View in EDS HTML Full Text PDF Full Text

MODIS-Based Estimation of Grassland Gross Primary Productivity in Inner Mongolia Using a ConvTransformer Deep Learning Model.

Saved in:
Bibliographic Details
Title: MODIS-Based Estimation of Grassland Gross Primary Productivity in Inner Mongolia Using a ConvTransformer Deep Learning Model.
Authors: Shi, Dingqi1,2 (AUTHOR), Yao, Yunjun1,2 (AUTHOR) yaoyunjun@bnu.edu.cn, Li, Yufu3 (AUTHOR), Zhang, Xueyi1,2,4 (AUTHOR), Zhang, Xiaotong1,2,5 (AUTHOR), Jiang, Bo1,2 (AUTHOR), Yu, Ruiyang2,5 (AUTHOR), Liu, Lu1,2,3 (AUTHOR), Xie, Zijing1,2,4 (AUTHOR), Fan, Jiahui1,2,5 (AUTHOR), Qiu, Fei1,2 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p2016. 22p.
Subjects: Transformer models, MODIS (Spectroradiometer), Deep learning, Spatiotemporal processes, Environmental monitoring, Carbon cycle
Geographic Terms: Inner Mongolia (China)
Abstract: Highlights: What are the main findings? A ConvTransformer framework was developed for estimating grassland GPP in Inner Mongolia. The model outperformed RF, GBRT, SVR, and EC-LUE, generating a daily 1 km GPP dataset (2003–2018). What are the implications of the main findings? The framework improves the representation of spatiotemporal dynamics in arid and semi-arid grasslands and enhances regional GPP estimation accuracy. The generated long-term high-resolution GPP dataset provides valuable support for carbon cycle research, ecological monitoring, and sustainable grassland management. Understanding ecosystem carbon processes relies heavily on the reliable assessment of gross primary productivity (GPP) yet remains challenging in the Inner Mongolia grasslands due to data scarcity and high uncertainty among existing products. We developed a ConvTransformer-based framework that exploits complementary information from satellite observations and meteorological datasets to enhance the representation of complex spatiotemporal dependencies in grassland ecosystems. Grounded in leave-one-site-out cross-validation across six eddy covariance sites, the model achieved average performance metrics of R2 = 0.59, RMSE = 1.40 g C m−2 d−1, Bias = −0.31 g C m−2 d−1, and KGE = 0.46, outperforming traditional machine learning models (RF, GBRT, and SVR) as well as the light use efficiency model (EC-LUE) in both accuracy and robustness. Using this framework, we generated a daily GPP dataset at spatial granularity of 1 km for the Inner Mongolia grasslands from 2003 to 2018. The results reveal a clear spatial gradient, with GPP decreasing from southeast to northwest. Comparisons with established products, including FLUXCOM, BESS V2, and PML V2, show strong spatial consistency and reduced discrepancies, supporting the reliability of the estimates. Overall, the proposed framework provides an effective approach for characterizing regional carbon dynamics and supports long-term ecological monitoring in semi-arid regions. [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: 194915149
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: MODIS-Based Estimation of Grassland Gross Primary Productivity in Inner Mongolia Using a ConvTransformer Deep Learning Model.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Shi%2C+Dingqi%22">Shi, Dingqi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yao%2C+Yunjun%22">Yao, Yunjun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> yaoyunjun@bnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Yufu%22">Li, Yufu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xueyi%22">Zhang, Xueyi</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaotong%22">Zhang, Xiaotong</searchLink><relatesTo>1,2,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Bo%22">Jiang, Bo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Ruiyang%22">Yu, Ruiyang</searchLink><relatesTo>2,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Lu%22">Liu, Lu</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Zijing%22">Xie, Zijing</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fan%2C+Jiahui%22">Fan, Jiahui</searchLink><relatesTo>1,2,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Fei%22">Qiu, Fei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p2016. 22p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22MODIS+%28Spectroradiometer%29%22">MODIS (Spectroradiometer)</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+cycle%22">Carbon cycle</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Inner+Mongolia+%28China%29%22">Inner Mongolia (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? A ConvTransformer framework was developed for estimating grassland GPP in Inner Mongolia. The model outperformed RF, GBRT, SVR, and EC-LUE, generating a daily 1 km GPP dataset (2003–2018). What are the implications of the main findings? The framework improves the representation of spatiotemporal dynamics in arid and semi-arid grasslands and enhances regional GPP estimation accuracy. The generated long-term high-resolution GPP dataset provides valuable support for carbon cycle research, ecological monitoring, and sustainable grassland management. Understanding ecosystem carbon processes relies heavily on the reliable assessment of gross primary productivity (GPP) yet remains challenging in the Inner Mongolia grasslands due to data scarcity and high uncertainty among existing products. We developed a ConvTransformer-based framework that exploits complementary information from satellite observations and meteorological datasets to enhance the representation of complex spatiotemporal dependencies in grassland ecosystems. Grounded in leave-one-site-out cross-validation across six eddy covariance sites, the model achieved average performance metrics of R2 = 0.59, RMSE = 1.40 g C m−2 d−1, Bias = −0.31 g C m−2 d−1, and KGE = 0.46, outperforming traditional machine learning models (RF, GBRT, and SVR) as well as the light use efficiency model (EC-LUE) in both accuracy and robustness. Using this framework, we generated a daily GPP dataset at spatial granularity of 1 km for the Inner Mongolia grasslands from 2003 to 2018. The results reveal a clear spatial gradient, with GPP decreasing from southeast to northwest. Comparisons with established products, including FLUXCOM, BESS V2, and PML V2, show strong spatial consistency and reduced discrepancies, supporting the reliability of the estimates. Overall, the proposed framework provides an effective approach for characterizing regional carbon dynamics and supports long-term ecological monitoring in semi-arid regions. [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=194915149
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18122016
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 2016
    Subjects:
      – SubjectFull: Transformer models
        Type: general
      – SubjectFull: MODIS (Spectroradiometer)
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Spatiotemporal processes
        Type: general
      – SubjectFull: Environmental monitoring
        Type: general
      – SubjectFull: Carbon cycle
        Type: general
      – SubjectFull: Inner Mongolia (China)
        Type: general
    Titles:
      – TitleFull: MODIS-Based Estimation of Grassland Gross Primary Productivity in Inner Mongolia Using a ConvTransformer Deep Learning Model.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Shi, Dingqi
      – PersonEntity:
          Name:
            NameFull: Yao, Yunjun
      – PersonEntity:
          Name:
            NameFull: Li, Yufu
      – PersonEntity:
          Name:
            NameFull: Zhang, Xueyi
      – PersonEntity:
          Name:
            NameFull: Zhang, Xiaotong
      – PersonEntity:
          Name:
            NameFull: Jiang, Bo
      – PersonEntity:
          Name:
            NameFull: Yu, Ruiyang
      – PersonEntity:
          Name:
            NameFull: Liu, Lu
      – PersonEntity:
          Name:
            NameFull: Xie, Zijing
      – PersonEntity:
          Name:
            NameFull: Fan, Jiahui
      – PersonEntity:
          Name:
            NameFull: Qiu, Fei
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 12
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1