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Spatiotemporal Dynamics of Cropland Topsoil Organic Carbon in Changchun, China, Based on Machine Learning and Multi-Source Geospatial Data.

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Title: Spatiotemporal Dynamics of Cropland Topsoil Organic Carbon in Changchun, China, Based on Machine Learning and Multi-Source Geospatial Data.
Authors: Xia, Jingyao1 (AUTHOR), Wen, Huiqing2 (AUTHOR), Li, Haoming1,3 (AUTHOR), Yang, Yadi1 (AUTHOR), Wang, Mingchang2,3 (AUTHOR), Li, Xiaoyan1,3 (AUTHOR) lxyan@jlu.edu.cn
Source: Remote Sensing. Jun2026, Vol. 18 Issue 11, p1781. 26p.
Subjects: Carbon in soils, Geospatial data, Soil fertility, Environmental engineering, Farms, Machine learning, Quantile regression
Geographic Terms: China, Manchuria (China), Changchun (Jilin Sheng, China)
Abstract: Highlights: What are the main findings? QRNN outperformed RF and XGBoost and was selected as the optimal model for SOCD prediction. SOCD showed a non-monotonic pattern with alternating decline and recovery phases, with consistently low values in the southwest. What are the implications of the main findings? Latitude, elevation, and mean annual temperature were stable dominant drivers of SOCD in Changchun croplands. Multi-source data and stage-based analysis improve SOC monitoring and support sustainable soil management. Soil organic carbon (SOC) of cropland is a key indicator of soil fertility and contributes to climate regulation and carbon storage. The understanding of SOCchanges in cropland in Northeast China still lacks high-precision long-term empirical evidence. This study is of great significance for ensuring national food security and regional sustainable development. Taking Changchun, a representative black soil region, as the study area, this study integrated 953 field samples with 19 predictors to estimate cropland soil organic carbon density (SOCD) from 2000 to 2022. The performance of quantile regression neural network (QRNN), random forest (RF), and extreme gradient boosting (XGBoost) models was compared. QRNN showed the best overall performance (R2 = 0.74, RMSE = 0.57 kg/m2, MAE = 0.40 kg/m2, and RPIQ = 2.46) and also exhibited greater stability in temporal-stage validation. Results indicated that SOCD exhibited an overall declining trend with intermittent recoveries, decreasing from 3.72 kg/m2 in 2000 to 3.36 kg/m2 in 2005, then increasing to 3.55 kg/m2 in 2010, slightly declining to 3.46 kg/m2 in 2015, and recovering to 3.63 kg/m2 in 2022. Spatially, SOCD remained low in the southwest, fluctuated markedly in the north, and was relatively stable in the central region. The analysis of the optimal parameter geographic detector (OPGD) showed that Y-latitude, elevation, and mean annual temperature (MAT) were stable dominant factors, while precipitation (PRE) and remote sensing variables showed stage-dependent effects. Interactions among multiple factors further enhanced the explanation of SOCD variations. These findings provide theoretical support for enhancing soil carbon retention and promoting long-term cropland sustainability in black soil areas. [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.)
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Spatiotemporal Dynamics of Cropland Topsoil Organic Carbon in Changchun, China, Based on Machine Learning and Multi-Source Geospatial Data.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Xia%2C+Jingyao%22">Xia, Jingyao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wen%2C+Huiqing%22">Wen, Huiqing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Haoming%22">Li, Haoming</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Yadi%22">Yang, Yadi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Mingchang%22">Wang, Mingchang</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xiaoyan%22">Li, Xiaoyan</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> lxyan@jlu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1781. 26p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Carbon+in+soils%22">Carbon in soils</searchLink><br /><searchLink fieldCode="DE" term="%22Geospatial+data%22">Geospatial data</searchLink><br /><searchLink fieldCode="DE" term="%22Soil+fertility%22">Soil fertility</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+engineering%22">Environmental engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Farms%22">Farms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Quantile+regression%22">Quantile regression</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22Manchuria+%28China%29%22">Manchuria (China)</searchLink><br /><searchLink fieldCode="DE" term="%22Changchun+%28Jilin+Sheng%2C+China%29%22">Changchun (Jilin Sheng, China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? QRNN outperformed RF and XGBoost and was selected as the optimal model for SOCD prediction. SOCD showed a non-monotonic pattern with alternating decline and recovery phases, with consistently low values in the southwest. What are the implications of the main findings? Latitude, elevation, and mean annual temperature were stable dominant drivers of SOCD in Changchun croplands. Multi-source data and stage-based analysis improve SOC monitoring and support sustainable soil management. Soil organic carbon (SOC) of cropland is a key indicator of soil fertility and contributes to climate regulation and carbon storage. The understanding of SOCchanges in cropland in Northeast China still lacks high-precision long-term empirical evidence. This study is of great significance for ensuring national food security and regional sustainable development. Taking Changchun, a representative black soil region, as the study area, this study integrated 953 field samples with 19 predictors to estimate cropland soil organic carbon density (SOCD) from 2000 to 2022. The performance of quantile regression neural network (QRNN), random forest (RF), and extreme gradient boosting (XGBoost) models was compared. QRNN showed the best overall performance (R2 = 0.74, RMSE = 0.57 kg/m2, MAE = 0.40 kg/m2, and RPIQ = 2.46) and also exhibited greater stability in temporal-stage validation. Results indicated that SOCD exhibited an overall declining trend with intermittent recoveries, decreasing from 3.72 kg/m2 in 2000 to 3.36 kg/m2 in 2005, then increasing to 3.55 kg/m2 in 2010, slightly declining to 3.46 kg/m2 in 2015, and recovering to 3.63 kg/m2 in 2022. Spatially, SOCD remained low in the southwest, fluctuated markedly in the north, and was relatively stable in the central region. The analysis of the optimal parameter geographic detector (OPGD) showed that Y-latitude, elevation, and mean annual temperature (MAT) were stable dominant factors, while precipitation (PRE) and remote sensing variables showed stage-dependent effects. Interactions among multiple factors further enhanced the explanation of SOCD variations. These findings provide theoretical support for enhancing soil carbon retention and promoting long-term cropland sustainability in black soil areas. [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.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/rs18111781
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 1781
    Subjects:
      – SubjectFull: Carbon in soils
        Type: general
      – SubjectFull: Geospatial data
        Type: general
      – SubjectFull: Soil fertility
        Type: general
      – SubjectFull: Environmental engineering
        Type: general
      – SubjectFull: Farms
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Quantile regression
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      – SubjectFull: China
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      – SubjectFull: Manchuria (China)
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      – SubjectFull: Changchun (Jilin Sheng, China)
        Type: general
    Titles:
      – TitleFull: Spatiotemporal Dynamics of Cropland Topsoil Organic Carbon in Changchun, China, Based on Machine Learning and Multi-Source Geospatial Data.
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            NameFull: Xia, Jingyao
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              M: 06
              Text: Jun2026
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              Y: 2026
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