Integrated Data Processing for Multi-Satellite Missions and Recovery of Marine Gravity Field

Motao, Huang; Zhai, Gangyi; Ouyang, Yongzhong; Lu, Xia; Chuanyong, Liu; Wang, Rui

doi:10.3319/tao.2008.19.1-2.103(sa)

Cited by 16 publications

(14 citation statements)

References 9 publications

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“…Crossover adjustment is used to integrate different satellite altimetry data (including ERM and GM data) or to determine the corrections that need to be applied to measurements on the basis of the difference between two observations from the same location (Huang et al, 1999;Huang et al, 2008). The classical crossover adjustment considers the radial orbit error to be one of the main sources of error affecting altimetry data and that error can be fully modeled by using either a time-or a distance-dependent polynomial (Wagner, 1985;Rummel, 1993).…”

Section: Crossover Adjustmentmentioning

confidence: 99%

“…The classical crossover adjustment considers the radial orbit error to be one of the main sources of error affecting altimetry data and that error can be fully modeled by using either a time-or a distance-dependent polynomial (Wagner, 1985;Rummel, 1993). The classical crossover adjustment was modified and simplified by Huang et al (Huang et al, 1999;Huang et al, 2008;Yuan et al, 2020a;Yuan et al, 2020b). Condition adjustment was applied to the crossover observation equation, and the new error model was used for least squares filtering and estimation along the satellite track.…”

Section: Crossover Adjustmentmentioning

confidence: 99%

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Construction of the Mean Sea Surface Model Combined HY-2A With DTU18 MSS in the Antarctic Ocean

Sun

Zhou

Yang

et al. 2021

Front. Environ. Sci.

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A new Mean Sea Surface (MSS) model called Shandong University of Science and Technology Antarctic Mean Sea Surface model (SDUST_ANT MSS) in the Antarctic Ocean is presented and validated in this paper. The SDUST_ANT MSS updates the DTU18 MSS with 6 years of Exact Repeat Mission (ERM) and Geodetic Mission (GM) data from HY-2A. Collinear adjustment was applied to all the ERM data to obtain the along-track mean sea surface height. Oceanic variability has been removed from the GM data. Crossover adjustment was applied to both the ERM and GM data. We constructed the HY-2A_MSS using HY-2A altimetry data based on optimal interpolation method. Several types of errors (such as white noise, residual effect of oceanic variability, and long wavelength bias) have been taken into account for the determination of MSS using optimal interpolation method. The SDUST_ANT MSS was constructed by mapping HY-2A_MSS onto the DTU18 MSS. The SDUST_ANT MSS was compared with DTU18 MSS and CNES_CLS15 MSS. At wavelengths below 150 km, differences between models are consistent with seafloor topographic gradient. At wavelengths above 150 km, differences are affected by the mesoscale activities and the altimetry errors in coastal areas. The errors of the three models, as indicated by their power spectral densities (PSDs), are of similar orders of magnitude. The absolute error is slightly smaller in SDUST_ANT than in CNES_CLS15 or DTU18.

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Section: Crossover Adjustmentmentioning

confidence: 99%

Section: Crossover Adjustmentmentioning

confidence: 99%

Construction of the Mean Sea Surface Model Combined HY-2A With DTU18 MSS in the Antarctic Ocean

Sun

Zhou

Yang

et al. 2021

Front. Environ. Sci.

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“…With application of GPS in navigation and positioning of shipborne observations, the accuracy of shipborne gravity has been improved. The precision of modern shipborne gravity with high resolution is approximately 1~3 mGal [18]. Therefore, the shipborne data after the adjustment are divided into two parts: data before 1990 and data since 1990.…”

Section: Shipborne Gravitymentioning

confidence: 99%

Refining Altimeter-Derived Gravity Anomaly Model from Shipborne Gravity by Multi-Layer Perceptron Neural Network: A Case in the South China Sea

et al. 2021

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Shipborne gravity can be used to refine altimeter-derived gravity whose accuracy is low in shallow waters and areas with complex submarine topography. As altimeter-derived gravity only within a small radius around the shipborne data can be corrected by traditional methods, a new method based on multi-layer perceptron (MLP) neural network is proposed to refine the altimeter-derived gravity. Input variables of MLP include the positional information at observation points and geophysical information (from our own South China Sea gravity anomaly model (SCSGA) V1.0 and bathymetry model ETOPO1) at grid points around observation points. Output variables of MLP are the refined residual gravity anomalies at observation points. Training shipborne data are classified into four cases to train four MLP models, which are used to predict the refined gravity anomaly model SCSGA V1.1. Then all of the training shipborne data are used for training an MLP model to predict the refined gravity anomaly model SCSGA V1.2. Assessed by testing shipborne data, the accuracy of SCSGA V1.2 is 0.14 mGal higher than that of SCSGA V1.0, and similar to that of SCSGA V1.1. Compared with the original gravity anomaly model (SCSGA V1.0), the accuracy of the refined gravity anomaly model (SCSGA V1.2) by MLP is improved by 4.4% in areas where the training data are concentrated, and also improved by 2.2% in other areas. Therefore, the method of MLP can be used to refine the altimeter-derived gravity model by shipborne gravity, overcoming the problem of limited correction radius for traditional methods.

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“…The standard deviation (SD) of satellite altimetry GA is generally about 2-5 mGal [24,32]. The root mean square (RMS) of shipborne gravity is approximately 1-3 mGal after considering crossover adjustments [33,34]. Considering the measurement error, navigation conditions, and the high-frequency noise, the SD of acoustic bathymetry results is usually 300 m [23].…”

Section: Seafloor Topography Inversionmentioning

confidence: 99%

Seafloor Topography Estimation from Gravity Anomaly and Vertical Gravity Gradient Using Nonlinear Iterative Least Square Method

Fan

et al. 2020

Remote Sensing

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Currently, seafloor topography inversion based on satellite altimetry gravity data provides the principal means to predict the global seafloor topography. Researchers often use sea surface geoid height or gravity anomaly to predict sea depth in the space domain. In this paper, a comprehensive discussion on seafloor topography inversion formulas in the space domain is presented using sea surface geoid height, gravity anomaly and introduces an approach that uses vertical gravity gradient. This would be the first study to estimate seafloor topography by vertical gravity gradient in the space domain. Further, a nonlinear iterative least-square inversion process is discussed. Using the search area for the Malaysia Airlines Flight MH370 as study site, we used the DTU17 gravity anomaly model and SIO V29.1 vertical gravity gradient to generate the seafloor topography. The results of the proposed bathymetric models were analyzed and compared with the DTU18 and SIO V20.1 bathymetric models. The experimental results show that the gravity anomaly and vertical gravity gradient in the study area are strongly correlated with the seafloor topography in the 20–200 km wavelength range. The optimal initial iteration values for seafloor topography variance and correlation length are 0.6365 km2 and 10.5′, respectively. Shipborne measurements from SONAR data were used as external checkpoints to evaluate the bathymetric models. The results show that the RMS for BAT_VGG_ILS (inversion model constructed by vertical gravity gradient) is smaller than for BAT_GA_ILS (inversion model constructed by gravity anomaly) and BAT_GA_VGG_ILS (inversion model constructed by gravity anomaly and vertical gravity gradient). The relative accuracy of the DTU18 bathymetry model was 9.27%, while the relative accuracy of the proposed seafloor models was higher than 4%. Within the 200 m difference range, the proportion of checkpoints for BAT_VGG_ILS was close to 95%, about 80% for BAT_GA_ILS and BAT_GA_VGG_ILS, and less than 50% for the DTU18. The results show that the nonlinear iterative least square method in the space domain is feasible.

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Integrated Data Processing for Multi-Satellite Missions and Recovery of Marine Gravity Field

Cited by 16 publications

References 9 publications

Construction of the Mean Sea Surface Model Combined HY-2A With DTU18 MSS in the Antarctic Ocean

Construction of the Mean Sea Surface Model Combined HY-2A With DTU18 MSS in the Antarctic Ocean

Refining Altimeter-Derived Gravity Anomaly Model from Shipborne Gravity by Multi-Layer Perceptron Neural Network: A Case in the South China Sea

Seafloor Topography Estimation from Gravity Anomaly and Vertical Gravity Gradient Using Nonlinear Iterative Least Square Method

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