A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis

被引:21
|
作者
Hong, Falu [1 ]
Zhan, Wenfeng [1 ,2 ]
Gottsche, Frank-M. [3 ]
Liu, Zihan [1 ]
Dong, Pan [1 ]
Fu, Huyan [1 ]
Huang, Fan [1 ]
Zhang, Xiaodong [4 ]
机构
[1] Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China
[2] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
[3] Karlsruhe Inst Technol KIT, Inst Meteorol & Climate Res, Hermann von Helmholtz Pl 1, D-76344 Eggenstein Leopoldshafen, Germany
[4] Shanghai Spaceflight Inst TT&C & Telecommun, Shanghai 201109, Peoples R China
基金
中国国家自然科学基金;
关键词
MODIS LST; PRODUCT; TRENDS; VEGETATION; CLIMATOLOGY; SVALBARD; DYNAMICS; DAYTIME; VIIRS; TERRA;
D O I
10.5194/essd-14-3091-2022
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Daily mean land surface temperatures (LSTs) acquired from polar orbiters are crucial for various applications such as global and regional climate change analysis. However, thermal sensors from polar orbiters can only sample the surface effectively with very limited times per day under cloud-free conditions. These limitations have produced a systematic sampling bias (Delta T-sb) on the daily mean LST (T-dm) estimated with the traditional method, which uses the averages of clear-sky LST observations directly as the T-dm. Several methods have been proposed for the estimation of the T-dm, yet they are becoming less capable of generating spatiotemporally seamless T-dm across the globe. Based on MODIS and reanalysis data, here we propose an improved annual and diurnal temperature cycle-based framework (termed the IADTC framework) to generate global spatiotemporally seamless T-dm products ranging from 2003 to 2019 (named the GADTC products). The validations show that the IADTC framework reduces the systematic Delta T-sb significantly. When validated only with in situ data, the assessments show that the mean absolute errors (MAEs) of the IADTC framework are 1.4 and 1.1 K for SURFRAD and FLUXNET data, respectively, and the mean biases are both close to zero. Direct comparisons between the GADTC products and in situ measurements indicate that the MAEs are 2.2 and 3.1 K for the SURFRAD and FLUXNET datasets, respectively, and the mean biases are -1.6 and -1.5 K for these two datasets, respectively. By taking the GADTC products as references, further analysis reveals that the T-dm estimated with the traditional averaging method yields a positive systematic Delta T-sb of greater than 2.0 K in low-latitude and midlatitude regions while of a relatively small value in high-latitude regions. Although the global-mean LST trend (2003 to 2019) calculated with the traditional method and the IADTC framework is relatively close (both between 0.025 to 0.029 K yr(-1)), regional discrepancies in LST trend do occur - the pixel-based MAE in LST trend between these two methods reaches 0.012 K yr(-1). We consider the IADTC framework can guide the further optimization of T-dm estimation across the globe, and the generated GADTC products should be valuable in various applications such as global and regional warming analysis. The GADTC products are freely available at (Hong et al., 2022).
引用
收藏
页码:3091 / 3113
页数:23
相关论文
共 50 条
  • [41] Estimating mean air temperature using MODIS day and night land surface temperatures
    Sun, Hao
    Chen, Yunhao
    Gong, Adu
    Zhao, Xiang
    Zhan, Wenfeng
    Wang, Mengjie
    THEORETICAL AND APPLIED CLIMATOLOGY, 2014, 118 (1-2) : 81 - 92
  • [42] Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution
    Kilibarda, Milan
    Hengl, Tomislav
    Heuvelink, Gerard B. M.
    Graeler, Benedikt
    Pebesma, Edzer
    Tadic, Melita Percec
    Bajat, Branislav
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2014, 119 (05) : 2294 - 2313
  • [43] Development and Analysis of a Long-Term, Global, Terrestrial Land Surface Temperature Dataset Based on HIRS Satellite Retrievals
    Siemann, Amanda L.
    Coccia, Gabriele
    Pan, Ming
    Wood, Eric F.
    JOURNAL OF CLIMATE, 2016, 29 (10) : 3589 - 3606
  • [44] Analysis and climatological interpretation of the evolution of global mean temperatures since 1880
    Zeltz, Eric
    PHYSIO-GEO, 2021, 16 : 49 - 70
  • [45] Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020-iMap World 1.0
    Liu, Han
    Gong, Peng
    Wang, Jie
    Wang, Xi
    Ning, Grant
    Xu, Bing
    REMOTE SENSING OF ENVIRONMENT, 2021, 258
  • [46] A global urban heat island intensity dataset: Generation, comparison, and analysis
    Yang, Qiquan
    Xu, Yi
    Chakraborty, T. C.
    Du, Meng
    Hu, Ting
    Zhang, Ling
    Liu, Yue
    Yao, Rui
    Yang, Jie
    Chen, Shurui
    Xiao, Changjiang
    Liu, Renrui
    Zhang, Mingjie
    Chen, Rui
    REMOTE SENSING OF ENVIRONMENT, 2024, 312
  • [47] SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022
    Zhang, Qiang
    Yuan, Qiangqiang
    Jin, Taoyong
    Song, Meiping
    Sun, Fujun
    EARTH SYSTEM SCIENCE DATA, 2022, 14 (10) : 4473 - 4488
  • [48] Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring
    Wu, Mingquan
    Li, Hua
    Huang, Wenjiang
    Niu, Zheng
    Wang, Changyao
    ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS, 2015, 17 (08) : 1396 - 1404
  • [49] Time series trends of land surface temperatures in Egypt: a signal for global warming
    Hereher, Mohamed E.
    ENVIRONMENTAL EARTH SCIENCES, 2016, 75 (17)
  • [50] Time series trends of land surface temperatures in Egypt: a signal for global warming
    Mohamed E. Hereher
    Environmental Earth Sciences, 2016, 75