# Challenging SWOT: Early Assessment of Level 2 High-Rate River Products in an Urbanized, Low Elevation Coastal Zone

Francisco Rodrigues Do Amaral, Tin Nguyen Trung, Thierry Pellarin, Nicolas Gratiot

<figure><img src="image_1.png" /></figure>

## To cite this version:

Francisco Rodrigues Do Amaral, Tin Nguyen Trung, Thierry Pellarin, Nicolas Gratiot. Challenging SWOT: Early Assessment of Level 2 High-Rate River Products in an Urbanized, Low Elevation Coastal Zone. IEEE Geoscience and Remote Sensing Letters, 2025, 22, pp.1 - 5. 10.1109/lgrs.2024.3501407. hal-04915647

HAL Id: hal-04915647

https://hal.science/hal-04915647v1

Submitted on 27 Jan 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Challenging SWOT: Early Assessment of Level 2 High-Rate River Products in an Urbanized, Low Elevation Coastal Zone

Francisco Rodrigues do Amaral<sup>©</sup>, Tin Nguyen Trung, Thierry Pellarin, and Nicolas Gratiot

**Abstract**— The surface water and ocean topography (SWOT) mission offers groundbreaking opportunities to observe fine-scale spatial changes in low elevation coastal zones (LECZs). This study explores the first SWOT data in one of the mission's most challenging environments: a data-scarce, tropical region with flat topography, heavy urbanization, and a river with a weak, tidally influenced slope. We focus on SWOT's water surface elevation (WSE) and water surface slope (WSS) products at the reach level, comparing the measurements to in situ data. Our analysis shows that about half of SWOT's WSE and WSS measurements fall within the desired error budgets, though WSS lacks linear correlation with in situ data. At this early stage, both SWOT's WSE and WSS require validation in such complex areas. However, as SWOT's high-resolution observations improve over time and are integrated with other data, they are expected to provide valuable insights into dynamic river and estuarine processes.

**Index Terms**— Estuary, low elevation coastal zone (LECZ), surface water and ocean topography (SWOT), tidal river, water surface elevation (WSE), water surface slope (WSS).

## I. INTRODUCTION

GEO PHYSICAL processes in low elevation coastal zones (LECZs) are primarily governed by the interaction between river discharge and coastal ocean dynamics [1]. The southern region of Vietnam, particularly Ho Chi Minh City (HCMC), is a densely populated LECZ with up to 30 000 inhabitants per square kilometer in its urban center [2]. HCMC is characterized by its extensive water system, including the tidal Saigon River and around 800 km of canals. With 90% of its urban area impermeable, the city experiences significant hydrological impacts [3], leading to frequent flooding from both river overflow and rainfall runoff [4]. This issue is exacerbated by increasing extreme rainfall events, making HCMC one of the most vulnerable coastal regions globally [5]. Despite these challenges, the Saigon basin remains largely under monitored, hindering effective water resource management and extreme event prevention efforts in HCMC.

Received 1 July 2024; revised 13 October 2024; accepted 6 November 2024. Date of publication 19 November 2024; date of current version 11 December 2024. This work was supported by the Center for Asian Research on Water (CARE). (Corresponding author: Francisco Rodrigues do Amaral.)

Francisco Rodrigues do Amaral, Thierry Pellarin, and Nicolas Gratiot are with CNRS, IRD, IGE, Université Grenoble Alpes, 38000 Grenoble, France (e-mail: francisco.amaral@univ-grenoble-alpes.fr; thierry.pellarin@univ-grenoble-alpes.fr; nicolas.gratiot@ird.fr).

Tin Nguyen Trung is with the Center for Asian Research on Water (CARE), Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City 700000, Vietnam (e-mail: tin.ngtrung19@gmail.com).

This article has supplementary downloadable material available at https://doi.org/10.1109/LGRS.2024.3501407, provided by the authors.

Digital Object Identifier 10.1109/LGRS.2024.3501407

Launched in December 2022, the surface water and ocean topography (SWOT) mission aims to transform our understanding of Earth's surface waters with high-resolution altimetric measurements of rivers, coastal regions, and oceans [6]. We hypothesize that SWOT data could enhance our knowledge of tidal river-estuary systems. This study explores the application of SWOT data for characterizing the Saigon River, a crucial waterway in HCMC's urban landscape. Despite its strategic importance, the Saigon River presents significant challenges for in situ monitoring due to its complex tidal regime and dense urbanization. SWOT data offer a promising approach to overcome these challenges, though radar interferometry faces difficulties due to flat topography, dense urbanization, and a river with a gentle water surface slope (WSS) influenced by strong coastal tidal forcing.

Here, we present an early assessment of SWOT level 2 high-rate river reach products namely, of water surface elevation (WSE) and WSS, over this region. We examine the accuracy and reliability of WSE and WSS, shedding light on their utility for future hydrological studies. Furthermore, we discuss the implications of our findings for leveraging SWOT data in data-scarce, tropical LECZs and enhancing our understanding of riverine processes in complex environmental settings.

## II. DATA AND METHODS

### A. In Situ Measurements

Three measurement locations for WSE along the Saigon River are used: Hobo 2 (H2), Hobo 3 (H3), and La Garden (LG) [black crosses in Fig. 1(c)]. The location selection was constrained by on-site factors such as inaccessibility to the riverbank and challenges in sensor installation, with two main criteria: 1) comprehensive coverage of key sections of the Saigon River within the center of Ho Chi Minh City and 2) maintaining a distance of approximately 10 km between sensors to match the spatial resolution of SWOT reach products.

Measurements were obtained using Onset HOBO U20L-01 water level loggers, measuring absolute pressure every 15 min. These devices have a typical error of 1 cm, a maximum error of 2 cm, and a resolution of 0.2 cm. To ensure accuracy, measurements were barometrically compensated using air pressure data. Since the vertical reference points of the pressure sensors were different and unknown, all signals were normalized for comparison [7]. Given that tidal fluctuations dominate the WSE signals throughout the river, normalization
<figure><img src="image_3.png" /><figcaption>Fig. 1. (a) Overview of the region. The green box highlights the study area depicted in (b). (b) Map of the study area. The area within the green box corresponds to the city center area depicted in (c). (c) City center area, displaying WSE gauges used to validate SWOT measurements: H2, H3, and LG marked as black crosses. The SWOT reaches are illustrated in color, with the center of each reach indicated by a colored dot.</figcaption></figure>

allowed for direct comparison between sensors by having all
signals fluctuate around zero. Additionally, to compare the
normalized in situ measurements with SWOT data, the former
were adjusted by 1.5 m to match SWOT's reference geoid
(EGM2008) corrected for tide effects [8].

From the WSE measurements, the WSS between two sen-
sors is computed as the difference between two measurement
locations divided by the along-river distance between them.

B. SWOT Mission and River Reach Products

SWOT provides observations for 10 km river reaches [see
colored Saigon river reaches in Fig. 1(c)] and nodes spaced
about 200 m apart. These measurement locations, predeter-
mined before launch, are cataloged in the SWOT river database
(SWORD) [9]. For most rivers, SWOT is expected to achieve a
minimum error of 10 cm in WSE and 1.7 cm/km in WSS [10].

The Saigon River is observed by two SWOT orbits: 90 and
271 [Fig. 1(a) red and blue, respectively], receiving observa-
tions on day 4 (orbit 90) and day 10 (orbit 271) within each
21-day cycle. These orbits collectively cover the entire river
[Fig. 1(b)] at least once per cycle. SWOT observations are
spatially heterogeneous due to nadir gaps in both orbits. From
the upstream Dau Tieng reservoir [Fig. 1(b), upper left corner],
the first 56 km are observed on day 10 [blue orbit 271 in
Fig. 1(b)]; the section from 56 to 94 km is observed on day 4
[red orbit 90 in Fig. 1(b)]; and the remaining section from
km 94 to the confluence with the Dongnai River is monitored
on both day 4 and day 10. In this study, we evaluate three
of the SWOT reaches that overlap with our in situ locations
[presented in orange, green and aquamarine in Fig. 1(c)].

Only data from SWOT's cycle 10 onward is available for
analysis as previous cycles did not include measurements over
this region. In this letter, we present the SWOT Level 2 River
Single-Pass Vector Reach Data Product (Version 2.0). This
dataset provides measurements of WSE and slope derived from
the high rate data stream from the KaRIn instrument. The asso-
ciated uncertainty is automatically computed by the SWOT

mission team. SWOT uncertainty estimation is a function of
the quality of the measured pixels from the KaRIn instrument.
Pixel-cloud information is aggregated into node-level data
which in turn is averaged over a reach to provide the reach
data product [11]. Pixel quality flags are provided by the
SWOT team and take the values of good, suspect, degraded,
or bad. The data are sourced from the Physical Oceanography
Distributed Active Archive Center (PODAAC) [12] and is
freely accessible to the community.

III. RESULTS

Fig. 2 shows WSE time-series for three in situ locations
across different SWOT cycles. Each plot features SWOT
measurements and estimated uncertainty for pass 90 and pass
271, except cycle 15 which only includes pass 271. SWOT
measurements are depicted as dots with uncertainty bars: blue
for those within the 10 cm error budget [10], and red for those
exceeding it. Measurements exceeding the error budget show
errors ranging from 0.12 to 7.99 m, as detailed in Table I.

To investigate the causes of these errors, we analyze the dark
water fraction (DWF) and the ratio of valid nodes (RVNs) to
total nodes within a SWOT reach. Table I presents the absolute
error, DWF, and RVN for each cycle-pass pair and location.
Of the 33 SWOT measurements, 14 fall within the 10 cm
error budget (highlighted in blue). For these measurements,
RVN ranges from 66% to 96%. However, even measurements
exceeding the error budget can show high RVN values. For
example, cycle 12, pass 271 has an error of 37 cm despite
an RVN of 100%. This suggests that a high RVN may not
always reflect measurement accuracy, as the overall error can
still exceed the budget even when all nodes are valid.

In Table I, the middle column shows the fraction of dark
water pixels within a reach. We find that few SWOT mea-
surements have a high fraction of dark water pixels. The two
largest errors (Cycle 11, pass 271, and Cycle 12, pass 271)
have no dark water pixels detected by SWOT, indicating that
<figure><img src="image_4.png" /><figcaption>Fig. 2. Timeseries of WSE at the three in situ locations H2, H3, and LG. The signals are displaced by 3.5 m. The two SWOT measurements per cycle (pass 90 and pass 271) are presented as dots with uncertainty bar. SWOT measurements within and outside of the error budget of 10 cm are presented in blue and red, respectively. Note that some inaccurate SWOT data fall outside of the area of the plot.</figcaption></figure>

<caption>TABLE I</caption>

SWOT WSE ERROR (IN M, LEFT COLUMN), DWF (IN %, MIDDLE COLUMN), AND RVN (IN %, RIGHT COLUMN) FOR EACH IN SITU LOCATION [H2, H3, AND LG IN Fig. 1(c)]. SWOT MEASUREMENTS THAT MEET THE ERROR BUDGET ARE HIGHLIGHTED IN BLUE

<table><thead><tr><th>Cycle</th><th>Pass</th><th colspan="3">H2</th><th colspan="3">H3</th><th colspan="3">LG</th></tr></thead><tbody><tr><td>10</td><td>90</td><td>0.01</td><td>0</td><td>87</td><td>0.01</td><td>1</td><td>79</td><td>0.06</td><td>3</td><td>68</td></tr><tr><td>10</td><td>271</td><td>0.01</td><td>0</td><td>72</td><td>0.08</td><td>5</td><td>79</td><td>0.17</td><td>1</td><td>87</td></tr><tr><td>11</td><td>90</td><td>0.05</td><td>11</td><td>96</td><td>0.25</td><td>18</td><td>92</td><td>0.21</td><td>37</td><td>98</td></tr><tr><td>11</td><td>271</td><td>7.95</td><td>0</td><td>37</td><td>0.16</td><td>0</td><td>79</td><td>0.04</td><td>1</td><td>89</td></tr><tr><td>12</td><td>90</td><td>0.48</td><td>63</td><td>55</td><td>0.08</td><td>47</td><td>66</td><td>0.14</td><td>14</td><td>91</td></tr><tr><td>12</td><td>271</td><td>7.99</td><td>0</td><td>38</td><td>0.96</td><td>0</td><td>77</td><td>0.37</td><td>0</td><td>100</td></tr><tr><td>13</td><td>90</td><td>0.06</td><td>6</td><td>85</td><td>0.12</td><td>19</td><td>62</td><td>0.14</td><td>12</td><td>94</td></tr><tr><td>13</td><td>271</td><td>0.07</td><td>0</td><td>76</td><td>0.01</td><td>0</td><td>91</td><td>0.33</td><td>0</td><td>87</td></tr><tr><td>14</td><td>90</td><td>0.13</td><td>1</td><td>69</td><td>0.28</td><td>4</td><td>98</td><td>0.05</td><td>1</td><td>89</td></tr><tr><td>14</td><td>271</td><td>0.49</td><td>0</td><td>73</td><td>0.21</td><td>0</td><td>79</td><td>0.01</td><td>4</td><td>98</td></tr><tr><td>15</td><td>271</td><td>0.14</td><td>4</td><td>52</td><td>0.13</td><td>7</td><td>79</td><td>0.08</td><td>14</td><td>92</td></tr></tbody></table>

measurement quality is not significantly impacted by dark water-related issues in these cases.

In Fig. 3, in situ WSE measurements are plotted against SWOT WSE measurements at three locations with a linear regression. Although only 14 out of 33 SWOT measurements

<figure><img src="image_5.png" /><figcaption>Fig. 3. Comparison between in situ and SWOT WSE.</figcaption></figure>

fall within the error budget, we see a strong linear correlation between SWOT and in situ WSE, with an $R^2$ of 0.92 and a near-zero $p$-value. The RMSE of 0.26 m, though over the budget, is still acceptable, which is promising for the region as SWOT measurements are likely to improve over time.

A similar analysis was done for WSS products (from Bayesian reconstruction algorithm, see supplemental material). Half of the 24 SWOT WSS measurements fall within the error budget, with RVN values ranging from 52% to 98%. For those outside the budget, errors range from 1.97 to 64 cm/km. There was no linear correlation between SWOT WSS and in situ, with an RMSE of 19.8 cm/km—about an order of magnitude higher than Saigon's WSS.

In order to explore possible causes of SWOT errors, the raised pixel-cloud quality flags can offer valuable insights. In this study, the most commonly occurring pixel-cloud quality flags [13] are as follows.

1) *Classification Suspect Flag:* Land/water classification information from the pixel-cloud is marked suspicious.

2) *Geolocation Suspect Flag:* Pixel-cloud geolocation information is marked suspect.

3) *Water Fraction Suspect Flag:* Water-fraction information from the pixel-cloud is suspiciously large.

4) *Geolocation Degraded Flag:* Pixel-cloud geolocation information is marked degraded. These are discussed in Section IV.

## IV. DISCUSSION

In this letter, we analyze SWOT Level 2 high-rate river products in a tidal river, focusing on WSE and WSS. Our findings indicate that both data require ground truth validation as estimated uncertainties can be misleading (see error bars in Fig. 2). This highlights the critical role of in situ data for effectively using SWOT measurements in challenging environments. We observe that uncertainty bars for pass 271 are often larger than those for pass 90. These uncertainty estimates depend on various factors, including proximity to the nadir point [14]. Since pass 271 intersects the Saigon River along the nadir track, locations like H2 are closer to the nadir gap than during pass 90 [see blue nadir track in Fig. 1(c)].
This proximity accounts for the high estimated uncertainty in
measurements that still meet the error budget.

Less than half (14 out of 33) of the analyzed SWOT WSE
data met the error budget of 10 cm. Similarly, for WSS
retrieved by SWOT, only half were within the error budget
of 1.7 cm/km. We found that the RVNs for a given reach
is not a reliable proxy for trusting a SWOT measurement;
while good measurements often have RVN values above 66%,
poor measurements can also arise from reaches with high RVN
values.

SWOT errors depend on the quality of the pixels measured
by the KaRIn instrument and used for WSE and WSS com-
putations. The classification information from the pixel-cloud
captured by SWOT distinguishes water pixels from land
pixels [13]. These data are used to aggregate high-resolution
pixel-cloud data to known river features from the prior river
database [9]. Once the pixels are assigned, ensemble measure-
ment quantities for each river feature are computed from the
assigned pixels. The pixel-cloud data are first aggregated to
the node locations, and then the node attributes are further
aggregated to generate the reach attributes [13]. The reach
attributes, which are the data used in this study, are expected
to meet the 10 cm error budget. However, as discussed
in Section III, SWOT presents errors above this budget of
which three main quality flags were raised: classification,
geolocation, and water fraction flags.

The classification flag relates to the number of pixels clas-
sified as water versus land. This flag could be attributed to the
Saigon River's role as a major commercial navigation route,
accommodating ships with drafts up to 9 m [15]. Constant
large container ship traffic, along with boat habitation [16],
may contribute to the difficulties SWOT faces in obtaining
WSE measurements. Additionally, the river is heavily pop-
ulated with water hyacinths that are visible from space [17]
and cover large areas of the water surface. Furthermore, slums,
particularly those on stilts over the river and canals in the urban
center [18], [19], could further complicate SWOT's ability to
distinguish between water and land pixels.

The classification flag is related to the DWF within a reach (Table I). Dark water can cause errors in SWOT measurements due to lower radar backscatter, which weakens the signal-to-noise ratio needed for accurate detection. This often occurs when the water surface is very smooth under low wind conditions [20], leading to specular reflection that causes radar signals to bounce away from the instrument, making the water appear dark. As a result, SWOT may misclassify or entirely miss the water, leading to measurement errors. However, only a small portion of measurements exhibit a high DWF and significant errors are not necessarily linked to high DWFs, as shown in Table I.

The geolocation flag pertains to the geolocation accuracy
of the pixels identified as water. Geolocation problems can
arise from phase unwrapping which is the process of resolv-
ing ambiguities in radar phase measurements, allowing for
the accurate retrieval of continuous WSEs from the radar
signals [21]. We found evidence of phase unwrapping errors
impacting SWOT measurements, particularly at the H2 loca-
tion for pass 271 (see supplemental material for an illustration

of this problem). The proximity of the H2 location to the nadir,
combined with the urban environment, increases the likelihood
of phase unwrapping issues.

Upon examining the 100 m raster and pixel cloud products
from SWOT, we found that in the H2 area, the river is
sometimes displaced by approximately 700 m at the bound-
ary between two pixel-cloud tiles. This displacement likely
accounts for the large errors observed, particularly in pass 271.
Additionally, this issue may be related to the over-detection of
water, which raises the water fraction flag. This flag is raised
when the fraction of water within a pixel exceeds acceptable
limits. The urban surroundings of the Saigon river are highly
impermeable, and rainwater can remain for extended periods,
potentially causing flooding [4]. Additionally, human-made
canals around it are ubiquitous. These static and ephemeral
water surfaces can reduce the quality of the measurements
and trigger this type of problem.

Another possible reason for the displacement of the river at
the H2 location is the layover effect between land and water
pixels. The example given in [6] illustrates rather well the
layover effect: suppose that a hill located a few kilometers
away from a river might have a distance to the satellite similar
to that of the river's center, and thus appear close to the river
center in a SWOT image. In contrast, the river banks, being
at a different distance from the satellite, could appear several
pixels away from the river center pixel. As a result, the top
of the hill could be closer to the river center than the river
banks. The layover phenomenon is well-known and has been
extensively studied [10].

The region under study is part of an extremely flat, LECZ,
with 65% of Ho Chi Minh City located at less than 1.5 m
above sea level [4]. Indeed, SWOT uncertainty is greatest
in flatter areas rather than in regions with complex topogra-
phy [22]. This is primarily due to the layover from topographic
features, such as skyscrapers and vegetation around and in
the river [6]. Additionally, the layover effect has a greater
impact on river slope measurements than on river height,
partly explaining the poorer results for SWOT WSS found
in this study. The Saigon River's slope fluctuates between -6
and +6 cm/km due to coastal tidal forces. As a result, the
magnitude of the SWOT WSS uncertainty is comparable to
the measured slope, increasing the potential for large errors
illustrating the need of validation against in situ in such
environments.

WSE and WSS are two hydraulic variables essential for
river discharge computation and are crucial for assessing a
river system. As presented in this letter, SWOT's capability
to observe these variables in a very flat, tropical, urbanized
coastal area is overall positive, as this environment may be the
most challenging for any satellite altimetry mission. However,
to fully use SWOT data, it is also important to consider how
sampling frequency impacts the derived hydraulic variables at
a given site [23]. In the Saigon River, SWOT's temporal fre-
quency is about three measurements per month due to its low
latitude and North-South extent, presenting a further challenge
to the usability of SWOT data for hydrological studies in the
area. Nonetheless, SWOT's unprecedented spatial resolution
can still provide valuable insights into the river's behavior.
This advantage can potentially be used to better calibrate
and validate hydraulic models once more SWOT data are
available [24].

In data-scarce regions like this one, in situ data remain cru-
cial despite SWOT’s capabilities. SWOT’s temporal resolution
constraints make it an additional tool rather than a replace-
ment, providing valuable data to complement in situ data
and hydraulic models. As the SWOT mission progresses and
more data become available, the quality of SWOT products
will improve and are thus, posed to become an increasingly
valuable resource for enhancing discharge estimates for the
Saigon River [7].

V. CONCLUSION

In this study, we analyze SWOT WSE and WSS products
in a challenging region characterized by flat topography,
a tropical urban environment, and a river with a weak slope
influenced by strong coastal tidal forces. Our analysis reveals
that only about half of the WSE and WSS measurements
meet the error budget and both require in situ validation for
reliability. Additionally, we identified phase unwrapping and
layover as significant sources of error at river locations near
the nadir gap.

This letter provides an early assessment of SWOT's poten-
tial in such coastal environments. We expect that SWOT's
high-resolution 2-D observations will improve over time, and
together with field data, other satellite data, and modeling
efforts, will yield new insights into dynamic phenomena in
this and similar coastal zones.

ACKNOWLEDGMENT

A special thanks go to Sylvain Biancamaria, Roger Fjortoft,
and Damien Desroches for their valuable insights.

REFERENCES

[1] A. J. F. Hoitink and D. A. Jay, "Tidal river dynamics: Implications for deltas," *Rev. Geophys.*, vol. 54, no. 1, pp. 240-272, Mar. 2016.

[2] T. T. N. Nguyen et al., "Nutrient dynamics and eutrophication assessment in the tropical river system of Saigon-Dongnai (southern Vietnam)," *Sci. Total Environ.*, vol. 653, pp. 370-383, Feb. 2019.

[3] G. Vachaud, N. Gratiot, and T. D. Tran Ngoc, "Ho chi Minh ville, des inondations à la submersion...," *EchoGéo*, vol. 52, pp. 1-14, Jul. 2020.

[4] F. Rodrigues do Amaral, N. Gratiot, T. Pellarin, and T. A. Tu, "Assessing typhoon-induced compound flood drivers: A case study in ho chi Minh city, Vietnam," *Natural Hazards Earth Syst. Sci.*, vol. 23, no. 11, pp. 3379-3405, Nov. 2023.

[5] L. P. Ho, T. Nguyen, N. X. Q. Chau, and K. D. Nguyen, "Integrated urban flood risk management approach in context of uncertainties: Case study ho chi Minh city," *La Houille Blanche*, vol. 6, no. 6, pp. 26-33, Dec. 2014.

[6] S. Biancamaria, D. P. Lettenmaier, and T. M. Pavelsky, "The SWOT mission and its capabilities for land hydrology," in *Remote Sensing and Water Resources*. Cham, Switzerland: Springer, May 2016, pp. 117-147.

[7] F. Rodrigues do Amaral, T. Pellarin, T. N. Trung, T. A. Tu, and N. Gratiot, "Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment," *PLOS Water*, vol. 3, no. 2, Feb. 2024, Art. no. e0000226.

[8] J. P.L. D-56413 Revision B. (2023). *SWOT Product Description Document: Level 2 KaRIn High Rate River Single Pass Vector (L2 HR RiverSP) Data Product*. Jet Propulsion Laboratory Internal Document. [Online]. Available: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/swotmissiondocs/pdd/D-56413SWOTProductDescriptionL2HRiverSP20231026RevBcite.pdf

[9] E. H. Altenau, T. M. Pavelsky, M. T. Durand, X. Yang, R. P. D. M. Frasson, and L. Bendezu, "The surface water and ocean topography (SWOT) mission river database (SWORD): A global river network for satellite data products," *Water Resour. Res.*, vol. 57, no. 7, Jul. 2021, Art. no. e2021WR030054.

[10] S. Desai, *Surface Water and Ocean Topography Mission (SWOT) Project Science Requirements Document*, document JPL D-61923, Rev. B, 2018.

[11] R. P. D. M. Frasson et al., "Automated river reach definition strategies: Applications for the surface water and ocean topography mission," *Water Resour. Res.*, vol. 53, no. 10, pp. 8164-8186, Oct. 2017.

[12] Surface Water Ocean Topography (SWOT). (2024). *Swot Level 2 River Single-Pass Vector Data Product*. [Online]. Available: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_2.0

[13] J. P.L. D-56411 Revision B. (2023). *Product Description Document, Level 2 KaRIn High Rate Water Mask Pixel Cloud Product (L2HRP IXC)*. Jet Propulsion Laboratory Internal Document. [Online]. Available: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/swotmissiondocs/pdd/D-56411SWOTProductDescriptionL2HRPIXC20231026RevBcite.pdf

[14] B. A. Williams, "Swot hydrology height and area uncertainty estimation," Jet Propulsion Lab, California Inst. Technol., Pasadena, CA, USA, Tech. Rep. JPL D-61923, 2018.

[15] K. R. Olson, "Saigon river valley: A navigation, trade, mitigation, invasion, liberation, and unification pathway," *Open J. Soil Sci.*, vol. 13, no. 2, pp. 46–82, 2023.

[16] L. Lahens et al., “Macroplastic and microplastic contamination assessment of a tropical river (Saigon River, Vietnam) transversed by a developing megacity,” *Environ. Pollut.*, vol. 236, pp. 661–671, May 2018.

[17] N. Janssens, L. Schreyers, L. Biermann, M. van der Ploeg, T.-K.-L. Bui, and T. van Emmerik, "Rivers running green: Water hyacinth invasion monitored from space," *Environ. Res. Lett.*, vol. 17, no. 4, May 2022, Art. no. 044069.

[18] S. Wust, J.-C. Bolay, and T. T. N. Du, "Metropolization and the ecological crisis: Precarious settlements in Ho Chi Minh City, Vietnam," *Environ. Urbanization*, vol. 14, no. 2, pp. 211-224, Oct. 2002.

[19] E. Givental, “The Ho Chi Minh City canals: assessing vulnerability and resilience factors,” *Yearbook Assoc. Pacific Coast Geographers*, vol. 76, no. 1, pp. 49–67, 2014.

[20] J. P.L. D-109532. (2024). *SWOT Science Data Products User Handbook*. Jet Propulsion Laboratory Internal Document. [Online]. Available: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/swotmissiondocs/D-109532SWOTUserHandbook20240502.pdf

[21] R. Fjørtoft et al., “KaRIn on SWOT: Characteristics of near-nadir Ka-band interferometric SAR imagery,” *IEEE Trans. Geosci. Remote Sens.*, vol. 52, no. 4, pp. 2172–2185, Apr. 2014.

[22] M. Durand et al., "How will radar layover impact SWOT measurements of water surface elevation and slope, and estimates of river discharge?" *Remote Sens. Environ.*, vol. 247, Sep. 2020, Art. no. 111883.

[23] J. Gehring, E. Beighley, and A. Stubbins, "Assessing the potential for the surface water and ocean topography (SWOT) mission for constituent flux estimations," *Frontiers Earth Sci.*, vol. 11, Sep. 2023, Art. no. 1201711.

[24] I. D. Lichtman, C. Banks, F. J. M. Calafat, C. Gommenginger, and P. Bell, *Validating SWOT in the Coastal Zone: A Radar Altimetry and Tide Gauge Case Study in the Bristol Channel and Severn River-Estuary system*. Vienna, Austria: EGU General Assembly 2024.