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Estimating soil salinity with different fractional vegetation cover using remote sensing
  • +6
  • Junrui Zhang,
  • Zhitao Zhang,
  • Junying Chen,
  • Haiying Chen,
  • Jiming Jin,
  • Jia Han,
  • Xintao Wang,
  • Zhishuang Song,
  • Guangfei Wei
Junrui Zhang
Northwest A&F University
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Zhitao Zhang
Northwest Agriculture and Forestry University
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Junying Chen
Northwest Agriculture and Forestry University
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Haiying Chen
Northwest Agriculture and Forestry University
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Jiming Jin
Northwest Agriculture and Forestry University
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Jia Han
Northwest Agriculture and Forestry University
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Xintao Wang
Northwest Agriculture and Forestry University
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Zhishuang Song
Northwest Agriculture and Forestry University
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Guangfei Wei
Northwest Agriculture and Forestry University
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Abstract

Soil salinization is a serious restrictive factor of sustainable agricultural development, and its monitoring accuracy is mainly influenced by such factors as mineral composition, organic matter, and Fractional Vegetation Cover (FVC). Previous research mostly focused on the first two factors and the study of FVC is scarce and unsystematic. In order to systematically explore the effect of FVC, we monitored the soil salinization with different vegetation coverage in Jiefangzha Irrigation District in Inner Mongolia using satellite remote sensing. From May to August 2018, we carried out field sampling at different depths (0-20cm, 0-40cm, 0-60cm) in each month, and calculated FVC and spectral covariates using GF-1 satellite images in the corresponding sampling period. Based on the FVC division criteria of Inner Mongolia, we took the following steps: (1) setting up control treatment A (the full data with undivided FVC,TA) and experimental treatment B (bare land, TB), C (mid-low FVC, TC), D (mid FVC, TD) and E (high FVC, TE); (2) conducting the Best Subset Selection (BSS) for all spectral covariates at different depths of each treatment; and (3) constructing the Soil Salt Content (SSC) inversion models by Partial Least Square Regression (PLSR), Cubist, and Extreme Learning Machine (ELM). The results indicated that classifying FVC could improve the stability and predictive ability of the models. The results can provide references for soil salinization prevention and agricultural production in Jiefangzha Irrigation District and other areas with the same vegetation cover.

Peer review status:ACCEPTED

25 Feb 2020Submitted to Land Degradation & Development
26 Feb 2020Submission Checks Completed
26 Feb 2020Assigned to Editor
22 Mar 2020Reviewer(s) Assigned
04 Apr 2020Review(s) Completed, Editorial Evaluation Pending
14 Apr 2020Editorial Decision: Revise Major
09 May 20201st Revision Received
11 May 2020Assigned to Editor
11 May 2020Submission Checks Completed
28 May 2020Review(s) Completed, Editorial Evaluation Pending
31 May 2020Editorial Decision: Revise Major
20 Jun 20202nd Revision Received
22 Jun 2020Submission Checks Completed
22 Jun 2020Assigned to Editor
05 Jul 2020Review(s) Completed, Editorial Evaluation Pending
20 Jul 2020Editorial Decision: Revise Minor
20 Jul 20203rd Revision Received
21 Jul 2020Submission Checks Completed
21 Jul 2020Assigned to Editor
21 Jul 2020Review(s) Completed, Editorial Evaluation Pending
05 Aug 2020Editorial Decision: Accept