Scholarlink

<p>Soil pH serves as a critical indicator of soil chemistry and fertility, and mapping its spatial distribution holds significant importance for effective crop management. Digital soil mapping (DSM) is a commonly employed method for making rapid and cost-effective quantitative predictions of soil properties and soil classes. In the present study, we mapped soil pH (0–15&#xa0;cm) on a regional scale in Karnataka using a combination of various environmental variables. Three distinct machine learning models, namely support vector machine (SVM), Cubist, and random forest (RF), were assessed using a dataset of 146,044 observations collected under various projects. The environmental covariates used for soil pH prediction encompassed terrain attributes, Landsat-8 data, vegetation indices, and climatic variables. Among these models, RF model exhibited the most acceptable results for predicting soil pH (<i>R</i><sup>2</sup><sub>val</sub> = 0.61, CCC<sub>val</sub> = 0.74, RMSE<sub>val</sub> = 0.66). On the other hand, the Cubist and SVM models displayed comparatively lower accuracy, explaining only about 46–49% of the variation. The inclusion of climatic variables and Landsat-8 data emerged as crucial factors for predicting soil pH. The study successfully produced high-resolution maps of soil pH for the entire state at a 90-m resolution, while also quantifying the associated uncertainty. These high-resolution maps have the potential to be valuable for decision-makers, stakeholders, and agricultural practitioners towards precision agriculture and land resource management.</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

Modeling Soil pH at regional scale using environmental covariates and machine learning algorithm

  • 期刊论文

  • 发表日期：

    2025年6月24日

查看全文
  • Ramakrishnappa Vasundhara,
  • Subramanian Dharumarajan,
  • Rajendra Hegde,
  • Pravash Chandra Moharana,
  • Beeman Kalaiselvi,
  • Hittanagi Prakash,
  • Jujin Seema,
  • Ayyappa Sathish,
  • Neralikere Lakkappa Rajesh,
  • Praveenkumar Naikodi,
  • Nitin Patil

摘要

Soil pH serves as a critical indicator of soil chemistry and fertility, and mapping its spatial distribution holds significant importance for effective crop management. Digital soil mapping (DSM) is a commonly employed method for making rapid and cost-effective quantitative predictions of soil properties and soil classes. In the present study, we mapped soil pH (0–15 cm) on a regional scale in Karnataka using a combination of various environmental variables. Three distinct machine learning models, namely support vector machine (SVM), Cubist, and random forest (RF), were assessed using a dataset of 146,044 observations collected under various projects. The environmental covariates used for soil pH prediction encompassed terrain attributes, Landsat-8 data, vegetation indices, and climatic variables. Among these models, RF model exhibited the most acceptable results for predicting soil pH (R2val = 0.61, CCCval = 0.74, RMSEval = 0.66). On the other hand, the Cubist and SVM models displayed comparatively lower accuracy, explaining only about 46–49% of the variation. The inclusion of climatic variables and Landsat-8 data emerged as crucial factors for predicting soil pH. The study successfully produced high-resolution maps of soil pH for the entire state at a 90-m resolution, while also quantifying the associated uncertainty. These high-resolution maps have the potential to be valuable for decision-makers, stakeholders, and agricultural practitioners towards precision agriculture and land resource management.