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Normalized Satellite-Derived Bathymetry Model from Landsat 8 Single-Band Image with Underwater Topography Trend for Nearshore Shallow Waters.

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Title: Normalized Satellite-Derived Bathymetry Model from Landsat 8 Single-Band Image with Underwater Topography Trend for Nearshore Shallow Waters.
Authors: Xu, Jiasheng1,2 (AUTHOR) jiashengxu@glut.edu.cn, Ge, Jinfeng2 (AUTHOR), Zhou, Guoqing1,2,3 (AUTHOR), Gao, Ertao1,2,4 (AUTHOR), Zhou, Xiang1,3,5 (AUTHOR), Huang, Yuejun2,6 (AUTHOR), Li, Juanfeng2,7 (AUTHOR), Yu, Yang4,8 (AUTHOR), Yang, Zhenyin1,5 (AUTHOR), Lei, Yao2,5 (AUTHOR), Zhu, Qiang1,3,6 (AUTHOR), Bai, Yuhang1,4,7 (AUTHOR), Teng, Qinghu5,8 (AUTHOR)
Source: Remote Sensing. Feb2026, Vol. 18 Issue 4, p660. 27p.
Subjects: Bathymetry, Bathymetric maps, Coastal mapping, Water depth, Landsat satellites, Submarine topography
Geographic Terms: Saipan (Northern Mariana Islands), Northern Mariana Islands, Hawaii, Molokai (Hawaii)
Abstract: Highlights: What are the main findings? We propose a normalized satellite-derived bathymetric (NSDB) model that innovatively integrates normalized underwater topography trends from low-resolution reference data with satellite imagery to reduce interference from water quality and seabed substrates. This model significantly outperforms four classic models across diverse waters (Weizhou Island, Saipan, Molokai), reducing RMSE by 20–45% and achieving R2 values up to 0.97. What are the implications of the main findings? This model provides a simple but accurate method for generating foundational nearshore bathymetric maps, overcoming accuracy limitations in challenging coastal environments. It represents a practical breakthrough for large-scale, cost-effective coastal bathymetric mapping with direct applications in navigation safety, coastal zone management, and geomorphic studies. Satellite-derived bathymetry holds significant value for acquiring nearshore bathymetric data. However, in coastal waters, bathymetry is affected by in-water particle scattering and seafloor substrate variability, leading to spatial inconsistency between the logarithmic green band profile derived from multispectral satellite imagery and the actual water depth profile. According to the position information of interpolated points and the inverse distance square relationship with the surrounding 16 points from low-reference bathymetric data (such as the bathymetric map from GEBCO, NOAA Electronic Navigational Charts), this model adopts a third-order inverse distance square bicubic convolution interpolation method to resample a high-resolution bathymetric map with the size of the satellite image. Normalized underwater topography trend data (derived from the low-resolution reference bathymetric map) were combined with normalized green band data to compute an averaged dataset. In this way, a linear bathymetric model was constructed. We invert this model's parameters and calculate the water depth by using the average data and reference points from reference bathymetric data. Validation tests were conducted across three test areas using independent validation bathymetric data: Weizhou Island, China (Case II waters); Saipan, Northern Mariana Islands, USA (Case I waters); and Molokai Island, Hawaii, USA (Case I waters). Each test area was studied using five error analysis methods (i.e., scatterplot, error histogram, regional bathymetric error, three check lines, and seven check points). Compared to four classic bathymetric models (i.e., single-band model, log-ratio model, ratio-log model, and multi-band model), the proposed model achieved lower root mean square errors (RMSE) of 2.08 m, 1.40 m, and 2.01 m in the three test areas, representing reductions of 35%, 43%, 45%, and 20% and overall averages of 48%, 62%, 64%, and 43%, respectively. Its goodness of fit (R2) reached 0.87, 0.97, and 0.97, showing improvements of at least 5%, 5%, 9%, and 9% and overall averages of 17%, 77%, 84%, and 12%, respectively. The results demonstrate that the proposed model significantly improves bathymetry accuracy while maintaining algorithmic simplicity, providing a new model for acquiring nearshore foundational bathymetric maps. [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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  Label: Title
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  Data: Normalized Satellite-Derived Bathymetry Model from Landsat 8 Single-Band Image with Underwater Topography Trend for Nearshore Shallow Waters.
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  Data: <searchLink fieldCode="AR" term="%22Xu%2C+Jiasheng%22">Xu, Jiasheng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jiashengxu@glut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Ge%2C+Jinfeng%22">Ge, Jinfeng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Guoqing%22">Zhou, Guoqing</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Ertao%22">Gao, Ertao</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Xiang%22">Zhou, Xiang</searchLink><relatesTo>1,3,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Yuejun%22">Huang, Yuejun</searchLink><relatesTo>2,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Juanfeng%22">Li, Juanfeng</searchLink><relatesTo>2,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Yang%22">Yu, Yang</searchLink><relatesTo>4,8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Zhenyin%22">Yang, Zhenyin</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lei%2C+Yao%22">Lei, Yao</searchLink><relatesTo>2,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Qiang%22">Zhu, Qiang</searchLink><relatesTo>1,3,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bai%2C+Yuhang%22">Bai, Yuhang</searchLink><relatesTo>1,4,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Teng%2C+Qinghu%22">Teng, Qinghu</searchLink><relatesTo>5,8</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2026, Vol. 18 Issue 4, p660. 27p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Bathymetry%22">Bathymetry</searchLink><br /><searchLink fieldCode="DE" term="%22Bathymetric+maps%22">Bathymetric maps</searchLink><br /><searchLink fieldCode="DE" term="%22Coastal+mapping%22">Coastal mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Water+depth%22">Water depth</searchLink><br /><searchLink fieldCode="DE" term="%22Landsat+satellites%22">Landsat satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Submarine+topography%22">Submarine topography</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Saipan+%28Northern+Mariana+Islands%29%22">Saipan (Northern Mariana Islands)</searchLink><br /><searchLink fieldCode="DE" term="%22Northern+Mariana+Islands%22">Northern Mariana Islands</searchLink><br /><searchLink fieldCode="DE" term="%22Hawaii%22">Hawaii</searchLink><br /><searchLink fieldCode="DE" term="%22Molokai+%28Hawaii%29%22">Molokai (Hawaii)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? We propose a normalized satellite-derived bathymetric (NSDB) model that innovatively integrates normalized underwater topography trends from low-resolution reference data with satellite imagery to reduce interference from water quality and seabed substrates. This model significantly outperforms four classic models across diverse waters (Weizhou Island, Saipan, Molokai), reducing RMSE by 20–45% and achieving R2 values up to 0.97. What are the implications of the main findings? This model provides a simple but accurate method for generating foundational nearshore bathymetric maps, overcoming accuracy limitations in challenging coastal environments. It represents a practical breakthrough for large-scale, cost-effective coastal bathymetric mapping with direct applications in navigation safety, coastal zone management, and geomorphic studies. Satellite-derived bathymetry holds significant value for acquiring nearshore bathymetric data. However, in coastal waters, bathymetry is affected by in-water particle scattering and seafloor substrate variability, leading to spatial inconsistency between the logarithmic green band profile derived from multispectral satellite imagery and the actual water depth profile. According to the position information of interpolated points and the inverse distance square relationship with the surrounding 16 points from low-reference bathymetric data (such as the bathymetric map from GEBCO, NOAA Electronic Navigational Charts), this model adopts a third-order inverse distance square bicubic convolution interpolation method to resample a high-resolution bathymetric map with the size of the satellite image. Normalized underwater topography trend data (derived from the low-resolution reference bathymetric map) were combined with normalized green band data to compute an averaged dataset. In this way, a linear bathymetric model was constructed. We invert this model's parameters and calculate the water depth by using the average data and reference points from reference bathymetric data. Validation tests were conducted across three test areas using independent validation bathymetric data: Weizhou Island, China (Case II waters); Saipan, Northern Mariana Islands, USA (Case I waters); and Molokai Island, Hawaii, USA (Case I waters). Each test area was studied using five error analysis methods (i.e., scatterplot, error histogram, regional bathymetric error, three check lines, and seven check points). Compared to four classic bathymetric models (i.e., single-band model, log-ratio model, ratio-log model, and multi-band model), the proposed model achieved lower root mean square errors (RMSE) of 2.08 m, 1.40 m, and 2.01 m in the three test areas, representing reductions of 35%, 43%, 45%, and 20% and overall averages of 48%, 62%, 64%, and 43%, respectively. Its goodness of fit (R2) reached 0.87, 0.97, and 0.97, showing improvements of at least 5%, 5%, 9%, and 9% and overall averages of 17%, 77%, 84%, and 12%, respectively. The results demonstrate that the proposed model significantly improves bathymetry accuracy while maintaining algorithmic simplicity, providing a new model for acquiring nearshore foundational bathymetric maps. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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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/rs18040660
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      – Code: eng
        Text: English
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        PageCount: 27
        StartPage: 660
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      – SubjectFull: Bathymetry
        Type: general
      – SubjectFull: Bathymetric maps
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      – SubjectFull: Coastal mapping
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      – SubjectFull: Water depth
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      – SubjectFull: Landsat satellites
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      – SubjectFull: Submarine topography
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      – SubjectFull: Saipan (Northern Mariana Islands)
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              Text: Feb2026
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