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A Large-Scale Evaluation of SWOT-Derived Water Surface Elevations: Precision Drivers and Strategies to Enhance Data Availability.

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Title: A Large-Scale Evaluation of SWOT-Derived Water Surface Elevations: Precision Drivers and Strategies to Enhance Data Availability.
Authors: Lappicy, Thiago1,2 (AUTHOR) thiago.lappicy@aluno.unb.br, Beltrão, Daniel1,2 (AUTHOR), Sales, Luana Oliveira1,3 (AUTHOR), Almeida, Tati1 (AUTHOR), Pessoa, Guilherme Gomes1,2 (AUTHOR), Souza, Saulo3 (AUTHOR), Frasson, Renato Prata de Moraes2 (AUTHOR), Cicerelli, Rejane Ennes1 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1609. 24p.
Subjects: Water levels, Satellite-based remote sensing, Ocean surface topography, Hydrological research, Data integrity, Lake hydrology, Lake ecology, Random forest algorithms
Geographic Terms: Brazil
Abstract: Highlights: What are the main findings? SWOT-derived WSE shows good agreement with in situ observations across 132 Brazilian lakes: Flag = 0 reduces the 68th percentile errors to below 12 cm but retains only 22% of observations, while Flag = 1 is affected by outliers (68th percentile errors below 21 cm). A Random Forest analysis identifies cross-track distance and lake geometry as the dominant drivers of WSE precision across three SWOT products (vector and raster products). What are the implications of the main findings? The SQRTL filter combines all Flag = 0 with cross-track constrained Flag = 1 observations, more than tripling usable data relative to Flag = 0 and achieving comparable precision (68th percentile of errors below 16 cm). This framework is transferable to other regions, showing potential to expand systematic lake monitoring for (un)gauged lakes. High-quality water surface elevation (WSE) measurements are critical in hydrological applications, yet no systematic evaluation of the Surface Water and Ocean Topography (SWOT) mission exists for Brazil's diverse lake systems, where satellite observations are essential given limited in situ monitoring. We evaluated WSE from SWOT over 132 Brazilian lakes, comparing LakeSP, Raster_250m, and Raster_100m products against field measurements over a 20-month period. The 68th percentile errors were under 29 cm for the full dataset, below 12 cm for Flag = 0, and below 21 cm for Flag = 1, indicating good agreement but also the presence of outliers and the need for data screening. A Random Forest analysis identified quality flags, lake geometry, and cross-track distance as key drivers of WSE precision. Flag = 0 is overly restrictive, retaining only 22% of observations, while Flag = 1 contains anomalous data. The SWOT Quality-Range Threshold for Lakes (SQRTL) filter combines Flag = 0 with cross-track constrained Flag = 1 observations. SQRTL more than triples data availability relative to Flag = 0, maintaining comparable precision (68th percentile below 16 cm) and reducing median revisit from 88–123 days to 16–18 days for raster products and from 25 to 14 days for LakeSP. These results provide the first large-scale SWOT WSE evaluation over Brazilian lakes and a transferable filtering framework applicable wherever SWOT and field observations overlap, with potential to extend monitoring to over 100,000 water bodies in the SWOT Prior Lake Database. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Large-Scale Evaluation of SWOT-Derived Water Surface Elevations: Precision Drivers and Strategies to Enhance Data Availability.
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  Data: <searchLink fieldCode="DE" term="%22Water+levels%22">Water levels</searchLink><br /><searchLink fieldCode="DE" term="%22Satellite-based+remote+sensing%22">Satellite-based remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean+surface+topography%22">Ocean surface topography</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrological+research%22">Hydrological research</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integrity%22">Data integrity</searchLink><br /><searchLink fieldCode="DE" term="%22Lake+hydrology%22">Lake hydrology</searchLink><br /><searchLink fieldCode="DE" term="%22Lake+ecology%22">Lake ecology</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Brazil%22">Brazil</searchLink>
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  Data: Highlights: What are the main findings? SWOT-derived WSE shows good agreement with in situ observations across 132 Brazilian lakes: Flag = 0 reduces the 68th percentile errors to below 12 cm but retains only 22% of observations, while Flag = 1 is affected by outliers (68th percentile errors below 21 cm). A Random Forest analysis identifies cross-track distance and lake geometry as the dominant drivers of WSE precision across three SWOT products (vector and raster products). What are the implications of the main findings? The SQRTL filter combines all Flag = 0 with cross-track constrained Flag = 1 observations, more than tripling usable data relative to Flag = 0 and achieving comparable precision (68th percentile of errors below 16 cm). This framework is transferable to other regions, showing potential to expand systematic lake monitoring for (un)gauged lakes. High-quality water surface elevation (WSE) measurements are critical in hydrological applications, yet no systematic evaluation of the Surface Water and Ocean Topography (SWOT) mission exists for Brazil's diverse lake systems, where satellite observations are essential given limited in situ monitoring. We evaluated WSE from SWOT over 132 Brazilian lakes, comparing LakeSP, Raster_250m, and Raster_100m products against field measurements over a 20-month period. The 68th percentile errors were under 29 cm for the full dataset, below 12 cm for Flag = 0, and below 21 cm for Flag = 1, indicating good agreement but also the presence of outliers and the need for data screening. A Random Forest analysis identified quality flags, lake geometry, and cross-track distance as key drivers of WSE precision. Flag = 0 is overly restrictive, retaining only 22% of observations, while Flag = 1 contains anomalous data. The SWOT Quality-Range Threshold for Lakes (SQRTL) filter combines Flag = 0 with cross-track constrained Flag = 1 observations. SQRTL more than triples data availability relative to Flag = 0, maintaining comparable precision (68th percentile below 16 cm) and reducing median revisit from 88–123 days to 16–18 days for raster products and from 25 to 14 days for LakeSP. These results provide the first large-scale SWOT WSE evaluation over Brazilian lakes and a transferable filtering framework applicable wherever SWOT and field observations overlap, with potential to extend monitoring to over 100,000 water bodies in the SWOT Prior Lake Database. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18101609
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 1609
    Subjects:
      – SubjectFull: Water levels
        Type: general
      – SubjectFull: Satellite-based remote sensing
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      – SubjectFull: Ocean surface topography
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      – SubjectFull: Hydrological research
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      – SubjectFull: Lake ecology
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      – SubjectFull: Random forest algorithms
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      – SubjectFull: Brazil
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              Text: May2026
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              Y: 2026
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