# Amazon’s 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River
Contraction

Fabien H. Wagner
Institute of the Environment and Sustainability,
University of California, Los Angeles, CA 90095 USA;
and NASA-Jet Propulsion Laboratory
California Institute of Technology, Pasadena, CA 91109, USA;
and CTREES.org, Pasadena, United States;
wagner.h.fabien<EMAIL_ADDRESS>
Samuel Favrichon
NASA-Jet Propulsion Laboratory
California Institute of Technology, Pasadena, CA 91109, USA ;
<EMAIL_ADDRESS>
Ricardo Dalagnol
Institute of the Environment and Sustainability,
University of California, Los Angeles, CA 90095 USA;
and NASA-Jet Propulsion Laboratory
California Institute of Technology, Pasadena, CA 91109, USA ;
and CTREES.org, Pasadena, United States;
<EMAIL_ADDRESS>
Mayumi CM Hirye
Quapá Lab
Faculty of Architecture and Urbanism
University of São Paulo—USP
São Paulo, SP, Brazil
<EMAIL_ADDRESS>
Adugna Mullissa
Institute of the Environment and Sustainability,
University of California, Los Angeles, CA 90095 USA;
and CTREES.org, Pasadena, United States;
<EMAIL_ADDRESS>
Sassan Saatchi
Institute of the Environment and Sustainability,
University of California, Los Angeles, CA 90095 USA;
and NASA-Jet Propulsion Laboratory
California Institute of Technology, Pasadena, CA 91109, USA
<EMAIL_ADDRESS>
and CTREES.org, Pasadena, United States

###### Abstract

The Amazon, the world’s largest rainforest, faces a severe historic drought.
The Rio Negro River, one of the major Amazon River tributaries, reaches its
lowest level in a century in October 2023. Here, we used a U-net deep learning
model to map water surfaces in the Rio Negro River basin every 12 days in 2022
and 2023 using 10 m spatial resolution Sentinel-1 satellite radar images. The
accuracy of the water surface model was high with an F1-score of 0.93. The 12
days mosaic time series of water surface was generated from the Sentinel-1
prediction. The water surface mask demonstrated relatively consistent
agreement with the Global Surface Water (GSW) product from Joint Research
Centre (F1-score: 0.708) and with the Brazilian Mapbiomas Water initiative
(F1-score: 0.686). The main errors of the map were omission errors in flooded
woodland, in flooded shrub and because of clouds. Rio Negro water surfaces
reached their lowest level around the 25th of November 2023 and were reduced
to 68.1% (9,559.9 km2) of the maximum water surfaces observed in the period
2022-2023 (14,036.3 km2). Synthetic Aperture Radar (SAR) data, in conjunction
with deep learning techniques, can significantly improve near real-time
mapping of water surface in tropical regions.

_Keywords_ Surface water mapping $\cdot$ Synthetic Aperture Radar (SAR)
$\cdot$ Deep Learning $\cdot$ U-net $\cdot$ Image segmentation $\cdot$ Near
real-time

##  1 Introduction

The Rio Negro River is among the largest tributaries of the left bank of the
Amazon River, extending from its sources in Colombia to Venezuela and Brazil,
making the Amazon the largest river basin in the world. The Rio Negro River
has a length of 2230 km, a catchment area of 696,000 km2, and an average flow
of 28,400 m3 per second, representing 14% of the annual average flow of the
Amazon basin (Alho et al., 2015; Filizola et al., 2009). It participates in
the complex hydrological processes of the Amazon River that play a key role in
the water, energy, and carbon cycles and interact with the global climate
system (Fassoni-Andrade et al., 2021). On the 26th of October 2023, the Rio
Negro River water level measured at the Port of Manaus (Brazil) reached its
lowest point since 1903, 12.7 m. The water level measurement at the port is
currently the most reliable data for the Rio Negro River, and to date, no
near-real-time data of the river height or extent derived from medium spatial
resolution satellite observation (5-30 m) is easily available over the Rio
Negro basin.

Generating maps of near-real-time water surfaces location and extents for the
Amazon region remains challenging. The primary obstacle is the persistent
cloud cover. Despite the availability of numerous medium spatial resolution
multispectral satellites like Landsat, CBERS, Sentinel-2, or PlanetScope,
which acquire data at frequencies of $\leq$ one month, the extensive cloud
cover impedes the creation of near-real-time observations of the state of the
river basin. As a result, most water surface products are generated based on a
yearly or multi-annual frequency (Pekel et al., 2016; Hess et al., 2015;
MapBiomas, 2024; Souza et al., 2019; Adeli et al., 2020).

Only Synthetic Aperture Radar (SAR), due to its cloud-penetrating capability,
appears as the data of choice for near real-time monitoring of surface water
in tropical regions with persistent clouds. Moreover, water surfaces of the
Amazon River in Synthetic Aperture Radar (SAR) images are easily
distinguishable from other land covers due to their characteristic lower
backscattering values compared to most terrestrial surfaces. These lower
backscattering values are likely attributed to the specular reflection and
smoothness of the water (Hess et al., 1995; Alsdorf et al., 2000).

There is a significant body of literature on the study of water levels,
extent, and wetland vegetation in the Amazon region using radar, employing
various instruments (SIR-C, RADARSAT-2, ALOS PALSAR, and ALOS2 ScanSAR,
PolSAR, ENVISAT, JERS-1), diverse bands such as X-, C-, and L-band, and
different polarizations (Hess et al., 1995; Alsdorf et al., 2000; Silva et
al., 2008; Sartori et al., 2011; Da Silva et al., 2012; Arnesen et al., 2013;
Hess et al., 2015; de Almeida Furtado et al., 2016; Cao et al., 2018; Canisius
et al., 2019), as reviewed in (Adeli et al., 2020) and (Fassoni-Andrade et
al., 2021). However, none of these studies provided near real-time and high-
resolution water surfaces because the instruments or missions were not
designed for this purpose. The Sentinel-1 C-band SAR satellite of the the
European Space Agency (ESA), with its instrument operating at a center
frequency of 5.405 GHz, enables image acquisition even in cloudy conditions.
Furthermore, with a spatial resolution of 10 m, a temporal resolution of 12
days, and an open data policy, it currently appears as the optimal satellite
program for measuring near-real-time variations in water surfaces at a fine
scale and has not been fully exploited in the Amazon (Fassoni-Andrade et al.,
2021).

To map water surfaces with Sentinel-1, several traditional methods are
available, such as spectral indices (Gao, 1996), machine learning (Huang et
al., 2018, 2021; Tang et al., 2022), dynamic thresholding (Bioresita et al.,
2018; Xing et al., 2018; Uddin et al., 2019; Tiwari et al., 2020; Markert et
al., 2020) and neural networks (Pham-Duc et al., 2017). However, recently, the
remote sensing scientific community is adapting to modern deep learning
approaches (Mayer et al., 2021). For instance, the U-Net convolutional neural
network can accurately map surface water from Sentinel-1 in tropical regions,
achieving a high validation accuracy with an F1-Score exceeding 0.92 (Mayer et
al., 2021; Ronneberger et al., 2015). Another study, which provides a
substantial dataset of flooding samples for training models, demonstrates that
a fully convolutional (FCNN) model outperforms thresholding algorithms in
identifying flooded areas (Bonafilia et al., 2020). Further research indicates
that the U-Net algorithm can learn features as meaningful as spectral indices
for flood segmentation (Konapala et al., 2021). In an attempt to map floods
with CNN-based methods and Sentinel-1 imagery with minimal pre-processing, it
was shown that CNNs, including the U-Net algorithm, can reduce the time
required to develop a flood map by 80%, while maintaining strong performance
across various locations and environmental conditions (Nemni et al., 2020).
Lastly, for near-real-time flood mapping using Sentinel-1, the U-Net model
demonstrated a notable improvement over thresholding techniques and enabled
fast processing (1 min per image) with a very low omission error (Katiyar et
al., 2021). Therefore, we used the U-Net convolutional neural network
(Ronneberger et al., 2015) to map water surfaces in Sentinel-1 images, as this
algorithm is currently one of the most successful for mapping water in
Sentinel-1 images and is an easy-to-use algorithm for binary segmentation
tasks (Konapala et al., 2021).

Several products covering the Rio Negro River have been developed in the last
decade. One of the most renowned and accurate water surface products is the
Global Surface Water (GSW) at 30 m resolution from the Joint Research Centre
(JRC) (Pekel et al., 2016). This dataset used the entire multi-temporal
orthorectified Landsat 5, 7, and 8 archive spanning the past 32 years
(1984-2016) and employed machine learning techniques to quantify global
surface water and its changes at 30 m spatial resolution (Pekel et al., 2016).
Specifically, using Google Earth Engine (Gorelick et al., 2017), they mapped
water surface in three million Landsat satellite images, identifying when
water was present, how occurrence changed, and the nature of changes in terms
of seasonality and persistence. The main limitation of this product is that it
is static and not available for recent years, thus cannot be used to monitor
drought episodes in near-real time. Another remarkable static dataset for the
region is the LBA-ECO LC-07 dataset of wetland extent for the Lowland Amazon
Basin (Hess et al., 2015). This dataset was derived from mosaics of Japanese
Earth Resources Satellite (JERS-1) Synthetic Aperture Radar (SAR) imagery for
the period October–November 1995 and May–July 1996. It provides a map of
wetland extent, vegetation type, and dual-season flooding state of the entire
lowland Amazon basin at 3 arc-seconds of spatial resolution ($\sim$90 m).
Finally, the most up-to-date and comparable to near-real-time dataset is the
annual water surface from the MapBiomas initiative (Souza et al., 2020;
MapBiomas, 2024; Souza et al., 2019), particularly from the year 2022. The
water surface is mapped by the MapBiomas initiative using machine learning
techniques and the Landsat Data Archive covering the period from 1985 to 2022
(scenes with cloud cover $<$ 70%), available in the Google Earth Engine
(Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI). This dataset is produced
with $>$ 84% average user accuracy (Souza et al., 2020, 2019). A limitation of
the product is that the map is produced yearly, so it cannot be used for near-
real-time mapping, for example, for the October - November 2023 drought. These
three water surface datasets are the most accurate and used for our study
region, and that is why we compared them with our results.

This work presents (i) the mapping of the water surface in the Rio Negro River
basin at a 10 m spatial resolution and a 12 days temporal resolution using
Sentinel-1 images and deep learning, (ii) the comparison of our water surface
mask with the Joint Research Centre (JRC) Global Surface Water (GSW) at 30 m
resolution, with the MapBiomas Water initiative water surface in 2022 at 30 m
resolution, with the LBA-ECO LC-07 dataset of wetland extent and vegetation
types and finally with the water level at the Port of Manaus, and (iii)
description of the Rio Negro River contraction during the 2023 drought event.

The water surface occurrence and recurrence on the 2022-2023 period are
available at https://doi.org/10.5281/zenodo.10552959.

##  2 Materials and Methods

###  2.1 Study site

Figure 1: Geographical location in blue of the entire Rio Negro River and its
tributaries before it forms the Amazon with the Solimões river at Manaus,
adapted from (FAO, 2022). The study area is represented with a dashed black
line, and the approximate extents of the 24 Sentinel-1 images taken for each
orbit are in magenta.

###  2.2 Sentinel-1 satellite images of the Rio Negro Basin

We used Sentinel-1 images from the global Sentinel-1 GRD archive at $\sim$ 10
m spatial resolution, covering the Rio Negro Basin in 2022 and 2023 in the
Countries of Brazil, Venezuela, Guyana and Columbia (Fig 1). These images were
acquired in Interferometric Wide Swath (IW) mode with the VV and VH
polarizations (dual-band cross-polarization, vertical transmit/vertical
receive and vertical transmit/horizontal receive). Since the dataset was not
contiguous over the basin, we retained only the largest contiguous part as our
study area ($\sim$ 695,912 km2, representing > 95% of the basin area), Fig 1.
Sentinel-1 was accessed on 2023-12-24 and 2024-01-03 from
https://registry.opendata.aws/sentinel-1/, and all images from the dates of
2022 and 2023 over the study area were downloaded (1536 images).

To obtain calibrated and georeferenced images suitable for our water
segmentation model, the VV and VH images in Geotiff were pre-processed using
the SNAP-Python (snappy) API (SNAP, ESA Sentinel Application Platform v9.0.0,
http://step.esa.int) of the Sentinel-1 Toolbox. The following steps were
applied: (i) orbit correction to properly orthorectify the image using orbit
metadata; (ii) GRD border noise removal to eliminate low-intensity noise and
invalid data on scene borders, crucial for working with the entire scene;
(iii) Thermal noise removal to minimize discontinuities between sub-swaths in
scenes acquired using multi-swath acquisition modes; (iv) Calibration of the
data to sigma-naught values, that is, computation of backscatter intensity
with the sensor calibration parameters from the metadata; (v) Terrain
correction (orthorectification) using the Global Earth Topography And Sea
Surface Elevation at 30 arc-second resolution (GETASSE30) as the digital
elevation model. The terrain-corrected values were converted to decibels via
log scaling (10*log10(x)), and the VV and VH image values ranging between -49
to 1 were scaled to 0-255 for storage as an 8-bit integer 2-band raster,
following the formula: (maximum(-49,minimum(1,x))+50)$\times$5.

###  2.3 Sentinel-1 mountain shade mask from SRTM

Topographic features create shadows in the SAR imagery that exhibit similar
backscatter values to the water surfaces and may be misclassified by the
model. Consequently, to exclude shaded areas from our results, we generated a
shade mask using the Shuttle Radar Topography Mission (SRTM) data at 30 m
(Farr et al., 2007) to identify mountains and shaded slopes. The SRTM data for
the study area were downloaded on 2023-12-29 from https://dwtkns.com/srtm30m/.
The tiles were mosaicked into a single image, aggregated by a factor of 3 (90
m spatial resolution) using the minimum value using GDAL (GDAL/OGR
contributors, 2023). Then, slope computation was performed by considering the
eight neighboring pixels with the R package terra (Hijmans, 2023). A value of
1 was assigned to slopes over 20 degrees, and 0 otherwise. The mask was then
converted to polygons (Pebesma and Bivand, 2023; Pebesma, 2018). Holes in the
polygons were removed (Dorman, 2023), and the convex hull envelop was computed
to ensure coverage of all mountains. Minor polygon errors (shaded areas over
rivers) were manually edited. Finally, these polygons were rasterized into
images matching the size and resolution of the images receiving the results of
water segmentation.

###  2.4 High resolution water masks and water levels

To compare and analyze our water segmentation results, we used four datasets
independent from Sentinel-1 observations over the region. The first product is
the Joint Research Centre (JRC) Global Surface Water (GSW) at 30 m resolution
based on Landsat data (Pekel et al., 2016). From the GSW product, we extracted
data on water occurrence (percent of observation of the pixel as water in the
time series, where 100 indicates constant water) and recurrence (the frequency
of water reappearing from year to year across the time-series) over the Rio
Negro basin. The datasets were accessed on 2023-12-21 from https://global-
surface-water.appspot.com/download. The second product is the annual water
surface of 2022 over our study area from the Mapbiomas Water initiative at 30
m resolution based on Landsat data (Souza et al., 2020). MapBiomas Project -
Collection [8] of the Annual Land Use Land Cover Maps of Brazil, accessed on
2024-01-13 through the link: https://brasil.mapbiomas.org/colecoes-mapbiomas/.
The water surface for the year 2022 (annual_water_coverage_2022) was selected
from the asset mapbiomas_water_collection2_annual_water_coverage_v1. The third
product is the LBA-ECO LC-07 Wetland dataset (Hess et al., 2015) accessed from
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1284 on 2024-12-01. The
dataset consists of maps of wetland extent, vegetation type and dual-season
flooding state of the entire lowland Amazon basin at 3 arc-seconds of spatial
resolution ($\sim$90 m). These map were derived from mosaics of Japanese Earth
Resources Satellite (JERS-1) Synthetic Aperture Radar (SAR) imagery for the
period October-November 1995 and May-July 1996. Full classes and values
description are available here:
https://daac.ornl.gov/LBA/guides/LC07_Amazon_Wetlands.html. The fourth product
is the daily Rio Negro water level measured at the port of Manaus (Brazil),
accessed on Jan 6 from https://www.portodemanaus.com.br/?pagina=nivel-do-rio-
negro-hoje.

####  2.4.1 Model Architecture

Figure 2: U-Net model architecture used for water surface estimation from
Sentinel-1 images, adapted from Ronneberger et al. (2015). The number of
channels is indicated above the cuboids, and the vertical numbers indicate the
row and column size in pixels. The operations (convolutions, skip connections,
max pooling and upsampling) performed in each layer and their sizes are
indicated by the colored arrows.

The water surface segmentation was performed using a classical U-net model
(Ronneberger et al., 2015), Fig. 2. Specifically, the U-net model returns the
probability of water surface presence in each pixel of a given input image.
The model takes 2-band (VH and VV) Sentinel-1 images with a size of 256
$\times$ 256 pixels as inputs. The output is a one-band mask with dimensions
256 $\times$ 256 pixels, containing 1 (indicating water surface with a pixel
probability $>=$0.5) or 0 (representing non-water pixel with a probability
$<$0.5). The model was implemented in the R language (R Core Team, 2016) using
the RStudio interface to Keras and TensorFlow 2.10 (Chollet et al., 2015;
Allaire and Chollet, 2016; Allaire and Tang, 2020; Abadi et al., 2015).

####  2.4.2 Network training

###  2.5 Training

To generate training samples for the U-net model’s water surface, we used a
water mask at 4.78 m resolution derived from a previous model using Planet
NICFI (Wagner et al., 2023; Planet, 2021). From this dataset covering Amazon
water bodies from 2015 to 2023 and tiled with the Planet NICFI tiling system,
we selected water masks from 2021 to 2022 that were fully covered by a
Sentinel-1 image (acquired $\leq$ 25 days apart from the NICFI mosaic date)
and had less than 25% cloud cover. Then water masks were randomly selected for
each monthly Planet mosaic date from 2021 and 2022, to constitute a data set
of 550 non-overlapping images. Additionally, 29 Planet NICFI-derived water
masks containing sandbanks in 2021 and 2022 were added to this dataset. This
strategy was implemented to have images for all seasons, use only non-
overlapping scenes to prevent overfitting, and include sandbanks observed
solely in the dry season, Fig. 1.

The Sentinel-1 images corresponding to the water mask tiles were pre-processed
as in section 2.2 and clipped to the water mask extents. Subsequently, the
water mask at 4.78 m resolution was projected onto a raster with the same
resolution and extent as the preprocessed and clipped Sentinel-1 image. Both
the clipped Sentinel-1 image and the corresponding water mask, having the same
size and resolution, were then cropped to a size of 1792 $\times$ 1792 pixels
and retiled to the size of 256 $\times$ 256 images for use in model training.
The complete sample comprises 59,387 and 6,057 Sentinel-1 image patches of 256
$\times$ 256 pixels and their associated water mask, for training and
validation, respectively.

Each image patch underwent a data augmentation process, involving random
vertical and horizontal flips. No additional data augmentation was necessary
due to the natural data augmentation provided by the acquisition of sampling
images at different dates (Wagner, 2021). Following data augmentation, the
images were fed into the U-net model.

During network training, we used a standard stochastic gradient descent
optimization. The loss function was designed as a sum of two terms: binary
cross-entropy and Dice coefficient-related loss of the predicted masks (Dice,
1945; Allaire and Chollet, 2016; Chollet et al., 2015) and finally the
optimizer Adam (Kingma and Ba, 2014) with a learning rate of 0.0001 was used.
We used the accuracy (i.e. the frequency with which the prediction matches the
observed value) as the metrics to assess the model performance.

The network was trained for 2000 epochs with a batch size of 32 images and the
model with the best accuracy was kept for prediction. The training of the
models took approximately 4.5 hours per 100 epochs using a NVIDIA A10G Tensor
Core (GPU) with a 24 GB memory.

###  2.6 Prediction

For the prediction of the water mask, the 1536 Sentinel images (64 dates
spanning 2021-12-02 to 2023-12-31 for each of the 24 Sentinel-1 tiles, Fig. 1)
were resized by adding columns and rows to have an aspect ratio of 4096 and a
128-pixel border. This adjustment was made to meet the input size requirements
for prediction. The standardized-size Sentinel-1 tiles were then subdivided
into sub-images of 4,224 $\times$ 4,224 pixels with a 128-pixel overlap
between them. The total number of sub-images to predict was 69,577.
Predictions were made on these 4,224 $\times$ 4,224 pixel images, and the
128-pixel border on each sub-image prediction was removed to mitigate border
artifacts (Ronneberger et al., 2015). To create the 11-day water mask mosaics,
the resulting 4096 $\times$ 4096 images were projected onto a regular grid
with a tiling system of 4096 $\times$ 4096 pixel images, maintaining the same
resolution as the central scene of Sentinel-1 over the study area during each
11-day period. In cases of overlap between predicted images, the maximum value
was retained, and in the absence of data, the previous mosaic values were used
to complete the time series. The final dataset, covering the period 2022-2023,
consisted of 62 mosaics with a temporal resolution of 12 days. The computation
time for predicting Rio Negro water masks using an RTX4090 GPU was 3.75 days.

###  2.7 Filtering for artifacts in Sentinel-1 images

Although the Sentinel-1 C-band SAR can theoretically penetrate clouds, some
images may still exhibit significant artifacts, potentially from convective
clouds as previously observed in this region (Doblas et al., 2020). This leads
to an increase in the values of the VV and VH bands, making it challenging for
the model to detect water. To address this artifact in the prediction, we
selected all tiles in our prediction grid with more than 500,000 pixels of
river (68 tiles) based on a water occurrence mask computed for the entire time
series (62 dates). For each of these 68 tiles, we identified the 5 images with
the largest anomalies in pixels classified as water in the water occurrence
mask and having fewer than two dates without water. Subsequently, all selected
prediction tiles underwent visual inspection, and in cases where significant
omissions of water pixels were clearly identified as errors, corrections were
made using the previous water mask mosaic date (111 images were corrected).

####  2.7.1 Segmentation accuracy assessment

The F1 score was computed for each sample $i$ (training and validation) as the
harmonic average of the precision and recall, eqn 1, where precision was the
ratio of the number of segments classified correctly as $i$ and the number of
all segments (true and false positive) and recall was the ratio of the number
of segments classified correctly as $i$ and the total number of segments
belonging to class $i$ (true positive and false negative). This score varies
between 0 (lowest value) and 1 (best value).

$\displaystyle F1_{i}$ $\displaystyle=$ $\displaystyle
2\times\frac{precision_{i}\times recall_{i}}{(precision_{i}+recall_{i})}$ (1)

##  3 Results

###  3.1 Model accuracy

Overall, the F1-score, precision and recall values were similar for the
training and validation sample, Table 1. The water surface model F1-scores of
the predictions on the training and validation samples were both high with a
value of 0.930, Table 1. The recall was lower than the precision indicating a
slightly higher rate of false negative than of false positive.

Table 1: F1-scores of the water surface segmentation in the training and validation samples. model | sample | nb images | precision | recall | F1-score
---|---|---|---|---|---
water mask | training | 59,387 | 0.935 | 0.926 | 0.930
validation | 6,057 | 0.934 | 0.926 | 0.930

Figure 3: Observation and prediction for 18 images of the validation dataset
with a F1-score above 0.97.

The model performs well on the validation dataset and can accurately capture
most of the variations in the river border, Fig. 3a1 - r2. It can segment well
very small islands, Fig. 3f2 and g2. However, the resolution of the mask may
be slightly degraded in certain areas, as seen in Fig. 3o2, p2, and q2. It is
important to note that the water surface masks for training and validation
come from a model at 5 m spatial resolution (and were aggregated at 10 m),
while Sentinel-1 spatial resolution is 10 m. Overall, the model performs very
well for non-water pixels and has very few commission errors. However, for
very small rivers, there are some omissions, even in the validation images
with the highest F1-score, where small rivers with one or two pixels in width
(10-20 m) are missed, Fig. 3b2, h2, i2, and j2.

Figure 4: Observations and predictions for six images of the validation
dataset with an F1-score below 0.5 or cloud artifacts.

For the images in validation with low F1-scores, errors can arise from the
omission of small rivers with widths less than 10 to 20 m, Fig. 4a1 - b2. An
inundation event in flooded shrubs was also missed by the model, Fig. 4c1 -
c2. Errors from clouds are visible in 4d2 and e2. These cloud errors are
recognizable as their borders are uncorrelated with the river pattern and
usually present in the entire Sentinel-1 image. Finally, some errors are due
to clouds in the Planet NICFI-derived water surface mask, Fig. 4f2, while
water surface is correctly mapped from the Sentinel-1 image.

Concerning the errors observed in the prediction, common omission errors
include the impact of clouds in the Sentinel image, resulting in an increase
in VH and VV bands, leading to the omission of water. We have filtered out
most of these large errors in post-processing (see section 2.7). The band of
water directly in front of the city of Manaus exhibits some omission errors,
but it is challenging to determine if it comes from the raw image, image
correction, or proximity to very high VH and VV values in the city that could
influence the model. This issue may be mitigated in the future near cities by
incorporating training samples in those areas. In our study, this affects a
negligible area in relation to the total water surface of the Rio Negro River.
Regarding commission errors, all mountain-shaded areas are predicted as water,
necessitating masking through alternative means, as they exhibit the exact
same pattern as water (see section 2.3). Additionally, a less significant
commission error in terms of area over our study area but consistently present
is airport landing runways.

###  3.2 LBA-ECO LC-07 Wetland dataset

Regarding the vegetation types and seasonal flooding states (wetland LC-07
data) of the pixels classified as water surface by our model, Table 2, 29.7%
fall into the open water class in the wetland LC-07 data. The next most
frequent classes were "Non-flooded shrub/Flooded shrub" (12.7%), Non-wetland
within Amazon Basin (12.0%), "Non-flooded forest/Flooded forest" (11.3%), and
"Flooded woodland/Flooded woodland" (8.1%). The high percentage of pixels
classified as "Non-wetland within Amazon Basin" (12.0%) is likely due to the
higher spatial resolution (10 m) of our dataset, which maps more smaller
rivers than the LC-07 wetlands dataset (90 m spatial resolution).

Table 2: Frequency and percentage of Wetland classes for the pixels classified as water by our model and for the pixels classified as non-water by our model but classified as water surface in the GWS dataset (false negative). |  | Our model | False negative | False negative
---|---|---|---|---
Vegetation type and dual-season flooding state from LBA-ECO LC-07 data |  | GWS | MapBiomas
Cover at Low Water Stage | Cover at High Water Stage | Freq. (%) | Freq. (%) | Freq. (%)
Non-wetland within Amazon Basin | Non-wetland within Amazon Basin | 3040627 (12) | 310982 (10.2) | 743987 (9.6)
Open water | Open water | 7564727 (29.7) | 338274 (11.1) | 256448 (3.3)
Open water | Aquatic macrophyte (flooded herbaceous) | 214580 (0.8) | 8275 (0.3) | 22046 (0.3)
Non-flooded bare soil or herbaceous | Open water | 894024 (3.5) | 317612 (10.4) | 392493 (5.0)
Non-flooded bare soil or herbaceous | Aquatic macrophyte (flooded herbaceous) | 590643 (2.3) | 199393 (6.5) | 343246 (4.4)
Aquatic macrophyte (flooded herbaceous) | Aquatic macrophyte (flooded herbaceous) | 366235 (1.4) | 121902 (4) | 223821 (2.9)
Non-flooded shrub | Open water | 48268 (0.2) | 2645 (0.1) | 1290 (0.0)
Non-flooded shrub | Flooded shrub | 3239111 (12.7) | 499891 (16.4) | 1700976 (21.9)
Flooded shrub | Open water | 496599 (2) | 206689 (6.8) | 318729 (4.1)
Flooded shrub | Flooded shrub | 25446 (0.1) | 3748 (0.1) | 4115 (0.1)
Non-flooded woodland | Flooded woodland | 501838 (2) | 30312 (1) | 17367 (0.2)
Flooded woodland | Flooded woodland | 2059197 (8.1) | 619445 (20.3) | 2003905 (25.7)
Non-flooded forest | Non-flooded forest | 1692959 (6.7) | 136088 (4.5) | 697255 (9.0)
Non-flooded forest | Flooded forest | 2863932 (11.3) | 94924 (3.1) | 343367 (4.4)
Flooded forest | Flooded forest | 1723735 (6.8) | 147859 (4.9) | 632938 (8.1)
Elevation $>$= 500 m, in Basin | Elevation $>$= 500 m, in Basin | 105759 (0.4) | 10394 (0.3) | 81478 (1.0)

###  3.3 Comparison with Mapbiomas Water Initiative data - year 2022

We found 24,761,019 and 24,774,540 pixels, corresponding to $\sim$ 22,285 and
$\sim$ 22,297 km2 of water surface at 30 m resolution from our model in 2022
and from the Map Biomass water surface in 2022, respectively. The F1-score
between water presence/absence in the Mapbiomas dataset and our water surface
at the same spatial resolution (30 m) was 0.686. The precision was 0.686,
indicating a high rate of false positives, which was expected as our dataset
is produced at a higher resolution (10m), allowing us to capture more rivers.
The recall was 0.686, similar to the precision value, indicating a significant
rate of false negatives. Main differences can be analyzed with the vegetation
and seasonal states of the pixels classified as false negative, Table 2.
Almost half of the false negatives fell in the classes annually flooded
woodland (25.7%) and seasonally flooded shrub (21.9%).

###  3.4 Comparison with Global Water Surface (JRC) - period 1984–2015)

We found 25,428,448 and 18,650,053 pixels ($\sim$ 22,886 and $\sim$16,785 km2)
of water surface at 30 m resolution from our model in the period 2022-2023 and
the GWS dataset, respectively. The F1-score between water presence/absence in
the GWS dataset and our water surface at the same spatial resolution (30 m)
was 0.708. The precision was 0.614 in relation to the GWS dataset, indicating
a high rate of false positives, as expected since our dataset is produced at a
higher resolution (10 m) allowing us to capture more rivers. The recall was
0.836, indicating a relatively low rate of false negatives. As for the
comparison with MapBiomas water surface, a product that is also based on
Landsat data, the two prominent classes in the false negatives were annually
flooded woodland (20.3%) and seasonally flooded shrub (16.4%).

###  3.5 Regional results and 2023 drought at 10 m spatial resolution

Figure 5: Daily water level of the Rio Negro measured at the Port of Manaus
and water surface estimated every 12 days with our Sentinel-1 based model for
the Rio Negro study area, for the period 2022-2023.

The correlation between the Rio Negro River water levels measured at the Port
of Manaus and the water area for the basin, measured from our model and
Sentinel-1 images, was high, with a value of 0.887, as shown in Fig. 5. The
minimum and maximum values of both curves always occurred with a maximum delay
of $\sim$ one month. For the highest water level around July, the water
surface is more variable, likely due to large new water surfaces from
inundation. For the lowest water levels, both curves exhibit a similar pattern
of variation, suggesting that water surfaces are more associated to the
river’s water level during the dry period of the year.

The median water surface area for the 2022-2023 period was 11,795.2 km2. The
maximum water surface was observed on July 9, 2022, reaching an area of
14,036.3 km2. The lowest water surface, 9,559.9 km2, was recorded on November
25, 2023, in the water surface mosaic composed of Sentinel-1 images taken from
the 14th to the 25th. During the 2023 drought, the water surface decreased to
68.1% of the maximum observed and 81.0% of the median observed during the
period 2022-2023.

Figure 6: Examples of our deep learning water segmentation results based on
Sentinel-1 images for the Rio Negro water surface near the junction with the
Branco River (a-c) and in near the Anavilhanas Archipel National Park (d-f),
for the maximum observed (2022-07-09, first column), the minimum observed
(2023 drought, 2023-11-25, second column) and water occurence per pixel in %
computed on the 2022-2023 period (third column). A white arrow in (f)
indicates omission errors at the border between two images in the same orbit.

Two of the major archipelagos in the Rio Negro River, near the junction with
the Rio Branco river and the Rio Jufari river, Fig. 6a-c, and near the
Anavilhanas Archipelago National Park, Fig. 6d-f, showed a remarkable pattern
of changes between the highest and lowest water surfaces estimated by our
model. During the highest measured water surface, some islands are still above
water but the river dominates the landscape, with 1055.6 km2 for the Rio
Branco junction Fig. 6a and 1711.6 km2 for the Anavilhanas Archipelago, Fig.
6d. During the lowest measured water surface on November 25, 2023, the water
surfaces were reduced by half in both archipelagos, with 549.5 km2 (52.1% of
the highest level) for the Rio Branco junction and 841.2 km2 (49.1% of the
highest level). The part of the river that disappears in 6b still has some
water during the drought, as verified in Planet NICFI images at 5 m resolution
for October and November. However, it remains undetected by the model for the
date of the lowest point and the three previous dates, and the river becomes
connected again the date after the lowest point. Also, the VV and VH bands
look more similar to sand banks than to water. This could be explained by thin
water paths that are undetected, very low water levels affecting radar
measurements (shoaling), or artifacts of the GRD border noise removal to
eliminate low-intensity noise and invalid data on scene borders in the image,
as this river is on the overlap of two Sentinel-1 orbits.

For both the junction with the Rio Branco river and the Anavilhanas
Archipelago, 40% of the water surface is permanent (occurrence of 100%), Fig.
6c and f. However, the seasonality differs, as the value of the 20th
percentile for water occurence at the river junction with the Rio Branco river
is 54%, while for Anavilhanas Archipelago, it is 85%. This indicates less
seasonality in water surface for the Anavilhanas Archipelago, and there is
more possibility of inundation at the junction with the Rio Branco river as
seen with reddish colors in 6c. An artifact indicating omission errors at the
border between two images in the same orbit is highlighted by a white arrow in
Fig. 6f. There is no overlap between images captured in the same orbit and
this artifact is likely attributed to a border effect inherent in the raw
Sentinel image.

##  4 Discussion

###  4.1 Mapping water surface of the Rio Negro River and perspectives

Here, for the first time, we demonstrate that medium spatial resolution
Sentinel-1 C-band SAR allows the accurate estimation of water surface extent
and changes of the Rio Negro River in near-real-time, with a 12-day temporal
resolution and 10 m spatial resolution. The water surface segmentation model
achieved an F1-score above 93% on the validation dataset, highlighting once
again the high capacity of deep learning to support water mapping in tropical
environments (Mayer et al., 2021; Bonafilia et al., 2020; Konapala et al.,
2021; Nemni et al., 2020). The good performance of the segmentation could be
explained by the unique water surface low backscattering value in comparison
to other land covers (Hess et al., 1995; Alsdorf et al., 2000). Our model
enables accurate near-real-time monitoring of the water surfaces and performed
particularly well during drought. For the period 2022-2023, we estimated a
median water surface area of 11,795.2 km2, a maximum of 14,036.3 km2, and a
minimum of 9,559.9 km2, observed during the 2023 drought, Fig. 5. In this
major drought, with water levels reaching the lowest observed since 1903, the
water surface decreased to 68.1% of the maximum observed and 81.0% of the
median observed during the period 2022-2023. When water levels are at their
lowest during the year, water level and water surface exhibit a similar
pattern of variation, suggesting that water surfaces are more associated with
the river’s water level during the dry period of the year. Climate extreme
events, such as major floods and droughts, have become more frequent in the
Amazon region in the last decades (Gloor et al., 2013; Marengo and Espinoza,
2016; da Silva et al., 2023), and our model could be used to monitor near-
real-time changes in water extents in this region. Furthermore, in combination
with recent data from the SWOT mission, which observes surface water storage
change and fluxes at the global scale (Biancamaria et al., 2016), it could be
used to model the real water balance of the Amazon River, in addition to its
water surface extent. Finally, in a broader context, as flooding affects large
regions in the world each year (Lin et al., 2019; Uddin et al., 2019), our
model could be tested for near-real-time mapping of flood events.

###  4.2 Limitations of the water surface model

The model made very few commission errors after masking for mountain shades.
Even though it does not represent a significant area, airport runways and
large roads ($\sim$ four lanes) were predicted as water surfaces, likely due
to low backscattering values in either polarization, resulting from specular
reflection over smooth surface, as observed in SAR images (Kumar, 2021). Most
mountain shades were predicted as water surfaces due to similar backscattering
values. We could have used SRTM at 30 m spatial resolution for terrain
correction, which is the conventional way. However, it was faster to process
the terrain correction of the 1536 images using the Global Earth Topography
And Sea Surface Elevation at 30 arc-second resolution (GETASSE30) digital
elevation model. Furthermore, most rivers were located at low altitudes and
far from mountain relief that could have caused shadows. Opting for an
independently created and manually corrected shadow mask was faster, taking
under half a day, than performing SRTM terrain correction at 30 meters in
preprocessing and addressing shadows within the model. Finally, even if there
are still a few shadows present on the map, they are not supposed to change in
area during the year and impact the seasonality of the water surface, as
acquisitions are the same throughout the year for an orbit.

The omission errors were more common than commission errors, and we identified
two main sources: clouds and seasonally flooded shrubs and trees. The
Sentinel-1 C-band SAR can penetrate most clouds, but some images (111) showed
significant artifacts, potentially from convective clouds, as already observed
in the Amazon region (Doblas et al., 2020). This leads to an increase in the
values of the VV and VH bands, making it challenging for the model to detect
water. Unlike multi-spectral satellite images, clouds are not easily detected
in the VH and VV bands, and only the large errors could be detected and
corrected when they were visible in the prediction mask (section 2.7). This is
likely the major problem of detecting water surface in near-real-time. The
second main source of omission errors was flooded vegetation, with $\geq$ 35%
of the omission errors located in the classes ’annually flooded woodland’ and
’seasonally flooded’ of the LBA-ECO LC-07 vegetation type and dual-season
flooding state dataset, Table 2. This might indicate that Sentinel-1 C-Band is
sensitive to the remaining vegetation structure above water during the flooded
period. These omission errors are mostly located in the Rio Negro interfluvial
wetlands, a region found 200 km North/North-West of the junction of Rio Negro
and Rio Branco rivers only flooded during the wet season (Hess et al., 2015;
Fassoni-Andrade et al., 2021). While this could be a problem for inundation
mapping, it has no effect on estimating the river contraction during a
drought. Studies that classify flooded forests and floodplain lakes with
emergent shrubs with radar usually use more SAR bands and polarisations, not
only C-band and VV or VH polarization, to benefit from the radar double-bounce
returns from water and vegetation surfaces (Alsdorf et al., 2000; Hess et al.,
1995; Wang et al., 1995). In 2024, the NISAR mission will be launched,
providing SAR data (L-band, 24 cm wavelength, and S-band, 10 cm wavelength)
distributed at 3 m spatial resolution and 12-day temporal resolution over land
(Rosen and Kumar, 2021). This new SAR data will likely complement the
Sentinel-1 data to improve the mapping of flooded forests and floodplain lakes
with emergent shrubs. There were few omissions in the water front of Manaus
which could have originated from the raw image, insufficient training data
near the city, image correction, or proximity to very high VH or VV values in
the city that could impact the model prediction. While it was not significant
in our study, it could have an impact in regions with heavily urbanized river
borders. Finally, a few omissions were found in a band of approximately 200 m
on the border between images of the same orbit (10 pixels of 10 m on each
side). Contrary to neighboring orbits, these images had no overlap between
them. This might be a border effect from the model, the raw data, or GRD
correction. If it comes from the model, it might be resolved by filling the
missing part of the image with the value in the neighboring image of the same
orbit. Finally, the backscatter coefficients, especially the VV polarization,
can also be influenced by wind-induced surface roughness over open water
(Pham-Duc et al., 2017) and/or low water levels causing shoaling (Bian et al.,
2020); and this might explain why water can disappear from the prediction,
Fig. 6b. However, it could also be due to a too small river width to be
detected or artifacts from GRD border noise removal as this river is on the
overlap of two Sentinel-1 orbits, and more investigation is needed.

###  4.3 Water surface segmentation model application on larger scale

While this study maps the water extent of one of the major Amazon river
tributaries, it still represents only around 20% of the water extent of the
Amazon basin (Souza et al., 2019), and only during 2 years. For the
application of this method on a larger scale, temporally or spatially, there
are three main challenges. The first is the dataset; Sentinel-1 data does not
provide a ready-to-use product for the user, and each image has to be
preprocessed by SNAP, which takes a significant amount of time ($>$ 30 seconds
per image) and limits the application on a larger scale for a single user on a
single machine. The second limitation is the absence of a standard Sentinel-1
tiling scheme, making it challenging for the user to easily find all images
corresponding to their region and date of interest without having to search on
external databases, which can be problematic for large datasets. The third
limitation is that in Google Earth Engine (Gorelick et al., 2017), the only
place where the Sentinel-1 images are preprocessed and ready to use, the use
of deep learning, while already tested and working (Mayer et al., 2021), would
likely have a prohibitive cost for large regional/country scales. To conclude,
users would benefit from an archive of ready-to-use preprocessed 12 days
Sentinel-1 mosaics with a standard tiling scheme..

## Author contributions

Conceptualization, F.H.W., S.F., R.D., M.C.M.H., A.M. and S.S.; methodology,
F.H.W., S.F., R.D., M.C.M.H., A.M. an S.S.; software, F.H.W. and S.F. and
A.M.; validation, F.H.W. and S.F.; formal analysis, F.H.W. and S.F.;
investigation, F.H.W., S.F, R.D., M.C.M.H., A.M. and S.S.; resources, S.S.;
data curation, F.H.W., S.F. and M.C.M.H. ; writing—original draft preparation,
F.H.W., S.F., R.D., M.C.M.H., A.M. and S.S.; writing—review and editing,
F.H.W., S.F., R.D., M.C.M.H., A.M. and S.S.; visualization, F.H.W.;
supervision, S.S.; project administration, S.S.; funding acquisition, S.S. All
authors have read and agreed to the published version of the manuscript.

## Funding

This research received no external funding.

## Data availability

The water surface occurrence and recurrence on the 2022-2023 period are
available at https://doi.org/10.5281/zenodo.10552959. Sentinel-1 data are
available at https://registry.opendata.aws/sentinel-1/.

## acknowledgments

The authors are grateful to the Grantham and High Tide Foundations for their
generous gift to UCLA and grants to CTrees for bringing new science and
technology to solve environmental problems. This work was partially conducted
at the Jet Propulsion Laboratory, California Institute of Technology under a
contract (80NM0018F0590) the National Aeronautics and Space Administration
(NASA).

## Conflicts of interest

The authors declare no conflict of interest. The funders had no role in the
design of the study; in the collection, analyses, or interpretation of data;
in the writing of the manuscript; or in the decision to publish the results.

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