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Province","award":["2021YFB3901403"],"award-info":[{"award-number":["2021YFB3901403"]}]},{"name":"a project on the identification and monitoring of potential geological hazards with remote sensing in Sichuan Province","award":["2021YFC3000405"],"award-info":[{"award-number":["2021YFC3000405"]}]},{"name":"a project on the identification and monitoring of potential geological hazards with remote sensing in Sichuan Province","award":["41941019"],"award-info":[{"award-number":["41941019"]}]},{"name":"a project on the identification and monitoring of potential geological hazards with remote sensing in Sichuan Province","award":["41801391"],"award-info":[{"award-number":["41801391"]}]},{"name":"a project on the identification and monitoring of potential geological hazards with remote sensing in Sichuan Province","award":["2020M673322"],"award-info":[{"award-number":["2020M673322"]}]},{"name":"a project on the identification and monitoring of potential geological hazards with remote sensing in Sichuan Province","award":["SKLGP2020Z012"],"award-info":[{"award-number":["SKLGP2020Z012"]}]},{"name":"a project on the identification and monitoring of potential geological hazards with remote sensing in Sichuan Province","award":["510201202076888"],"award-info":[{"award-number":["510201202076888"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Gaofen-3 is the first Chinese spaceborne C-band SAR satellite with multiple polarizations. The Gaofen-3 satellite\u2019s data has few applications for monitoring landslides at present, and its potential for use requires further investigation. Consequently, we must evaluate and analyze the landslide interference quality and displacement monitoring derived from the Gaofen-3 SAR satellite\u2019s data, particularly in high and steep, mountainous regions. Based on the nine Gaofen-3 SAR datasets gathered in 2020\u20132021, this study used DInSAR technology to track landslide displacement in Mao County, Sichuan Province, utilizing data from Gaofen-3. Our findings were compared to SENTINEL-1 and ALOS-2 data for the same region. This study revealed that due to its large spatial baseline, Gaofen-3\u2019s SAR data have a smaller interference effect and weaker coherence than the SENTINEL-1 and ALOS-2 SAR data. In addition, the displacement sensitivity of the Gaofen-3 and SENTINEL-1 data (C-band) is higher than that of the ALOS-2 data (L-band). Further, we conducted a study of observation applicability based on the geometric distortion distribution of the three forms of SAR data. Gaofen-3\u2019s SAR data are very simple to make layover and have fewer shadow areas in hilly regions, and it theoretically has more suitable observation areas (71.3%). For its practical application in mountainous areas, we introduced the passive geometric distortion analysis method. Due to its short incidence angle (i.e., 25.8\u00b0), which is less than the other two satellites\u2019 SAR data, only 39.6% of the Gaofen-3 SAR data in the study area is acceptable for suitable observation areas. This study evaluated and analyzed the ability of using Gaofen-3\u2019s data to monitor landslides in mountainous regions based on the interference effect and observation applicability analysis, thereby providing a significant reference for the future use and design of Gaofen-3\u2019s data for landslide monitoring.<\/jats:p>","DOI":"10.3390\/rs14174425","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4425","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Evaluating and Analyzing the Potential of the Gaofen-3 SAR Satellite for Landslide Monitoring"],"prefix":"10.3390","volume":"14","author":[{"given":"Ningling","family":"Wen","sequence":"first","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fanru","family":"Zeng","sequence":"additional","affiliation":[{"name":"Sichuan Water Conservancy College, Chengdu 611231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8989-3113","authenticated-orcid":false,"given":"Keren","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"},{"name":"State Key Laboratory of Geological Disaster Prevention and Geological Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, MNR, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3177-037X","authenticated-orcid":false,"given":"Saied","family":"Pirasteh","sequence":"additional","affiliation":[{"name":"GeoAI, Smarter Map and LiDAR Lab, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610097, China"},{"name":"Department of Geotechnics and Geomatics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geological Disaster Prevention and Geological Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/MGRS.2019.2954395","article-title":"Entering the era of Earth-Observation based landslide warning system","volume":"8","author":"Dai","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105181","DOI":"10.1016\/j.enggeo.2019.105181","article-title":"Surface displacements of the heifangtai terrace in northwest china measured by x and c-band insar observations","volume":"259","author":"Shi","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1007\/s10346-017-0900-1","article-title":"Measuring displacements of the thompson river valley landslides, south of ashcroft, bc, canada, using satellite insar","volume":"15","author":"Journault","year":"2018","journal-title":"Landslides"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tom\u00e1s, R., Pag\u00e1n, J.I., Navarro, J.A., Cano, M., Pastor, J.L., Riquelme, A., Cuevas-Gonz\u00e1lez, M., Crosetto, M., Barra, A., and Monserrat, O. (2019). Semi-automatic identification and pre-screening of geological\u2013geotechnical deformational processes using persistent scatterer interferometry datasets. Remote Sens., 11.","DOI":"10.3390\/rs11141675"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1080\/15481603.2022.2100054","article-title":"Interpretation and sensitivity analysis of the LOS displacements from InSAR in landslide measurement","volume":"59","author":"Dai","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1080\/19475705.2016.1238850","article-title":"Landslides investigations from geoinformatics perspective: Quality, challenges, and recommendations. Geomatics","volume":"8","author":"Pirasteh","year":"2017","journal-title":"Nat. Hazards Risk"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"An, M., Sun, Q., Hu, J., Tang, Y., and Zhu, Z. (2018). Coastline detection with gaofen-3 sar images using an improved fcm method. Sensors, 18.","DOI":"10.3390\/s18061898"},{"key":"ref_8","unstructured":"National Space Administration (2022, July 05). Launch of Gaofen-3 Satellite, Available online: http:\/\/www.sastind.gov.cn\/n152\/n6641041\/index.html."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kang, W., Xiang, Y., Wang, F., Wan, L., and You, H. (2018). Flood detection in gaofen-3 sar images via fully convolutional networks. Sensors, 18.","DOI":"10.3390\/s18092915"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, W., Yang, J., Zhao, J., Shi, H., and Yang, L. (2018). An unsupervised change detection method using time-series of polsar images from radarsat-2 and gaofen-3. Sensors, 18.","DOI":"10.3390\/s18020559"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Xia, J. (2021). Flood detection using multiple chinese satellite datasets during 2020 china summer floods. Remote Sens., 14.","DOI":"10.3390\/rs14010051"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dong, H., Xu, X., Wang, L., and Pu, F. (2018). Gaofen-3 polsar image classification via xgboost and polarimetric spatial information. Sensors, 18.","DOI":"10.3390\/s18020611"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fang, Y., Zhang, H., Mao, Q., and Li, Z. (2018). Land cover classification with gf-3 polarimetric synthetic aperture radar data by random forest classifier and fast super-pixel segmentation. Sensors, 18.","DOI":"10.3390\/s18072014"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, F. (2021). Comparing methods for segmenting supra-glacial lakes and surface features in the mount everest region of the himalayas using chinese gaofen-3 sar images. Remote Sens., 13.","DOI":"10.3390\/rs13132429"},{"key":"ref_15","first-page":"460","article-title":"Monitoring the motion of the yiga glacier using gf-3 images","volume":"45","author":"Wang","year":"2020","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y., and Wei, S. (2019). Automatic ship detection based on retinanet using multi-resolution gaofen-3 imagery. Remote Sensing, 11.","DOI":"10.3390\/rs11050531"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"An, Q., Pan, Z., and You, H. (2018). Ship detection in gaofen-3 sar images based on sea clutter distribution analysis and deep convolutional neural network. Sensors, 18.","DOI":"10.3390\/s18020334"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5707","DOI":"10.1109\/JSTARS.2021.3083287","article-title":"Evaluation of gaofen-3 c-band sar for soil moisture retrieval using different polarimetric decomposition models","volume":"14","author":"Zhang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, L., Meng, Q., Yao, S., Wang, Q., Zeng, J., Zhao, S., and Ma, J. (2018). Soil moisture retrieval from the chinese gf-3 satellite and optical data over agricultural fields. Sensors, 18.","DOI":"10.3390\/s18082675"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Han, L., Wang, C., Yu, T., Gu, X., and Liu, Q. (2020). High-precision soil moisture mapping based on multi-model coupling and background knowledge, over vegetated areas using chinese gf-3 and gf-1 satellite data. Remote Sens., 12.","DOI":"10.3390\/rs12132123"},{"key":"ref_21","first-page":"1","article-title":"Characterizing ancient channel of the yellow river from spaceborne sar: Case study of chinese gaofen-3 satellite","volume":"19","author":"Li","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ding, Y., Liu, M., Li, S., Jia, D., Zhou, L., Wu, B., and Wang, Y. (August, January 28). Mountainous landslide recognition based on gaofen-3 polarimetric sar imagery. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900478"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jia, W., Mengfei, W., and Jiang, D. (September, January 26). Detecting recent landslide activities in yigong and surrounding areas in eastern tibet of china based on gf-3 sar amplitude imagery. Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324187"},{"key":"ref_24","first-page":"883","article-title":"Investigation on earthquake-induced landslide in jiuzhaigou using full polarimetric gf-3 sar images","volume":"23","author":"Li","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_25","unstructured":"China Earthquake Network Center (2022, July 05). Earthquake. Available online: https:\/\/news.ceic.ac.cn\/."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10346-017-0915-7","article-title":"The Maoxian landslide as seen from space: Detecting precursors of failure with Sentinel-1 data","volume":"15","author":"Intrieri","year":"2018","journal-title":"Landslides"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.3390\/s17102419","article-title":"The sar payload design and performance for the gf-3 mission","volume":"17","author":"Sun","year":"2017","journal-title":"Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shang, M., Han, B., Ding, C., Sun, J., Zhang, T., Huang, L., and Meng, D. (2018). A high-resolution sar focusing experiment based on gf-3 staring data. Sensors, 18.","DOI":"10.3390\/s18040943"},{"key":"ref_29","first-page":"269","article-title":"System design and key technologies of the gf-3 satellite","volume":"46","author":"Qingjun","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Han, B., Ding, C., Zhong, L., Liu, J., Qiu, X., Hu, Y., and Lei, B. (2018). The gf-3 sar data processor. Sensors, 18.","DOI":"10.3390\/s18030835"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Xiao, F., Ding, Z., Ke, M., and Zeng, T. (2017). Sliding spotlight mode imaging with gf-3 spaceborne sar sensor. Sensors, 18.","DOI":"10.3390\/s18010043"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Potin, P., Rosich, B., Grimont, P., Miranda, N., Shurmer, I., O\u2019Connell, A., Torres, R., and Krassenburg, M. (2016, January 6\u20139). Sentinel-1 mission status. Proceedings of the EUSAR 2016: 11th European Conference on Synthetic Aperture Radar, Hamburg, Germany.","DOI":"10.1109\/IGARSS.2015.7326401"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"Gmes sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Arikawa, Y., Saruwatari, H., Hatooka, Y., and Suzuki, S. (2014, January 13\u201318). ALOS-2 launch and early orbit operation result. Proceedings of the 2014 IEEE geoscience and remote sensing symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947212"},{"key":"ref_35","first-page":"2425","article-title":"Monitoring Ground Subsidence in High-intensity Mining Area by lntegrating DInSAR and Offset-tracking Technology","volume":"22","author":"Xu","year":"2020","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mao, W., Liu, G., Wang, X., Xie, Y., He, X., Zhang, B., Xiang, W., Wu, S., Zhang, R., and Fu, Y. (2022). Using Range Split-Spectrum Interferometry to Reduce Phase Unwrapping Errors for InSAR-Derived DEM in Large Gradient Region. Remote Sens., 14.","DOI":"10.3390\/rs14112607"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1007\/s12517-017-3207-6","article-title":"Microwave d-insar technique for assessment of land subsidence in kolkata city, india","volume":"10","author":"Suganthi","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_38","first-page":"2","article-title":"InSAR and Landsat ETM+ incorporating with CGPS and SVM to determine subsidence rates and effects on Mexico City","volume":"8","author":"Poreh","year":"2019","journal-title":"Geoenvironmental Disasters"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, L., Liang, R., Shi, X., Dai, K., Cheng, J., and Cao, J. (2021). Detecting and analyzing the displacement of a small-magnitude earthquake cluster in rong county, china by the gacos based insar technology. Remote Sens., 13.","DOI":"10.3390\/rs13204137"},{"key":"ref_40","first-page":"66","article-title":"Early warning and monitoring of geohazards based on D-InSAR technology","volume":"30","author":"Li","year":"2021","journal-title":"Eng. Surv. Mapp."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.rse.2016.09.009","article-title":"Monitoring activity at the daguangbao mega-landslide (China) using sentinel-1 tops time series interferometry","volume":"186","author":"Dai","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_42","first-page":"1450","article-title":"Quantitative Analysis of Sentinel-1 lmagery Geometric Distortion and Their Suitability Along Sichuan-Tibet Railway","volume":"46","author":"Dai","year":"2021","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1706","DOI":"10.1016\/j.asr.2021.04.013","article-title":"Spatio-temporal evolutionary characteristics of gongga mountain glaciers and their response to climate","volume":"68","author":"Shi","year":"2021","journal-title":"Adv. Space Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4425\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:23:48Z","timestamp":1760142228000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,5]]},"references-count":43,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174425"],"URL":"https:\/\/doi.org\/10.3390\/rs14174425","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,5]]}}}