Scholarlink

<p>The gross primary productivity (GPP) of Shanxi Province, China, plays an important role in the carbon cycle of the Loess Plateau ecosystem. However, Shanxi Province lacks carbon flux stations, leading to imprecise GPP estimation results. Additionally, few studies have explored the drivers of long-term GPP change in Shanxi Province. Therefore, in this study, we aimed to estimate the GPP in Shanxi Province from 2001 to 2022 and determine the driving factors of long-term GPP trends. To this end, we proposed an improved GPP estimation method based on the CatBoost model. Our CatBoost GPP model reduces model overfitting in few-shot scenarios and effectively captures the time dependence in time-series data. In addition, it integrates the change characteristics of vegetation ecological indicators and topography constraints, improving GPP estimation accuracy. Subsequently, we explored the spatial and temporal variations driving force through methods such as Theil-Sen Median trend analysis and Geodetectors. Our results show that (1) Compared with existing methods, the proposed CatBoost GPP method achieved superior site-level accuracy, with an <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_8927_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(R^{2}\)</EquationSource> </InlineEquation> value of 0.890, root mean square error (RMSE) of 1.155 gC<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_8927_Article_IEq2.gif" Format="GIF" Height="20" Rendition="HTML" Resolution="72" Type="Linedraw" Width="74" /> </InlineMediaObject> <EquationSource Format="TEX">\(\,\textrm{m}^{-2}\,\textrm{day}^{-1}\)</EquationSource> </InlineEquation>, and mean absolute error (MAE) of 0.772 gC<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_8927_Article_IEq2.gif" Format="GIF" Height="20" Rendition="HTML" Resolution="72" Type="Linedraw" Width="74" /> </InlineMediaObject> <EquationSource Format="TEX">\(\,\textrm{m}^{-2}\,\textrm{day}^{-1}\)</EquationSource> </InlineEquation>. Furthermore, we compared our results with previous GPP products to further assess the regional-level accuracy; (2) The GPP in Shanxi Province displayed a fluctuating increase, with a growth rate of 20.58 gC<InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_8927_Article_IEq4.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="78" /> </InlineMediaObject> <EquationSource Format="TEX">\(\,\textrm{m}^{-2}\,\textrm{year}^{-1}\)</EquationSource> </InlineEquation> from 2001 to 2022. The overall spatial variation was characterized by low GPP in the northwest and high GPP in the southeast. The GPP change was mainly characterized by weak anti-persistence; thus, approximately 58.8% of the area may experience degradation in the future; and (3) Land use type significantly influenced GPP changes in Shanxi, with the restoration and improvement of grassland being the main contributor to the increase in GPP. The interaction between precipitation and temperature had the most complex and significant impact on GPP, affecting approximately 62.05% of the study area. The results of this study provide a theoretical basis for ecological protection and sustainable development in Shanxi Province.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

GPP estimation based on CatBoost and analysis of change driving factors in Shanxi Province, China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

查看全文
  • Yujie Li,
  • Xuanguang Liu,
  • Zhenchao Zhang,
  • Jun Lang

摘要

The gross primary productivity (GPP) of Shanxi Province, China, plays an important role in the carbon cycle of the Loess Plateau ecosystem. However, Shanxi Province lacks carbon flux stations, leading to imprecise GPP estimation results. Additionally, few studies have explored the drivers of long-term GPP change in Shanxi Province. Therefore, in this study, we aimed to estimate the GPP in Shanxi Province from 2001 to 2022 and determine the driving factors of long-term GPP trends. To this end, we proposed an improved GPP estimation method based on the CatBoost model. Our CatBoost GPP model reduces model overfitting in few-shot scenarios and effectively captures the time dependence in time-series data. In addition, it integrates the change characteristics of vegetation ecological indicators and topography constraints, improving GPP estimation accuracy. Subsequently, we explored the spatial and temporal variations driving force through methods such as Theil-Sen Median trend analysis and Geodetectors. Our results show that (1) Compared with existing methods, the proposed CatBoost GPP method achieved superior site-level accuracy, with an \(R^{2}\) value of 0.890, root mean square error (RMSE) of 1.155 gC \(\,\textrm{m}^{-2}\,\textrm{day}^{-1}\) , and mean absolute error (MAE) of 0.772 gC \(\,\textrm{m}^{-2}\,\textrm{day}^{-1}\) . Furthermore, we compared our results with previous GPP products to further assess the regional-level accuracy; (2) The GPP in Shanxi Province displayed a fluctuating increase, with a growth rate of 20.58 gC \(\,\textrm{m}^{-2}\,\textrm{year}^{-1}\) from 2001 to 2022. The overall spatial variation was characterized by low GPP in the northwest and high GPP in the southeast. The GPP change was mainly characterized by weak anti-persistence; thus, approximately 58.8% of the area may experience degradation in the future; and (3) Land use type significantly influenced GPP changes in Shanxi, with the restoration and improvement of grassland being the main contributor to the increase in GPP. The interaction between precipitation and temperature had the most complex and significant impact on GPP, affecting approximately 62.05% of the study area. The results of this study provide a theoretical basis for ecological protection and sustainable development in Shanxi Province.