France. His main research themes are the soil degradation and recovery of degraded land, and the characterization of the biological and physico-chemical quality of surface waters.

<figure><img src="image_18.png" /></figure>

**Nicolas Taburet** has been working in the domain of satellite data analysis for 17 years. After the Ph.D. degree in astronomy from the Paris-Saclay University in 2010, he worked for 4 years with CNRS as a Research Engineer and then joined Collecte Localisation Satellites (CLS) Group, Ramonville-Saint-Agne, France, in 2016 and developed an expertise in altimetry CalVal studies over inland waters. He is involved in the current Sentinel-3 Mission Performance Centre activities and Sentinel-6 CalVal activities. He has also ensured the product management of the Continental Water Level products for the Theia/Hydroweb since 2016 and the Copernicus Global Land Cryosphere and Water service since 2018. Since december 2020, he leads the Performance and Valorization of altimetric data over Continental Waters team, CLS.

<figure><img src="image_19.png" /></figure>

**Julien Renou** received the Ph.D. degree in seismology from the Institut de Physique du Globe de Paris, Paris, France, in 2021.

He is currently an Engineer working in the field of space altimetry applied to the study of continental waters. Before joining Collecte Localisation Satellites (CLS), Ramonville-Saint-Agne, France, in 2021 as well as a Postdoctorate from the University of Oxford, Oxford, U.K. He is now part of the team for the performance and valorization of continental waters at CLS, where he is involved in the Calibration and

Validation (Cal/Val) of several altimetric missions such as CryoSat-2, Sentinel-3, and the recent SWOT mission. His primary studies focus on assessing the quality of products provided by these missions and developing approaches to better estimate the performance of products over rivers and lakes. He is specifically responsible for the hydrology thematic within the MPC-S3 and CryoTempo projects. His main research focused on the study and modeling of the earthquake's initiation at global scale.

<figure><img src="image_20.png" /></figure>

**Nicolas Picot** received the Graduate degree from the Ecole Nationale Superieure d'Ingenieur en Constructions Aeronautiques, Metz, France, in 1990, with a specialization in Space Technologies.

In 1990, he joined the "Institut National des Sciences de l'Univers" as a Research Engineer within the TOPEX/Poseidon CNES project, working on validation techniques and ionospheric modeling using Doris signals. He participated in the CalVal phases of the ERS1 and Topex/Poseidon altimeter projects.

In 1996, he joined the CNES TOPEX/POSEIDON project team in charge of product processing, quality, and altimetry expertise. He was involved in the development of the ground processing center for the Jason-1 and ENVISAT (altimetry payload) projects. He then carried out the same activity for Jason-2, Jason-3, SARAL, and other altimetry missions. He is currently responsible for the Sentinel3 project with CNES and involved in the SWOT project.

<figure><img src="image_21.png" /></figure>

**Maxime Vayre** received the Engineering degree in electronics and signal processing, ENSEEIHT, Toulouse, France, in 2016.

He has been a Scientific Engineer with the Performance and Valorization of Satellite Observations over Inland Waters unit, Collecte Localisation Satellites, Ramonville-Saint-Agne, France, since January 2017. He is involved in altimetry Calibration/Validation studies and product generation over inland waters for CNES and Copernicus projects. He contributed to the CCI+ precursor project for the river discharge ECV, to

provide elevation data from radar altimetry and to assess the quality of discharge products. He also participates in the SWOT validation as part of the CNES Cal/Val activities.

<figure><img src="image_22.png" /></figure>

**Manuela Grippa** received the Graduate degree in physics from the University of Milan, Milan, Italy, in 1997 and the Ph.D. degree in remote sensing of the atmosphere from the Department of Physics, Heriot-Watt University, Edinburgh, U.K., in 2001.

Her research then focused on land applications of microwave remote sensing with a first postdoctoral fellowship on Radar Remote Sensing of Soil Moisture with the School of Earth, Environmental and Geographical Sciences, University of Edinburgh, Edinburgh, U.K., and a second one on passive microwave

remote sensing of snow with CESBIO, Toulouse, France. She is currently a Researcher with GET, Toulouse, France. During the last 15 years, her main research interests have focused on the assessment of eco-hydrological changes in the Sahelian region, in particular concerning the evolution of surface waters, using optical and radar remote sensing combined with in situ measurements and modeling approaches.