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Detecting the precursors of giant landslides constitutes the premise of landslide monitoring and early warning, and remote sensing is a powerful means to achieve this goal. In this work, we aim to summarize the basic types and evolutionary principles of giant landslide precursors, describe the remote sensing methods capable of identifying those precursors, and present typical cases of related sliding. Based on a review of the literature and an analysis of remote sensing imagery, the three main types of remote sensing techniques for capturing the geomorphological, geotechnical, and geoenvironmental precursors of giant landslides are optical, synthetic aperture radar (SAR), and thermal infrared methods, respectively. Time-series optical remote sensing data from medium-resolution satellites can be used to obtain abundant information on geomorphological changes, such as the extension of cracks and erosion ditches, and band algebraic analysis, image enhancement, and segmentation techniques are valuable for focusing on the locations of geomorphological landslide precursors. SAR sensors have the ability to monitor the slight slope deformation caused by unfavorable geological structures and can provide precursor information on imminent failure several days before a landslide; furthermore, persistent scatterer interferometric SAR has significant advantages in large-scale surface displacement monitoring. Thermal infrared imagery can identify landslide precursors by monitoring geoenvironmental information, especially in permafrost regions where glaciers are widely distributed; the reason may be that freeze\u2013thaw cycles and snowmelt caused by increased temperatures affect the stability of the surface. Optical, SAR, and thermal remote sensing all exhibit unique advantages and play an essential role in the identification of giant landslide precursors. The combined application of these three remote sensing technologies to obtain the synthetic geomorphological, geotechnical, and geoenvironmental precursors of giant landslides would greatly promote the development of landslide early warning systems.<\/jats:p>","DOI":"10.3390\/rs14174399","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Remote Sensing Precursors Analysis for Giant Landslides"],"prefix":"10.3390","volume":"14","author":[{"given":"Hengxing","family":"Lan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6398-3161","authenticated-orcid":false,"given":"Langping","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Quanwen","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Naiman","family":"Tian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jianbing","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710064, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,4]]},"reference":[{"key":"ref_1","unstructured":"Varnes, D.J., and Cruden, D. 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