APPLICATION OF THERMAL INFRARED REMOTE SENSING OF SATELLITE FOR ANALYSIS OP PEAT AND OTHER SOILS

doi:10.6046/gtzyyg.1992.01.06

Download: PDF(2170 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

The analyses of Surface temperature, Vegetation Index and Land coverage at a peatland by Landsat TMdata demonstrate that the surface state and the surface temperature is influential by Vegetation. From the analysis of Landsat TM data the following is demonstrated. 1. The growing of Sasa lowers the surface temperature. 2. The surface temperature decreases with the increase of Vegetation Index. 3. The relationship of surface temperature and Vegetation Index in summer can be presented as: Ts= (-14～-18 )VI + (30～40) therefore, biological amount in summer may be estimated from the surface tern perature.

Keywords ASTER data Land surface temperature Geothermal resource Changbaishan volcano

Issue Date: 02 August 2011

	Service

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

	XU Jun-Qiang
	BAI Chao-Jun
	LIU Jia-Yi
	YANG Tian-Chun
	XIAO Qiao-Ling

Cite this article:

XU Jun-Qiang,BAI Chao-Jun,LIU Jia-Yi, et al. APPLICATION OF THERMAL INFRARED REMOTE SENSING OF SATELLITE FOR ANALYSIS OP PEAT AND OTHER SOILS[J]. REMOTE SENSING FOR LAND & RESOURCES, 1992, 4(1): 34-39.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.1992.01.06 OR https://www.gtzyyg.com/EN/Y1992/V4/I1/34

[1]	BO Yingjie, ZENG Yelong, LI Guoqing, CAO Xingwen, YAO Qingxiu. Impacts of floating solar parks on spatial pattern of land surface temperature[J]. Remote Sensing for Natural Resources, 2022, 34(1): 158-168.
[2]	YUAN Qianying, MA Caihong, WEN Qi, LI Xuemei. Vegetation cover change and its response to water and heat conditions in the growing season in Liupanshan poverty-stricken area[J]. Remote Sensing for Land & Resources, 2021, 33(2): 220-227.
[3]	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.
[4]	FAN Jiazhi, LUO Yu, TAN Shiqi, MA Wen, ZHANG Honghao, LIU Fulai. Accuracy evaluation of the FY-3C/MWRI land surface temperature product in Hunan Province[J]. Remote Sensing for Land & Resources, 2021, 33(1): 249-255.
[5]	ZHOU Fangcheng, TANG Shihao, HAN Xiuzhen, SONG Xiaoning, CAO Guangzhen. Research on reconstructing missing remotely sensed land surface temperature data in cloudy sky[J]. Remote Sensing for Land & Resources, 2021, 33(1): 78-85.
[6]	Bing ZHAO, Kebiao MAO, Yulin CAI, Xiangjin MENG. Study of the temporal and spatial evolution law of land surface temperature in China[J]. Remote Sensing for Land & Resources, 2020, 32(2): 233-240.
[7]	Chenchao XIAO, Xiaojuan WU, Daming WANG, Yongbin CHU. Oil-gas information extraction and prospective area prediction based on hydrocarbon microseepage theory: A case study of Salamat Basin in Central Africa[J]. Remote Sensing for Land & Resources, 2019, 31(4): 120-127.
[8]	Di WU, Jian CHEN, Man SHI, Bangyong QIN, Shengyang LI. Reconstruction of land surface temperature time-series datasets of FY-2F based on Savitzky-Golay filter[J]. Remote Sensing for Land & Resources, 2019, 31(2): 59-65.
[9]	Junnan XIONG, Wei LI, Weiming CHENG, Chunkun FAN, Jin LI, Yunliang ZHAO. Spatial variability and influencing factors of LST in plateau area: Exemplified by Sangzhuzi District[J]. Remote Sensing for Land & Resources, 2019, 31(2): 164-171.
[10]	Jianyu LIU, Ling CHEN, Wei LI, Genhou WANG, Bo WANG. An improved method for extracting alteration related to the ductile shear zone type gold deposits using ASTER data[J]. Remote Sensing for Land & Resources, 2019, 31(1): 229-236.
[11]	Ying WU, Sulin JIANG, Zhenhui WANG. Effect of radio-frequency interference on the retrieval of land surface temperature from microwave radiation imager[J]. Remote Sensing for Land & Resources, 2018, 30(4): 90-96.
[12]	Min YANG, Guijun YANG, Yanjie WANG, Yongfeng ZHANG, Zhihong ZHANG, Chenhong SUN. Remote sensing analysis of temporal-spatial variations of urban heat island effect over Beijing[J]. Remote Sensing for Land & Resources, 2018, 30(3): 213-223.
[13]	Jian YU, Yunjun YAO, Shaohua ZHAO, Kun JIA, Xiaotong ZHANG, Xiang ZHAO, Liang SUN. Estimating latent heat flux over farmland from Landsat images using the improved METRIC model[J]. Remote Sensing for Land & Resources, 2018, 30(3): 83-88.
[14]	Junwei HUA, Shanyou ZHU, Guixin ZHANG. Downscaling land surface temperature based on random forest algorithm[J]. Remote Sensing for Land & Resources, 2018, 30(1): 78-86.
[15]	Min YANG, Guijun YANG, Xiaoning CHEN, Yongfeng ZHANG, Jingni YOU. Generation of land surface temperature with high spatial and temporal resolution based on FSDAF method[J]. Remote Sensing for Land & Resources, 2018, 30(1): 54-62.