View in EDS HTML Full Text PDF Full Text

Sensitivity of Vegetation Greenness to Multi-Depth Soil Moisture on the Mongolian Plateau: Nonlinear Responses Revealed by RF–SHAP.

Saved in:
Bibliographic Details
Title: Sensitivity of Vegetation Greenness to Multi-Depth Soil Moisture on the Mongolian Plateau: Nonlinear Responses Revealed by RF–SHAP.
Authors: Tong, Siqin1,2 (AUTHOR), Zhang, Shubo1,2 (AUTHOR), Yang, Xueliang1,3 (AUTHOR), Ren, Jinyuan1,4 (AUTHOR), Dorjsuren, Altantuya1,3 (AUTHOR), Bayarsaikhan, Sainbuyan2,4 (AUTHOR), Bao, Gang1,2,3 (AUTHOR), Huang, Xiaojun1,2,4 (AUTHOR), Bao, Yuhai1,2 (AUTHOR) baoyuhai@imnu.edu.cn
Source: Remote Sensing. Aug2026, Vol. 18 Issue 15, p2600. 22p.
Subjects: Soil moisture, Normalized difference vegetation index, Arid regions, Random forest algorithms, Ecological resilience, Climate change, Shapley Additive Explanations, Vegetation greenness
Geographic Terms: Mongolian Plateau (China & Mongolia)
Abstract: Highlights: What are the main findings? NDVI sensitivity peaks in subsurface soil moisture (7–28 cm) across the Mongolian Plateau, showing clear vertical stratification. Vegetation sensitivity increases significantly (1982–2022) with nonlinear threshold responses and a shift toward deeper soil water dependence. What are the implications of the main findings? Dryland ecosystems exhibit strengthening water limitation and enhanced vegetation–water feedback under climate change. Deep soil moisture becomes increasingly critical for ecosystem stability and vegetation resilience. Under the context of climate change, investigating vegetation sensitivity to soil moisture in arid and semi-arid regions is crucial for assessing ecosystem stability. Nevertheless, how vegetation sensitivity varies among different soil depths, whether it exhibits nonlinear threshold responses, and how such sensitivity has evolved over recent decades remain poorly understood in the Mongolian Plateau. This study focuses on the Mongolian Plateau and uses long-term NDVI data (1982–2022) together with ERA5-Land multilayer soil moisture reanalysis products. A random forest model combined with SHAP (Shapley Additive Explanations), an interpretable machine learning approach, was employed to quantitatively evaluate vegetation NDVI sensitivity to soil moisture at different soil depths. The spatial patterns, nonlinear response characteristics, and temporal evolution trends of vegetation sensitivity were systematically analyzed, and the main driving factors underlying sensitivity changes were identified. The results show that NDVI sensitivity to soil moisture exhibits a pronounced vertical differentiation. Sensitivity is highest in the subsurface layer (7–28 cm), followed by the surface layer (0–7 cm), and lowest in the deep layer (28–100 cm). A significant nonlinear relationship exists between NDVI sensitivity and soil moisture, with vegetation responses markedly intensifying under low-moisture conditions, indicating clear threshold effects. Areas with high sensitivity are mainly distributed in typical grassland and desert-steppe regions, reflecting strong water-limitation characteristics. Over the past 40 years, vegetation sensitivity to soil moisture has shown an overall increasing trend, particularly in semi-arid regions, suggesting an intensifying ecosystem response to water stress. SHAP-based attribution analysis further indicates that the NDVI trend is the strongest positive contributing factor across all three soil moisture layers, revealing a positive feedback mechanism of vegetation greening–enhanced water dependence. Meanwhile, improved surface soil moisture reduces vegetation reliance on deeper soil water, reflecting a distinct interlayer substitution effect. This study systematically characterizes the spatiotemporal dynamics and driving mechanisms of vegetation sensitivity from a multilayer soil moisture perspective, providing scientific insight into ecosystem responses to climate change in arid and semi-arid regions, and offering useful implications for regional ecological management and adaptive strategies. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 196272280
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Sensitivity of Vegetation Greenness to Multi-Depth Soil Moisture on the Mongolian Plateau: Nonlinear Responses Revealed by RF–SHAP.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Tong%2C+Siqin%22">Tong, Siqin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shubo%22">Zhang, Shubo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xueliang%22">Yang, Xueliang</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Jinyuan%22">Ren, Jinyuan</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dorjsuren%2C+Altantuya%22">Dorjsuren, Altantuya</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bayarsaikhan%2C+Sainbuyan%22">Bayarsaikhan, Sainbuyan</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bao%2C+Gang%22">Bao, Gang</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Xiaojun%22">Huang, Xiaojun</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bao%2C+Yuhai%22">Bao, Yuhai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> baoyuhai@imnu.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Aug2026, Vol. 18 Issue 15, p2600. 22p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Soil+moisture%22">Soil moisture</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Arid+regions%22">Arid regions</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+resilience%22">Ecological resilience</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Shapley+Additive+Explanations%22">Shapley Additive Explanations</searchLink><br /><searchLink fieldCode="DE" term="%22Vegetation+greenness%22">Vegetation greenness</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Mongolian+Plateau+%28China+%26+Mongolia%29%22">Mongolian Plateau (China & Mongolia)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? NDVI sensitivity peaks in subsurface soil moisture (7–28 cm) across the Mongolian Plateau, showing clear vertical stratification. Vegetation sensitivity increases significantly (1982–2022) with nonlinear threshold responses and a shift toward deeper soil water dependence. What are the implications of the main findings? Dryland ecosystems exhibit strengthening water limitation and enhanced vegetation–water feedback under climate change. Deep soil moisture becomes increasingly critical for ecosystem stability and vegetation resilience. Under the context of climate change, investigating vegetation sensitivity to soil moisture in arid and semi-arid regions is crucial for assessing ecosystem stability. Nevertheless, how vegetation sensitivity varies among different soil depths, whether it exhibits nonlinear threshold responses, and how such sensitivity has evolved over recent decades remain poorly understood in the Mongolian Plateau. This study focuses on the Mongolian Plateau and uses long-term NDVI data (1982–2022) together with ERA5-Land multilayer soil moisture reanalysis products. A random forest model combined with SHAP (Shapley Additive Explanations), an interpretable machine learning approach, was employed to quantitatively evaluate vegetation NDVI sensitivity to soil moisture at different soil depths. The spatial patterns, nonlinear response characteristics, and temporal evolution trends of vegetation sensitivity were systematically analyzed, and the main driving factors underlying sensitivity changes were identified. The results show that NDVI sensitivity to soil moisture exhibits a pronounced vertical differentiation. Sensitivity is highest in the subsurface layer (7–28 cm), followed by the surface layer (0–7 cm), and lowest in the deep layer (28–100 cm). A significant nonlinear relationship exists between NDVI sensitivity and soil moisture, with vegetation responses markedly intensifying under low-moisture conditions, indicating clear threshold effects. Areas with high sensitivity are mainly distributed in typical grassland and desert-steppe regions, reflecting strong water-limitation characteristics. Over the past 40 years, vegetation sensitivity to soil moisture has shown an overall increasing trend, particularly in semi-arid regions, suggesting an intensifying ecosystem response to water stress. SHAP-based attribution analysis further indicates that the NDVI trend is the strongest positive contributing factor across all three soil moisture layers, revealing a positive feedback mechanism of vegetation greening–enhanced water dependence. Meanwhile, improved surface soil moisture reduces vegetation reliance on deeper soil water, reflecting a distinct interlayer substitution effect. This study systematically characterizes the spatiotemporal dynamics and driving mechanisms of vegetation sensitivity from a multilayer soil moisture perspective, providing scientific insight into ecosystem responses to climate change in arid and semi-arid regions, and offering useful implications for regional ecological management and adaptive strategies. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=196272280
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18152600
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 2600
    Subjects:
      – SubjectFull: Soil moisture
        Type: general
      – SubjectFull: Normalized difference vegetation index
        Type: general
      – SubjectFull: Arid regions
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Ecological resilience
        Type: general
      – SubjectFull: Climate change
        Type: general
      – SubjectFull: Shapley Additive Explanations
        Type: general
      – SubjectFull: Vegetation greenness
        Type: general
      – SubjectFull: Mongolian Plateau (China & Mongolia)
        Type: general
    Titles:
      – TitleFull: Sensitivity of Vegetation Greenness to Multi-Depth Soil Moisture on the Mongolian Plateau: Nonlinear Responses Revealed by RF–SHAP.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Tong, Siqin
      – PersonEntity:
          Name:
            NameFull: Zhang, Shubo
      – PersonEntity:
          Name:
            NameFull: Yang, Xueliang
      – PersonEntity:
          Name:
            NameFull: Ren, Jinyuan
      – PersonEntity:
          Name:
            NameFull: Dorjsuren, Altantuya
      – PersonEntity:
          Name:
            NameFull: Bayarsaikhan, Sainbuyan
      – PersonEntity:
          Name:
            NameFull: Bao, Gang
      – PersonEntity:
          Name:
            NameFull: Huang, Xiaojun
      – PersonEntity:
          Name:
            NameFull: Bao, Yuhai
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Text: Aug2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 15
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1