03 Dec 2020

03 Dec 2020

Review status: this preprint is currently under review for the journal ESSD.

A fine-resolution soil moisture dataset for China in 2002–2018

Xiangjin Meng1,2,, Kebiao Mao1,3,, Fei Meng4, Jiancheng Shi5,6, Jiangyuan Zeng6, Xinyi Shen7, Yaokui Cui8, Lingmei Jiang6, and Zhonghua Guo1 Xiangjin Meng et al.
  • 1School of Physics and Electronic-Engineering, Ningxia University, Yinchuan 750021, China
  • 2School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
  • 3Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
  • 4School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250100, China
  • 5National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China
  • 6State 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, China
  • 7Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
  • 8School of Earth and Space Sciences, Peking University, Beijing, China, 100871
  • These authors contributed equally to this work.

Abstract. 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 over 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°, monthly) for China from 2002–2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products (including the AMSR-E/2 Level 3 products and the SMOS-INRA-CESBIO (SMOS-IC) 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: −0.024, −0.030 and −0.016 m3/m3, unbiased root mean square error (ubRMSE): 0.051, 0.048 and 0.042, correlation coefficient (R): 0.82, 0.88, and 0.90 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 downward trend (slope = −0.167, R = 0.750) 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 the Zenodo at (Meng et al., 2020).

Xiangjin Meng et al.

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Xiangjin Meng et al.

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A fine-resolution soil moisture dataset for China in 2002~2018 Xiangjin Meng, Kebiao Mao, Fei Meng, Jiancheng Shi, Jiangyuan Zeng, Xinyi Shen, Yaokui Cui, Lingmei Jiang, and Zhonghua Guo

Xiangjin Meng et al.


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Short summary
Soil moisture is one of the key parameters for flood forecast, drought detection, crop yield estimation and hydrological modeling. The coarse spatial resolution of passive microwave data imposes great limitations. To improve the spatio-temporal resolution of soil moisture products, we built a spatial weight decomposition model to improve the resolution of soil moisture products. The validation and application analysis indicate that new product can meet application needs.