Contribution of Land Surface Temperature (TCI) to Vegetation Health Index: A Comparative Study Using Clear Sky and All-Weather Climate Data Records

被引:38
|
作者
Bento, Virgilio A. [1 ]
Trigo, Isabel F. [1 ,2 ]
Gouveia, Celia M. [1 ,2 ]
DaCamara, Carlos C. [1 ]
机构
[1] Univ Lisbon, Fac Ciencias, Inst Dom Luiz, P-1749016 Lisbon, Portugal
[2] Inst Portugues Mar Atmosfera IP, Rua C Aeroporto, P-1749077 Lisbon, Portugal
关键词
drought monitoring; land surface temperature; vegetation health index; Meteosat; climate data records; standardized precipitation-evapotranspiration index; DROUGHT VARIABILITY; LONG-TERM; THERMAL INERTIA; IMPACT; SPACE; FREQUENCY; EVENTS; ALBEDO;
D O I
10.3390/rs10091324
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The Vegetation Health Index (VHI) is widely used for monitoring drought using satellite data. VHI depends on vegetation state and thermal stress, respectively assessed via (i) the Vegetation Condition Index (VCI) that usually relies on information from the visible and near infra-red parts of the spectrum (in the form of Normalized Difference Vegetation Index, NDVI); and (ii) the Thermal Condition Index (TCI), based on top of atmosphere thermal infrared (TIR) brightness temperature or on TIR-derived Land Surface Temperature (LST). VHI is then estimated as a weighted average of VCI and TCI. However, the optimum weights of the two components are usually not known and VHI is usually estimated attributing a weight of 0.5 to both. Using a previously developed methodology for the Euro-Mediterranean region, we show that the multi-scalar drought index (SPEI) may be used to obtain optimal weights for VCI and TCI over the area covered by Meteosat satellites that includes Africa, Europe, and part of South America. The procedure is applied using clear-sky Meteosat Climate Data Records (CDRs) and all-sky LST derived by combining satellite and reanalysis data. Results obtained present a coherent spatial distribution of VCI and TCI weights when estimated using clear- and all-sky LST. This study paves the way for the development of a future VHI near-real time operational product for drought monitoring based on information from Meteosat satellites.
引用
收藏
页数:20
相关论文
共 34 条
  • [1] Generation of an all-weather land surface temperature product from MODIS and AMSR-E data
    Duan, Si-Bo
    Li, Zhao-Liang
    Leng, Pei
    Han, Xiao-Jing
    Chen, Yuanyuan
    INTERNATIONAL CONFERENCE ON INTELLIGENT EARTH OBSERVING AND APPLICATIONS 2015, 2015, 9808
  • [2] Retrievals of all-weather daytime land surface temperature from FengYun-2D data
    Zhang, Xiaoyu
    Wang, Chenguang
    Zhao, Hong
    Lu, Zehui
    OPTICS EXPRESS, 2017, 25 (22): : 27210 - 27224
  • [3] Reconstruction of all-weather land surface temperature based on a combined physical and data-driven model
    Zhang, Xuepeng
    Gou, Peng
    Zhang, Fengjiao
    Huang, Yingshuang
    Wang, Zhe
    Li, Guangchao
    Xing, Jianghe
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (32) : 78865 - 78878
  • [4] Reconstruction of all-weather land surface temperature based on a combined physical and data-driven model
    Xuepeng Zhang
    Peng Gou
    Fengjiao Zhang
    Yingshuang Huang
    Zhe Wang
    Guangchao Li
    Jianghe Xing
    Environmental Science and Pollution Research, 2023, 30 : 78865 - 78878
  • [5] GENERATION OF ALL-WEATHER MODIS-LIKE LAND SURFACE TEMPERATURE BASED ON DATA FUSION METHOD
    Zhang, Xuepeng
    Gou, Peng
    Huang, Yingshuang
    Zhang, Fengjiao
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 7451 - 7454
  • [6] 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
  • [7] Improved fusion model for generating hourly fine scale land surface temperature data under all-weather condition
    Adeniran, Ibrahim Ademola
    Nazeer, Majid
    Wong, Man Sing
    Zhu, Rui
    Yang, Jinxin
    Chan, Pak-Wai
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2024, 131
  • [8] Reconstruction of Hourly All-Weather Land Surface Temperature by Integrating Reanalysis Data and Thermal Infrared Data From Geostationary Satellites (RTG)
    Ding, Lirong
    Zhou, Ji
    Li, Zhao-Liang
    Ma, Jin
    Shi, Chunxiang
    Sun, Shuai
    Wang, Ziwei
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [9] A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution
    Xu, Shuo
    Cheng, Jie
    Zhang, Quan
    REMOTE SENSING, 2021, 13 (11)
  • [10] A two-step deep learning framework for mapping gapless all-weather land surface temperature using thermal infrared and passive microwave data
    Wu, Penghai
    Su, Yang
    Duan, Si-bo
    Li, Xinghua
    Yang, Hui
    Zeng, Chao
    Ma, Xiaoshuang
    Wu, Yanlan
    Shen, Huanfeng
    REMOTE SENSING OF ENVIRONMENT, 2022, 277