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Zheng Duan

Universitetslektor

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Reconstructing long-term global satellite-based soil moisture data using deep learning method

Författare

  • Yifan Hu
  • Guojie Wang
  • Xikun Wei
  • Feihong Zhou
  • Giri Kattel
  • Solomon Obiri Yeboah Amankwah
  • Daniel Fiifi Tawia Hagan
  • Zheng Duan

Summary, in English

Soil moisture is an essential component for the planetary balance between land surface water and energy. Obtaining long-term global soil moisture data is important for understanding the water cycle changes in the warming climate. To date several satellite soil moisture products are being developed with varying retrieval algorithms, however with considerable missing values. To resolve the data gaps, here we have constructed two global satellite soil moisture products, i.e., the CCI (Climate Change Initiative soil moisture, 1989–2021; CCIori hereafter) and the CM (Correlation Merging soil moisture, 2006–2019; CMori hereafter) products separately using a Convolutional Neural Network (CNN) with autoencoding approach, which considers soil moisture variability in both time and space. The reconstructed datasets, namely CCIrec and CMrec, are cross-evaluated with artificial missing values, and further againt in-situ observations from 12 networks including 485 stations globally, with multiple error metrics of correlation coefficients (R), bias, root mean square errors (RMSE) and unbiased root mean square error (ubRMSE) respectively. The cross-validation results show that the reconstructed missing values have high R (0.987 and 0.974, respectively) and low RMSE (0.015 and 0.032 m3/m3, respectively) with the original ones. The in-situ validation shows that the global mean R between CCIrec (CCIori) and in-situ observations is 0.590 (0.581), RMSE is 0.093 (0.093) m3/m3, ubRMSE is 0.059 (0.058) m3/m3, bias is 0.032 (0.037) m3/m3 respectively; CMrec (CMori) shows quite similar results. The added value of this study is to provide long-term gap-free satellite soil moisture products globally, which helps studies in the fields of hydrology, meteorology, ecology and climate sciences.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • MERGE: ModElling the Regional and Global Earth system

Publiceringsår

2023-02

Språk

Engelska

Publikation/Tidskrift/Serie

Frontiers in Earth Science

Volym

11

Dokumenttyp

Artikel i tidskrift

Förlag

Frontiers Media S. A.

Ämne

  • Environmental Sciences related to Agriculture and Land-use

Nyckelord

  • data reconstruction
  • deep learning
  • long-term
  • satellite-based
  • soil moisture

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 2296-6463