A METHOD FOR ESTIMATING 1 KM ALL-WEATHER HOURLY LAND SURFACE TEMPERATURE

被引:0
|
作者
Yan, Jianan [1 ,2 ]
Chen, Hong [3 ]
Wu, Hua [1 ,2 ]
Wang, Ning [4 ]
Ma, Lingling [4 ]
机构
[1] Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] China Aero Geophys Survey & Remote Sensing Ctr Na, Beijing 100083, Peoples R China
[4] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Quantitat Remote Sensing Informat Technol, Beijing 100094, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
all weather; annual temperature cycle; land surface temperature; DIURNAL CYCLE;
D O I
10.1109/IGARSS46834.2022.9884511
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Land Surface Temperature (LST) is one of the important parameters in thermal environment monitoring. Satellite thermal remote sensing is the major way to obtain spatial-temporal information of LST. However, limited by the cloud contamination and the trade-off between spatial and temporal resolution, current temperature products are difficult to provide all- weather LST. In this paper, a method is proposed to obtain all-weather hourly LST. It consists of two main steps: 1) reconstruction of LST under cloudy-sky by using enhanced annual temperature cycle (ATCE) model and 2) establishment of relationship between LST and air temperature which is used for the acquisition of hourly LST. In the end, the performance of the method is analyzed through the artificial data which is created by masking the origin images. And the results show that the proposed method is valuable for generating all-weather hourly LST.
引用
收藏
页码:3664 / 3667
页数:4
相关论文
共 50 条
  • [41] Fusion of All-Weather Land Surface Temperature From AMSR-E and MODIS Data Using Random Forest Regression
    Zhang, Quan
    Cheng, Jie
    Wang, Ninglian
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [42] Estimation of evapotranspiration using all-weather land surface temperature and variational trends with warming temperatures for the River Source Region in Southwest China
    Ma, Yanfei
    Zhou, Ji
    Liu, Shaomin
    Zhang, Weike
    Zhang, Yuan
    Xu, Ziwei
    Song, Lisheng
    Zhao, Haigen
    JOURNAL OF HYDROLOGY, 2022, 613
  • [43] Advances in Methodology and Generation of All-Weather Land Surface Temperature Products From Polar-Orbiting and Geostationary Satellites: A comprehensive review
    Jia, Aolin
    Liang, Shunlin
    Wang, Dongdong
    Mallick, Kanishka
    Zhou, Shugui
    Hu, Tian
    Xu, Shuo
    IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, 2024, 12 (04) : 218 - 260
  • [44] Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data
    Jia, Aolin
    Liang, Shunlin
    Wang, Dongdong
    REMOTE SENSING OF ENVIRONMENT, 2022, 278
  • [45] A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003-2019
    Song, Peilin
    Zhang, Yongqiang
    Guo, Jianping
    Shi, Jiancheng
    Zhao, Tianjie
    Tong, Bing
    EARTH SYSTEM SCIENCE DATA, 2022, 14 (06) : 2613 - 2637
  • [46] Contribution of Land Surface Temperature (TCI) to Vegetation Health Index: A Comparative Study Using Clear Sky and All-Weather Climate Data Records
    Bento, Virgilio A.
    Trigo, Isabel F.
    Gouveia, Celia M.
    DaCamara, Carlos C.
    REMOTE SENSING, 2018, 10 (09)
  • [47] A novel TIR-derived three-source energy balance model for estimating daily latent heat flux in mainland China using an all-weather land surface temperature product
    Yang, Junming
    Yao, Yunjun
    Shao, Changliang
    Li, Yufu
    Fisher, Joshua B.
    Cheng, Jie
    Chen, Jiquan
    Jia, Kun
    Zhang, Xiaotong
    Shang, Ke
    Yu, Ruiyang
    Guo, Xiaozheng
    Xie, Zijing
    Liu, Lu
    Ning, Jing
    Zhang, Lilin
    AGRICULTURAL AND FOREST METEOROLOGY, 2022, 323
  • [48] Estimation of all-sky 1 km land surface temperature over the conterminous United States
    Li, Bing
    Liang, Shunlin
    Liu, Xiaobang
    Ma, Han
    Chen, Yan
    Liang, Tianchen
    He, Tao
    REMOTE SENSING OF ENVIRONMENT, 2021, 266
  • [49] Retrievals of all-weather daytime air temperature from MODIS products
    Zhu, Wenbin
    Lu, Aifeng
    Jia, Shaofeng
    Yan, Jiabao
    Mahmood, Rashid
    REMOTE SENSING OF ENVIRONMENT, 2017, 189 : 152 - 163
  • [50] All-weather road drivable area segmentation method based on CycleGAN
    Chen Jiqing
    Wei Depeng
    Long Teng
    Luo Tian
    Wang Huabin
    VISUAL COMPUTER, 2023, 39 (10): : 5135 - 5151