25 Apr 2022
25 Apr 2022
Status: this preprint is currently under review for the journal ESSD.

SGD-SM 2.0: An Improved Seamless Global Daily Soil Moisture Long-term Dataset From 2002 to 2022

Qiang Zhang1, Qiangqiang Yuan2, Taoyong Jin2, Meiping Song3, and Fujun Sun4 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
  • 3CHIRS, Information Science and Technology College, Dalian Maritime University, China
  • 4CASIC Research Institute of Intelligent Decision Engineering, Beijing, China

Abstract. Satellite-based daily soil moisture products inevitably exist the drawbacks of low-coverage rate in global land, because of the satellite orbit covering scopes and the limitations of soil moisture retrieving models. To solve this issue, Zhang et al. (2021) generated seamless global daily soil moisture (SGD-SM 1.0) products for the years 2013~2019. Nevertheless, there are still several shortages in SGD-SM 1.0 products, especially on temporal range, sudden extreme weather condition, and sequential time-series information. In this work, we develop an improved seamless global daily soil moisture (SGD-SM 2.0) dataset from 2002 to 2022, to overcome above shortages. SGD-SM 2.0 uses three sensors AMSR-E, AMSR2 and WindSat. Global daily precipitation products are assimilated into the proposed reconstructing model. We propose an integrated long short-term memory convolutional neural network (LSTM-CNN) to fill the gaps and missing regions in daily soil moisture products. In-situ validation and time-series validation testify the reconstructing accuracy and availability of SGD-SM 2.0 (R: 0.672, RMSE: 0.096, MAE: 0.078). The time-series curves of the improved SGD-SM 2.0 are consistency with the original daily time-series soil moisture and precipitation distribution. Compared with SGD-SM 1.0, the improved SGD-SM 2.0 outperforms on reconstructing accuracy and time-series consistency. SGD-SM 2.0 products are recorded at (Zhang et al., 2022).

Qiang Zhang et al.

Status: open (until 30 Jun 2022)

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Qiang Zhang et al.

Data sets

SGD-SM 2.0 Qiang Zhang, Qiangqiang Yuan, Taoyong Jin

Qiang Zhang et al.


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Short summary
Compared with previous SGD-SM 1.0, SGD-SM 2.0 enlarges the temporal scope of seamless global daily soil moisture products from 2002 to 2022. Through assimilating auxiliary precipitation information with LSTM-CNN model, SGD-SM 2.0 can consider the sudden extreme weather condition for single day in global daily soil moisture products. SGD-SM 2.0 is significant for full-coverage global daily hydrologic monitoring, rather than averaging as the monthly–quarter–yearly results.