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

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Hu, YifanNanjing University of Information Science and Technology (author)

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

Article/chapterEnglish2023

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2023-02-03
Frontiers Media SA,2023

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LIBRIS-ID:oai:lup.lub.lu.se:ccbdc626-6b89-4e4e-bb2e-385e8f3d9e04
https://lup.lub.lu.se/record/ccbdc626-6b89-4e4e-bb2e-385e8f3d9e04URI
https://doi.org/10.3389/feart.2023.1130853DOI

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Language:English
Summary in:English

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Subject category:ref swepub-contenttype

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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.

Subject headings and genre

LANTBRUKSVETENSKAPER Annan lantbruksvetenskap Miljö- och naturvårdsvetenskap hsv//swe
AGRICULTURAL SCIENCES Other Agricultural Sciences Environmental Sciences related to Agriculture and Land-use hsv//eng
data reconstruction
deep learning
long-term
satellite-based
soil moisture

Added entries (persons, corporate bodies, meetings, titles ...)

Wang, GuojieNanjing University of Information Science and Technology (author)
Wei, XikunNanjing University of Information Science and Technology (author)
Zhou, FeihongNanjing University of Information Science and Technology (author)
Kattel, GiriNanjing University of Information Science and Technology,University of Melbourne,Tsinghua University (author)
Amankwah, Solomon Obiri YeboahNanjing University of Information Science and Technology (author)
Hagan, Daniel Fiifi TawiaNanjing University of Information Science and Technology (author)
Duan, ZhengLund University,Lunds universitet,BECC: Biodiversity and Ecosystem services in a Changing Climate,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,MERGE: ModElling the Regional and Global Earth system,Institutionen för naturgeografi och ekosystemvetenskap,Centre for Environmental and Climate Science (CEC),Faculty of Science,Dept of Physical Geography and Ecosystem Science(Swepub:lu)zh7785du (author)
Nanjing University of Information Science and TechnologyUniversity of Melbourne (creator_code:org_t)

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In:Frontiers in Earth Science: Frontiers Media SA112296-6463

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By the author/editor: Hu, Yifan; Wang, Guojie; Wei, Xikun; Zhou, Feihong; Kattel, Giri; Amankwah, Solomo ...; show more...; Hagan, Daniel Fi ...; Duan, Zheng; show less...

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