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Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously，which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements，and the geometric distortion introduced by the side-looking orbit. In this paper，from landslide detection and monitoring perspective，the four major challenges of satellite radar technologies are discussed：① The phase decorrelation caused by heavy vegetation can be weakened by use of synthetic aperture radar (SAR) imagery with a long radar wavelength (e. g. S-band or L-band)，a short temporal resolution，and/or a high spatial resolution (e. g. 1 m or even higher)，and/or advanced interferometric SAR (InSAR) time series，and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range splitspectrum interferometry techniques.② Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The generic atmospheric correction online service (GACOS) which is developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis.③ The geometric distortions such as shadows and layovers can be pre-analyzed using an external digital elevation model (DEM) for medium-spatial-resolution SAR data； in contrast，for high-resolution SAR data，a machine learning approach can be used to identify water bodies，shadow and layover areas without a requirement of a high-spatial-resolution DEM.④ Residual topographic phase exhibits in areas with high buildings or steep slopes，which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations (e. g. ground-based radar, LiDAR and GNSS) to develop an automated landslide detection and monitoring system. It is expected that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)