Height Precision of SAR Altimeter and Conventional Radar Altimeter Based on Flight Experimental Data

Lingwei, Shi; Xu, Ke; Liu, Peng; Yang, Shuangbao; Wang, Lei; Yu, Xiaowei

doi:10.1109/jstars.2016.2550030

Cited by 6 publications

(2 citation statements)

References 38 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, various re-tracking algorithms based on the Brown model have been proposed for altimetry parameter estimation. The conventional altimetry parameter estimation algorithm is the least squares (LS) algorithm [13], [14]. Although LS is efficient, it is very susceptible to noise.…”

Section: Introductionmentioning

confidence: 99%

Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning

Fang,

Sun,

Huang

et al. 2023

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

View full text Add to dashboard Cite

The along-track resolution of conventional airborne synthetic aperture radar altimetry (SARAL), where the coherent processing interval (CPI) is the burst length, is not a full resolution due to beam limited. For a fully focused radar altimeter image, a novel airborne SARAL processing algorithm based on a long CPI is proposed in this paper. The long CPI is capable of achieving higher azimuthal resolution than that in the conventional altimeter with limited number of pulses. To conquer the problem of atmosphere turbulence and in accordance of motion deviation of the airborne platform, a sub-aperture phase gradient auto-focusing (PGA) framework is introduced to alleviate non-systematic phase errors (NsPE). In this framework, NsPE is estimated and compensated in along-track so that a fully-focused delayed Doppler map (DDM) can be guaranteed. Finally, the height parameter estimation is performed to the one-dimensional altimeter echoes after multi-looking processing of DDM to improve the estimation accuracy. Given that the conventional re-tracking algorithm is sensitive to noise, which may degrade the estimation accuracy, a flexible Bayesian method is designed in a hierarchical manner for the SARAL re-tracking. The SARAL raw data is utilized in the experiment. The results of image entropy and RMSE demonstrate the effectiveness and superiority of the proposed algorithm both qualitatively and quantitatively.Index Terms-Synthetic aperture radar altimeter (SARAL), coherent processing interval (CPI), phase gradient auto-focusing (PGA), hierarchical Bayesian learning.

show abstract

Section: Introductionmentioning

confidence: 99%

Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning

Fang,

Sun,

Huang

et al. 2023

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

View full text Add to dashboard Cite

show abstract

“…There are two main types of altimeters that provide satellite altimetry data: radar altimeters and laser altimeters. Radar altimetry satellites have been in operation since the 1990s and include TOPEX/Poseidon [14], Geosat, ERS-1/2 [15], Jason-1/2/3 [16], Sentinel-3A and B [17,18], SARAL/Altika [19], CryoSat-2 [20], ENVISAT [21], Sentinel6 [22] and HY-2 [23]. These satellites have facilitated the convenient extraction of water level data at large spatial scales, over long periods, and of high precision.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Analysis of Water Level Changes in Mekong River from ICESat-2 Spaceborne Laser Altimetry

Lao

Wang

Nie

et al. 2022

Water

View full text Add to dashboard Cite

The monitoring and analysis of the water level in the Mekong River is of major importance in water resources security, management, and geostrategic cooperation. This study proposed a new approach for monitoring and analysis of changes in water level of the Mekong River by using ICESat-2 spaceborne laser altimetry. River water levels were extracted from ICESat-2 inland water level data (ATL13). Then, the remote-sensed water level was quantitatively compared with the in situ water level measured by hydrological stations. Finally, the responses of water level to natural and anthropogenic factors were explored. The results showed that the ATL13 products provided river water level data with a high precision (root mean square error (RMSE) = 0.24 m, bias = −0.11 m) and a low measurement uncertainty (median of standard deviations (MSD) = 0.04). The analysis indicated that ATL13 products under different beam intensities and acquisition times can be applied to derive river water level. However, the use of nighttime measurements achieved slightly higher accuracies. The seasonal characteristics of river water level (flooding season from May to October and dry season from November to April of the next year) were because of natural factors, such as upstream flow and rainfall. A comparison of water level changes among different periods showed that hydropower development generally decreased the range of water level in the flooding season and increased water level in the dry season, thereby mitigating the current uneven spatial and temporal distribution characteristics of water resources in Mekong basin.

show abstract

Denoising Smooth Signals Using a Bayesian Approach: Application to Altimetry

Halimi

Buller

McLaughlin

et al. 2017

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

View full text Add to dashboard Cite

This paper presents a novel Bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous signals that can have a numerical or an analytical expression with respect to some parameters. The proposed Bayesian model takes into account the Gaussian properties of the noise and the smooth evolution of the successive signals. In addition, a gamma Markov random field prior is assigned to the signal energies and to the noise variances to account for their known properties. The resulting posterior distribution is maximized using a fast coordinate descent algorithm whose parameters are updated by analytical expressions. The proposed algorithm is tested on satellite altimetric data demonstrating good denoising results on both synthetic and real signals. In comparison with state-of-the-art algorithms, the proposed strategy provides a good compromise between denoising quality and necessary reduced computational cost. The proposed algorithm is also shown to improve the quality of the altimetric parameters when combined with a parameter estimation or a classification strategy.

show abstract

Height Precision of SAR Altimeter and Conventional Radar Altimeter Based on Flight Experimental Data

Cited by 6 publications

References 38 publications

Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning

Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning

Monitoring and Analysis of Water Level Changes in Mekong River from ICESat-2 Spaceborne Laser Altimetry

Denoising Smooth Signals Using a Bayesian Approach: Application to Altimetry

Contact Info

Product

Resources

About