Assessment of ERA-Interim and ERA5 reanalysis data on atmospheric corrections for InSAR

Zhang, Zhenyi; Lou, Yidong; Zhang, Weixing; Wang, Hua; Zhou, Yaozong; Bai, Jingna

doi:10.1016/j.jag.2022.102822

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…In the case that the meteorological reanalysis data of ERA5 and the acquisition time of SAR image data are not completely synchronized, four variables, including air temperature, potential, specific humidity, and barometric pressure values, are extracted from the pressure layers closest to the acquisition time of the SAR images, assuming that the atmospheric parameters of the two adjacent moments are linearly varying [23]. The reanalysis parameters of each layer are interpolated to the grounding point using a bilinear interpolation method to calculate the ZTD and to compensate for the height difference between the grounding point and the atmosphere layers [24]. Then the hydrostatic delay and the wet delay of the main image and the auxiliary image are calculated respectively.…”

Section: Era5 Dataset Correction Methodsmentioning

confidence: 99%

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial–Temporal Variability

Yang,

Zuo,

Guo

et al. 2023

Sensors

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The tropospheric delay caused by the temporal and spatial variation of meteorological parameters is the main error source in interferometric synthetic aperture radar (InSAR) applications for geodesy. To minimize the impact of tropospheric delay errors, it is necessary to select the appropriate tropospheric delay correction method for different regions. In this study, the interferogram results of the InSAR, corrected for tropospheric delay using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 atmospheric reanalysis dataset (ERA5) methods, are presented for the study area of the junction of the Hengduan Mountains and the Yunnan–Kweichow Plateau, which is significantly influenced by the plateau monsoon climate. Four representative regions, Eryuan, Binchuan, Dali, and Yangbi, are selected for the study and analysis. The phase standard deviation (STD), phase–height correlation, and global navigation satellite system (GNSS) data were used to evaluate the effect of tropospheric delay correction by integrating topographic, seasonal, and meteorological factors. The results show that all three methods can attenuate the tropospheric delay, but the correction effect varies with spatial and temporal characteristics.

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Section: Era5 Dataset Correction Methodsmentioning

confidence: 99%

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial–Temporal Variability

Yang,

Zuo,

Guo

et al. 2023

Sensors

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“…In addition to considering natural vegetation factors, this study also takes into account the influence of meteorological parameters on atmospheric CO 2 concentration. Given the significant impact of meteorological factors on the temporal and spatial variations of CO 2 concentration, key meteorological factors affecting concentration include wind speed, temperature, and humidity [33,34]. ERA5, the fifth-generation ECMWF global climate and weather reanalysis dataset, features a spatial resolution of 0.25 • × 0.25 • and a temporal resolution of 1 h, distributed on a grid.…”

Section: Meteorological Datamentioning

confidence: 99%

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Hu,

Zhang,

Feng

et al. 2024

Remote Sensing

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Carbon satellites are an important observation tool for analyzing ground carbon emission. From the perspective of the Earth’s scale, the spatiotemporal sparse characteristics of raw data observed from carbon satellite requires the accurate interpolation of data, and based on only this work, people predict future carbon emission trends and formulate appropriate management and conservation strategies. The existing research work has not fully considered the close correlation between data and seasons, as well as the characteristics accumulated over a long time scale. In this paper, firstly, by employing extreme random forests and auxiliary data, we reconstruct a daily average CO2 dataset at a resolution of 0.25°, and achieve a validated determination coefficient of 0.92. Secondly, introducing technologies such as Time Convolutional Networks (TCN), Channel Attention Mechanism (CAM), and Long Short-Term Memory networks (LSTM), we conduct atmospheric CO2 concentration interpolation and predictions. When conducting predictive analysis for the Yangtze River Delta region, we train the model by using quarterly data from 2016 to 2020; the correlation coefficient in summer is 0.94, and in winter it is 0.91. These experimental data indicate that compared to other algorithms, this algorithm has a significantly better performance.

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“…Therefore, researchers have attempted to correct the tropospheric delay in InSAR using external auxiliary data, which estimated the zenith total delay (ZTD) in the line-of-sight (LOS) direction for each SAR data acquisition moment using external auxiliary data. The commonly used external data sources fall into several categories: meteorological reanalysis data represented by ERA5 (European Center for Medium-Range Weather Forecasts Reanalysis v5) [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]; numerical weather forecast models represented by WRF (weather research and forecasting model) [ 37 , 38 , 39 , 40 ]; precipitable water vapor (PWV) products represented using MERIS (medium-resolution imaging spectrometer), Sentinel-3 OLCI (ocean and land color instrument); and MODIS (moderate-resolution imaging spectroradiometer) [ 41 , 42 , 43 , 44 ]. These data types originate from different sensors, and exhibit spatial continuity, clearly representing the lateral heterogeneity of tropospheric water vapor content.…”

Section: Introductionmentioning

confidence: 99%

An Interferometric Synthetic Aperture Radar Tropospheric Delay Correction Method Based on a Global Navigation Satellite System and a Backpropagation Neural Network: More Suitable for Areas with Obvious Terrain Changes

Qiu,

Chen,

Yao

et al. 2023

Sensors

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Atmospheric delay correction remains a major challenge for interferometric synthetic aperture radar (InSAR) technology. In this paper, we first reviewed several commonly used methods for tropospheric delay correction in InSAR. Subsequently, considering the large volume and high temporal resolution of global navigation satellite system (GNSS) station measurement data, we proposed a method for spatial prediction of the InSAR tropospheric delay phase based on the backpropagation (BP) neural network and GNSS zenith total delay (ZTD). Using 42 Sentinel-1 interferograms over the Los Angeles area in 2021 as an example, we validated the accuracy of the BP + GNSS method in spatially predicting ZTD and compared the correction effects of BP + GNSS and five other methods on interferograms using the standard deviation (StaD) and structural similarity (SSIM). The results demonstrated that the BP + GNSS method reduced the root-mean-square error (RMSE) in spatial prediction by approximately 95.50% compared to the conventional interpolation method. After correction using the BP + GNSS method, StaD decreased in 92.86% of interferograms, with an average decrease of 52.03%, indicating significantly better correction effects than other methods. The SSIM of the BP + GNSS method was lower in mountainous and high-altitude areas with obvious terrain changes in the east and north, exhibiting excellent and stable correction performance in different seasons, particularly outperforming the GACOS method in autumn and winter. The BP + GNSS method can be employed to generate InSAR tropospheric delay maps with high temporal and spatial resolution, effectively addressing the challenge of removing InSAR tropospheric delay signals in areas with significant terrain variations.

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Assessment of ERA-Interim and ERA5 reanalysis data on atmospheric corrections for InSAR

Cited by 10 publications

References 38 publications

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial–Temporal Variability

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial–Temporal Variability

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

An Interferometric Synthetic Aperture Radar Tropospheric Delay Correction Method Based on a Global Navigation Satellite System and a Backpropagation Neural Network: More Suitable for Areas with Obvious Terrain Changes

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