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Effects of multi-temporal environmental variables on SOC spatial prediction models in coastal wetlands of a Chinese delta
  • +4
  • Yiming Xu,
  • Bin Li,
  • Junhong Bai,
  • Guangliang Zhang,
  • Xin Wang,
  • Scot Smith,
  • Shudong Du
Yiming Xu
Beijing Technology and Business University

Corresponding Author:xuyiming@btbu.edu.cn

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Bin Li
Beijing Technology and Business University
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Junhong Bai
Beijing Normal University
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Guangliang Zhang
Beijing Normal University
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Xin Wang
Beijing Normal University
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Scot Smith
University of Florida
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Shudong Du
Beijing Normal University
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Abstract

Mapping the SOC distributions in coastal wetlands plays an important role in assessing ecosystem services, predicting the greenhouse effects and investigating global carbon cycle. Few research has explored the relationships of SOC and environmental variables with seasonal changes, and the effects of multi-temporal environmental variables on Digital Soil Mapping (DSM). The results showed that the relationships between SOC and environmental variables in different months varied significantly in coastal wetlands of the Yellow River Delta (YRD). In general, the environmental variables in wet season showed stronger correlations and higher importance scores with SOC compared with those in dry season. In addition, SOC prediction models based on multi-temporal data in wet season and mono-temporal data in April had stronger prediction performance compared with those based on multi-temporal data in dry season. As a result, data fusion of multi-temporal data did not necessarily contribute to the model performance enhancement. Relative homogenous soil-landscape attributes and spectral characteristics in coastal wetlands of the YRD in dry season could not accurately explain the strong spatial variation of SOC in this area, and it might be the major reason that caused the stronger model performance of soil prediction models based on wet season than those based on dry season. Therefore, the accurate spatial prediction of soil properties requires the characterization of the seasonal dynamics of soil-landscape relationships. In general, the findings of this research demonstrated that the selection of the environmental variables in the establishment of DSM model should consider the seasonal effects of environmental variables.
10 Dec 2021Submitted to Land Degradation & Development
11 Dec 2021Submission Checks Completed
11 Dec 2021Assigned to Editor
20 Dec 2021Reviewer(s) Assigned
31 Jan 2022Review(s) Completed, Editorial Evaluation Pending
22 Feb 2022Editorial Decision: Revise Major
23 Mar 20221st Revision Received
24 Mar 2022Submission Checks Completed
24 Mar 2022Assigned to Editor
24 Apr 2022Review(s) Completed, Editorial Evaluation Pending
24 Jun 2022Editorial Decision: Accept
Nov 2022Published in Land Degradation & Development volume 33 issue 17 on pages 3557-3567. 10.1002/ldr.4408