Preprints
https://doi.org/10.5194/essd-2020-353
https://doi.org/10.5194/essd-2020-353

  23 Nov 2020

23 Nov 2020

Review status: a revised version of this preprint is currently under review for the journal ESSD.

SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Productions (2013–2019)

Qiang Zhang1, Qiangqiang Yuan2,3, Jie Li2, Yuan Wang2, Fujun Sun4, and Liangpei Zhang1 Qiang Zhang et al.
  • 1State Key Laboratory of Information Engineering, Survey Mapping and Remote Sensing, Wuhan University, China
  • 2School of Geodesy and Geomatics, Wuhan University, China
  • 3Collaborative Innovation Center of Geospatial Technology, Wuhan University, China
  • 4Beijing Electro-mechanical Engineering Institute, Beijing, China

Abstract. High quality and long-term soil moisture productions are significant for hydrologic monitoring and agricultural management. However, the acquired daily soil moisture productions are incomplete in global land (just about 30 %∼80 % coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieving algorithms. To solve this inevitable problem, we develop a novel 3D spatio-temporal partial convolutional neural network (CNN) for Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture productions gap-filling. Through the proposed framework, we generate the seamless global daily (SGD) AMSR2 soil moisture long-term productions from 2013 to 2019. To further validate the effectiveness of these productions, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. Results show that the seamless global daily soil moisture productions have reliable cooperativity with the selected in-situ values. The evaluation indexes of the reconstructed (original) dataset are R: 0.683 (0.687), RMSE: 0.099 m3/m3 (0.095 m3/m3), and MAE: 0.081 m3/m3 (0.078 m3/m3), respectively. Temporal consistency of the reconstructed daily soil moisture productions is ensured with the original time-series distribution of valid values. Besides, the spatial continuity of the reconstructed regions is also accorded with the context information (R: 0.963∼0.974, RMSE: 0.065∼0.073 m3/m3, and MAE: 0.044∼0.052 m3/m3). More details of this work are released at https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2/. This dataset can be downloaded at https://zenodo.org/record/3960425 (Zhang et al., 2020. DOI:https://doi.org/10.5281/zenodo.3960425).

Qiang Zhang et al.

 
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Qiang Zhang et al.

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SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Productions (2013-2019) Qiang Zhang, Qiangqiang Yuan, Jie Li, Yuan Wang, Fujun Sun, and Liangpei Zhang https://doi.org/10.5281/zenodo.3960425

Qiang Zhang et al.

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Short summary
The acquired daily soil moisture products are always incomplete in global land (just about 30 %~80 % coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieving algorithms. To solve this inevitable problem, we generate seamless, global, daily (SGD) AMSR2 soil moisture long-term productions from 2013 to 2019. These productions are significant for full coverage global daily hydrologic monitoring, rather than averaging as the monthly/quarter/yearly results.