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Location of the study region in Eastern Florida coastal waters in the United States (together with the depth classes derived from the GMRT profile including the transect orientation from 1 to 40 (transect 1 is the southernmost and transect 40 is the northernmost).

Location of the study region in Eastern Florida coastal waters in the United States (together with the depth classes derived from the GMRT profile including the transect orientation from 1 to 40 (transect 1 is the southernmost and transect 40 is the northernmost).

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Synthetic aperture radar (SAR) imagers are active microwave sensors that could overcome many challenges of passive optical bathymetry inversion, yet their capacity to yield accurate high-resolution bathymetric mapping is not studied sufficiently. In this study, we evaluate the feasibility of applying fast Fourier transform (FFT) to SAR data in coas...

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... Therefore, accurate information regarding a reservoir's bathymetry is required if sustainability in aquatic resources and developments that require water resources is to be achieved [4]. Several approaches have been proposed for reservoir's bathymetric pattern retrieval, and these include shipborne sonar or radar technique [5], spatial interpolation techniques based on field-measured depths data [6], topographic databased approach [7], multispectral remote-sensing-based techniques [8], synthetic aperture radar (SAR) [9], and LiDAR systems [10]. However, all these approaches are not without limitations. ...
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Information pertaining to a reservoir’s bathymetry is of utmost significance for water resource sustainability and management. The current study evaluated and compensated the reservoir’s bathymetric patterns established using radial basis function (RBF) approaches. Water depth data were acquired by conventionally rolling out a measuring tape into the water. The water depth data were split into three (3) categories, i.e., training data, validation data, and test dataset. Spatial variations in the field-measured bathymetry were determined through descriptive statistics. The thin-plate spline (TPS), multiquadric function (MQF), inverse multiquadric (IMQF), and Gaussian function (GF) were integrated into RBF to establish bathymetric patterns based on the training data. Spatial variations in bathymetry were assessed using Levene’s k-comparison of equal variance. The coefficient of determination (R2), root mean square error (RMSE) and absolute error of mean (AEM) techniques were used to evaluate the uncertainties in the interpolated bathymetric patterns. The regression of the observed estimated (ROE) was used to compensate for uncertainties in the established bathymetric patterns. The Levene’s k-comparison of equal variance technique revealed variations in the predicted bathymetry, with the standard deviation of 8.94, 6.86, 4.36, and 9.65 for RBF with thin-plate spline, multi quadric function, inverse multiquadric function, and Gaussian function, respectively. The bathymetric patterns predicted with thin-plate spline, multiquadric function, inverse multiquadric function, and Gaussian function revealed varying accuracy, with AEM values of −1.59, −2.7, 2.87, and −0.99, respectively, R2 values of 0.68, 0.62, 0.50, and 0.70, respectively, and RMSE values of 4.15, 5.41, 5.80 and 3.38, respectively. The compensated mean bathymetric values for thin-plate spline, multiquadric function, inverse multiquadric function, and Gaussian-based RBF were noted to be 18.21, 17.82, 17.35, and 18.95, respectively. The study emphasized the ongoing contribution of geospatial technology towards inland water resource monitoring.
... Therefore, accurate information regarding a reservoir's bathymetry is required if sustainability in aquatic resources and developments that require water resources is to be achieved [4]. Several approaches have been proposed for reservoir's bathymetric pattern retrieval, and these include shipborne sonar or radar technique [5], spatial interpolation techniques based on field-measured depths data [6], topographic databased approach [7], multispectral remote-sensing-based techniques [8], synthetic aperture radar (SAR) [9], and LiDAR systems [10]. However, all these approaches are not without limitations. ...
Article
Full-text available
Information pertaining to a reservoir's bathymetry is of utmost significance for water resource sustainability and management. The current study evaluated and compensated the reservoir's bathymetric patterns established using radial basis function (RBF) approaches. Water depth data were acquired by conventionally rolling out a measuring tape into the water. The water depth data were split into three (3) categories, i.e., training data, validation data, and test dataset. Spatial variations in the field-measured bathymetry were determined through descriptive statistics. The thin-plate spline (TPS), multiquadric function (MQF), inverse multiquadric (IMQF), and Gaussian function (GF) were integrated into RBF to establish bathymetric patterns based on the training data. Spatial variations in bathymetry were assessed using Levene's k-comparison of equal variance. The coefficient of determination (R 2), root mean square error (RMSE) and absolute error of mean (AEM) techniques were used to evaluate the uncertainties in the interpolated bathymetric patterns. The regression of the observed estimated (ROE) was used to compensate for uncertainties in the established bathymetric patterns. The Levene's k-comparison of equal variance technique revealed variations in the predicted bathymetry, with the standard deviation of 8.94, 6.86, 4.36, and 9.65 for RBF with thin-plate spline, multi quadric function, inverse multiquadric function, and Gaussian function, respectively. The bathymetric patterns predicted with thin-plate spline, multiquadric function, inverse multiquadric function, and Gaussian function revealed varying accuracy, with AEM values of −1.59, −2.7, 2.87, and −0.99, respectively, R 2 values of 0.68, 0.62, 0.50, and 0.70, respectively, and RMSE values of 4.15, 5.41, 5.80 and 3.38, respectively. The compensated mean bathymetric values for thin-plate spline, multiquadric function, inverse multiquadric function, and Gaussian-based RBF were noted to be 18.21, 17.82, 17.35, and 18.95, respectively. The study emphasized the ongoing contribution of geospatial technology towards inland water resource monitoring.
... This study uses harmonic modeling and quantile mapping, which have been seldom utilized in this context to date, to show that passive sensors still offer great potential in the detection of tropical and sub-tropical agriculture. Harmonic regression, i.e., discrete Fourier transformation, was used in remote sensing applications before, for example in ship targeting [45], spectroscopy [46], and bathymetry [47]. Despite its potential, it has not been widely used for specific vegetation or crop pattern detection to date. ...
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In response to economic and environmental challenges like sea-level rise, salinity intrusion, groundwater extraction, sand mining, and sinking delta phenomena, the demand for solutions to adapt to changing conditions in riverine environments has increased significantly. High-quality analyses of land use and land cover (LULC) dynamics play a critical role in addressing these challenges. This study introduces a novel high-spatial resolution satellite-based approach to identify sub-seasonal LULC dynamics in the Mekong River Delta (MRD), employing a three-year (2021–2023) Sentinel-1 and Sentinel-2 satellite data time series. The primary obstacle is discerning detailed vegetation dynamics, particularly the seasonality of rice crops, answered through quantile mapping, harmonic regression with Fourier transform, and phenological metrics as inputs to a random forest machine learning classifier. Due to the substantial data volume, Google’s cloud computing platform Earth Engine was utilized for the analysis. Furthermore, the study evaluated the relative significance of various input features. The overall accuracy of the classification is 82.6% with a kappa statistic of 0.81, determined using comprehensive reference data collected in Vietnam. While the purely pixel-based approach has limitations, it proves to be a viable method for high-spatial resolution satellite image time series classification of the MRD.
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Accurate modeling of bathymetry has a paramount importance in various marine applications, including navigation, resource exploration, and environmental studies. In this study, we present an innovative approach to enhance bathymetry modeling in the Persian Gulf and the Sea of Oman by employing data assimilation techniques with geodetic observation data. Our approach involves the integration of satellite altimetry missions, the XGM2019e gravity model, and ship-borne marine gravity data to extract the gravity anomaly. By utilizing variance component estimation (VCE), we integrate these three data sources to estimate the final gravity anomaly. Comparing ship-borne gravity anomaly control profiles with altimetry, XGM2019e and the final gravity anomaly reveals the superior accuracy of the final gravity anomaly compared to altimetry and the XGM2019e gravity model. Next, we utilize the final gravity anomaly in the Parker physical model to estimate the bathymetry. In order to modification the estimated bathymetry and achieve local calibration, we employ the 3D variational (3DVAR) data assimilation method, assimilating echo sounder observations to improve the bathymetry estimation. The assimilated bathymetry is then validated by comparing it with control points derived from echo sounder observations. The results demonstrate that data assimilation has the potential to enhance the accuracy of bathymetry estimation derived from the physical model. Following the data assimilation process in the physical model, our focus shifts to modeling the residual error between the echo sounder observations and the assimilated bathymetry. To tackle this, we propose the utilization of a Multi-Layer Perceptron (MLP) algorithm to model the residual between the assimilated model and the echo sounder observations. The results indicate that employing the MLP algorithm for residual modeling leads to improved accuracy at the control points.