# Sentinel-2 MSI image time series reveal hydrological and geomorphological control of the sedimentation processes in an Amazonian hydropower dam

Diego Alves E Santos, Jean-Michel Martinez, Diogo Olivetti, André Zumak, David Guimarães, Keila Aniceto, Ednaldo Severo, Osmair Ferreira, Tristan Harmel, Mauricio Cordeiro, et al.

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To cite this version:

Diego Alves E Santos, Jean-Michel Martinez, Diogo Olivetti, André Zumak, David Guimarães, et al. "Sentinel-2 MSI image time series reveal hydrological and geomorphological control of the sedimentation processes in an Amazonian hydropower dam." International Journal of Applied Earth Observation and Geoinformation, 2024, 128, pp.103786. 10.1016/j.jag.2024.103786. hal-04904080

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# Sentinel-2 MSI image time series reveal hydrological and geomorphological control of the sedimentation processes in an Amazonian hydropower dam

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Diego R. Alves e Santos<sup>a</sup>, Jean-Michel Martinez<sup>a,b,*</sup>, Diogo Olivetti<sup>a</sup>, André Zumak<sup>c</sup>, David Guimarães<sup>a,b</sup>, Keila Aniceto<sup>c</sup>, Ednaldo Severo<sup>c</sup>, Osmair Ferreira<sup>a</sup>, Tristan Harmel<sup>a</sup>, Mauricio Cordeiro<sup>b</sup>, Naziano Fillizola<sup>c</sup>, Bruna Sell<sup>d</sup>, Daniel Fernandes<sup>d</sup>, Camila Souto<sup>d</sup>, Henrique Roig<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Inland water

Hydropower

Madeira River

Run-of-the-river

Water color

Siltation

Sediment transport

Geomorphology

Remote sensing

## ABSTRACT

Monitoring sediment transport in a run-of-river hydropower dam was investigated to detect and quantify the siltation processes using Sentinel-2/MSI satellite images. We developed a method allowing us to assess the fate of sediment discharge in reservoirs and to map the locations of eroding and silting stream cross sections. This monitoring was achieved by retrieving the seasonal and interannual variation in suspended sediment concentration (SPM) longitudinally and by developing indices to detect local deviations from the overall SPM trend. A comparison between Sentinel-2 reflectance, processed for atmospheric and glint correction, and field measurements showed fine accuracy for the b8a/b4 band ratio (RMSE = 0.23 sr⁻¹, N = 50) as well as a very good SPM retrieval performance for a wide range of concentrations [10–926 mg/L] with an RMSLE of 0.23 mg/L. SPM trend analysis demonstrated that significant (p < 0.001) sedimentation processes (i.e., decreases in SPM concentration upstream to downstream) occur for most parts of the hydrological cycle except during peak flooding when there was no significant SPM siltation. The overall rate of SPM decrease is –0.52 mg/L/km, varying from +0.11 mg/L/km down to –1.10 mg/L/km. We developed a deviation index to detect local anomalies from the overall upstream–downstream satellite-derived SPM trend. A satellite-derived map of eroding and silting reaches is produced evidencing the control of the sediment transport pattern by geomorphological features, such as rapids. Using independent bathymetric surveys, we showed that satellite data allow the locations of eroding and silting river reaches to be correctly mapped.

## 1. Introduction

Sediment accumulation in reservoirs, which are used for hydro-power, irrigation, fish farming or flood control, represents one of the major environmental modifications introduced by anthropogenic activities across continental and oceanic ecosystems (Nilsson et al., 2005). It has been estimated that 5 Gt·yr⁻¹ of sediment is annually stored by dams worldwide (Vörösmarty et al., 2003), leading to a dramatic reduction in fluvial nutrient-rich sediment (mostly clay and silts) and sand particle fluxes to lowland floodplains, estuaries and coastal

environments of approximately 49 % between 1950 and 2010 (Svivitski et al., 2022). The sediment-depleted waters flowing through downstream river reaches induce bank and channel erosion and subsidence of estuaries (Blum & Roberts, 2012), altering both terrestrial and aquatic environments. On the other hand, the sediment accumulation process in reservoirs, usually referred to as siltation, severely limits the exploitation of the reservoir by reducing the water storage capacity (Baskaran et al., 2015; Falinski & Penn, 2018) but also by inducing degraded water quality, as pollutants are stored along with sediments at the bottom of artificial ponds (Bábek et al., 2020; Palanques et al., 2014). In a recent

<footnote><sup>a</sup> University of Brasília, Institute of Geosciences, Graduate Program in Applied Geosciences and Geodynamics, Geoprocessing Laboratory (GeoLab), Brasília, DF, Brazil</footnote>

<footnote><sup>b</sup> Géosciences Environnement Toulouse (GET), UMR5563, Institut de Recherche pour le Développement (IRD) / Centre National de la Recherche Scientifique (CNRS) /
Université Toulouse 3, 14 Avenue Edouard Belin, 31400 Toulouse, France</footnote>

<footnote><sup>c</sup> Federal University of Amazonas, H2A group, Manaus, AM, Brazil</footnote>

<footnote><sup>d</sup> Jirau Energia – Road BR-364, KM 824, Porto, Velho, Brazil</footnote>

<footnote>* Corresponding author at: University of Brasília, Institute of Geosciences, Graduate Program in Applied Geosciences and Geodynamics, Geoprocessing Laboratory (GeoLab), Brasília, DF, Brazil.</footnote>

<footnote>E-mail addresses: jean-michel.martinez@ird.fr (J.-M. Martinez), nazianofilizola@ufam.edu.br (N. Fillizola).</footnote>
study, (Yao et al., 2023a) showed a global reduction in water storage capacity using surface area estimates and altimetric data from a remote sensing dataset. They suggested that the primary factor controlling this water storage reduction is related to siltation processes that have a larger impact than hydroclimatic variability over the last 30 years.

More than 50 000 large dams higher than 15 m have been built in the late century (World Commission on Dams), and the environmental problems generated by these massive modifications of water and sediment fluxes in river networks have raised multiple concerns about the construction of new reservoirs in recent decades (Martínez & Castillo, 2016; Poff & Hart, 2002). The run-of-river reservoir model has been increasingly implemented in recent years because it limits the water volume storage and the water residence time in reservoirs, which should limit both geomorphological and ecological impacts. Although a large body of literature exists on the impact of large storage reservoirs, the evaluation of the impact of run-of-river systems in relation to their environmental impacts is much more limited (Almeida et al., 2019; Anderson et al., 2015). In particular, it is important to monitor and understand the sedimentation processes in run-of-river reservoirs to promote sustainable management of the existing and planned dam networks in river catchments.

The assessment of reservoir sedimentation using satellite data has been proposed based on different approaches. One approach consists in multitemporal mapping of the waterbody extent combined with water level data to construct lake bathymetry and estimate storage losses as a function of time (Yao et al., 2023b). This approach can bring fine results, either using optical or SAR images or both (Pena-Luque et al. 2021), but requires that the reservoir dries out in order to map the reservoir bottom which restricts the application of this technique to reservoir experimenting extreme water level variations. Another approach is to map sediment fluxes in the river stream and reservoirs using water color techniques. Condé et al. (2019) used MODIS images to retrieve turbidity profiles along stream on large reservoirs evidencing SPM decrease from the upstream part of the reservoir to the lacustrine region. The average satellite-retrieved turbidity loss rate was used to compute reservoir sediment trapping efficiency and was compared to a conventional approach proposed by Brune (1953), based on river inflow and water storage data, showing good agreement for six reservoirs (Condé et al. 2019). Moragoda et al. (2023) proposed to assess reservoir sediment trapping efficiency using long-term average satellite-derived SPM data (from 1984 to 2018) combined with river discharge estimates to assess upstream and downstream sediment discharge and derive local sediment budgets. However, both studies offer insufficient spatial or temporal resolution to monitor sediment transport and siltation within reservoirs which are known to be highly variable and strongly impacted by climate change induced extreme hydrological events (Rosas et al., 2020).

In this work, we propose and validate a method to monitor sediment transport as a function of the hydrological cycle and to map siltation areas within an hydropower dams using high-resolution satellite data (i. e., Sentinel-2/MSI sensor). There is currently no method available to map silting or eroding areas in reservoirs from water color data, the main obstacle being that siltation is a process occurring at the water column bottom which is invisible from water color spaceborne sensors for most inland waterbodies, especially in sediment-laden waters. The satellite-derived SPM assessment is restricted to a thin layer in inland turbid waters (Condé et al. 2019), of about one-meter deep, and there is a need to verify if the sediment transport pattern revealed from space is effectively related to the bottom processes. Consequently, it requires to simultaneously acquire data at the river surface and field bathymetric surveys over consecutive years as siltation/erosion processes may vary at different time scales (from a few days to decades). This unique dataset has been gathered for this work analyzing Sentinel-2/MSI images acquired during 4 years and bathymetric surveys acquired at different locations over the same time span.

We analyzed the behavior of the recently constructed Jirau run-of-

river dam located along the Madeira River, which is the main tributary of the Amazon River in terms of sediment discharge, representing approximately half of the total sediment fluxes carried by the Amazon to the Atlantic Ocean (Vauchel et al., 2017). The number of constructed and planned dams in the Amazon has largely increased in the last two decades, and there is an urgent need to monitor their impact to promote a sustainable plan for hydropower across the Amazon basin (Flecker et al., 2022). The real impact of the Madeira River run-of-river dams either from a hydrological, geomorphological or ecological perspective is not clearly defined (Cella-Ribeiro et al., 2017). In particular, contradictory results have been presented on the impact of run-of-river dams on the Madeira River regarding siltation, showing either downstream suspended sediment depletion (Latrubesse et al., 2017) or no significant variation (Almeida et al., 2019).

There is a large literature on the retrieval of SPM concentration using remote sensing. For turbid waters, most popular empirical or semi-analytical models relies on single band (Nechad et al. 2010) or band ratio formulations (Doxaran et al., 2002; Qiu et al. 2013; Martinez et al., 2015). Most important limitation is related to the reflectance saturation for moderate turbidity levels that implies to use near infrared channels where light absorption by particles remains low in relation to pure water absorption and light backscattering. Hybrid models have been developed (Han et al., 2016; Balasubramanian et al., 2020) to tackle the variability of coastal/inland water optical properties, where the different optically active components can contribute to the water leaving radiance, in order to apply separate models as a function of some predefined water types.

For this work, we rely on previous articles that analyzed the optical properties of Madeira River water from experimental (Espinoza Villar et al., 2013) and theoretical perspectives (Pinet et al., 2017). These works demonstrated that these river waters experience seasonal variation in the suspended particle size distribution that can be accounted for using a band ratio model with red and infrared wavelengths. This band ratio model allows fine-scale monitoring of the SPM concentration and for a wide range of concentrations, as the Madeira River presents a strong suspended sediment load for most of the hydrological cycle. Ten new field campaigns have been conducted since 2021 to validate the reflectance and satellite-derived SPM estimates. Sentinel-2 data processing steps are presented before showing the field/satellite matchup results. We analyzed SPM spatial trends to retrieve sediment transport processes as a function of hydrology and geomorphological features. Applying the trend test and calculating the trend amplitude, we retrieved occurrence data on siltation processes. In the discussion, we mapped the locations of reaches presenting either siltation or erosion and compared the results with field bathymetric surveys acquired at 6 different reaches to understand the efficiency of satellite data in mapping siltation processes in run-of-river reservoirs.

## 2. Materials and methods

### 2.1. The Madeira river and the Jirau dam

The Madeira River is formed by the confluence of the Beni and Mamoré Rivers at the border between Brazil and Bolivia in the southwest Amazon. The Madeira River is considered a mega river (Latrubesse et al., 2017) and is among the ten largest rivers in the world, being the third-largest contributor in terms of water discharge to the Amazon River, with an estimated average annual discharge of 26,500 m³/s at its confluence with the Amazon River. Solid discharge is the largest contributor in the Amazon basin, representing approximately 50 % of the Amazon River total sediment discharge to the Atlantic Ocean, transporting 419 Mt/year (Espinoza Villar et al., 2012; Labadessa, 2011; Rivera et al., 2019; Vauchel et al., 2017; Vergasta et al., 2021).

The Madeira River headwaters are in the Andes in Bolivian and Peruvian territory, with strong erosion rates up to 3000 tons/km²/year (Pepin et al., 2013). Outside the Andes ranges, the Madeira River
tributaries drain lowland areas, less than 200 m in altitude, characterized by low erosion rates and extensive areas of inundation. The drainage area at the Porto Velho station, the nearest hydrological station located downstream of the Jirau run-of-river dam, is 975,379 km², representing 16% of the total area of the Amazon basin (Vauchel et al., 2017; Vergasta et al., 2021).

The Jirau dam extends 7,875 m across the river, and the reservoir area is 395.21 km² during peak flooding. The reservoir water level is artificially maintained between two thresholds depending on the hydrological period, representing a water level variation of 7.5 m (https://www.jirauenergia.com.br/conheca-a-uhe). The backwater of the reservoir waters extends approximately 158 km upstream of the dam depending on the location of the Madeira River discharge. Table 1 lists the locations of eleven river reaches that were sampled for suspended sediment concentration and radiometric measurement during 10 field sampling trips. Eight of these sample sections are in the Jirau Reservoir and were used for satellite SPM estimation validation using the field-collected data.

Fig. 1 displays the Jirau dam area, which mostly follows the former river mainstream with a marked sinuosity and some islands. Several rapids were flooded during the reservoir filling and are marked as yellow stars.

## 2.2. River discharge data

Fig. 2 shows the daily river discharges for each year from 2017 to 2020 for which we had complete records at the water gauge located approximately 3 km downstream of the Jirau dam. The discharge exhibits a monomodal seasonal flood pattern, typical of the Amazonian rivers, with peak flooding occurring from March to April (mean river discharge of 37,086 m³/s) and a lower stage occurring from August to early November (mean river discharge of 3,114 m³/s). Table 2 presents the minimum and maximum mean monthly water discharges for each year. Over the period considered, the minimum discharge was recorded during October 2020 (2,634 m³/s), and the maximum discharge occurred during March 2019 (42,130 m³/s).

## 2.3. Field-sampling measurements

Ten field campaigns (C1 to C10) were carried out each month from March 2021 to January 2022 with 150 radiometric measurements and SPM concentration samples. For each of the eleven river reaches visited, three different points at 25, 50, and 75% of the total stream width were sampled. Depending on the SPM concentration level, 500 ml or 1 l HDPE bottles were used for water sampling at the river surface. Samples were filtered through 0.45 µm cellulose acetate filters. The filters were dried in an oven at 105 °C for one hour and then cooled for one hour in the desiccator to perform the preweighing three times to determine their initial weight. The filtered material was dried for two hours in an oven at 105 °C. After drying, the material was cooled in a desiccator for 1 h 30

<table><caption>**Table 1**
Field sampling sections and distance to the dam (km).</caption><thead><tr><td>River</td><td>Sampling section</td><td>Distance to the dam</td></tr></thead><tbody><tr><td>Beni</td><td>Beni (BENI)*</td><td>230</td></tr><tr><td>Mamoré</td><td>Mamoré (MAM)*</td><td>230</td></tr><tr><td>Madeira</td><td>Abunã (ABU)</td><td>145</td></tr><tr><td>Madeira</td><td>Pedermeiras (PED)</td><td>130</td></tr><tr><td>Madeira</td><td>Tamborete (TAM)</td><td>110</td></tr><tr><td>Madeira</td><td>Paredão (PAR)</td><td>85</td></tr><tr><td>Madeira</td><td>Velha Mutum (VMUT)</td><td>60</td></tr><tr><td>Madeira</td><td>Palmeiral (PAL)</td><td>45</td></tr><tr><td>Madeira</td><td>Jirau (JIR)</td><td>5</td></tr><tr><td>Madeira</td><td>Estação (EST)</td><td>3</td></tr><tr><td>Madeira</td><td>Jaci (JACI)*</td><td>-40</td></tr></tbody></table>

min and then weighed three times to obtain the final weight.

Radiometric measurements were realized using TriOS RAMSES hyperspectral radiometers following the above water measurement protocol presented by (Mobley, 1999). The estimation of the remote-sensing ($R_{rs}$) reflectance was performed as follows:

$$R_{rs}(\theta, \phi, \lambda) = \frac{L_u(\theta, \phi, \lambda) - \rho(\theta, \phi, \lambda) \cdot L_d(\theta, \phi, \lambda)}{E_d(\lambda)} \quad (1)$$

where $L_u$ is the upwelling water-leaving radiance, $L_d$ is the downwelling radiance, $E_d$ is the downwelling irradiance, $\theta$ and $\phi$ are the polar (from nadir) and azimuthal (from sun) directions, respectively, and $\lambda$ is the spectral wavelength. The factor $\rho$, which represents the water surface reflectance factor for the downwelling light at the air-water interface, is a function of the acquisition geometry, wavelength and local rugosity. All measurements were realized between 9:30 AM and 14:30 PM corresponding to low solar zenith angle conditions (<45°). For each measurement, the factor $\rho$ was adjusted according to the method presented in Harmel et al. (2012) based on full vector radiative transfer computations using the vector radiative transfer model OSOAA (Chami et al., 2015), which quantifies the directional variations in the water-leaving reflectance and degree of polarization considering all the related variables as well as the water roughness. For this processing, the open source Python package (https://github.com/Tristanovsk/trios) was used.

## 2.4. Bathymetric surveys

Bathymetric surveys were performed independently of this work, twice a year, at six different river reaches by the hydropower company to monitor siltation processes in the reservoir. Surveys were realized with an ecobathymeter BAthy 500-MF that operates at four different frequencies (33, 40, 50, 200 kHz), delivering depth estimates with an accuracy greater than 0.1 % for river depths shallower than 100 m. The ecobathymeter was coupled to a DGPS receiver using the OmniSTAR virtual base station service. Fig. 3 shows the bathymetric surveys for 4 reaches comparing the 2018 and 2022 results acquired at peak flooding.

Surveys were used to compute the river cross section $A_{year}$ (or wetted area) for each year and each river reach. We considered the surveys performed during the peak flooding stage (February or March) to ensure that during the reservoir's maximum water level operation, all the river reaches were completely flooded and could be fully measured. We defined the cross-sectional ratio CSR as the ratio between the 2018 and 2022 cross-sections with CSR = 100(1 - $A_{2018}/A_{2022}$). A positive CSR value indicates erosion (larger wetted area in 2022 than in 2018) for a specific river reach, a null value indicates stability, and a negative value indicates siltation (i.e., sediment accumulation).

## 2.5. Sentinel-2 image Processing: GRS and water detection algorithms

Sentinel-2 MSI Level 2A products were obtained using the Glint Removal for Sentinel-2 (GRS) (Harmel et al., 2018) algorithm, which allows correction of both atmospheric artifacts and sunglint effects from level-1C data. The GRS algorithm is based on the spectral characteristics of the sunglint component, measured by the radiance reaching the atmosphere, the direct transmittance reaching the surface and that which leaves the surface. It is a function of the bidirectional reflectance distribution function (BRDF) in waters with diffusing surfaces, as in the case of water with waves caused by winds that generate a myriad of images of the sun that will reach the sensor. The algorithm aims to derive reflectance values from the air-water interface in the SWIR bands, where the atmosphere is weakly impacted by the scattering of gases and aerosols. In this range, the radiance that leaves the water can be considered null because of the high absorption of incident light by the water column. GRS atmospheric correction was applied to Level-1C images generated by MSI Sentinel-2 sensors, which were resampled to the same 20 m spatial resolution as the SWIR band. The gas absorption

<footnote>*River reaches located outside the Jirau reservoir (either upstream or downstream).</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 1. Map of the Jirau run-of-river reservoir showing the location of the main hydrological and geomorphological features as well as the field sampling sections.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 2. Madeira River daily river discharges at the Jirau dam during the 2017–2020 period.</figcaption></figure>

**Table 2**

Madeira River minimum and maximum mean monthly river discharge recorded for each year from 2017 to 2020.

<table><thead><tr><th>Year</th><th>Q (mín) m³/s</th><th>Q (max) m³/s</th></tr></thead><tbody><tr><td>2017</td><td>3,160</td><td>31,501</td></tr><tr><td>2018</td><td>3,436</td><td>37,976</td></tr><tr><td>2019</td><td>3,225</td><td>42,130</td></tr><tr><td>2020</td><td>2,634</td><td>36,737</td></tr><tr><td>Meanvalues</td><td>3,114</td><td>37,086</td></tr></tbody></table>

correction and the correction for the diffuse radiance of the atmosphere for aerosol optical thickness (AOT) were made. Atmospheric pressure and gas concentrations were retrieved from Copernicus Atmosphere Monitoring Service dataset (CAMS). Then, the BRDF was calculated, the sunglint radiance was computed for all bands, and finally, sunglint radiance correction was performed.

Finally, a set of 329 images containing at least 5 % cloud free water pixels in each image were available covering one of the two frames that matched the Jirau reservoir (T20LKQ and T20LLQ tiles as delivered by ESA).

Each Level 2A image produced using the GRS code was masked using the WaterDetect code (Cordeiro et al., 2021), which uses an unsupervised machine learning algorithm for pixel clustering, allowing a very
<figure><img src="image_7.png" /><figcaption>Fig. 3. Bathymetric surveys conducted prior to this work by the Jirau hydropower company at different cross-sections along the Jirau reservoir. Blue lines represent 2018 surveys, and red lines represent 2022 surveys. Shallower depths in the most recent survey indicates local siltation over the period of interest. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

reliable assessment of the extent of water bodies in individual Sentinel-2 images. WaterDetect had been run independently on Sentinel-2 level 2A images originating from the MAJA atmospheric correction to benefit from the cloud mask that has been shown to be very robust (Baetens et al., 2019). The MAJA correction is based on the Multi-Mission Atmospheric Correction and Cloud Screening - MACCS-ATCOR algorithm that identifies clouds and shadows by multitemporal detection in which each image of a time series is chronologically compared with the set of images and clouds. These different steps are summarized in Fig. 4.

To elaborate the SPM map time series, we used the radiometric SPM retrieval model developed specifically for the Madeira River (Espinoza

Villar et al., 2013), which is based on near infrared and red band ratios. This spectral ratio has been shown to limit the reflectance sensitivity to grain-size variability and other acquisition artifacts. Using the apparent optical water properties dataset (Espinoza Villar et al., 2012; Martinez et al., 2015) and the MSI Spectral Response Function - SRF for bands 4 (central wavelength 665 nm) and 8a (central wavelength 865 nm), we derived a new parametrization for the SPM retrieval model:

$$ SPM = 945,1 \cdot \left( \frac{R_{rs}(b8a)}{R_{rs}(b4)} \right)^{1,9463} \quad (2) $$

<figure><img src="image_8.png" /><figcaption>Fig. 4. Flowchart of the present work. The different codes used in this study appear in red and are described in sections 2.4 and 2.5. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
## 2.6. Sen's slope and Mann-Kendall statistical methods

Spatial trends in SPM concentration were analyzed in terms of monthly median values, assessed over 50 consecutive river reaches from upstream to downstream of the Jirau reservoir, using the Mann-Kendall test (Kendall, 1975; Mann, 1945) and Sen's slope estimator (Sen, 1968). The MK test was applied to determine if the data series reflected increasing or decreasing trends from upstream to downstream of the reservoir (Shi & Wang, 2015). This test does not depend on the magnitude and statistical distribution of the data and provides three parameters: the S statistic, Kendall's tau (τ) correlation and the significance level. A negative value of S indicates a negative trend representing a decrease in the surface SPM concentration along the reservoir from upstream to downstream, indicating sedimentation processes. A positive value of S would indicate a positive trend representing an increase in SPM concentration along the reservoir, indicating sediment resuspension. Sen's slope test is a nonparametric method that is used to estimate the slope of an existing linear trend. Consequently, when a significant trend in the SPM concentration from upstream to downstream was found with the MK test, Sen's slope estimator was applied to assess the trend magnitude.

To identify the local SPM concentration maximum and minimum along the reservoir, we computed an index that represents the deviation of the mean satellite-retrieved SPM concentration from Sen's linear trend estimate for each of the river reaches. The deviation index was computed as follows:

$$SPM_{DEV} = 100 \cdot \frac{C_i^{sat} - C_i^{trend}}{C_i^{sat}} \qquad (3)$$

where $C_i^{sat}$ represents the satellite-retrieved SPM concentration for river reach i, and $C_i^{trend}$ is the SPM concentration computed using Sen's estimate linear trend for the same river reach i. A positive deviation indicates a higher satellite-retrieved SPM concentration than the linear trend, indicating a local increase in the sediment load at the river surface. In contrast, a negative deviation indicates a lower SPM concentration than the linear trend, pointing at a local decrease in the suspended sediment concentration. The index was used for different periods of the hydrological cycle to assess both spatial and temporal variability of the SPM concentration as a function of hydraulics and geomorphological features.

## 2.7. Statistical indicators

Statistical indicators were used to compare satellite and field datasets for both SPM and Rrs. Statistics were calculated in log space to evaluate SPM retrieval performance because concentration data spans over two order of magnitude and that linear indicators are highly impacted by large values. Three statistical indicators were computed in log space: Root Mean Squared Log Error (RMSLE), Mean Absolute Log Error (MAE) and Mean Bias Log Error (MBLE). These indicators are calculated as follows:

$$RMSLE = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (\log10SPM_{Estimated}(i) - \log10SPM_{Measured}(i))^2} \qquad (4)$$

$$MAE = \frac{1}{N} \sum_{i=1}^{N} |\log10SPM_{Estimated}(i) - \log10SPM_{Measured}(i)| \qquad (5)$$

$$MBLE = \frac{1}{N} \sum_{i=1}^{N} (\log10SPM_{Estimated}(i) - \log10SPM_{Measured}(i)) \qquad (6)$$

To assess the satellite-derived $R_{rs}$ estimate accuracy compared to field measurements, that vary over a much more limited range than SPM, we used the linear Root Mean Squared Error (RMSE) indicator:

$$RMSE = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (X_{Estimated}(i) - X_{Measured}(i))^2} \qquad (7)$$

## 3. Results

### 3.1. Validation of satellite-retrieved remote sensing reflectance and SPM concentration using field data

The accuracy of the S2/MSI $R_{rs}$, processed using the GRS algorithm, as well as the accuracy of the satellite-derived SPM concentration estimates, were assessed using field measurements acquired during the 10 field sampling trips realized from March 2021 to January 2022. Using the S2/MSI spectral response function, field hyperspectral radiometric records were used to simulate satellite $R_{rs}$ for further comparison with satellite products. Fig. 5 displays the matchups between satellite and field measurements for both $R_{rs}$ band 8a (left panel) and SPM concentration (right panel). Both field and satellite data were assessed at the river reach scale for the different sampling locations presented in Fig. 1. Satellite data were assessed along a buffer zone of approximately 300 m upstream and downstream of the field measurement with a maximum 7-day delay between satellite and field measurements, representing 50 matchups after selecting cloud-free pixels. Field measurements consisted of 3 water samples taken equidistantly from the right to the left river bank with simultaneous radiometric data collection. The values presented in Fig. 5 represent the mean value for each date and location, and the error bars represent the standard deviation of all points collected at a given date and location, either at the river surface (3 points) or from all the pixels from each buffer zone. Regarding the radiometric matchups, the infrared band (b8a) shows very good agreement between the field and satellite data with an RMSE of 0.011 sr⁻¹ and a bias of 0.0017 sr⁻¹. The red band (b4) shows fine agreement with an RMSE of 0.013 sr⁻¹ (relative value of 27 %) and a bias of 0.0095 sr⁻¹. Finally, for the b8a/b4 band ratio, the RMSE between the field and satellite data is of 0.23 and the bias of -0.10. For SPM concentration assessment, we used logarithmic statistics considering the large range of SPM concentration variation with a minimum concentration of 10.4 mg/L, a maximum value of 925.9 mg/L and a mean value of 270.0 mg/L. Considering the 50 match-ups, RMSLE was 0.23 mg/L, MAE was 0.17 mg/L and MBLE was 0.15 mg/L. The SPM retrieval performs well for the whole range of concentrations, ensuring a fine-resolution capacity for suspended sediment monitoring for all hydrological periods.

### 3.2. SPM mapping using Sentinel-2/MSI images

#### 3.2.1. Overall view of the suspended sediment transport pattern

Fig. 6 (upper panel) shows two satellite-derived SPM maps of the Jirau reservoir for two contrasting periods of the hydrological cycle. Each map represents a composite of maps produced from images acquired during the high-water period (left) and during the low-water period (right). For each period, pixelwise median values were computed from all the maps available since 2017, between February and April and between June and September for the high-water and low-water periods, respectively. In addition to using a specific water pixel retrieval mask for each individual image, a general mask was delineated to focus only on the active river stream, removing adjacent floodplain lakes that do not contribute actively to the sediment transport processes within the dam system. These maps show an overall decreasing trend in SPM concentration but with several features (i.e., increased concentration) along the reservoir that match the location of some rapids. Fig. 6 (lower panel) displays closer views of the dam region for two dates, 23 April 2021 and 12 August 2018, which are the high-water period (left) and the low-water period (right), respectively. Satellite-derived SPM maps reveal multiple patterns linked with the hydrodynamical and sediment transport processes in the dam region. The most striking
<figure><img src="image_9.png" /><figcaption>Fig. 5. Matchups between satellite and field measurements for both Sentinel-2/MSI Band 8a $R_{rs}$ (left panel) and SPM concentration (right panel). Field data were collected during 10 sampling trips from March 2021 to February 2022.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 6. Upper panel: satellite-derived SPM concentration maps (mg/L⁻¹) of the run-of-river reservoir during the high-water period (left) and during the low-water period (right). Lower panel: satellite-derived SPM concentration maps (mg/L⁻¹) of the most downstream part of the run-of-river reservoir for both the high-water period (left) and low-water period (right). For all maps, the dam appears at the upper right with the river flowing from the left to the right. The SPM map was masked to focus on the river active stream.</figcaption></figure>
pattern is the probable sediment resuspension occurring downstream of the dam in high water. Importantly, full hydropower generation occurs only during the high-water period and during the low-water period, only a limited fraction of the turbines actually generates hydroelectricity because of the low water discharge, which explains the lower resuspension processes during the dry season.

### 3.2.2. SPM seasonal variation along the reservoir

We analyzed SPM variation as a function of distance within the reservoir lake and for different periods of the hydrological cycle. The availability of satellite-derived SPM estimates surpasses any field measurement collection, as space-borne sensors provide a complete spatial and temporal view of the SPM concentration at the river surface. As the Madeira River presents a unique annual flood, SPM variations were retrieved for each month, making it possible to monitor every moment of the hydrological cycle from the lower water-level stage (August-September) to peak flooding (March-April). River discharge and water velocity vary as a function of the hydrological cycle and are known to control sedimentation processes in a reservoir environment. Consequently, a close analysis of SPM variability may reveal the occurrence of sedimentation processes, i.e., loss of SPM in the water column, or resuspension processes, i.e., an increase in SPM in the water column from the river bottom. Fig. 7 displays the interannual mean SPM concentration variation for each month and for three locations in the reservoir located upstream (ABU station), in the middle (VTM station) and near the dam (JIR station). Satellite data reveal a global pattern related to the hydrological cycle with low SPM concentrations from May to October, encompassing the end of the water-decreasing stage, the low water stage and the beginning of the water-rising stage. The SPM concentration level increases in November and reaches a maximum value in February. There is an almost systematic decrease in SPM concentration from upstream to downstream, but the amplitude of this decrease varies as a function of the period. It is worthwhile to note a slight inversion in SPM concentration from upstream to downstream for March, which represents the peak flooding period.

We further investigated the occurrence of the sedimentation process by analyzing the SPM variation as a function of distance from the reservoir. Fig. 8 displays SPM variation as a function of distance for each month starting from the Madeira River, outside the backwater region, downstream to the dam. For this figure, SPM values were calculated as median values averaged from all the years and for each month. The most important geomorphological features are identified, such as the five main rapids that have been totally flooded during dam construction from upstream to downstream: the Tamborete, Paredão, Mutum, Avião

and Jirau rapids. The trend line was calculated based on Sen's test, which delivers trend estimates. The analysis of the spatial trend confirms that a global trend of sedimentation exists during most parts of the year except during peak flooding (March and April).

Statistically significant decreasing trends were observed ($p < 0.001$) for the SPM from May to January (Table 3), meaning that sedimentation processes occurred most of the time except during peak flooding from March to April. The overall trend, considering the whole dataset, has been a decrease in SPM at a rate of -0.52 mg/L/km across the reservoir. Regarding the different hydrological periods, the SPM trend was -0.36 mg/L/km, -0.62 mg/L/km and -0.57 mg/L/km for the decreasing stage, rising stage and dry period, respectively. During March, there was a decreasing SPM concentration trend (i.e., -0.22 mg/L/km) but with a low significance ($p < 0.1$), and during April, there was an increasing trend in SPM concentration (i.e., +0.11 mg/L/km) but with very low significance ($p < 0.2$), indicating rather stable SPM values from upstream to downstream during the peak flooding period.

## 4. Discussion

The use of sunglint correction for near-nadir view spaceborne sensors such as Sentinel-2/MSI has been shown to be an important step towards robust assessment of $R_{rs}$ over water bodies, especially over low-latitudes where the sun zenith angle is generally low during the satellite acquisitions. Tavares et al. (2021) observed that the application of sunglint correction over tropical lagoons, using either GRS code or ACOLITE (Vanhellemont & Ruddick, 2018) correction codes, improved $R_{rs}$ accuracy by about 33% on average for all bands. Harmel et al. (2018) assessed the accuracy of GRS for Sentinel-2/MSI images comparing satellite measurements with AERONET water-leaving radiance data. The authors observed that $r^2$ increased from 0.50 to 0.78 while RMSE decreased from about 50%, passing from 0.84 to 0.40 mW.cm$^{-2}$.sr$^{-1}$.µm$^{-1}$ considering all bands from the visible spectrum to the near infrared. Over several floodplain lakes along the Amazon River, Maciel et al. (2019) observed a significant improvement in the Rrs retrieval accuracy after glint correction and for all spectral bands, independently of the atmosphere correction method. These authors, using the mean absolute percentage error as a measure of matchups accuracy, found that the retrieval error improved from 119% to 17% when using glint correction and ACOLITE atmospheric correction code.

We compared the Rrs and SPM retrieval results achieved in this study with previous works. Maciel et al. (2019) adopted, for matchup assessment, a maximum time span between satellite acquisition and field measurement of 4 days for several Amazonian lakes. The matchup procedure used for this study consider samples collected up to 7 days, based on a previous study that showed that the decorrelation time for both river discharge and SPM concentration in the lowland Amazonian rivers varies between 30 and 40 days (Mangiarotti et al. 2013). This large decorrelation time makes possible to compare samples collected within a few days and is explained by the large catchment contributing to the water and sediment discharge in the Amazonian lowlands, about 1 million square kilometers for the Madeira River near Jirau dam, that gathers the contribution of hundreds of rivers during the rainy season resulting in a regular annual monomodal flood pulse downstream (Fig. 2).

Assessing $R_{rs}$ retrieval accuracy by comparing field measurements and Sentinel-2 images processed with GRS atmospheric and sunglint correction code, Tavares et al. (2021) showed that the RMSE varied from 0.013 to 0.0074 for band 4 and band 8, respectively. Those values are very close to the accuracy reported in this work, as we showed that RMSE varied from 0.013 to 0.011 for band 4 and band 8, respectively. Balasubramanian et al. (2020) proposed a model for SPM retrieval and compares its accuracy with five other models for sediment-laden waters using a dataset very similar to our dataset in terms of SPM range (minimum concentration of 50 mg/L-1, a maximum value of 1190 mg/L and a mean value of 150 mg/L). Balasubramanian et al. (2020) show

<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Monthly averaged remote sensing-derived SPM concentrations retrieved from 2017 to 2021 for three different virtual stations located upstream (ABU station), in the middle of the reservoir (VTM station) and downstream (JIR station) near the reservoir dam. The error bars represent the monthly-wise standard deviation of all SPM assessment for the observation period.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Monthly averaged satellite-derived SPM variation retrieved from 2017 to 2021. The results are presented for each month and as a function of distance along the reservoir. For each month, Señs estimated linear trend for the corresponding month is displayed. The position of the reservoir dam and of the flooded rapids are indicated, and the river flows from the left to the right.</figcaption></figure>

that RMSLE varies from 0.22 to 0.71 mg/L and MAE varies from 0.17 to 0.65 mg/L for the different models evaluated, very close to our model assessment with RMSLE and MAE being 0.23 mg/L and 0.17 mg/L respectively. Han et al. (2016) developed and tested thirteen hybrid model for SPM retrieval over coastal waters including a red/infrared band ratio model for sediment-laden waters. Han et al. (2016) reports a RMSLE varying between 0.22 and 0.91 mg/L matching the same accuracy level presented by Balasubramanian and by our work.

We demonstrated that remote sensing-based SPM monitoring can be achieved efficiently within hydropower dams, allowing us to retrieve suspended sediment transport patterns driven by hydrodynamic processes. However, it would be interesting to go one step further by relating the sediment transport processes at the river surfaces, as retrieved with satellite data, to the transport patterns occurring at the river/reservoir bottom to understand if remote sensing can indirectly map the presence of sedimentation and/or erosion zones within the water course. This is of special importance, as the widespread method for reservoir managers to assess reservoir siltation is to conduct bathymetric surveys to map the variation at the water body bottom.

Notwithstanding the overall SPM decreasing trend along the reservoir, the detailed analysis of Fig. 8 reveals various local patterns with resuspension or sedimentation near the main rapids, indicating complex

hydraulic processes all along the reservoir. To identify those patterns and to relate them to the processes occurring at the reservoir bottom, we used the SPM<sub>DEV</sub> index, which represents the deviation of the satellite-retrieved SPM concentration from Sen's linear trend estimates. The SPM<sub>DEV</sub> deviation index was computed using all the images available from 2018 to 2021 to analyze where the most relevant deviation, either positive or negative, occurred and to relate those variations to the run-of-river dam hydraulics and geomorphological features. Fig. 9 shows the SPM<sub>DEV</sub> deviation index, expressed in terms of percentage, for all the river reaches with red tones indicating positive deviation (i.e., increase in SPM concentration relative to the linear trend), blue tones denoting negative deviation (i.e., decrease in SPM concentration) and white tones indicating null deviation from the linear trend. It is worthwhile to note that a null deviation does not mean that there is no SPM increase or decrease from one reach to the next but, instead, that there is neither excess siltation nor resuspension in relation to the linear trend.

The variability of the SPM<sub>DEV</sub> values reveals patterns related to hydraulics and geomorphology. Fig. 8 reveals an overall trend of siltation for most of the reservoir, while the analysis presented in Fig. 9 shows that there are areas where siltation or erosion occurs more intensively. We observed two broad regions presenting stronger negative deviations (i.e., SPM<sub>DEV</sub> lower than -5%) in the upstream and downstream parts,
<table><caption>Table 3<br>Spatial trend of the satellite-retrieved SPM concentration along the reservoir as a function of the month and water stage compiled for the 2017–2021 period. Negative values indicate a decrease in SPM from the river to the reservoir (i.e., sedimentation) while positive values indicate an increase in SPM (i.e., resuspension). Significance of the trends following the Mann–Kendall test: *** (p value < 0.001), ** (p value < 0.01), * (p value < 0.05), + (p value < 0.1), ++ (p value < 0.2).</caption><thead><tr><th></th><th>Jan</th><th>Feb</th><th>Mar</th><th>Apr</th><th>May</th><th>June</th><th>July</th><th>Aug</th><th>Sept</th><th>Oct</th><th>Nov</th><th>Dec</th><th>2017-2021</th><th>Dry</th><th>Rising</th><th>Flood</th><th>Low Water</th></tr></thead><tbody><tr><th>p value</th><td>***</td><td>***</td><td>+</td><td>++</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><th>Trend (mg/L/km)</th><td>-0.47</td><td>-1.10</td><td>-0.22</td><td>0.11</td><td>-0.25</td><td>-0.40</td><td>-0.37</td><td>-0.86</td><td>-0.59</td><td>-0.41</td><td>-0.68</td><td>-0.77</td><td>-0.52</td><td>-0.58</td><td>-0.64</td><td>-0.42</td><td>-0.37</td></tr></tbody></table>

separated by a region in the middle of the reservoir exhibiting positive or null SPM<sub>DEV</sub> values. Reservoir reaches showing positive (red color) SPM<sub>DEV</sub> values mostly match the positions of flooded rapids. The most important rapid that still subsists at the river surface is the Paredão rapids, which generate a constant SPM increase at the river surface. Cella-Ribeiro et al., reported that the river water velocity varied between 0.9 and 1.4 m/s in normal river stretches but that the velocity may increase up to 2.5 m/s in those rapids, leading to increased sediment transport capacity. Very low SPM<sub>DEV</sub> values can be seen for the downstream reaches within the reservoir. This pattern matches the bathymetric surveys presented by (De Souza et al., 2018) that revealed significant siltation in this downstream part of the reservoir, up to 20 m of siltation in the last 10 km of the reservoir between 2011 (i.e., before the reservoir construction) and 2017 (after 3 years of operation).

We further checked the ability of the SPM<sub>DEV</sub> index to correctly predict riverbed siltation or erosion by comparing satellite-derived assessments with field bathymetric reports. We compared the river cross section ratio index CSR, assessed from the 2018 and 2022 surveys, with the SPM<sub>DEV</sub> deviation index to assess if the satellite data correctly predicted the location of eroding and silting reaches as well as the magnitude of the siltation or erosion processes at each river reach. Fig. 10 shows how the CSR and SPM<sub>DEV</sub> deviation index vary for the 6 different river reaches from upstream to downstream. The satellite data successfully identified the presence of either erosion or silting reaches, demonstrating that satellite images may efficiently support reservoir managers in mapping the evolution of the reservoir bottom bathymetry. While the CSR index is based on bathymetric surveys that monitor the river wetted-area variation, the SPM<sub>DEV</sub> index relates to suspended sediment variation retrieved from satellite images. Both assessments are not fully linked, but they both provide insight into the local sediment transport balance either from the water column perspective (satellite data) or from the reservoir bottom perspective (bathymetric surveys). Suspended sediment concentration at the river surface and in the water column, i.e., the “wash-load” is directly linked to the stream hydraulic characteristics (shear stress velocity, water depth and river reach geometry), which also control the suspended sediment deposition and transport at the riverbed (i.e., the bedload). Vertical concentration profiles of suspended sediment in rivers results from an equilibrium between the sediment settling velocity and the upward flux of sediment due to turbulent mixing. Even for rivers presenting large depth, such as in the Amazon basin, it has been shown that the vertical suspended sediment concentration distribution is fully controlled by local river reach hydraulic conditions and that the surface estimate is a good predictor of the average concentration over the whole water column (Santini et al., 2019, Pinet et al., 2019). However, the equilibrium between the sediment settling velocity and the turbulent mixing varies as a function of particle size, with coarser particles being transported mostly in the lower part of the water column or directly at the riverbed. Consequently, as the upwelling radiance captured by satellite data originates from a thin layer at the water surface in sediment-laden waters (Martinez et al., 2015), satellite-derived SPM estimates are mostly representative of the fine fraction of the wash load (i.e., silt and clays) and do not consider coarse particles (i.e., sand material). We conclude that, despite not monitoring the entire wash-load and bedload sediment transport (i.e., neither the whole water column nor the entire range of particle sizes), the satellite observations provide sufficient insights to detect the evolution of riverbed formation and particularly sediment accumulation within reservoirs. Finally, the quantitative assessment of sediment accumulation at the reservoir bottom may still be beyond reach from space, as the relationship between wash load and bedload is extremely variable as a function of hydraulic conditions and catchment characteristics (Turowski et al., 2010).

Regular bathymetric surveys at specific reaches represent the standard procedure for hydropower companies to monitor the progression of siltation or erosion processes in reservoirs. This method is preferred to full bathymetric surveys that are too costly to be realized on a yearly or
<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Maps of the SPM<sub>DEV</sub> deviation index that represents the deviation (%) of the mean satellite-retrieved SPM concentration from Seán's linear trend estimate, computed for all the images from 2017 to 2021. Red tones indicate SPM concentration in excess (resuspension) in relation to the linear trend, while blue tones indicate SPM concentration depletion (siltation) in relation to the linear trend. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 10.** Comparison of the SPM<sub>DEV</sub> deviation index derived from satellite data with the cross-section ratio (CSR) retrieved from field surveys acquired in 2018 and 2022.</figcaption></figure>

seasonal basis. As an example, for United States, it has been estimated that half of the total storage capacity is surveyed every 17 years and that half of the total number of all reservoirs is surveyed only every 31 years, which can be considered far too limiting for the management of siltation

processes (Podolak & Doyle, 2015). However, surveys acquired at some specific reaches within reservoirs, even on a more regular basis, do not make it possible to fully assess the siltation processes and are usually hardly available to the water research community, as those data are considered confidential by the companies operating dams. We show that remote sensing can be used to independently assess or to complete conventional field monitoring of siltation processes thanks to its synoptic vision. It would make possible to better assess the impact of a dam or of a group of dams (dam cascade) in river systems in relation to sediment transport. Land use changes in watersheds and the increased occurrence of extreme events will likely increase sediment transport toward reservoirs (Moran et al., 2018) and will dramatically intensify siltation problems. It has been estimated that the annual water storage capacity loss varies between 0.5 and 1 % (Palmieri Alessandro et al., 2003) and may only increase with the intensification of the hydrological cycle. Mechanical dredging and flushing are two main methods used to remove sediment and recover lost storage capacity in reservoirs. However, those techniques are very complex and time-consuming operations that would benefit from frequent remote sensing monitoring to better assess their impact and plan their realization.

## 5. Conclusions

We proposed a method to map the sediment transport pattern in reservoirs, which is an important issue in terms of both environmental impacts and hydropower operations. Sediment storage in reservoirs
represents a major threat worldwide to river and estuarine systems, in unbalancing geomorphological processes with accelerated erosion processes downstream of dams. In contrast, sediment accumulation in reservoirs limits the viability of hydropower generation as the water storage capacity is progressively hindered.

We applied a glint removal algorithm jointly to an atmospheric correction scheme (i.e., GRS code) that allowed us to produce fine Rrs estimates. Relying on a large database of radiometric and SPM field measurements, it was possible to show that satellite-derived maps produced robust SPM estimates (RMSLE = 0.23 mg/L) for a large range of concentrations from approximately 10 to 1000 mg/L. We have shown that the joint analysis of a large image dataset makes it possible to finely retrieve both the spatial and temporal dynamics of siltation/resuspension processes. By applying statistical trend tests and through trend amplitude assessment, it was possible to detect how siltation processes occur as a function of the hydrological cycle. In particular, satellite data revealed that there was no SPM siltation during peak flooding when the water and sediment discharge reached maximum values.

The work presented in this article may be reproduced using other satellite data but some important processing steps and sensors characteristics should be considered. We evaluate that glint removal is an important step for water color monitoring, especially over tropics where the sun zenith angle is most of the time very low at the time of the satellite acquisition, increasing the probability of sunglit occurrence for nadir viewing sensors such as Sentinel-2 or Landsat. Another important point is the availability of frequent images in order to capture all the periods of the hydrological cycle implying to use satellite constellation.

Analyzing SPM deviation to the mean trend from an upstream to downstream direction, it was possible to delineate reaches marked by siltation or erosion processes, disclosing the complexity of the sediment transport in a run-of-river reservoir. Bathymetric surveys are the most common method used by reservoir managers to quantify siltation processes. Although satellite data reveal the SPM concentration at the surface of the water column, SPM deviation maps matched the bathymetric surveys and correctly identified the reaches that were either impacted by erosion or siltation. Our results pave the way for the global use of satellite images to assess and quantify the global impact of run-of-river dams on sediment transport processes. In particular, it will be possible to detect the dams that are most impacting sediment transport, supporting better planning and management practices at the watershed scale.

### CRediT authorship contribution statement

**Diego R. Alves e Santos:** Formal analysis, Validation, Writing – original draft, Investigation, Data curation. **Jean-Michel Martinez:** Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing. **Diogo Olivetti:** Formal analysis, Investigation, Project administration, Validation, Writing – original draft, Data curation. **André Zumak:** Formal analysis, Investigation, Validation, Writing – original draft, Data curation. **David Guimarães:** Formal analysis, Methodology, Validation, Writing – original draft, Software. **Keila Aniceto:** Investigation, Validation, Writing – original draft, Data curation. **Ednaldo Severo:** Investigation, Validation, Writing – original draft, Data curation. **Osmair Ferreira:** Formal analysis, Investigation, Validation, Writing – original draft, Data curation. **Tristan Harmel:** Formal analysis, Investigation, Validation, Writing – original draft. **Mauricio Cordeiro:** Formal analysis, Investigation, Software. **Naziano Fillizola:** Funding acquisition, Investigation, Writing – original draft. **Bruna Sell:** Project administration, Supervision, Writing – original draft. **Daniel Fernandes:** Project administration, Supervision, Writing – original draft, Data curation. **Camila Souto:** Project administration, Supervision, Writing – original draft, Data curation. **Henrique Roig:** Conceptualization, Formal analysis, Funding acquisition, Investigation, Project

administration, Resources, Supervision, Writing – original draft.

### 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.

### Data availability

Data will be made available on request.

### Acknowledgments

This research was funded by Jirau Energia through the Research and Development Program regulated by the Brazilian National Electric Energy Agency – ANEEL (PD-06631-0010/2020). We are also grateful for the AQUASENSE research group and HYBAM long-term observatory for providing equipment for the field work. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

### Data Availability Statement.

Bathymetric surveys are available upon request at the Brazilian Water Agency (www.ana.gov.br). GRS code has been published (Harmel et al. 2018) and is being progressively made available for a selection of Sentinel-2 tiles at the French Data Center (https://hydroweb.next.theiand.fr/) including over South America. MAJA code is available at github.com/olivierhagolle/Start_maja and the WaterDetect code is available at github.com/cordmaur/WaterDetect. Field Radiometric and SPM measurements are available upon request.

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