A fine-resolution soil moisture dataset for China in 2002–2018
<p>Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data i...
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Copernicus Publications
2021-07-01
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
X. Meng X. Meng K. Mao F. Meng J. Shi J. Shi J. Zeng X. Shen Y. Cui L. Jiang Z. Guo |
spellingShingle |
X. Meng X. Meng K. Mao F. Meng J. Shi J. Shi J. Zeng X. Shen Y. Cui L. Jiang Z. Guo A fine-resolution soil moisture dataset for China in 2002–2018 Earth System Science Data |
author_facet |
X. Meng X. Meng K. Mao F. Meng J. Shi J. Shi J. Zeng X. Shen Y. Cui L. Jiang Z. Guo |
author_sort |
X. Meng |
title |
A fine-resolution soil moisture dataset for China in 2002–2018 |
title_short |
A fine-resolution soil moisture dataset for China in 2002–2018 |
title_full |
A fine-resolution soil moisture dataset for China in 2002–2018 |
title_fullStr |
A fine-resolution soil moisture dataset for China in 2002–2018 |
title_full_unstemmed |
A fine-resolution soil moisture dataset for China in 2002–2018 |
title_sort |
fine-resolution soil moisture dataset for china in 2002–2018 |
publisher |
Copernicus Publications |
series |
Earth System Science Data |
issn |
1866-3508 1866-3516 |
publishDate |
2021-07-01 |
description |
<p>Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing
technology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data
imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05<span class="inline-formula"><sup>∘</sup></span>, monthly) for China from
2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including
AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d’Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation
data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between
optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates
that the accuracy of the new dataset is satisfactory (bias: <span class="inline-formula">−</span>0.057, <span class="inline-formula">−</span>0.063 and <span class="inline-formula">−</span>0.027 <span class="inline-formula">m<sup>3</sup> m<sup>−3</sup></span>; unbiased root mean square error
(<span class="inline-formula">ubRMSE</span>): 0.056, 0.036 and 0.048; correlation coefficient (<span class="inline-formula"><i>R</i></span>): 0.84, 0.85 and 0.89 on monthly, seasonal and annual scales,
respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past
17 years, China's soil moisture has shown cyclical fluctuations and a slight downward trend and can be summarized as wet in the south and dry in
the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic
and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in Zenodo at
<a href="https://doi.org/10.5281/zenodo.4738556">https://doi.org/10.5281/zenodo.4738556</a> (Meng et al., 2021a).</p> |
url |
https://essd.copernicus.org/articles/13/3239/2021/essd-13-3239-2021.pdf |
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doaj-9b8f609b95f44810bb627df95575e3af2021-07-07T06:01:24ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162021-07-01133239326110.5194/essd-13-3239-2021A fine-resolution soil moisture dataset for China in 2002–2018X. Meng0X. Meng1K. Mao2F. Meng3J. Shi4J. Shi5J. Zeng6X. Shen7Y. Cui8L. Jiang9Z. Guo10School of Physics and Electronic-Engineering, Ningxia University, Yinchuan 750021, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaHulunbeir Grassland Ecosystem Research station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, ChinaSchool of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250100, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing, 100190, ChinaState Key Laboratory of Remote Sensing Science, jointly sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, ChinaState Key Laboratory of Remote Sensing Science, jointly sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, ChinaDepartment of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USASchool of Earth and Space Sciences, Peking University, Beijing, China, 100871State Key Laboratory of Remote Sensing Science, jointly sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, ChinaSchool of Physics and Electronic-Engineering, Ningxia University, Yinchuan 750021, China<p>Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05<span class="inline-formula"><sup>∘</sup></span>, monthly) for China from 2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d’Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: <span class="inline-formula">−</span>0.057, <span class="inline-formula">−</span>0.063 and <span class="inline-formula">−</span>0.027 <span class="inline-formula">m<sup>3</sup> m<sup>−3</sup></span>; unbiased root mean square error (<span class="inline-formula">ubRMSE</span>): 0.056, 0.036 and 0.048; correlation coefficient (<span class="inline-formula"><i>R</i></span>): 0.84, 0.85 and 0.89 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a slight downward trend and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in Zenodo at <a href="https://doi.org/10.5281/zenodo.4738556">https://doi.org/10.5281/zenodo.4738556</a> (Meng et al., 2021a).</p>https://essd.copernicus.org/articles/13/3239/2021/essd-13-3239-2021.pdf |