A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry

Cahalane, Conor; Magee, Aidan; Monteys, Xavier; Casal, Gema; Hanafin, J. A.; Harris, Paul

doi:10.1016/j.rse.2019.111414

Cited by 81 publications

(40 citation statements)

References 39 publications

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“…8(a), an underestimation was found when the underwater elevation is lower (i.e., corresponding to a greater water depth). This phenomenon has also been mentioned in many previous studies [31,48,49]. Hence, the future study can be focused on quantifying the relationship between the bathymetric bias and the underwater elevation.…”

Section: Discussionsupporting

confidence: 60%

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: Discussionsupporting

confidence: 60%

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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“…For satellite remote sensing, the ICESat-2 mission provides bathymetric LiDAR (Light Detection And Ranging) data with global coverage every 91 days (Parrish et al 2019;Ma et al 2020), but the spatial resolution (across track: 280 m) is lower compared to very high-resolution satellite image sensors (e.g., WorldView-3, Landsat 8, Pleiades, QuickBird, RapidEye, etc.) used for multispectral approaches (Sagawa et al 2019;Cahalane et al 2019). In summary, although laser bathymetry provides better depth performance in general, image based techniques are frequently employed due to lower costs and better availability of image data.…”

Section: Introductionmentioning

confidence: 99%

BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

Mandlburger

Kölle

Nübel

et al. 2021

PFG

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Besides airborne laser bathymetry and multimedia photogrammetry, spectrally derived bathymetry provides a third optical method for deriving water depths. In this paper, we introduce BathyNet, an U-net like convolutional neural network, based on high-resolution, multispectral RGBC (red, green, blue, coastal blue) aerial images. The approach combines photogrammetric and radiometric methods: Preprocessing of the raw aerial images relies on strict ray tracing of the potentially oblique image rays, considering the intrinsic and extrinsic camera parameters. The actual depth estimation exploits the radiometric image content in a deep learning framework. 3D water surface and water bottom models derived from simultaneously captured laser bathymetry point clouds serve as reference and training data for both image preprocessing and actual depth estimation. As such, the approach highlights the benefits of jointly processing data from hybrid active and passive imaging sensors. The RGBC images and laser data of four groundwater supplied lakes around Augsburg, Germany, captured in April 2018 served as the basis for testing and validating the approach. With systematic depth biases less than 15 cm and a standard deviation of around 40 cm, the results satisfy the vertical accuracy limit Bc7 defined by the International Hydrographic Organization. Further improvements are anticipated by extending BathyNet to include a simultaneous semantic segmentation branch.

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“…The error term in Equation ( 8) is then calculated for each pair date. Under the assumption that the error at the prediction date is similar to that at the pair date that is close to the prediction date, the temporal distance is computed using Equation (10) and then used as a weighting value in Equation (9). That is, if the specific pair date is close to the prediction date, a higher weighting is assigned to the error term at the specific pair date.…”

Section: Estimation Of Temporal Trends At a Fine Spatial Resolutionmentioning

confidence: 99%

“…For example, geostationary satellite data with high temporal resolutions provide rich temporal information to monitor environmental changes on global and regional scales [3][4][5][6][7], but their spatial resolutions are too coarse to be applied in local analyses (such data are hereafter referred to as dense time-series with coarse spatial resolution (DTCS) data). In contrast, high spatial resolution data can be used in local analyses, such as urban area monitoring [8][9][10][11][12], but their poor temporal resolutions are unsuitable for use in the detection of 2 of 21 short-term changes (such data are hereafter referred to as sparse time-series with fine spatial resolution (STFS) data).…”

Section: Introductionmentioning

confidence: 99%

A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions

2020

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Dense time-series with coarse spatial resolution (DTCS) and sparse time-series with fine spatial resolution (STFS) data often provide complementary information. To make full use of this complementarity, this paper presents a novel spatiotemporal fusion model, the spatial time-series geostatistical deconvolution/fusion model (STGDFM), to generate synthesized dense time-series with fine spatial resolution (DTFS) data. Attributes from the DTCS and STFS data are decomposed into trend and residual components, and the spatiotemporal distributions of these components are predicted through novel schemes. The novelty of STGDFM lies in its ability to (1) consider temporal trend information using land-cover-specific temporal profiles from an entire DTCS dataset, (2) reflect local details of the STFS data using resolution matrix representation, and (3) use residual correction to account for temporary variations or abrupt changes that cannot be modeled from the trend components. The potential of STGDFM is evaluated by conducting extensive experiments that focus on different environments; spatially degraded datasets and real Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images are employed. The prediction performance of STGDFM is compared with those of a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM). Experimental results indicate that STGDFM delivers the best prediction performance with respect to prediction errors and preservation of spatial structures as it captures temporal change information on the prediction date. The superiority of STGDFM is significant when the difference between pair dates and prediction dates increases. These results indicate that STGDFM can be effectively applied to predict DTFS data that are essential for various environmental monitoring tasks.

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A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry

Cited by 81 publications

References 39 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

BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions

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