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https://doi.org/10.5194/essd-2020-59
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-59
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 08 May 2020

Submitted as: data description paper | 08 May 2020

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This preprint is currently under review for the journal ESSD.

A new dataset of satellite observation-based global surface soil moisture covering 2003–2018

Yongzhe Chen1,2, Xiaoming Feng1, and Bojie Fu1,2 Yongzhe Chen et al.
  • 1State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
  • 2University of Chinese Academy of Sciences, Beijing 100049, PR China

Abstract. Soil moisture is an important variable linking the atmosphere and the terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture is still lacking at global scale. In this study, we conducted data calibration and fusion of 11 well-acknowledged microwave remote sensing-based soil moisture products since 2003 through neural network approach, with SMAP soil moisture data applied as the fundamental training target. The training efficiency proves to be high (R2 = 0.95) due to the selection of 9 quality impact factors of microwave soil moisture products and the elaborate organization structure of multiple various neural networks(5 rounds of simulation; 8 substeps; 74 independent neural networks; and > 106 regional subnetworks). We achieved global satellite monitoring of surface soil moisture during 2003–2018 at 0.1° resolution. This new dataset, once validated against the International Soil Moisture Network (ISMN) records, is supposed to be superior to the existing products (ASCAT-SWI, GLDAS Noah, ERA5-Land, CCI/ECV and GLEAM), and is applicable to studying both the spatial and temporal patterns. It suggests an increase in global mean surface soil moisture, and reveals that the surface moisture decline on rainless days is highest in summers over the low-latitudes but highest in winters over most mid-latitude areas. Notably, the error propagation with the extension of the simulation period to the past is well controlled, indicating that the fusion algorithm will be more meaningful in future when more advanced sensors are in operation. The dataset can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.912597 (Chen, 2020).

Yongzhe Chen et al.

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A new dataset of satellite observation-based global surface soil moisture covering 2003-2018 Yongzhe Chen https://doi.org/10.1594/PANGAEA.912597

Yongzhe Chen et al.

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Latest update: 31 May 2020
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Short summary
Soil moisture can greatly influence the ecosystem, but is hard to monitor at global scale. In this study, by calibrating and combining 11 different products derived from satellite observation, we developed a new global surface soil moisture dataset spanning from 2003 to 2018 with high accuracy. Using this new dataset, not only the global long-term trends can be derived, but also the seasonal variation and spatial distribution of surface soil moisture at different latitudes can be better studied.
Soil moisture can greatly influence the ecosystem, but is hard to monitor at global scale. In...
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