Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2010, Vol. 22 Issue (1) : 60-64     DOI: 10.6046/gtzyyg.2010.01.10
Technology and Methodology |
The Relationship Between Inter-annual Variations of Land Surface
Temperature and Climate Factors in the Yangtze River Delta
XU Yong-ming 1, 2, QIN Zhi-hao 1,3, SHEN Yan 4
1.International Institute for Earth System Sciences, Nanjing University, Nanjing 210093, China;2.School of Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China;3.Institute of Natural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;4.National Meteorological Information Center , Beijing 100081, China
Download: PDF(1142 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

With the Yangtze River delta as the study area, this paper quantitatively analyzed the temporal responses of land

surface temperature annual variability to climate factors based on MODIS LST products and meteorological observation data. First,

the Harmonic Analysis of Time Series (HANTS) algorithm was employed to distill LST harmonics (periodical fluctuation characters)

and reconstruct cloud-free LST time-series. The solar radiation of the study area was calculated and its impact on LST inter-

annual was investigated by time lag cross-correlation analysis. The high correlation coefficient (mean coefficient is 0.991 6)

indicates the sensitivity of LST seasonal variations to solar radiance, and lag days show that the peak time of LST is about 20

days later than solar radiance. The analysis between the inter-annual variations of land surface temperature and air temperature

shows that air temperature has significant correlations with LST and the air temperature delays about 5 days relative to LST

seasonal fluctuation.

Keywords Small satellite      Remote sensing      Cyber Beijing     
Issue Date: 22 March 2010
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
LIN Xiao-feng
Cite this article:   
LIN Xiao-feng. The Relationship Between Inter-annual Variations of Land Surface
Temperature and Climate Factors in the Yangtze River Delta[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(1): 60-64.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.01.10     OR     https://www.gtzyyg.com/EN/Y2010/V22/I1/60
[1] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[3] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[4] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[5] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[6] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[7] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[8] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[9] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[10] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[11] 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.
[12] YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[13] LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[14] AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[15] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech