A Method to Derive Bathymetry for Dynamic Water Bodies Using ICESat-2 and GSWD Data Sets

Xu, Nan; Ma, Yi; Zhou, Hui; Zhang, Wenhao; Zhang, Zhe; Wang, Xiaohua

doi:10.1109/lgrs.2020.3019396

Cited by 25 publications

(19 citation statements)

References 19 publications

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“…The ICESat-2 ATL03 products on 22/10/2018, 22/02/2019, and 12/04/2019 when ICESat-2 flew over the study area were downloaded from the NSIDC (National Snow and Ice Data Center). The ICESat-2 lidar points from three strong beams were used to generate the along-track topography profiles with an along-track interval of 0.7 m and a cross-track distance of 3.3 km [38]. In the standard ATL03 products, the lidar points including both signal and noise points were based on the WGS84 (World Geodetic System 84) ellipsoidal height.…”

Section: Study Area and Datamentioning

confidence: 99%

“…In the standard ATL03 product, the lidar points include both signal and noise points [40,41] and cannot be directly used. The signal points were detected from the raw noisy points using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, which has been verified for its good performance [38]. The DBSCAN method is a clustering analysis algorithm, i.e., a point in a cluster will be identified as signal when the point density of its neighboring points is larger than a threshold (the minimum number of points MinPts) [42].…”

Section: A Prior Bathymetric Data Extractionmentioning

confidence: 99%

“…The threshold MinPts is the key parameter in the DBSCAN algorithm, and the neighborhood is normally defined as a circle within a given radius or an ellipse. In this study, we used an adaptive MinPts in a previous study [38], which was proposed to detect the signal photons from ICESat-2 data. Due to the different solar backgrounds in the daytime and at night, the radius of the circular neighborhood was set to 1.5 m in the daytime to avoid noise photons regarding as signal, whereas the radius Ra was 2.5 m at night to search more possible bathymetric signal photons.…”

Section: A Prior Bathymetric Data Extractionmentioning

confidence: 99%

“…The errors of the pointing bias, atmospheric delay, and solid tide have been corrected in the ATL03 products. Based on previous studies, the bathymetric errors were further corrected for the detected signal points [24,38]. After the above processes, the lidar signal points provided along-track above water topography and underwater topography near the shore of the study area, which were used as the prior bathymetric information for the Sentinel-2 multispectral images.…”

Section: A Prior Bathymetric Data Extractionmentioning

confidence: 99%

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Deriving Highly Accurate Shallow Water Bathymetry From Sentinel-2 and ICESat-2 Datasets by a Multitemporal Stacking Method

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Empirical models have been widely used to retrieve shallow water bathymetry from multispectral/hyperspectral satellite imagery. In traditional studies on deriving the topography and monitoring its temporal changes, a single date satellite image without clouds corresponded to a bathymetric map and multi-date images corresponded to multiple bathymetric maps. The satellite image noise caused by various environmental conditions and satellite sensors can inevitably introduce errors or gaps in deriving bathymetric maps. Also, empirical models are limited in some remote areas due to the lack of prior bathymetric points. In this study, using only satellite data, including multi-temporal Sentinel-2 images and ICESat-2 data, a multi-temporal stacking method was developed to derive highly accurate and cloud free shallow water bathymetry with accuracy of approximately 1 m and the depth range exceeding 22 m. The proposed method was tested and validated by an airborne bathymetric lidar. To be specific, our method using multi-temporal Sentinel-2 images can achieve a mean RMSE of 1.08 m (R 2 = 0.94) by comparing with in-situ airborne lidar data around Ganquan Island, which is better than the result (R 2 = 0.92, RMSE = 1.46 m) derived from single date image based methods. Also, the gaps in a bathymetric map due to clouds or other noise can be avoidable benefitting from the stacking of multiple date satellite images. In the future, this satellite data driven method can be further extended to the globe to produce highly accurate and cloud free bathymetry around clear shallow water benefited from prior ICESat-2 bathymetric data.

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Section: Study Area and Datamentioning

confidence: 99%

Section: A Prior Bathymetric Data Extractionmentioning

confidence: 99%

Section: A Prior Bathymetric Data Extractionmentioning

confidence: 99%

Section: A Prior Bathymetric Data Extractionmentioning

confidence: 99%

See 2 more Smart Citations

Deriving Highly Accurate Shallow Water Bathymetry From Sentinel-2 and ICESat-2 Datasets by a Multitemporal Stacking Method

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In addition, benefitting from the bathymetric capacity and much denser ground/bottom points, the simulated ICESat-2 data (based on the airborne MABEL data) [51,52] or measured ICESat-2 data [40,[53][54][55] were used to obtain the along-track underwater bottom points and further generate the bathymetric maps with satellite images, e.g., the Landsat, Sentinel-2, and GSWD (Global Surface Water Dataset) products [4]. Although these used datasets and some steps of the method (e.g., detecting the signal photons) are similar to this study, the specific applications and other steps (e.g., the data fusion) are quite different.…”

Section: Difference From Classical Studies Using Satellite Lidarsmentioning

confidence: 99%

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

Zhang

et al. 2020

Remote Sensing

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With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R2 and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km3/year and −1.29 km3/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R2 and RMSE were 1.00 and 0.36 km3, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km3/year and −0.58 km3/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth’s surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period.

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Applications ofICESat‐2Photon Data in the Third Pole Environment

Dandabathula

2022

Advances in Remote Sensing Technology and the Three Poles

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A Method to Derive Bathymetry for Dynamic Water Bodies Using ICESat-2 and GSWD Data Sets

Cited by 25 publications

References 19 publications

Deriving Highly Accurate Shallow Water Bathymetry From Sentinel-2 and ICESat-2 Datasets by a Multitemporal Stacking Method

Deriving Highly Accurate Shallow Water Bathymetry From Sentinel-2 and ICESat-2 Datasets by a Multitemporal Stacking Method

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

Applications ofICESat‐2Photon Data in the Third Pole Environment

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