Modelling the ground heat flux of an urban area using remote sensing data

被引:49
|
作者
Rigo, G. [1 ]
Parlow, E. [1 ]
机构
[1] Univ Basel, Inst Meteorol, Dept Environm Sci, Climatol & Remote Sensing, Basel, Switzerland
关键词
D O I
10.1007/s00704-006-0279-8
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
During the Basel Urban Boundary Layer Experiment (BUBBLE) conducted in 2002, micrometeorological in-situ data were collected for different sites using a variety of instruments. This provides a unique data set for urban climate studies. Nevertheless, the spatial distribution of energy and heat fluxes can only be taken into account with remote sensing methods or numerical models. Therefore, multiple satellite images from different platforms (NOAA-AVHRR, MODIS and LANDSAT ETM+) were acquired, processed and analysed. In addition, a high resolution digital elevation model (DEM) and a 1 m resolution digital surface model (DSM) of a large part of the city of Basel was utilized. This paper focuses on the calculation and modelling of the ground (or storage) heat flux density using remotely sensed data combined with in-situ measurements using three different approaches. First, an empirical regression function was generated to estimate the storage heat flux from NDVI values second approach used the Objective Hysteresis Model (OHM) which is often used for in-situ measurements. The last method used information of the geometric parameters of urban street canyons, computed from the high resolution digital urban surface model. Modelled and measured data are found to be in agreement within +/- 30 Wm(-2) and result in a coefficient of determination (R-2) of 0.95.
引用
收藏
页码:185 / 199
页数:15
相关论文
共 50 条
  • [1] Modelling the ground heat flux of an urban area using remote sensing data
    G. Rigo
    E. Parlow
    [J]. Theoretical and Applied Climatology, 2007, 90 : 185 - 199
  • [2] Validation of remote sensing of bare soil ground heat flux
    van der Tol, Christiaan
    [J]. REMOTE SENSING OF ENVIRONMENT, 2012, 121 : 275 - 286
  • [3] Estimation of storage heat flux in an urban area using ASTER data
    Kato, Soushi
    Yamaguchi, Yasushi
    [J]. REMOTE SENSING OF ENVIRONMENT, 2007, 110 (01) : 1 - 17
  • [4] An intercomparison of regional latent heat flux estimation using remote sensing data
    Jiang, L
    Islam, S
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2003, 24 (11) : 2221 - 2236
  • [5] Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data
    Bonsoms, Josep
    Boulet, Gilles
    [J]. REMOTE SENSING, 2022, 14 (08)
  • [6] Mapping dustfall distribution in urban areas using remote sensing and ground spectral data
    Yan, Xing
    Shi, Wenzhong
    Zhao, Wenji
    Luo, Nana
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2015, 506 : 604 - 612
  • [7] Urban growth dynamics and modelling using remote sensing data and multivariate statistical techniques
    Kumar, Manish
    Singh, R. B.
    Pravesh, Ram
    Kumar, Pankaj
    Tripathi, Dinesh Kumar
    Sahu, Netrananda
    [J]. CURRENT SCIENCE, 2018, 114 (10): : 2080 - 2091
  • [8] Ontologies in remote sensing data processing for urban area description
    Ryumkin, Alexandr I.
    Kabanov, Mikhail M.
    Kapustin, Sergey N.
    [J]. 20TH INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS: ATMOSPHERIC PHYSICS, 2014, 9292
  • [9] A Deep Learning Framework Approach for Urban Area Classification Using Remote Sensing Data
    Nijhawan, Rahul
    Jindal, Radhika
    Sharma, Himanshu
    Raman, Balasubramanian
    Das, Josodhir
    [J]. PROCEEDINGS OF 3RD INTERNATIONAL CONFERENCE ON COMPUTER VISION AND IMAGE PROCESSING, CVIP 2018, VOL 1, 2020, 1022 : 449 - 456
  • [10] Development of normalized soil area index for urban studies using remote sensing data
    Javed, Akib
    Shao, Zhenfeng
    Bai, Bin
    Yu, Zhuoyang
    Wang, Jiabing
    Ara, Iffat
    Huq, Md. Enamul
    Ali, Md. Yeamin
    Saleem, Nayyer
    Ahmad, Muhammad Nasar
    Sumari, Neema Simon
    Mardia
    [J]. GEOFIZIKA, 2023, 40 (01) : 29 - 49