# Monitoring of Riverine Aquatic Vegetation Using Satellite PlanetScope Imagery: Feasibility, Limitations and Prospects

Léo Rasse, Julien Godfroy, Géraldine Nogaro, Florian Cordier, Danaë Feldis, Sophie Meunier, Sara Puijalon, Hervé Piégay

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Léo Rasse, Julien Godfroy, Géraldine Nogaro, Florian Cordier, Danaë Feldis, et al. Monitoring of Riverine Aquatic Vegetation Using Satellite PlanetScope Imagery: Feasibility, Limitations and Prospects. Ecohydrology, 2026, 19 (1), pp.E70178. 10.1002/eco.70178. hal-05474560

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RESEARCH ARTICLE

OPEN ACCESS

# Monitoring of Riverine Aquatic Vegetation Using Satellite PlanetScope Imagery: Feasibility, Limitations and Prospects

Léo Rasse<fnref n="1" /> ID | Julien Godfroy² ID | Géraldine Nogaro³ ID | Florian Cordier³,⁴ ID | Danaë Feldis<fnref n="1" /> | Sophie Meunier⁵ |
Sara Puijalon<fnref n="1" /> ID | Hervé Piégay⁶ ID

**Correspondence:** Léo Rasse (leo.rasse@univ-lyon1.fr) | Sara Puijalon (sara.puijalon@univ-lyon1.fr)

**Received:** 10 October 2025 | **Revised:** 6 January 2026 | **Accepted:** 10 January 2026

**Keywords:** aquatic vegetation | fluvial environment | remote sensing | time series

## ABSTRACT

Spectral interference induced by the water and the spatial resolution of many satellite images (≥ 10 m) limit the efficiency of remote sensing for monitoring riverine aquatic plant stands. In this study, the potential of using PlanetScope satellite images (3 m in spatial resolution, ~daily acquisition) for monitoring seasonal and interannual aquatic vegetation surface area was evaluated. Airborne images (≤ 0.2 m) acquired on four dates on three aquatic plant stands were used to create, through visual interpretation, reference maps indicating whether the pixels of each PlanetScope image acquired at ±8 days correspond to aquatic vegetation or nonvegetated aquatic areas. For each PlanetScope image, the green normalized difference vegetation index (GNDVI) was calculated and centred on the mean (GNDVI centrered) to distinguish aquatic vegetation from nonvegetated aquatic areas while minimizing variations in their spectral signature over time. Reference maps from the date when aquatic vegetation was the least developed were used to calculate the GNDVI centrered classification threshold. To reduce classification errors from radiometric inconsistencies, the frequency at which pixels of PlanetScope images acquired at ±8 days from airborne images were classified as aquatic vegetation was also calculated. Aquatic vegetation was then empirically defined as pixels with a frequency ≥ 85%. Although the classification of PlanetScope images indicates that low abundances of aquatic vegetation cannot be detected, our results show that large changes in stand surface area can be monitored using a multidate classification threshold, thus providing new opportunities for the monitoring of riverine aquatic vegetation on large scales.

## 1 | Introduction

The awareness that ecological patterns observed at a specific scale are influenced by processes operating at both finer and broader scales marked a significant shift in ecology, emphasizing the need for multiscale approaches (Levin 1992; Reuter et al. 2010; Wiens 1989). For a long time, the time consumption and logistical cost of field surveys and experiments have limited

the ability of ecologists to conduct large-scale studies (Estes et al. 2018). However, these studies are often not suitable for predicting the dynamics operating at broader scales because of the nonlinearity of patterns and processes across scales (Jarvis 1995; Levin 1992; Turner 1989). Recently, rapid advances in technology and analytical methods have opened up new possibilities for overcoming these limitations (Stocks et al. 2019). In particular, aerial and satellite remote sensing approaches have been

<footnote n="1">¹Universite Claude Bernard Lyon 1, LEHNA UMR 5023, CNRS, ENTPE, F-69622, Villeurbanne, France | ²Univ. Grenoble Alpes, INRAE, LESSEM, St-Martin-d'Hères, France | ³EDF Research and Development, National Hydraulics and Environment Laboratory (LNHE), Chatou, France | ⁴LHSV, ENPC, Institut Polytechnique de Paris, EDF R&D, Chatou, France | ⁵EDF-DTG, Service Etudes Eau et Environnement, Saint-Martin-le-Vinoux, France | ⁶ENS Lyon, EVS UMR 5600, CNRS, F-69362, Lyon, France</footnote>

<footnote>Sara Puijalon and Hervé Piégay are co-leaders of the project.</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2026 The Author(s). Ecohydrology published by John Wiley & Sons Ltd.</footnote>
increasingly applied to study large-scale ecological patterns and processes (Estes et al. 2018), such as the increase in the effects of meteorology and climate conditions on the duration of the growing season of boreal forests or the responses of riparian forest greenness to climatic and hydrological conditions along the latitudinal gradient of a river corridor (Lochin et al. 2025; Zhou et al. 2001). By increasing the extent, frequency and duration of ecological observations, remote sensing approaches have rapidly been applied to complement traditional field surveys and experiments and enable the study of patterns and processes across multiple scales (Estes et al. 2018; Kerr and Ostrovsky 2003).

In aquatic ecosystems, previous studies have shown the efficiency of satellite remote sensing for providing measurements of aquatic vegetation properties, such as biomass, cover and species composition (Espel et al. 2020; Vahtmäe et al. 2021; Yuan and Zhang 2008). Most of these studies have used machine learning models trained from field data to predict vegetation properties over an entire image on large spatial scales. However, the applicability of these models is often restricted to images acquired on short time scales. In particular, as both aquatic vegetation and nonvegetated aquatic areas present similar spectral signatures because of the strong absorption, reflectance and scattering of solar radiation by water (Cho and Lu 2010; Klemas 2016), small changes in their spectral signatures, induced by changes in vegetation properties and environmental conditions, can significantly affect the performance of models (Tuominen and Lipping 2014; Yuan and Zhang 2008; Zhao et al. 2012). Consequently, the development of multiple models, which are based on multiple field datasets, is often necessary for long-term monitoring of aquatic vegetation (Roelfsema et al. 2014). However, such methods are labour-intensive and are not suitable for studying the response of aquatic vegetation to events that are difficult to anticipate, such as floods (Ibáñez et al. 2012; Tena et al. 2017).

In recent years, various methods have been used to analyse time series satellite imagery of large lakes to reconstruct the past trajectories of large and homogeneous areas of aquatic vegetation without relying on field observation data (Dai et al. 2021; Luo et al. 2025; Wang et al. 2023). However, these conditions strongly differ from those of fluvial systems, where aquatic vegetation forms patches or homogeneous vegetated areas with widths rarely exceeding 100 m because of channel width limitations and heterogeneity in environmental conditions within the channel (Biggs et al. 2018; Cornacchia et al. 2020; Schoelynck et al. 2012). Consequently, the spatial resolution of the satellite images often used in lake studies (10–30 m) may not be adequate for fluvial systems, as many pixels may include a mixture of aquatic vegetation and nonvegetated aquatic areas. This results in spectral characteristics that are less distinguishable from those of nonvegetated aquatic areas because of the spectral interference induced by water (Klemas 2016; Silva et al. 2008; Yuan and Zhang 2008). Additionally, fluvial environments can present rapid changes in water height, both at a daily or a seasonal scale, because of the natural flow regime of streams and rivers or hydropeaking (Bejarano et al. 2018). However, these variations can reduce the spectral separability between aquatic vegetation and water when water height increases, thereby limiting the number of satellite images that can be used for monitoring aquatic plant communities (Cho et al. 2012).

The recent launch of commercial satellite constellations providing archives of high-resolution images has opened up new opportunities to overcome the constraints induced by the medium spatial resolution imagery (> 10 m) of satellite series traditionally used for temporal monitoring, such as Landsat and Sentinel, as well as by the restricted temporal resolution and access to archives of satellite images with high-spatial resolution, such as Quickbird and WorldView (< 3 m). Among these constellations, the PlanetScope nanosatellite constellation, launched in 2017, provides near-daily global coverage with images acquired at a spatial resolution of 3.7 to 4.1 m, depending on the altitude of the sensor, and resampled to 3 m for consistency (Roy et al. 2021). Although PlanetScope satellites lack an onboard radiometric calibration system and present greater radiometric uncertainties in the images compared with Landsat and Sentinel imagery (Huang and Roy 2021; Leach et al. 2019; Wegmueller et al. 2021), the extensive archive of high-spatial and temporal resolution imagery, already covering 8 years, provides important data to monitor riverine aquatic vegetation and investigate its responses to past and future changes in environmental conditions.

This study aimed to test the feasibility of using PlanetScope images to reconstruct and monitor the temporal trajectories of riverine aquatic vegetation. Specifically, we tested the following two hypotheses: (1) aquatic vegetation could be distinguished from nonvegetated aquatic areas, and (2) the temporal trajectories of riverine aquatic vegetation could be detected without relying on field observations for each new period of interest. For that purpose, we worked on large aquatic plant stands in the Rhône River (France), representing extensive vegetated areas composed of one or several submerged species and spanning hundreds of metres in length and dozens of metres in width (Figure 1).

## 2 | Materials and Methods

### 2.1 | Study Site and Selection of Aquatic Plant Stands

This study was carried out on the Rhône River, which is a highly anthropized river with a total length of 812 km distributed between Switzerland (300 km) and France (512 km). During the second half of the 20th century, a major engineering programme was implemented on the Rhône River to produce hydroelectricity. This consisted of a series of 20 dams and 17 derivation canals bypassing the original channel (Vázquez-Tarrío et al. 2019). This development resulted in an alteration of the flow regime: when the discharge is lower than the hydropower plant capacity, the bypassed original channels of the Rhône River receive only residual discharge. Conversely, excess discharge exceeding the capacity of hydropower plants is redirected into these bypassed channels during high-flow events (Vázquez-Tarrío et al. 2019). Additionally, hydropower plants generate large fluctuations in weekly and subdaily discharge and water height because of variations in electricity demand (Capra et al. 2017).

The aquatic plant stands selected for this study were located on a section of the Rhône River near the city of Baix (Figure 2; 44.712689 N and 4.7763454 E). This site was selected as stakeholders face major challenges in managing aquatic vegetation
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Large aquatic plant stands observed in the summer of 2023 on the Rhône River near the city of Baix. Photo credit: Sara Puijalon (top photos) and Léo Rasse (bottom photos).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**FIGURE 2** | Location of the three aquatic plant stands in the study area.</figcaption></figure>

because of the large amount of infrastructure developed to pro-
duce electricity (hydro- and nuclear power plants). Moreover,
high-spatial resolution airborne images (0.15–0.2 m) acquired
annually during the summers of 2017, 2018, 2019 and 2023 were
available and could be used as truth data for developing and test-
ing models applied to PlanetScope imagery for detecting aquatic
vegetation (Table 1). These images included the red, green and
blue spectral bands and were acquired as part of monitoring
programmes conducted by Electricité de France (EDF) and the
Institut national de l'information géographique et forestière

(IGN). The primary objectives of these programmes were to monitor the dynamics of aquatic vegetation upstream of a nuclear power plant, as well as the evolution of the French territory, respectively. Based on these high-spatial resolution airborne images, aquatic plant stands were selected according to their size to ensure that they could be detected with the spatial resolution of PlanetScope imagery. In total, three aquatic plant stands, composed of submerged species whose canopies can reach the water surface depending on the season, were selected: Stands 1 and 2 are located upstream of the bypassed original channel, whereas Stand 3 is located downstream (Figure 2). Among the 4 years for which high-resolution airborne images were available, the maximum width and length of the three stands ranged from 50 to 120 m and 800 to 1130 m, respectively (Table 2).

2.2 | Delineation of the Study Area Around Stands

For each stand (1-3), the four high-spatial resolution airborne
images (2017 to 2023) were overlaid in QGIS 3.22.6 (QGIS
Development Team 2021) to delineate the edges of their max-
imum extent (Figure 3, Step 1). To ensure that potential over-
growth could be captured on subsequent dates, the maximum
extent of stands was extended with a 50-m buffer created in
QGIS 3.22.6 (QGIS Development Team 2021). Finally, the 50-m
buffer created around each stand was adjusted by removing the
first 10 m from the river bank to avoid potential interference
with riparian vegetation, such as canopy coverage and shadow-
ing (Visser et al. 2016). The surface of the study area surround-
ing each stand is provided in Table 2.
<table><caption>**TABLE 1** | Characteristics of high-resolution airborne images and ranges of discharge and water height corresponding to the minimum and maximum values recorded at the hydrometric station of Viviers on the day the airborne images were acquired.</caption><thead><tr><th>Source</th><th>Date</th><th>Spatial resolution (m)</th><th>Min.-max. discharge (m<sup>3</sup>·s<sup>-1</sup>)</th><th>Min.-max. water height (m)</th></tr></thead><tbody><tr><td>EDF</td><td>2017-08-28</td><td>0.15</td><td>515-805</td><td>0.56-0.73</td></tr><tr><td>EDF</td><td>2018-09-10</td><td>0.15</td><td>553-765</td><td>0.60-0.73</td></tr><tr><td>EDF</td><td>2019-08-25</td><td>0.15</td><td>802-1220</td><td>0.60-0.90</td></tr><tr><td>IGN</td><td>2023-06-25</td><td>0.2</td><td>794-1310</td><td>0.56-0.97</td></tr></tbody></table>

<table><caption>**TABLE 2** | Spatial characteristics of the aquatic plant stands and the study area. The maximum width and length were measured on the high-resolution airborne images acquired by EDF and IGN.</caption><thead><tr><th>Stand</th><th>Maximum width of the stand (m)</th><th>Maximum length of the stand (m)</th><th>Surface of the study area (m<sup>2</sup>)</th></tr></thead><tbody><tr><td>1</td><td>110</td><td>930</td><td>124,600</td></tr><tr><td>2</td><td>50</td><td>1130</td><td>90,297</td></tr><tr><td>3</td><td>120</td><td>800</td><td>149,871</td></tr></tbody></table>

## 2.3 | Selection of PlanetScope Satellite Images

The PlanetScope constellation currently consists of sensors from three distinct generations (PlanetScope 2025); each includes the blue, green, red and near-infrared spectral bands but with differences in bandwidth (Huang and Roy 2021; Roy et al. 2021). In this study, two datasets of PlanetScope images were used (totalling 712 images), with each dataset including the three generations of sensors. The first dataset (15, 25 and 23 images for Stands 1, 2 and 3, respectively) included all the cloud-free PlanetScope images (visual interpretation of the study area created around each stand) acquired within ±8 days of the acquisition date of the four high-resolution airborne images (Figure 3, Step 2). A 16-day period was chosen because no major variation in discharge occurred during this time, thereby minimizing the risk of changes in areas of aquatic plant stands (e.g., due to vegetation uprooting) or spectral interference caused by large fluctuations in water level (Table 1). Additionally, we assumed that no significant changes in aquatic vegetation would occur within a 16-day period at the level of the aquatic plant stands. The second dataset (201, 217 and 231 images for Stands 1, 2 and 3, respectively) included all the cloud-free PlanetScope images acquired between 29 March 2017 and 17 September 2023 (Figure 3).

For both datasets, only PlanetScope images acquired at low discharge were downloaded to avoid spectral interference induced by high water height and turbidity levels induced during high-flow events. In this study, the threshold discharge was set as 1200 m³·s⁻¹ at the hydrometric station of Viviers, located ~20 km downstream of Stand 3 (Hydroportail 2025). It was chosen because it corresponds approximately to the highest discharge at which high-resolution airborne images were acquired (Table 1), and it represents the average discharge exceeded approximately once every 2 days, thereby enabling the use of a large number of PlanetScope images. The PlanetScope images were downloaded in surface reflectance after being atmospherically corrected and

harmonized to Sentinel-2 by Planet Labs Inc. (PlanetScope 2025). The harmonization tool provided by Planet Labs Inc. applies, for each spectral band, a controlled linear model between the pixels of PlanetScope scenes and those of a multiyear Sentinel-2 seasonal reference model, thereby improving radiometric consistency across images within and between sensor generations (PlanetScope 2025). Additionally, as PlanetScope images can present radiometric inconsistencies within images, all downloaded images were also visually inspected to exclude those with spectral bands presenting radiometric quality issues (Figure S1; Huang and Roy 2021). Following the removal of PlanetScope images from the dataset, the number of PlanetScope images used to detect aquatic vegetation during the 16-day period associated with each high-resolution airborne image varied from 3 to 8 (Figure S2).

## 2.4 | Truth Data

For the first dataset of PlanetScope images, the pixels of each image were converted into a grid (one pixel = one cell) in QGIS 3.22.6 (QGIS Development Team 2021) to create a reference map considering the difference in spatial resolution between the airborne and PlanetScope images (Figure 3, Step 3). The grid was overlaid onto the corresponding high-resolution airborne images, and the pixels were manually classified through visual interpretation into either aquatic vegetation or nonvegetated aquatic areas in QGIS 3.22.6 (QGIS Development Team 2021). Pixels were classified as aquatic vegetation if at least 25% of the pixel was covered by aquatic vegetation (visual interpretation); otherwise, they were classified as nonvegetated aquatic areas, whether or not the bare substrate was visible on airborne images. For Stand 1, the reference map could not be created in 2023 because of the difficulty in determining, through visual interpretation, whether the pixels in a large area corresponded to aquatic vegetation or biofilm-covered sediments.

## 2.5 | Image Processing

For both datasets of PlanetScope images, the green normalized difference vegetation index (GNDVI) was calculated with the following formula:

$$ \text{GNDVI} = \frac{(\rho\text{NIR} - \rho\text{G})}{(\rho\text{NIR} + \rho\text{G})} \quad (1) $$

where $\rho$NIR and $\rho$G are the reflectance in the near-infrared and green spectral bands, respectively (GNDVI; Gitelson et al. 1996). This index is sensitive to chlorophyll and can be used for
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<figure><img src="image_10.png" /><figcaption>**FIGURE 3** | Workflow for classifying PlanetScope imagery.</figcaption></figure>
monitoring vegetation (Gitelson et al. 1996). Unlike the NDVI, which is widely used in terrestrial environments, the GNDVI was employed because water absorbs less green light than red light (Klemas 2016; Tuominen and Lipping 2014). Owing to the potential variations in the spectral characteristics of aquatic vegetation and nonvegetated aquatic areas induced by changes in vegetation (e.g., plant species phenology), water properties (e.g., variations in water height and turbidity) or in PlanetScope image properties (e.g., solar zenith angle and differences in bandwidth between sensors of different PlanetScope generations) (Huang and Roy 2021; Tuominen and Lipping 2014; Zhao et al. 2012), the following formula was used to centre the GNDVI (GNDVI centrated) of each pixel in a PlanetScope image relative to the average GNDVI of all pixels in the image in order to improve consistency across dates and years (Figure 3, Step 4):

$$GNDVI_{centred} = GNDVI - \overline{GNDVI} \quad (2)$$

where GNDVI represents the average GNDVI of the respective PlanetScope image in the study area surrounding stands.

## 2.6 | Differences in GNDVI centrated and Classification of Aquatic Vegetation and Nonvegetated Aquatic Areas

For each PlanetScope image from the first dataset, the difference in GNDVI centrated between aquatic vegetation and nonvegetated aquatic areas was tested with linear mixed-effects models by using the ‘lme4’ package (v1.1.35.5, Bates et al. 2015) from the R environment (v4.2.0, R Core Team 2022). These models were applied to each year (2017, 2018, 2019 and 2023), with the GNDVI centrated as the response variable, the class (aquatic vegetation or nonvegetated aquatic area) as a fixed effect, and aquatic plant stands as a random effect. The R ‘lmerTest’ package (v3.1.3, Kuznetsova et al. 2017) was subsequently used to determine the p values of each model using the Satterthwaite method.

To determine the optimal GNDVI centrated threshold for distinguishing aquatic vegetation from nonvegetated aquatic areas across all PlanetScope images, a logistic regression was used, with the GNDVI centrated of the PlanetScope image acquired on all stands within ±8 days of the 2018 high-resolution airborne images (dataset 1) as the response variable and the class as the explanatory variable (Figure 3). Following initial tests that were either inconclusive or did not improve classification accuracy when using a single GNDVI centrated threshold per stand or year, this analysis included the GNDVI centrated of all pixels from all PlanetScope images of the three stands acquired within ±8 days of the 2018 high-resolution airborne image (4, 8 and 12 images for Stands 1, 2 and 3, respectively; Figure S2). This approach enabled the testing of whether a single multivariate classification threshold could be used to detect aquatic vegetation, thereby extending the spatial and temporal monitoring of aquatic plant stands. Data from 2018 were selected because the aquatic vegetation of the three aquatic plant stands was sparse, with plants that often did not reach the water surface, probably due to the removal or growth limitations of aquatic vegetation induced by higher discharges in 2018 compared to other years (Figures S2 and S3). Consequently, we expected the threshold for distinguishing aquatic vegetation and nonvegetated aquatic areas to

be stricter, as the spectral differences between aquatic vegetation and nonvegetated aquatic areas decrease with lower vegetation cover and greater plant submergence (Cho and Lu 2010; Yuan and Zhang 2008).

Receiver operating characteristic analysis was performed using the R ‘pROC’ package (Robin et al. 2011) on the predicted probabilities generated by the logistic regression model to measure sensitivity and specificity (Fawcett 2006), both of which were used to identify the optimal GNDVI centrated threshold (Figure 3, Step 5). The former represents the proportion of actual vegetation correctly classified, whereas the latter represents the proportion of water accurately classified. The optimal GNDVI centrated threshold was ultimately determined with Youden’s statistic (Youden 1950), ensuring the best balance between correctly classified aquatic vegetation and nonvegetated aquatic areas. For all PlanetScope images, pixels with a predicted probability exceeding the optimal threshold were classified as aquatic vegetation, and those below this threshold were classified as nonvegetated aquatic areas. To minimize potential misclassifications caused by radiometric issues, such as within-sensor radiometric inconsistencies, sunglints or skyglints (Tuominen and Lipping 2014; Visser et al. 2016; Wegmueller et al. 2021), the classification frequency of pixels classified as aquatic vegetation was further calculated for all PlanetScope images acquired within ±8 days of the acquisition date of the four high-resolution airborne images (Figure 3, Step 5). This frequency index was calculated by dividing the number of times a pixel was classified as aquatic vegetation by the total number of PlanetScope images classified within each 16-day period. Depending on the stand and the year considered, the number of PlanetScope images used to calculate the frequency index varied from 3 to 8 (Figure S2). On the basis of empirical observations, pixels with a classification frequency ≥ 85% were ultimately classified as aquatic vegetation, whereas the remaining pixels were classified as nonvegetated aquatic areas.

## 2.7 | Assessment of Accuracy

For each year of acquisition and stand, the classification accuracy was assessed by calculating two indices commonly used in remote sensing (Figure 3, Step 6). The first, the producer accuracy, measures the proportion of observed classes (aquatic vegetation and nonvegetated aquatic areas) that are correctly classified. It is equivalent to the sensitivity (for aquatic vegetation) and specificity (for nonvegetated aquatic areas) measured with receiver operating characteristic analysis. The second, the user accuracy, assesses the proportion of classified classes that correspond to actual observations. These indices were calculated for both aquatic vegetation and nonvegetated aquatic areas using the following formulas:

$$\text{Producer accuracy} = \frac{Pclassif}{Ptot_{obs}} \quad (3)$$

$$\text{User accuracy} = \frac{Pclassif}{Ptot_{classif}}, \quad (4)$$

where Pclassif is the number of correctly classified pixels for the class, Ptotobs is the total number of pixels observed for the class and Ptotclassif is the total number of classified pixels in the class.
The effectiveness of the GNDVI centred and frequency thresholds for detecting changes in the surface area of aquatic plant stands was further assessed by performing, for each stand, a linear regression between the surface area derived from the classification maps (A pred ) and those from the reference maps (A obs ; Figure 3, Step 6). Additionally, the relative error was calculated with the following formula to assess whether the surface area of the aquatic plant stands was overestimated or underestimated (Figure 3, Step 6):

$$
\text{Relative error} = \frac{(A_{\text{pred}} - A_{\text{obs}})}{A_{\text{obs}}} * 100 \quad (5)
$$

Finally, the GNDVI centred and frequency thresholds were applied to the second dataset of PlanetScope images (i.e., those acquired between 29 March 2017 and 17 September 2023) to test the fea- sibility of detecting intra- and interannual dynamics (Figure 3, Step 2). For that purpose, the GNDVI centred threshold was ap- plied to PlanetScope images within 16-day periods including at least three available images to align with the minimum num- ber of images used in dataset 1, calculate the frequency index used for minimizing misclassification caused by radiometric issues and enable the monitoring of aquatic plant stands over a large number of periods. For each 16-day period identified, only those separated by at least 30days were considered to capture significant changes in the surface area of aquatic plant stands (Figure 3, Step 6). The 30-day interval corresponds to the time between the last image of the preceding 16-day period and the first image of the following 16-day period. This selection process resulted in the use of 3 to 14 PlanetScope images per period and stand considered (Figure S2).

3 | Results

3.1 | Differences in GNDVI centred Between Aquatic Vegetation and Nonvegetated Aquatic Areas

For all the studied years, the GNDVI centred of aquatic vegetation was significantly greater than that of nonvegetated aquatic areas (F$_{1,183×10}$ 3 = 160 × 10$^{3}$, p < 0.001; F$_{1,228×10}$ 3 = 186 × 10$^{2}$, p < 0.001; F$_{1,265×10}$ 3 = 282 × 10$^{3}$, p < 0.001; and F$_{1,172×10}$ 3 = 133 × 10$^{3}$, p < 0.001 for 2017, 2018, 2019 and 2023, respectively, Figure S4). Moreover, the variance explained by the random effect (aquatic plant stand) was low (< 0.001 across all the years), indicating little variation in the GNDVI centred among the stands. Across all years, the regression slope was less pronounced in 2018 (− 0.04) than in other years (− 0.14, − 0.17 and − 0.24 for 2017, 2019 and 2023, respectively), indicating a smaller difference in GNDVI centred between aquatic vegetation and nonvegetated aquatic areas than in other years.

3.2 | Assessment of Classification Accuracy

The accuracy of the classifications obtained with both GNDVI centred and frequency thresholds (≥ or < 85% for aquatic vegetation and nonvegetated aquatic areas, respectively) was variable across stands and years. The producer and user ac- curacies for nonvegetated aquatic areas were consistently high, exceeding 85% (Figure 4). In contrast, the accuracies for

aquatic vegetation greatly varied across stands and years, rang- ing from 5% to 92% (Figure 4). A visual comparison of the mis- classifications of PlanetScope images with the corresponding high-resolution airborne images revealed that low producer ac- curacies were primarily due to the failure to detect sparsely and narrowly vegetated areas, which were often no wider than one or two pixels (i.e., ~5 m) and areas where plants did not reach the water surface (Figure S5). Stand 1 in 2017 was an exception, with large and densely vegetated areas undetected. With respect to user accuracy, low values were attributed mainly to an over- estimation of the aquatic vegetation surrounding the observed vegetation (Figure S5).

Although the surface areas of the aquatic plant stands measured on high-resolution airborne images were either high or low, with no intermediate values, the relationships between the predicted and observed surface areas were highly significant for Stands 2 and 3 (F$_{1,2}$ = 91.3, p = 0.01 and F$_{1,2}$ = 41.5, p = 0.02, respectively; Figure 5). In contrast, no significant relationship was found for Stand 1 (F$_{1,1}$ = 8.8, p = 0.2), although changes between the states with large surface areas and those with small surface areas were detected (Figure 5). Additionally, the mean relative errors in the surface area derived from the classification maps were − 0.3% and 4.5% for Stands 2 and 3, respectively, indicating no signif- icant deviation from the 1:1 line. In contrast, the mean relative error was − 42.5% for Stand 1, indicating an underestimation of the surface area.

3.3 | Intra- and Interannual Dynamics of Aquatic Plant Stands

The application of the GNDVI centred and frequency thresholds to the second dataset of PlanetScope images (i.e., images ac- quired between 29 March 2017 and 17 September 2023) revealed a constant pattern with clear intra- and interannual changes in areas of aquatic plant stands (Figure 6). Specifically, intra- annual changes were characterized by a peak in the surface area of aquatic plant stands in summer when the discharge was low. At the interannual timescale, summer surface areas were high in years with long periods of low flows without major events during the vegetative period (2017, 2019, 2020, 2022 and 2023) and low in years with high-discharge events ( > 3000 m$^{3}$.s$^{-1}$) and short periods of low flows (2018 and 2021).

4 | Discussion

The scattering, absorption and reflectance of the solar radi- ation caused by the water and the substances it carries create challenging conditions for using remote sensing approaches to monitor aquatic vegetation (Cho et al. 2012; Tuominen and Lipping 2014). Although aquatic vegetation and water are spec- trally distinct in shallow waters with low turbidity (Tuominen and Lipping 2014), their small spectral differences make the use of medium spatial resolution imagery ( > 10 m) traditionally used for temporal monitoring challenging in environments where aquatic vegetation does not exceed hundreds of metres in width and length (Silva et al. 2008; Yuan and Zhang 2008). The ob- jective of the present study was to test the feasibility of using time series satellite imagery (PlanetScope) with high spatial and
<figure><img src="image_11.png" /><figcaption>**FIGURE 4** | (A) Producer and (B) user accuracies of aquatic vegetation (green bars) and nonvegetated aquatic areas (blue bars).</figcaption></figure>

temporal resolution to monitor riverine aquatic vegetation over large scales and assess current limitations and future needs.

**4.1 | Feasibility and Limitations of Using PlanetScope Imagery for Monitoring Aquatic Vegetation**

In the present study, we hypothesized that aquatic vegetation could be distinguished from nonvegetated aquatic areas in PlanetScope satellite images and that temporal trajectories of aquatic vegetation could be detected without the need for new field observations for each period of interest. Consistent with our first hypothesis, our results revealed that the GNDVI centrered of aquatic vegetation was greater than that of nonvegetated aquatic areas, although this difference appeared to decrease with decreasing vegetation density and extent and increasing plant submersion. While other vegetation indices have been

developed in aquatic environments, such as the normalized difference aquatic vegetation index and the water adjusted vegetation index, these have primarily been used to detect floating and emergent aquatic plant species, whose spectral signature may be less affected by the water than that of submerged species (Villa et al. 2014; Zhao et al. 2012). The use of the GNDVI instead of these vegetation indices was driven by the more accurate classifications obtained during initial tests performed on a small set of PlanetScope images. Additionally, the GNDVI uses the green spectral band, which penetrates the water column more effectively than other spectral bands and may therefore be less affected by variations in water height (Klemas 2016). However, as there were no in situ measurements of water depth, it was not possible to quantify the effect of the canopy depth below the water surface on the efficiency with which vegetation indices detect submerged aquatic vegetation. Further studies are therefore necessary to investigate this effect, determine the most suitable vegetation index and estimate classification uncertainties.
<figure><img src="image_12.png" /><figcaption>**FIGURE 5** | Predicted and observed surface areas of the three aquatic plant stands derived from the classification and reference maps (i.e., the grid with the spatial resolution of PlanetScope images). The 1:1 line is represented by the black continuous line.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**FIGURE 6** | Changes in the surface area of aquatic plant stands between 4 April 2017 and 12 September 2023. Each point corresponds to the central date of the 16-day period selected to detect aquatic vegetation using PlanetScope images. The mean daily discharges recorded at the hydrometric station of Viviers (~20 km downstream of Stand 3) are displayed on the grey line.</figcaption></figure>

Consistent with our second hypothesis, the comparison of the stand surface area derived from the classification and reference maps indicated that large changes in the surface area of aquatic plant stands could be detected with a multidate classification threshold, without requiring new field observations for each period of interest. Although only a few observations were available, the observation of a logical and constant pattern characterized by clear intra- and interannual dynamics further reinforces these results. In particular, the dynamics of the three aquatic plant stands observed from 2017 to 2024 are similar to those observed in temperate rivers, with a peak of stand surface areas in the summer when discharges are low and water temperatures are high, as well as a reduced summer surface area

during high-flow years compared with low-flow years (Cotton et al. 2006; Sand-Jensen et al. 1999).

Overall, the aquatic vegetation observed on high-resolution airborne images was detected accurately, although low producer and user accuracies were observed for certain stands and years. These misclassifications were primarily due to the nondetection of areas where the vegetation was limited, sparse and narrowly distributed with plants that do not reach the water surface, as well as the overestimation of aquatic vegetation in areas surrounding the observed vegetation. Such classification errors could be attributed to the reduced solar radiation reaching plants with increasing plant submersion, as well as to the lower spectral distinction between aquatic vegetation and nonvegetated aquatic areas when the vegetation cover and biomass are low (Phinn et al. 2008; Yuan and Zhang 2008). Additionally, a border effect induced by the resampling of PlanetScope images at 3 m associated with geometric errors, which can reach ~10 m (Houborg and McCabe 2018), may have affected the frequency at which pixels were classified as aquatic vegetation, resulting in an underestimation in narrow vegetated areas and an overestimation along the edges of larger vegetated areas. Consequently, small changes in aquatic vegetation may not be detectable with PlanetScope imagery, although the ongoing effort in developing algorithms to improve the geometric and radiometric quality of images, as well as the rapid development of new sensors having a higher spatial and spectral resolution, may overcome these limitations (Chen et al. 2025; Houborg and McCabe 2018; Leach et al. 2019; Wegmueller et al. 2021).

## 4.2 | Future Challenges

The feasibility of using PlanetScope images for monitoring riverine aquatic vegetation provides new opportunities to investigate patterns and processes at the stand level. However, future studies are necessary to generalize the method presented in this study to other contexts (e.g., differences in plant species, growth forms and water turbidity). In particular, although the classification thresholds ($GNDVI_{centred}$ and frequency thresholds) determined in this study enabled the monitoring of the three aquatic plant stands, they were derived from the data acquired on these aquatic plant stands in 2018, as well as from empirical observations and other initial tests that were much less conclusive. However, these thresholds may not be adapted to other aquatic plant stands, particularly in river reaches that have contrasting environmental conditions, such as differences in water clarity or the presence of biofilms covering sediments in shallow waters (Espel et al. 2020; Tuominen and Lipping 2014). Additionally, the classifications obtained in our study could differ according to the number of PlanetScope images used in each 16-day period for calculating the frequency at which pixels were classified as aquatic vegetation. Future studies are therefore necessary to test the applicability of the classification thresholds used in our study to other aquatic plant stands, determine the optimal number of images required for satisfactory classification and maintain consistent error rates across different time periods. Identifying and quantifying the different sources of uncertainty inherent in the classifications would help both scientists and practitioners
correctly interpret and use them in analyses and decision-making processes (Werther and Burggraaff 2023).

By enabling the monitoring of aquatic plant stands on large spatial and temporal scales, satellite remote sensing could be used to understand the effects of environmental conditions and anthropogenic pressures on the dynamics of riverine aquatic vegetation (Luo et al. 2023; Wang et al. 2023; Zhao et al. 2013). For instance, although the dynamics of aquatic vegetation are considered to be strongly influenced by the hydrological conditions of streams and rivers (Franklin et al. 2008; Riis et al. 2008; Riis and Biggs 2003), their response to different flow events (high and low flows) remains largely unknown because of the unpredictability of these events (Ibáñez et al. 2012). Consequently, the daily acquisition of PlanetScope imagery should overcome these limitations by enabling the survey of aquatic plant stands before and after each event. The possibility of conducting long-term surveys should also involve investigating the effects of multiple hydrological components, i.e., the timing, magnitude, duration, frequency and rate of change of hydrological events (Poff et al. 1997), on the interannual dynamics of aquatic vegetation. By enabling the study of riverine aquatic vegetation on large scales, PlanetScope images could also be used complementarily with field surveys and laboratory experiments to investigate how the information scales from fine to large scales and vice versa, which is necessary for understanding the mechanisms underlying ecological processes (Levin 1992; Millington 2021; Wu and Li 2006). Such advances in monitoring riverine aquatic vegetation and understanding the factors and mechanisms driving its dynamics would be particularly useful for supporting river managers and stakeholders in addressing management issues related to riverine aquatic vegetation, such as improvement in water quality or boat navigation (Thomaz 2021; Verhofstad and Bakker 2019).

## 5 | Conclusion

Our study demonstrated the feasibility of using PlanetScope images for monitoring riverine aquatic plant stands over a period of seven growing seasons (from 2017 to 2024) and potentially identifying the drivers of growing or scouring patterns. Although some misclassifications were observed, our results demonstrated that PlanetScope images could be used for detecting large changes in stand surface area during intra- and interannual periods, opening new opportunities for studying the factors and mechanisms driving the dynamics of aquatic plant stands on large spatial and temporal scales. Future studies are necessary to test whether the classification thresholds used in our study could be applied to other aquatic plant stands and river reaches presenting contrasting environmental conditions.

### Author Contributions

**Léo Rasse:** conceptualization; data curation; formal analysis; investigation; methodology; visualization; writing – original draft. **Julien Godfroy:** conceptualization; formal analysis; investigation; methodology; visualization; writing – original draft. **Géraldine Nogaro:** conceptualization; funding acquisition; methodology; project administration; supervision; visualization; writing – original draft. **Florian Cordier:** conceptualization; funding acquisition; methodology; project administration; supervision; visualization; writing – original draft.

**Danaë Feldis:** conceptualization; data curation; formal analysis; investigation; methodology; visualization. **Sophie Meunier:** data curation; funding acquisition; visualization. **Sara Puijalon:** conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; supervision; visualization; writing – original draft. **Hervé Piégay:** conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; supervision; visualization; writing – original draft.

### Acknowledgements

This study was conducted under the aegis of Rhône Basin Long-Term Environmental Research (ZABR, Zone Atelier Bassin du Rhône). The authors thank the anonymous reviewer for his time and thoughtful evaluation of our manuscript.

### Funding

This work was supported by Electricité de France and the H₂O'Lyon Graduate School [ANR-17-EURE-0018] of the Université de Lyon (UdL), within the programme 'Investissements d'Avenir' operated by the French National Research Agency (ANR).

### Data Availability Statement

The data that support the findings are available from the corresponding author upon reasonable request.

### References

Bates, D., M. Mächler, B. Bolker, and S. Walker. 2015. "Fitting Linear Mixed-Effects Models Using lme4." *Journal of Statistical Software* 67, no. 1: 1–48. https://doi.org/10.18637/jss.v067.i01.

Bejarano, M. D., R. Jansson, and C. Nilsson. 2018. "The Effects of Hydropeaking on Riverine Plants: A Review." *Biological Reviews* 93, no. 1: 658–673. https://doi.org/10.1111/brv.12362.

Biggs, H. J., V. I. Nikora, C. N. Gibbins, et al. 2018. "Coupling Unmanned Aerial Vehicle (UAV) and Hydraulic Surveys to Study the Geometry and Spatial Distribution of Aquatic Macrophytes." *Journal of Ecohyddraulics* 3, no. 1: 45–58. https://doi.org/10.1080/24705357.2018.1466666.

Capra, H., L. Plichard, J. Bergé, et al. 2017. "Fish Habitat Selection in a Large Hydropeaking River: Strong Individual and Temporal Variations Revealed by Telemetry." *Science of the Total Environment* 578: 109–120. https://doi.org/10.1016/j.scitotenv.2016.10.155.

Chen, R., W. Yang, X. Chen, et al. 2025. "Harmoni-Planet: A Holistic Harmonization Method for PlanetScope Constellation Imagery Leveraging a Graph-Based Greedy Optimization Strategy." *Remote Sensing of Environment* 330: 114986. https://doi.org/10.1016/j.rse.2025.114986.

Cho, H. J., and D. Lu. 2010. "A Water-Depth Correction Algorithm for Submerged Vegetation Spectra." *Remote Sensing Letters* 1, no. 1: 29–35. https://doi.org/10.1080/01431160903246709.

Cho, H. J., D. Mishra, and J. Woo. 2012. "Remote Sensing of Submerged Aquatic Vegetation." In *Remote Sensing—Applications*, edited by B. Escalante, 297–308. Intech.

Cornacchia, L., G. Wharton, G. Davies, et al. 2020. "Self-Organization of River Vegetation Leads to Emergent Buffering of River Flows and Water Levels." *Proceedings of the Royal Society B: Biological Sciences* 287, no. 1931: 20201147. https://doi.org/10.1098/rspb.2020.1147.

Cotton, J. A., G. Wharton, J. A. B. Bass, C. M. Heppell, and R. S. Wotton. 2006. "The Effects of Seasonal Changes to In-Stream Vegetation Cover on Patterns of Flow and Accumulation of Sediment." *Geomorphology* 77, no. 3–4: 320–334. https://doi.org/10.1016/j.geomo.rph.2006.01.010.
Dai, Y., L. Feng, X. Hou, and J. Tang. 2021. "An Automatic Classification Algorithm for Submerged Aquatic Vegetation in Shallow Lakes Using Landsat Imagery." *Remote Sensing of Environment* 260: 112459. https://doi.org/10.1016/j.rse.2021.112459.

Espel, D., S. Courty, Y. Auda, D. Sheeren, and A. Elger. 2020. "Submerged Macrophyte Assessment in Rivers: An Automatic Mapping Method Using Pléiades Imagery." *Water Research* 186: 12. https://doi.org/10.1016/j.watres.2020.116353.

Estes, L., P. R. Elsen, T. Treuer, et al. 2018. "The Spatial and Temporal Domains of Modern Ecology." *Nature Ecology & Evolution* 2, no. 5: 819-826. https://doi.org/10.1038/s41559-018-0524-4.

Fawcett, T. 2006. "An Introduction to ROC Analysis." *Pattern Recognition Letters* 27, no. 8: 861-874. https://doi.org/10.1016/j.patrec.2005.10.010.

Franklin, P., M. Dunbar, and P. Whitehead. 2008. "Flow Controls on Lowland River Macrophytes: A Review." *Science of the Total Environment* 400, no. 1-3: 369-378. https://doi.org/10.1016/j.scitotenv.2008.06.018.

Gitelson, A. A., Y. J. Kaufman, and M. N. Merzlyak. 1996. "Use of a Green Channel in Remote Sensing of Global Vegetation From EOS-MODIS." *Remote Sensing of Environment* 58, no. 3: 289-298. https://doi.org/10.1016/S0034-4257(96)00072-7.

Houborg, R., and M. F. McCabe. 2018. "A Cubesat Enabled Spatiotemporal Enhancement Method (CESTEM) Utilizing Planet, Landsat and MODIS Data." *Remote Sensing of Environment* 209: 211-226. https://doi.org/10.1016/j.rse.2018.02.067.

Huang, H., and D. P. Roy. 2021. "Characterization of Planetscope-0 Planetscope-1 Surface Reflectance and Normalized Difference Vegetation Index Continuity." *Science of Remote Sensing* 3: 100014. https://doi.org/10.1016/j.srs.2021.100014.

Hydroportail. 2025. "Station Hydrométrique—V453 0010 02: Le Rhône à Viviers." https://hydro.eaufrance.fr/stationhydro/V45301002/fiche.

Ibáñez, C., N. Caiola, A. Rovira, and M. Real. 2012. "Monitoring the Effects of Floods on Submerged Macrophytes in a Large River." *Science of the Total Environment* 440: 132-139. https://doi.org/10.1016/j.scitotenv.2012.07.073.

Jarvis, P. G. 1995. "Scaling Processes and Problems." *Plant, Cell & Environment* 18, no. 10: 1079-1089. https://doi.org/10.1111/j.1365-3040.1995.tb00620.x.

Kerr, J. T., and M. Ostrovsky. 2003. "From Space to Species: Ecological Applications for Remote Sensing." *Trends in Ecology & Evolution* 18, no. 6: 299-305. https://doi.org/10.1016/S0169-5347(03)00071-5.

Klemas, V. V. 2016. "Remote Sensing of Submerged Aquatic Vegetation." In *Seafloor Mapping Along Continental Shelves*, edited by C. W. Finkl and C. Makowski, vol. 13, 125-140. Springer. https://doi.org/10.1007/978-3-319-25121-9_5.

Kuznetsova, A., P. B. Brockhoff, and R. H. B. Christensen. 2017. "lmerT-est Package: Tests in Linear Mixed Effects Models." *Journal of Statistical Software* 82, no. 13: 1-26. https://doi.org/10.18637/jss.v082.i13.

Leach, N. R., N. Coops, and N. Obrknezev. 2019. "Normalization Method for Multi-Sensor High Spatial and Temporal Resolution Satellite Imagery With Radiometric Inconsistencies." *Computers and Electronics in Agriculture* 164: 104893. https://doi.org/10.1016/j.compag.2019.104893.

Levin, S. A. 1992. "The Problem of Pattern and Scale in Ecology." *Ecology* 73, no. 6: 1943-1967. https://doi.org/10.2307/1941447.

Lochin, P., H. Piégay, J. C. Stella, K. K. Caylor, L. Vaudor, and M. Bliss Singer. 2025. "Drivers of Spatiotemporal Patterns of Riparian Forest NDVI Along a Hydroclimatic Gradient." *Ecohydrology* 18, no. 2: e2729. https://doi.org/10.1002/eco.2729.

Luo, J., H. Duan, Y. Xu, et al. 2025. "Global Trends and Regime State Shifts of Lacustrine Aquatic Vegetation." *Innovation* 6, no. 3: 100784. https://doi.org/10.1016/j.xinn.2024.100784.

Luo, J., G. Ni, Y. Zhang, et al. 2023. "A New Technique for Quantifying Algal Bloom, Floating/Emergent and Submerged Vegetation in Eutrophic Shallow Lakes Using Landsat Imagery." *Remote Sensing of Environment* 287: 113480. https://doi.org/10.1016/j.rse.2023.113480.

Millington, J. 2021. "Scale and Hierarchy in Landscape Ecology." In *The Routledge Handbook of Landscape Ecology*, edited by R. Francis, J. Millington, G. Perry, and E. Minor, 1st ed., 49-66. Routledge.

Phinn, S., C. Roelfsema, A. Dekker, V. Brando, and J. Anstee. 2008. "Mapping Seagrass Species, Cover and Biomass in Shallow Waters: An Assessment of Satellite Multi-Spectral and Airborne Hyper-Spectral Imaging Systems in Moreton Bay (Australia)." *Remote Sensing of Environment* 112, no. 8: 3413-3425. https://doi.org/10.1016/j.rse.2007.09.017.

PlanetScope. 2025. "Planet Documentation." https://docs.planet.com/data/imagery/planetscope/.

Poff, N. L., J. D. Allan, M. B. Bain, et al. 1997. "The Natural Flow Regime: A Paradigm for River Conservation and Restoration." *Bioscience* 47, no. 11: 769-784. https://doi.org/10.2307/1313099.

QGIS Development Team. 2021. "QGIS Geographic Information System (Version 3.22.6) [Open Source Geospatial Foundation]." https://qgis.org.

R Core Team. 2022. "R: A Language and Environment for Statistical Computing." R Foundation for Statistical Computing. https://www.R-project.org/.

Reuter, H., F. Jopp, J. M. Blanco-Moreno, C. Damgaard, Y. Matsinos, and D. L. DeAngelis. 2010. "Ecological Hierarchies and Self-Organisation—Pattern Analysis, Modelling and Process Integration Across Scales." *Basic and Applied Ecology* 11, no. 7: 572-581. https://doi.org/10.1016/j.baae.2010.08.002.

Riis, T., and B. J. F. Biggs. 2003. "Hydrologic and Hydraulic Control of Macrophyte Establishment and Performance in Streams." *Limnology and Oceanography* 48, no. 4: 1488-1497. https://doi.org/10.4319/lo.2003.48.4.1488.

Riis, T., A. M. Suren, B. Clausen, and K. Sand-Jensen. 2008. "Vegetation and Flow Regime in Lowland Streams." *Freshwater Biology* 53, no. 8: 1531-1543. https://doi.org/10.1111/j.1365-2427.2008.01987.x.

Robin, X., N. Turck, A. Hainard, et al. 2011. "pROC: An Open-Source Package for R and S+ to Analyze and Compare ROC Curves." *BMC Bioinformatics* 12, no. 1: 77. https://doi.org/10.1186/1471-2105-12-77.

Roelfsema, C. M., M. Lyons, E. M. Kovacs, et al. 2014. "Multi-Temporal Mapping of Seagrass Cover, Species and Biomass: A Semi-Automated Object Based Image Analysis Approach." *Remote Sensing of Environment* 150: 172-187. https://doi.org/10.1016/j.rse.2014.05.001.

Roy, D. P., H. Huang, R. Houborg, and V. S. Martins. 2021. "A Global Analysis of the Temporal Availability of PlanetScope High Spatial Resolution Multi-Spectral Imagery." *Remote Sensing of Environment* 264: 112586. https://doi.org/10.1016/j.rse.2021.112586.

Sand-Jensen, K., K. Andersen, and T. Andersen. 1999. "Dynamic Properties of Recruitment, Expansion and Mortality of Macrophyte Patches in Streams." *International Review of Hydrobiology* 84, no. 15: 497-508. https://doi.org/10.1002/iroh.199900044.

Schoelnick, J., T. de Groote, K. Bal, W. Vandenbruwaene, P. Meire, and S. Temmerman. 2012. "Self-Organised Patchiness and Scale-Dependent Bio-Geomorphic Feedbacks in Aquatic River Vegetation." *Ecography* 35, no. 8: 760-768. https://doi.org/10.1111/j.1600-0587.2011.07177.x.

Silva, T. S. F., M. P. F. Costa, J. M. Melack, and E. M. L. M. Novo. 2008. "Remote Sensing of Aquatic Vegetation: Theory and Applications." *Environmental Monitoring and Assessment* 140, no. 1-3: 131-145. https://doi.org/10.1007/s10661-007-9855-3.
Stocks, J. R., M. P. Rodgers, J. B. Pera, and D. M. Gilligan. 2019. "Monitoring Aquatic Plants: An Evaluation of Hydroacoustic, On-Site Digitising and Airborne Remote Sensing Techniques." *Knowledge and Management of Aquatic Ecosystems* 420, no. 27: 9. https://doi.org/10.1051/kmae/2019016.

Tena, A., D. Vericat, L. E. Gonzalo, and R. J. Batalla. 2017. "Spatial and Temporal Dynamics of Macrophyte Cover in a Large Regulated River." *Journal of Environmental Management* 202: 379-391. https://doi.org/10.1016/j.jenvman.2016.11.034.

Thomaz, S. M. 2021. "Ecosystem Services Provided by Freshwater Macrophytes." *Hydrobiologia* 850: 2757-2777. https://doi.org/10.1007/s10750-021-04739-y.

Tuominen, J., and T. Lipping. 2014. "Feasibility of Benthic Cover-Type Mapping in Turbid Waters Near Estuaries Using Hyperspectral Remote Sensing." *Journal of Coastal Research* 298: 1131-1139. https://doi.org/10.2112/JCOASTRES-D-12-00240.1.

Turner, M. G. 1989. "Landscape Ecology: The Effect of Pattern on Process." *Annual Review of Ecology and Systematics* 20: 171-197. https://doi.org/10.1146/annurev.es.20.110189.001131.

Vahtmäe, E., J. Kotta, L. Lõugas, and T. Kutser. 2021. "Mapping Spatial Distribution, Percent Cover and Biomass of Benthic Vegetation in Optically Complex Coastal Waters Using Hyperspectral CASI and Multispectral Sentinel-2 Sensors." *International Journal of Applied Earth Observation and Geoinformation* 102: 1-14. https://doi.org/10.1016/j.jag.2021.102444.

Vázquez-Tarrío, D., M. Tal, B. Camenen, and H. Piégay. 2019. "Effects of Continuous Embankments and Successive Run-of-the-River Dams on Bedload Transport Capacities Along the Rhône River, France." *Science of the Total Environment* 658: 1375-1389. https://doi.org/10.1016/j.scitotenv.2018.12.109.

Verhofstad, M. J. J. M., and E. S. Bakker. 2019. "Classifying Nuisance Submerged Vegetation Depending on Ecosystem Services." *Limnology* 20, no. 1: 55-68. https://doi.org/10.1007/s10201-017-0525-z.

Villa, P., M. Bresciani, F. Braga, and R. Bolpagni. 2014. "Comparative Assessment of Broadband Vegetation Indices Over Aquatic Vegetation." *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing* 7, no. 7: 3117-3127. https://doi.org/10.1109/JSTARS.2014.2315718.

Visser, F., K. Buis, V. Verschoren, and J. Schoelynck. 2016. "Mapping of Submerged Aquatic Vegetation in Rivers From Very High-Resolution Image Data, Using Object-Based Image Analysis Combined With Expert Knowledge." *Hydrobiologia* 812, no. 1: 157-175. https://doi.org/10.1007/s10750-016-2928-y.

Wang, Y., Z. Gong, and H. Zhou. 2023. "Long-Term Monitoring and Phenological Analysis of Submerged Aquatic Vegetation in a Shallow Lake Using Time-Series Imagery." *Ecological Indicators* 154: 110646. https://doi.org/10.1016/j.ecolind.2023.110646.

Wegmueller, S. A., N. R. Leach, and P. A. Townsend. 2021. "LOESS Radiometric Correction for Contiguous Scenes (LORACCS): Improving the Consistency of Radiometry in High-Resolution Satellite Image Mosaics." *International Journal of Applied Earth Observation and Geoinformation* 97: 102290. https://doi.org/10.1016/j.jag.2020.102290.

Werther, M., and O. Burggraaff. 2023. "Dive Into the Unknown: Embracing Uncertainty to Advance Aquatic Remote Sensing." *Journal of Remote Sensing* 3: 0070. https://doi.org/10.34133/remotesensing.0070.

Wiens, J. A. 1989. "Spatial Scaling in Ecology." *Functional Ecology* 3, no. 4: 385. https://doi.org/10.2307/2389612.

Wu, J., and H. Li. 2006. "Concepts of Scale and Scaling." In *Scaling and Uncertainty Analysis in Ecology*, edited by J. Wu, K. Jones, H. Li, and O. Loucks, 3-15. Springer Netherlands. https://doi.org/10.1007/1-4020-4663-4_1.

Youden, W. J. 1950. "Index for Rating Diagnostic Tests." *Cancer* 3, no. 1: 32-35. https://doi.org/10.1002/1097-0142(1950)3:1%253C32::AID-CNCRC2820030106%253E3.0.CO;2-3.

Yuan, L., and L.-Q. Zhang. 2008. "Mapping Large-Scale Distribution of Submerged Aquatic Vegetation Coverage Using Remote Sensing." *Ecological Informatics* 3, no. 3: 245-251. https://doi.org/10.1016/j.ecoinf.2008.01.004.

Zhao, D., H. Jiang, T. Yang, Y. Cai, D. Xu, and S. An. 2012. "Remote Sensing of Aquatic Vegetation Distribution in Taihu Lake Using an Improved Classification Tree With Modified Thresholds." *Journal of Environmental Management* 95, no. 1: 98-107. https://doi.org/10.1016/j.jenvman.2011.10.007.

Zhao, D., M. Lv, H. Jiang, Y. Cai, D. Xu, and S. An. 2013. "Spatial- Temporal Variability of Aquatic Vegetation in Taihu Lake Over the Past 30 Years." *PLoS ONE* 8, no. 6: 1-8. https://doi.org/10.1371/journal.pone.0066365.

Zhou, L., C. J. Tucker, R. K. Kaufmann, D. Slayback, N. V. Shabanov, and R. B. Myneni. 2001. "Variations in Northern Vegetation Activity Inferred From Satellite Data of Vegetation Index During 1981 to 1999." *Journal of Geophysical Research: Atmospheres* 106, no. D17: 20069-20083. https://doi.org/10.1029/2000JD000115.

## Supporting Information

Additional supporting information can be found online in the Supporting Information section. **Figure S1:** Example of radiometric inconsistencies observed on PlanetScope images (represented for the NIR spectral band only). In this example, Image A was conserved in PlanetScope imagery used for the classification of aquatic vegetation and nonvegetated aquatic areas, whereas Image B was removed from the dataset because there was a clear distinction in the NIR values between the two areas. **Figure S2:** Number of PlanetScope images used to detect aquatic vegetation in each 16-day period analysed in datasets 1 (white squares) and 2 (grey squares). Each square corresponds to the central date of each 16-day period. Vertical arrows correspond to the date at which high-resolution airborne images were acquired. The mean daily discharges recorded at the hydrometric station of Viviers (~20 km downstream of Stand 3) are displayed on the grey line. **Figure S3:** Aerial images of Stand 1 in the summers of 2017, 2018 and 2019. Across the 3 years, the aquatic vegetation was less developed in 2018 than in the other years, probably due to higher discharges leading to plant uprooting or growth limitations. Photo credit: EDF. **Figure S4:** Mean GNDVI centrered of aquatic vegetation (green bars) and nonvegetated aquatic areas (blue bars) measured on all PlanetScope images acquired within ±8 days of the acquisition dates of the high-resolution airborne images in 2017, 2018, 2019 and 2023. Error bars indicate the standard deviation. Dotted lines indicate a GNDVI centrered of 0. In each panel, *n* indicates the number of PlanetScope images for which the GNDVI centrered of aquatic vegetation and nonvegetated aquatic areas was measured. **Figure S5:** Examples of misclassifications. Photo credit: EDF.