THE CHOICE AND APPLICATION OF RADARSAT DATA PRODUCTS TO THE FIELD OF GEOLOGICAL EXPLORATION

doi:10.6046/gtzyyg.1999.04.02

Download: PDF(610 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Radarsat is the most advanced satellite SAR system now. It has varied beam modes and offers the different type of remote sensing data products. Prior to the selection of suitable data products, the related influencing factors should be considered carefully. Focus on the interpretations of geological structure and lithology, this paper discusses and analyses the main influencing factors about data choice to the field of geological exploration as following: beam mode, Look anagle (beam position), acquisition time of the data, satellite's orbit and the level of data handling. Then proposing recommendations of the data products selection to the field of geological exploration were recommended, it is helpful to the geologicl survey by Radarsat SAR data Products.

Keywords Google Earth Tourism planning GIS Virtual roaming

Issue Date: 02 August 2011

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	LI Juan
	HAO Zhi-Gang
	LI Qi
	LI Qing-Chun

Cite this article:

LI Juan,HAO Zhi-Gang,LI Qi, et al. THE CHOICE AND APPLICATION OF RADARSAT DATA PRODUCTS TO THE FIELD OF GEOLOGICAL EXPLORATION[J]. REMOTE SENSING FOR LAND & RESOURCES, 1999, 11(4): 8-16.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.1999.04.02 OR https://www.gtzyyg.com/EN/Y1999/V11/I4/8

[1]	LI Dong, TANG Cheng, ZOU Tao, HOU Xiyong. Detection and assessment of the physical state of offshore artificial reefs[J]. Remote Sensing for Natural Resources, 2022, 34(1): 27-33.
[2]	ZANG Liri, YANG Shuwen, SHEN Shunfa, XUE Qing, QIN Xiaowei. A registration algorithm of images with special textures coupling a watershed with mathematical morphology[J]. Remote Sensing for Natural Resources, 2022, 34(1): 76-84.
[3]	SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[4]	FANG Mengyang, LIU Xiaohuang, KONG Fanquan, LI Mingzhe, PEI Xiaolong. A method for creating annual land cover data based on Google Earth Engine: A case study of the Yellow River basin[J]. Remote Sensing for Natural Resources, 2022, 34(1): 135-141.
[5]	ZHENG Xiucheng, ZHOU Bin, LEI Hui, HUANG Qiyu, YE Haolin. Extraction and spatio-temporal change analysis of the tidal flat in Cixi section of Hangzhou Bay based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(1): 18-26.
[6]	WU Yijie, KONG Xuesong. Simulation and development mode suggestions of the spatial pattern of “ecology-agriculture-construction” land in Jiangsu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 238-248.
[7]	YAO Jinxi, ZHANG Zhi, ZHANG Kun. An analysis of the characteristics, causes, and trends of spatio-temporal changes in vegetation in the Nuomuhong alluvial fan based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(1): 249-256.
[8]	LAI Peiyu, HUANG Jing, HAN Xujun, MA Mingguo. An analysis of impacts from water impoundment in Three Gorges Dam Project on surface water in Chongqing area base on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2021, 33(4): 227-234.
[9]	WANG Shuang, ZHANG Lei, ZHANG Junyong, WANG Yile. Characteristics of GIS applications in national fitness[J]. Remote Sensing for Natural Resources, 2021, 33(4): 265-271.
[10]	SHU Huiqin, FANG Junyong, LU Peng, GU Wanfa, WANG Xiao, ZHANG Xiaohong, LIU Xue, DING Lanpo. Research on fine recognition of site spatial archaeology based on multisource high-resolution data[J]. Remote Sensing for Land & Resources, 2021, 33(2): 162-171.
[11]	ZHAO Longxian, DAI Jingjing, ZHAO Yuanyi, JIANG Qi, LIU Tingyue, FU Minghai. A study of mine site selection of the Duolong ore concentration area in Tibet based on RS and GIS technology[J]. Remote Sensing for Land & Resources, 2021, 33(2): 182-191.
[12]	YE Wantong, CHEN Yihong, LU Yinhao, Wu Penghai. Spatio-temporal variation of land surface temperature and land cover responses in different seasons in Shengjin Lake wetland during 2000—2019 based on Google Earth Engine[J]. Remote Sensing for Land & Resources, 2021, 33(2): 228-236.
[13]	MIAO Miao, XIE Xiaoping. Spatial-temporal evolution analysis of Rizhao coastal zone during 1988—2018 based on GIS and RS[J]. Remote Sensing for Land & Resources, 2021, 33(2): 237-247.
[14]	MOU Xiaoli, LI He, HUANG Chong, LIU Qingsheng, LIU Gaohuan. Application progress of Google Earth Engine in land use and land cover remote sensing information extraction[J]. Remote Sensing for Land & Resources, 2021, 33(2): 1-10.
[15]	CHEN Hong, GUO Zhaocheng, HE Peng. Spatial and temporal change characteristics of vegetation coverage in Erhai Lake basin during 1988—2018[J]. Remote Sensing for Land & Resources, 2021, 33(2): 116-123.