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Dear Colleagues,

In recent decades, SAR Interferometry (InSAR) and Global Navigation Satellite Systems (GNSSs) have made a giant stride in locating geohazards and monitoring the Earth surface. Both methods of geodetic measurements from air and space have changed the way people understand the process of Earth systems. As more SAR and GNSS data are obtained from satellite, airborne, and ground-based platforms, the effective managements of huge datasets have been an issue for scientific communities. Researchers have strived for the development of data management and started to embody artificial intelligence for solving problems inherent in those big data (e.g., noise reduction, identification of geohazards). Furthermore, sensing and processing technologies of SAR and GNSS have been greatly improved and enabled us to capture signals of our interests that could be buried among noise sources. Consequently, future advances in InSAR and GNSS will make us obtain more precise and temporally and spatially dense measurements.

This Special Issue will gather original research articles, reviews, technical notes, and letters to provide the future insights of InSAR and GNSS development in data management and sensor and processing technologies. Research studies are not limited to the single use of InSAR or GNSS, but synergetic use with InSAR, GNSS, and other sensors (i.e., LiDAR, optical images, field survey) is also welcome. We also encourage studies including new ideas of big data analysis, noise reduction, identification of geohazards, and automated processing in InSAR and GNSS.

Dr. Jin-Woo Kim
Dr. Yusuf Eshqi Molan
Guest Editors

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Keywords

Advances in InSAR and GNSS
Artificial intelligence
Machine learning
Integrated use of geodetic measurements
Developments in sensing and processing technology

Published Papers (2 papers)

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Research

18 pages, 4417 KiB

Open AccessArticle

Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

by Huineng Yan, Wujiao Dai, Hongzhi Liu, Han Gao, Wesley R. Neely and Wenbin Xu

Remote Sens. 2022, 14(14), 3293; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143293 - 08 Jul 2022

Cited by 5 | Viewed by 1672

Abstract

The fusion of global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) deformation data can leverage the advantages of GNSS high temporal resolution and InSAR high spatial resolution, and obtain more abundant deformation data for constraints on geophysical structural and mechanical parameters. Existing studies seldom consider the spatial heterogeneity of largescale deformation data, which easily leads to obvious spatial aggregation of errors in the results of fusion. Here, we propose a novel multiresolution segmentation fusion (MRSF) method that uses a multiresolution segmentation algorithm to automatically classify the spatial heterogeneity of InSAR deformation data with similar deformation characteristics. We applied the MRSF method to the fusion of GNSS and InSAR deformation data covering the central valley aquifer system (CVAS) in southern California to verify its precision and robustness. Results show that the MRSF method can accurately reflect spatiotemporal evolution characteristics of displacement data and reliably estimate deformation for the times and locations of missing data. We then tested this method for geophysical parameter estimation by constructing three different sets of data, including dense GNSS sites, sparse GNSS sites, and sparse GNSS sites fused with InSAR data using MRSF, to invert the slip distribution of the Cascadia subduction zone. Results show that the inverted slip of the fused InSAR and GNSS data is comparable to that of the dense GNSS sites. Therefore, the MRSF method can obtain deformation results with high precision and high spatiotemporal resolution and effectively compensate for the lack of data caused by sparse GNSS sites during the geophysical inversion process. Full article

(This article belongs to the Special Issue New Insights in InSAR and GNSS Measurements)

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18 pages, 8241 KiB

Open AccessArticle

Accuracy of Code GNSS Receivers under Various Conditions

by Weronika Magiera, Inese Vārna, Ingus Mitrofanovs, Gunārs Silabrieds, Artur Krawczyk, Bogdan Skorupa, Michal Apollo and Kamil Maciuk

Remote Sens. 2022, 14(11), 2615; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112615 - 30 May 2022

Cited by 10 | Viewed by 2460

Abstract

The main objective of this research work was to study the accuracy of GNSS code receivers under poor sky visibility conditions based on measurements on three different objects (point, line, and surface) and additionally to test results on point positioning with good sky visibility conditions. The measurement was based on 3 smartphones (in the same mode to check repeatability) and 2 handheld receivers (working in GPS+GLONASS modes). The methodology was based on the RTK technique, whose coordinates were assumed as a reference. Based on the results, the significant influence of measuring in the vicinity of high trees on the obtained accuracy was observed for both the precise geodetic equipment and the tested code receivers. More favorable results of point positioning were observed when using mobile phones. On the other hand, in the case of measurement in motion, the handheld receivers guaranteed higher accuracy. Moreover, the study showed that handheld receivers might achieve a better accuracy than smartphones, and that position might be determined with a greater accuracy and reliability. Furthermore, handheld receivers were characterized by a smaller number of outliers. Full article

(This article belongs to the Special Issue New Insights in InSAR and GNSS Measurements)

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