A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019)

Panpan Yao; Hui Lu; Jiancheng Shi; Tianjie Zhao; Kun Yang; Michael H. Cosh; Daniel J. Short Gianotti; Dara Entekhabi

doi:10.1038/s41597-021-00925-8

Scientific Data (May 2021)

A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019)

Panpan Yao,
Hui Lu,
Jiancheng Shi,
Tianjie Zhao,
Kun Yang,
Michael H. Cosh,
Daniel J. Short Gianotti,
Dara Entekhabi

Affiliations

Panpan Yao: Department of Earth System Science, Tsinghua University
Hui Lu: Department of Earth System Science, Tsinghua University
Jiancheng Shi: National Space Science Center, Chinese Academy of Sciences
Tianjie Zhao: State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences
Kun Yang: Department of Earth System Science, Tsinghua University
Michael H. Cosh: Hydrology and Remote Sensing Laboratory (HRSL), United States Department of Agriculture-Agricultural Research Service (USDA-ARS)
Daniel J. Short Gianotti: Parsons Laboratory, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology
Dara Entekhabi: Parsons Laboratory, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology

DOI: https://doi.org/10.1038/s41597-021-00925-8
Journal volume & issue: Vol. 8, no. 1
pp. 1 – 16

Abstract

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Measurement(s) wetness of soil • surface soil Technology Type(s) microwave spectroscopy • satellite imaging Factor Type(s) temporal resolution • spatial resolution Sample Characteristic - Environment planetary surface Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14533554

Published in Scientific Data

ISSN: 2052-4463 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/sdata/

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