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Big Earth Data Volume 4, 2020 - Issue 2: Understanding climate change using big Earth data
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Review Article

Linking observation, modelling and satellite-based estimation of global land evapotranspiration

Jiahua Zhanga Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;b College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, ChinaCorrespondence[email protected]
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Yun Baia Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;c Remote Sensing and Digital Earth Center, College of Sciences and Technology, Qingdao University, Qingdao, ChinaView further author information
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Hao Yand National Meteorological Center, China Meteorological Administration, Beijing, ChinaView further author information
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Huadong Guoa Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;b College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, ChinaView further author information
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Shanshan Yanga Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;b College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, ChinaView further author information
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Jinwen Wanga Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;b College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, ChinaView further author information
Pages 94-127 | Received 09 Jan 2020, Accepted 09 Mar 2020, Published online: 27 Apr 2020

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Gensuo Jia. (2020) The role of big Earth data in understanding climate change. Big Earth Data 4:2, pages 91-93.
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Articles from other publishers (9)

P. Hulsman, J. Keune, A. Koppa, J. Schellekens & D. G. Miralles. (2023) Incorporating Plant Access to Groundwater in Existing Global, Satellite‐Based Evaporation Estimates. Water Resources Research 59:8.
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Ali Khoshnazar, Gerald A. Corzo Perez, Vitali Diaz, Milad Aminzadeh & Roberto Adolfo Cerón Pineda. (2022) Wet-environment Evapotranspiration and Precipitation Standardized Index (WEPSI) for drought assessment and monitoring. Hydrology Research 53:11, pages 1393-1413.
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Xinlei He, Shaomin Liu, Tongren Xu, Kailiang Yu, Pierre Gentine, Zhe Zhang, Ziwei Xu, Dandan Jiao & Dongxing Wu. (2022) Improving predictions of evapotranspiration by integrating multi-source observations and land surface model. Agricultural Water Management 272, pages 107827.
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Zelin Luo, Mengjing Guo, Peng Bai & Jing Li. (2022) Different Vegetation Information Inputs Significantly Affect the Evapotranspiration Simulations of the PT-JPL Model. Remote Sensing 14:11, pages 2573.
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Ikhlas Ghiat, Hamish R. Mackey & Tareq Al-Ansari. (2021) A Review of Evapotranspiration Measurement Models, Techniques and Methods for Open and Closed Agricultural Field Applications. Water 13:18, pages 2523.
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V. Douna, V. Barraza, F. Grings, A. Huete, N. Restrepo-Coupe & J. Beringer. (2021) Towards a remote sensing data based evapotranspiration estimation in Northern Australia using a simple random forest approach. Journal of Arid Environments 191, pages 104513.
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Fei Wang, Shengtian Yang, Yang Wei, Qian Shi & Jianli Ding. (2021) Characterizing soil salinity at multiple depth using electromagnetic induction and remote sensing data with random forests: A case study in Tarim River Basin of southern Xinjiang, China. Science of The Total Environment 754, pages 142030.
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Volkan Senyurek, Fangni Lei, Dylan Boyd, Ali Cafer Gurbuz, Mehmet Kurum & Robert Moorhead. (2020) Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations. Remote Sensing 12:21, pages 3503.
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Clara Chew & Eric Small. (2020) Description of the UCAR/CU Soil Moisture Product. Remote Sensing 12:10, pages 1558.
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