InSAR and GNSS data fusion for improved urban heat island estimation using local climate zone classification

被引:1
|
作者
Tasan, Melika [1 ]
Voosoghi, Behzad [2 ]
Haji-Aghajany, Saeid [3 ]
Khalili, Mohammad Amin [4 ]
Di Martire, Diego [4 ]
机构
[1] Wroclaw Univ Environm & Life Sci, Fac Environm Engn & Geodesy, Dept Civil Engn, PL-50363 Wroclaw, Poland
[2] KN Toosi Univ Technol, Fac Geodesy & Geomatics Engn, Tehran 154331996, Iran
[3] Wroclaw Univ Environm & Life Sci, Inst Geodesy & Geoinformat, Norwida 25, PL-50375 Wroclaw, Poland
[4] Federico II Univ Naples, Dept Earth Environm & Resource Sci, Monte St Angelo Campus, I-80126 Naples, Italy
关键词
UHI; GNSS; InSAR; LCZ; Temperature; MODEL;
D O I
10.1016/j.jag.2024.103906
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The phenomenon of the Urban Heat Island (UHI) is a common feature in city climates, impacting habitat quality and public health. The UHI refers to the temperature difference between metropolitan and countryside areas. This article introduces a new methodology for determining UHI using a high-resolution temperature map created by fusing Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite Systems (GNSS) measurements. The validity of this method has been assessed by comparing the UHI results with the outputs of the Weather Research and Forecasting (WRF) model. Using the new approach, temperature determination focuses on the moist segment of the tropospheric delay. The wet tropospheric delay is divided into turbulent and non-turbulent components, with the first segment calculated using InSAR and the second using GNSS observations. After generating high-resolution temperature maps to compute the temperature difference between urban and non-urban regions and defining the UHI index, the research area was categorized into various classes based on land cover using the Local Climate Zone Classification (LCZ) approach. Finally, after calculating the UHI in different regions, the results were evaluated against the WRF model outputs. According to the statistical evaluations, the Root Mean Square Error (RMSE) of the UHI index obtained from the novel method and the WRF model outputs ranges from 0.7 to 0.4 Kelvin. The determination coefficient (R2) also varies from 0.85 to 0.9 in different months. These statistical markers illustrate the significant effectiveness of the suggested technique in computing the UHI phenomenon.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] Regional thermal climate prediction and mitigation strategy of local urban heat island
    Song, Xiaocheng
    Liu, Jing
    Lin, Yaoyu
    Liu, Lin
    Wang, Dan
    Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology, 2015, 47 (02): : 25 - 30
  • [42] Local Climate Zone classification for climate-based urban planning using Landsat 8 Imagery (A case study in Yogyakarta Urban Area)
    Pradhesta, Y. F.
    Nurjani, E.
    Arijuddin, B. I.
    INTERNATIONAL CONFERENCE ON TROPICAL METEOROLOGY AND ATMOSPHERIC SCIENCES, 2019, 303
  • [43] Local climate zone classification with different source data in Xi'an, China
    He, Shan
    Zhang, Yunwei
    Gu, Zhaolin
    Su, Junwei
    INDOOR AND BUILT ENVIRONMENT, 2019, 28 (09) : 1190 - 1199
  • [44] The urban morphology classification under local climate zone scheme based on the improved method - A case study of Changsha, China
    Chen, Yaping
    Hu, Yinze
    URBAN CLIMATE, 2022, 45
  • [45] EXPLORING SENTINEL-1 DATA FOR LOCAL CLIMATE ZONE CLASSIFICATION
    Hu, Jingliang
    Zhu, Xiao Xiang.
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 4677 - 4680
  • [46] Dynamics and controls of urban heat sink and island phenomena in a desert city: Development of a local climate zone scheme using remotely-sensed inputs
    Nassar, Ahmed K.
    Blackburn, G. Alan
    Whyatt, J. Duncan
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2016, 51 : 76 - 90
  • [47] Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones
    Zhang, Guichen
    Ghamisi, Pedram
    Zhu, Xiao Xiang
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (10): : 7623 - 7642
  • [48] Urban heat island estimation from improved selection of urban and rural stations by DTW algorithm
    Yonghong Hu
    Gensuo Jia
    Jinlong Ai
    Yong Zhang
    Meiting Hou
    Yapeng Li
    Theoretical and Applied Climatology, 2021, 146 : 443 - 455
  • [49] Urban heat island estimation from improved selection of urban and rural stations by DTW algorithm
    Hu, Yonghong
    Jia, Gensuo
    Ai, Jinlong
    Zhang, Yong
    Hou, Meiting
    Li, Yapeng
    THEORETICAL AND APPLIED CLIMATOLOGY, 2021, 146 (1-2) : 443 - 455
  • [50] A DYNAMIC END-TO-END FUSION FILTER FOR LOCAL CLIMATE ZONE CLASSIFICATION USING SAR AND MULTI-SPECTRUM REMOTE SENSING DATA
    Feng, Pengming
    Lin, Youtian
    He, Guangjun
    Guan, Jian
    Wang, Jin
    Shi, Huifeng
    IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 4231 - 4234