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<p>Spatial information on soil carbon content and&#xa0;storage is essential for land use planning, carbon sequestration, and climate change mitigation. This study aimed to map soil organic carbon (SOC) and SOC stocks across Karnataka, India (191,791 km<sup>2</sup>), via large datasets (n = 144,197) compiled from multiple sources. Two different approaches based on the data source were used for building the prediction model: Model A, which uses all datasets (n = 144,197), and Model B, which uses district wise models (11 districts, n = 139,704) with an additional model for the remaining data (n = 4,493). Model B showed the best performance, with the lowest RMSE (average of 0.28%), and was thus selected for mapping. The SOC stock was estimated by applying a class pedotransfer function to infer missing soil bulk density measurements, for which the soil textural class map of Karnataka was predicted via the random forest classification algorithm, and then average bulk density values for each textural class were used. The average SOC stock across Karnataka was 1.46 ± 0.54&#xa0;kg&#xa0;m<sup>−2</sup>, ranging from 0.05–8.14&#xa0;kg&#xa0;m<sup>−2</sup>. Among different soil types, Ultisols had the highest SOC stock (4,209&#xa0;Mg&#xa0;km<sup>−2</sup>), and Aridisols had the lowest. Similarly, Grasslands stored the highest amount of SOC stock (4,654&#xa0;Mg&#xa0;km<sup>−2</sup>) followed by forest (3,337&#xa0;Mg&#xa0;km<sup>−2</sup>) and agricultural lands had the least SOC stock (1,688&#xa0;Mg&#xa0;km<sup>−2</sup>). This study provides a comprehensive SOC stock map for Karnataka, which is crucial for effective land use planning, prioritize areas for carbon sequestration, and support national SOC inventories in India.</p>

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Prediction and mapping of soil organic carbon stock via large datasets coupled with pedotransfer functions

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Subramanian Dharumarajan,
  • Kabindra Adhikari,
  • Ranabir Chakraborty,
  • Beeman Kalaiselvi,
  • Ramakrishnappa Vasundhara,
  • Manickam Lalitha,
  • Rajendra Hegde,
  • Hittangi Prakash,
  • Sreekumar Parvathy,
  • C. G. Kusuma,
  • Ayyappa Sathish,
  • N. L. Rajesh,
  • Praveenkumar Naikodi,
  • K. T. Gurumurthy,
  • P. L. Patil,
  • Venkataramanappa Ramamurthy

摘要

Spatial information on soil carbon content and storage is essential for land use planning, carbon sequestration, and climate change mitigation. This study aimed to map soil organic carbon (SOC) and SOC stocks across Karnataka, India (191,791 km2), via large datasets (n = 144,197) compiled from multiple sources. Two different approaches based on the data source were used for building the prediction model: Model A, which uses all datasets (n = 144,197), and Model B, which uses district wise models (11 districts, n = 139,704) with an additional model for the remaining data (n = 4,493). Model B showed the best performance, with the lowest RMSE (average of 0.28%), and was thus selected for mapping. The SOC stock was estimated by applying a class pedotransfer function to infer missing soil bulk density measurements, for which the soil textural class map of Karnataka was predicted via the random forest classification algorithm, and then average bulk density values for each textural class were used. The average SOC stock across Karnataka was 1.46 ± 0.54 kg m−2, ranging from 0.05–8.14 kg m−2. Among different soil types, Ultisols had the highest SOC stock (4,209 Mg km−2), and Aridisols had the lowest. Similarly, Grasslands stored the highest amount of SOC stock (4,654 Mg km−2) followed by forest (3,337 Mg km−2) and agricultural lands had the least SOC stock (1,688 Mg km−2). This study provides a comprehensive SOC stock map for Karnataka, which is crucial for effective land use planning, prioritize areas for carbon sequestration, and support national SOC inventories in India.