Preprints
https://doi.org/10.5194/essd-2022-137
https://doi.org/10.5194/essd-2022-137
 
08 Jun 2022
08 Jun 2022
Status: this discussion paper is a preprint. It has been under review for the journal Earth System Science Data (ESSD). The manuscript was not accepted for further review after discussion.

An 8-day composited 36 km SMAP soil moisture dataset from 1979 to 2015 produced using a random forest and historical CCI data

Haoxuan Yang1, Qunming Wang1, Wei Zhao2, and Peter M. Atkinson3,4 Haoxuan Yang et al.
  • 1College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China
  • 2Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
  • 3Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YR, UK
  • 4Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK

Abstract. Soil moisture (SM) plays a significant role in many natural and anthropogenic systems which are essential to supporting life on Earth. Thus, accurate measurement and assessment of changes in soil moisture globally is of great value, including long-term historical assessment. Since the on-board cycle and detailed parameters of disparate sensors are different, the European Space Agency established the Climate Change Initiative (CCI) program to harmonize the available multisource SM data, producing long time-series surface SM datasets starting from 1978 to the present. However, the Soil Moisture Active Passive (SMAP) mission, launched in 2015, has shown more satisfactory performance in both spatial accuracy and in capturing pattern of temporal changes. In this paper, a random forest (RF) model was proposed to extend the superior SMAP dataset historically (named RF_SMAP), using the corresponding CCI time-series. We assumed that the temporal changes in the SMAP dataset are similar generally to those in the available CCI dataset. Accordingly, the RF model was constructed using the CCI SM v05.2 data, which was migrated to the prediction of the RF_SMAP dataset. The available in-situ SM data and the real SMAP data from 2015 to 2019 were used as references to validate the predicted RF_SMAP data. It was shown that compared with the CCI dataset, the predicted RF_SMAP dataset is closer to the in-situ SM data and the real SMAP data. Thus, the RF_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring.

Haoxuan Yang et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-137', Anonymous Referee #1, 26 Jul 2022
  • RC2: 'Review of "An 8-day composited 36 km SMAP soil moisture dataset from 1979 to 2015 produced using a random forest and historical CCI data" by Haoxuan Yang et al. submitted to ESSD', Anonymous Referee #2, 18 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-137', Anonymous Referee #1, 26 Jul 2022
  • RC2: 'Review of "An 8-day composited 36 km SMAP soil moisture dataset from 1979 to 2015 produced using a random forest and historical CCI data" by Haoxuan Yang et al. submitted to ESSD', Anonymous Referee #2, 18 Sep 2022

Haoxuan Yang et al.

Data sets

The 8-day composited 36 km SMAP soil moisture dataset from 1979 to 2015 Haoxuan Yang, Qunming Wang, Wei Zhao, and Peter M. Atkinson https://doi.org/10.6084/m9.figshare.17621765

Haoxuan Yang et al.

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Short summary
A random forest (RF) model was proposed to extend the superior SMAP dataset (named RF_SMAP) from 1979 to 2015, using the corresponding CCI time-series. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring.