Using Deep Learning and Radar Backscatter for Mapping River

Water Surface

Diana Orlandi

, Federico A. Galatolo

, Mario C. G. A. Cimino

, Carolina Pagli

, Nicola Perilli

Joao A. Pompeu

and Itxaso Ruiz

Dept. of Information Engineering, University of Pisa, Italy

Dept. of Earth Science, University of Pisa, Italy

Dept. of Civil and Industrial Engineering, University of Pisa, Italy

BC3, Basque Centre for Climate Change, Leioa, Spain

{itxaso.ruiz, joao.pompeu}@bc3research.org

Keywords: Hydrological Remote Sensing, River Water Surface Mapping, Radar Backscatter, Convolutional Neural

Network, Attention.

Abstract: In the last decades, the effects of global warming combined with growing anthropogenic activities have caused

a mismatch in the water supply-demand, resulting in a negative impact on numerous Mediterranean rivers

regime and on the functionality of related ecosystem services. Thus, for water management and mitigation of

the potential hazards, it is fundamental to efficiently map areal extents of river water surface. Synthetic

Aperture Radar (SAR) is one of the satellite technologies applied for hydrological studies, but it has a spatial

resolution which is limited for the study of rivers. On the other side, deep learning technology exhibits a high

modelling potential with low spatial resolution data. In this paper, a method based on convolutional neural

networks is applied to the SAR backscatter coefficient for detecting river water surface. Our experimental

study focuses on the lower reach of Mijares river (Eastern Spain), covering a period from Apr 2019 to Sept

2022. Results suggest that radar backscattering has high potential in modelling water river trends, contributing

to the monitoring of the effects of climate change and impacts on related ecosystem services. To assess the

effectiveness of the method, the output has been validated with the Normalized Difference Water Index

(NDWI).

1 INTRODUCTION

In hydrology, the ability to regularly assess the river

water surface is of utmost importance for several

purposes: water accountability, water allocation,

flooding mitigation, and the reinforcement of the

ecosystem services. In the literature, satellite based

remote sensing has been used to monitor the areal

extent of surface water bodies (Frappart et al., 2021;

Botha et al., 2020). Most of the research focuses on

studying flooding events (Carreño-Conde et al.,

2019; Quiròs and Gagnon, 2022; Tran et al., 2022),

while the monitoring of the areal extent of river water

surface is a more complex task, with fewer studies

(Filippucci et al., 2022). Specifically, remote sensing

data include different technologies, ranging from

radar to multispectral. Radar data is not affected by

weather conditions (e.g. clouds) while it is by

vegetation. In particular, this is relevant for the

monitoring of the extents of water in narrow river,

given the limited number of water pixels. In contrast,

the spatial resolution provided by multispectral

satellites, especially for optical bands (such as

Sentinel-2 used in this study), is higher than

resolution of Sentinel-1 SAR data. For this reason, the

Synthetic Aperture Radar (SAR) is less used to map

the extent of river water surface respect to the optical

data.

On the other hand, there is a high potential in deep

learning technology for its capabilities of mapping

and image features identification (Ronneberger et al.,

2015), also applied to SAR data under low resolution

conditions (Jiang et al., 2022; Orlandi et al., 2022).

Specifically, the U-Net convolutional neural network

has been recently experimented for the mapping of

the extent of lake water surface. U-Net carries out the

semantic segmentation task, partitioning the image

into different regions, for corresponding classes (e.g.

216

Orlandi, D., Galatolo, F., Cimino, M., Pagli, C., Perilli, N., Pompeu, J. and Ruiz, I.

Using Deep Learning and Radar Backscatter for Mapping River Water Surface.

DOI: 10.5220/0011975000003473

In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2023), pages 216-221

ISBN: 978-989-758-649-1; ISSN: 2184-500X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

surface water, vegetation, and so on) (Ronneberger et

al., 2015). In the context of image segmentation, U-

Net is equipped with a spatial attention mechanism,

to highlight only the relevant parts of the image

during training. As a result, the computational

resources on the irrelevant part of the image are low,

with better generalisation capability.

In this paper, a U-Net architecture is used on the

radar backscatter coefficient with the purpose of

efficiently identifying and mapping areal extents of

river water surface. Satellite multispectral data are

used for validating the method. The paper is

structured as follows. Section 2 discusses the case

study, i.e., the area and the data sets achieved. Section

3 covers the overall methods in terms of Multispectral

data pre-processing, SAR data pre-processing, and U-

Net segmentation. Experimental results are discussed

in Section 4. Finally, Section 4 draws also the

conclusions.

2 CASE STUDY

2.1 Study Area

The study area (Figure 1) focuses on the last 20 km of

the Mijares river in Eastern Spain (Pompeu et al.,

2021). Along the river path there are the Arenoso (93

), Sichar (49 Mm

) and Maria Cristina (18.4

) reservoirs, which support the agricultural needs

and guarantee the water supply to the Sichar dam (0.2

) downstream (Macian-Sorribes et al., 2015). In

the lower reach of the Mijares river (Figure 1), the

alluvial plain is characterized by meandering

sequence of fine to coarse sediments, which grade to

deltaic successions in the Almazora and Buriana

plains. In the last decades, the region has experienced

hotter seasons, a concentration of the total annual

rainfall (MedECC, 2020), and an overall decrease of

precipitation in the period 1980-2012 with respect to

the period 1940-2012 in 3-7%. Overall, the area has

available water resources of 335.7 hm

/year and a

water demands of 268.23 hm

/year (Confederación

Hidrográfica del Júcar, 2019). There are records

historically significant torrential floods that could not

be correctly gauged, which are only expected to rise

given the increasingly unstable weather patterns

forecasted for the watershed (Masson-Delmotte et al.,

2021).

2.2 Datasets

Our data sets consist of images acquired by Sentinel-

2 and Sentinel-1A satellites, provided by the

European Space Agency (ESA). Sentinel-2 is a

Multispectral satellite, acquiring images in 13 bands.

For this work, we used 36 Level-1C images (Table 1)

covering a period from October 2019 to August 2022,

considering all the 13 bands but also selecting

particular bands, such as B3 and B8 (visible and near-

infrared, respectively) to build a river mask. Sentinel-

1A is a SAR satellite operating in the C-band. We

used 104 Single Look Complex (SLC) images,

acquired in Descending orbit and in Interferometric

Wide swath beam mode (Table 1), covering the time

period from April 2019 to September 2022.

Table 1: SAR and Multispectral datasets.

Satellite Sentinel-1A Sentinel-2

Product Level Single Look

Complex (SLC)

Multispectral

Instrument (MSI) -

Level-1C

Tiles - T31TBE, T30TY

Spatial

Resolution

(

)

20 × 20 10 × 10

Orbit Descending (path

8, frame 458

)

Acquisition

mode

Interferometric

Wide swath (IW)

Revisit time 12 days -

Polarization VV -

3 METHODS

3.1 Multispectral Images

Pre-Processing

To identify the areal extent of the river water surface

the NDWI is computed, i.e., a satellite-derived index

utilizing visible and near-infrared wavelengths not

affected by meteorological conditions (e.g. Tran et

al., 2022). Specifically, NDWI has been computed on

36 Sentinel-2 images, using band 3 (visible) and 8

(near-infrared) through the following formula:

𝑁𝐷𝑊𝐼 =

𝐵3 − 𝐵8

𝐵3 + 𝐵8

)

Then, based on the NDWI, a polygon of the areal

extent of the water has been created for each image.

This process has been also validated by comparing

each polygon to all the 13 bands in the image. Thus,

the final Water Surface Mask (WSM) has been

created by comparing the 36 different water polygons

generated. As a final step, in order to give to the U-

Net architecture some validation multispectral maps

to train the network, the 36 different NDWI maps

have been translated into binary images. Each pixel

Using Deep Learning and Radar Backscatter for Mapping River Water Surface

217

Figure 1: Study area (red rectangle) and river drainage network.

of a binary image is set to 0 outside the WSM, and 1

to mark the presence of water within the final WSM,

where the selection of the threshold water/non-water

set to -0.1 (Figure 2) has been validated comparing

with all the bands of the Multispectral images. The

column on the right in Figure 2 shows the flow chart.

Figure 2: SAR (left) and Multispectral (right) data pre-

processing.

3.2 SAR Images Pre-processing

A series of 104 SAR images have been processed to

obtain the backscatter coefficient from the raw radar

images. Figure 2 left shows the workflow. First, the

thermal noise removal has been applied, choosing the

VV (Vertical-Vertical) polarization. This option was

preferred, instead of VH polarization. Indeed,

differently from other works focused on floodings

events (Carreño-Conde et al., 2019; Tran et al.,

2022), in this research the general scarce presence of

water requires a stronger backscatter value provided

by VV polarization. The following step was the

radiometric calibration. To achieve a radiometrically

calibrated backscatter, σ is set to 0, from the

amplitudes stored in the SLC image. Subsequently,

the azimuth debursting is carried out to merge all

bursts using the TOPSAR-Deburst method, followed

by the Multilook step with a window size of 1×1 in

Range and Azimuth, respectively. Figure 3 shows

three different filters that have been tested to remove

the remaining speckle: Lee, IDAN and Lee Sigma.

Differently from other works (e.g. Carreño-Conde et

al., 2019), in this study, in terms of accuracy and

better visual estimation of the presence of water, the

Lee Sigma filter gives the best results, compared to

both Lee which appears noisier and to IDAN that

provides less details. Then, the image is projected

from Slant Range onto Ground Range (SRGR).

Finally, the Terrain-Correction geocoding has been

applied using the Digital Elevation Model of the

NASA Shuttle Radar Topography Mission 1 arcsec of

30 m spatial resolution.

3.3 SAR Image Semantic Segmentation

Overall, the task of detecting the river water surface

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218

Figure 3: Speckle-Filters, Lee (a), IDAN (b), Lee Sigma (c).

is tackled as a SAR image segmentation task.

Specifically, a 128×128-pixel SAR image is

considered as an input. A particular Convolutional

Neural Network, known as U-Net (Ronneberger et

al., 2015) is used for the SAR image segmentation

task. A U-Net consists of a contracting and an

expanding path, to capture context and precise

localization, respectively. A U-Net can be trained

from very few images, outperforming the other

approaches (Qin et al., 2020).

Figure 4 shows two examples of image

segmentation, after 100 training iterations (images a-

e), and after 30,000 training iterations (f-j) of the U-

Net. Specifically, image (a) and (f) are two examples

of raw input. Second, the known WSM has been used

to filter the initial raw input (b and g). Third, the

reference truth data (c) and (h) are derived from the

Multispectral images. Fourth, images (d) and (i) show

the output provided by the U-Net. Finally, images (e)

and (j) represent the binarized water/non-water

outputs: pixel values larger or equal than 0.5 are set

to 1, otherwise they are set to 0.

(a) (f)

(b) (g)

(d) (i)

(e) (j)

Figure 4: Samples after 100 training iterations (a-e), and

after 30,000 training iterations (f-j); raw input (a,f); water

surface mask (b,g); multispectral bands (c,h); raw output

(d,i); discretized output (e,j).

Using Deep Learning and Radar Backscatter for Mapping River Water Surface

219

4 RESULTS AND DISCUSSION

To carry out the proposed research, we have used an

open-source implementation of the U-Net (Wang,

2023). The generated source code has been made

publicly available (Galatolo, 2023). Table 2 shows

the U-Net hyperparameters settings, achieved via grid

search. Figure 5 shows the cross-entropy loss against

the iterations. In this image, it can be read that after

computing 6000 iterations the system achieves good

performances, about 0.013. Figure 6 shows the

outputs of the U-Net, one performed on the SAR

images and the other one obtained with Multispectral

images, both representing the area in the image

covered by the water, hereafter called as the

“Normalized River Water Extent (NRWE)”. The

results obtained using SAR and optical images are

promising. Indeed, there is a good similarity between

SAR outputs and Optical water masks (Figure 4).

Moreover, it can also be appreciated a similar

seasonal trend in the NRWE over time. Finally, the

Mean Absolute Error (MAE) between the SAR-based

NRWE and Optical- based NRWE is 0.072.

5 CONCLUSIONS

In this paper we analysed the Mijares river (Eastern

Spain) from April 2019 to September 2022. In

particular, we focused on its lower reach that can be

considered a challenging task given that this area is

often drought prone and it has little detectable water

for the most part of the year, yet it registers recurring

floods. Differently from the majority of case studies

in the literature using remote sensing to map flooding,

wide rivers and large water surfaces, here we used a

Convolutional Neural Network for detecting river

water surfaces from SAR data, using Multispectral

data as a ground truth. Specifically, a data pipeline for

satellite data pre-processing is first presented, and

then the U-Net architecture is parameterized and

trained. The adopted approach, which provided

promising early experimental results in the river

water surfaces detection through radar backscatter,

can be considered as a first step to further investigate

the same satellite data sets over a longer period, with

the final aim of monitoring the temporal variations

and the effects of the climate change in a fragile

ecosystem such as rivers. Lastly, to encourage

scientific collaborations, the source code used for this

work has been made publicly available (Galatolo,

2023).

Table 2: U-Net hyperparameters settings.

Paramete

Descri

tion Value

(

)

no. initial channels 8

dim mults no. channels multi

liers [1, 2, 4]

blocks per stage no. convolutional

erations

er sta

[2, 2, 2]

self-attentions

er stage

no. self-attention blocks per

stage

[0, 0, 1]

channels input channels 1

resnet

rou

s no. normalization

rou

s 2

consolidate

upsample fmaps

feature maps consolidation true

weight

standardize

weight standardization false

attention heads no. attention heads 2

attention dim

hea

size of attention head 16

training

window size

window size of training

sam

les

128

training batch

size

no. of samples per iteration 32

learning rate amount of weight change in

res

onse to the erro

0.001

Figure 5: Cross-entropy loss against iterations.

Figure 6: Normalized River Water Extent.

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220

ACKNOWLEDGEMENTS

This work has been partially supported by: (i) the

National Center for Sustainable Mobility

MOST/Spoke10, funded by the Italian Ministry of

University and Research, in the framework of the

National Recovery and Resilience Plan; (ii) the

PRA_2022_101 project “Decision Support Systems

for territorial networks for managing ecosystem

services”, funded by the University of Pisa; (iii) the

Ministry of University and Research (MUR) as part

of the PON 2014-2020 “Research and Innovation"

resources – Green/Innovation Action – DM MUR

1061/2022"; (iv) the Italian Ministry of University

and Research (MUR), in the framework of the

"Reasoning" project, PRIN 2020 LS Programme,

Project number 2493 04-11-2021; (v) the Italian

Ministry of Education and Research (MIUR) in the

framework of the FoReLab project (Departments of

Excellence).

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