Preprints
https://doi.org/10.5194/essd-2022-426
https://doi.org/10.5194/essd-2022-426
14 Feb 2023
 | 14 Feb 2023
Status: this preprint has been withdrawn by the authors.

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

Haoxuan Yang, Qunming Wang, Wei Zhao, and Peter Atkinson

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 data 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 temporal characteristics extracted from the CCI SM v05.2 data (coupled with three terrain characteristics and two location characteristics), which was migrated to the prediction of the RF_SMAP dataset. The available in-situ SM data and the real SMAP data from April 2015 to April 2016 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. Moreover, the historical RF_SMAP dataset is more accurate than the widely used Global Land Evaporation Amsterdam Model (GLEAM) dataset in terms of average root mean square error (RMSE), bias (Bias), and Kling-Gutpa efficiency (KGE). Thus, the RF_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset, with an unbiased root mean square error (ubRMSE) of 0.035. 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.

This preprint has been withdrawn.

Haoxuan Yang, Qunming Wang, Wei Zhao, and Peter Atkinson

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-426', Anonymous Referee #1, 15 Mar 2023
    • CC2: 'Reply on RC1', Haoxuan Yang, 22 Mar 2023
    • AC1: 'Reply on RC1', Q. Wang, 24 Mar 2023
  • CC1: 'Comment on essd-2022-426', yuyang Ma, 19 Mar 2023
    • CC3: 'Reply on CC1', Haoxuan Yang, 22 Mar 2023
    • AC2: 'Reply on CC1', Q. Wang, 24 Mar 2023
  • RC2: 'Comment on essd-2022-426', Anonymous Referee #2, 28 Mar 2023
    • AC3: 'Reply on RC2', Q. Wang, 14 Apr 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-426', Anonymous Referee #1, 15 Mar 2023
    • CC2: 'Reply on RC1', Haoxuan Yang, 22 Mar 2023
    • AC1: 'Reply on RC1', Q. Wang, 24 Mar 2023
  • CC1: 'Comment on essd-2022-426', yuyang Ma, 19 Mar 2023
    • CC3: 'Reply on CC1', Haoxuan Yang, 22 Mar 2023
    • AC2: 'Reply on CC1', Q. Wang, 24 Mar 2023
  • RC2: 'Comment on essd-2022-426', Anonymous Referee #2, 28 Mar 2023
    • AC3: 'Reply on RC2', Q. Wang, 14 Apr 2023
Haoxuan Yang, Qunming Wang, Wei Zhao, and Peter Atkinson

Data sets

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

Haoxuan Yang, Qunming Wang, Wei Zhao, and Peter Atkinson

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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.
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