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Modern remote sensing (RS) technologies, combined with Geographic Information Systems (GIS), have revolutionized agricultural monitoring and resource management by enabling site-specific observations and decision-making. This chapter explores the integration of digital soil mapping (DSM), geostatistical techniques, and GIS tools for soil fertility evaluation and conservation planning. DSM facilitates the generation of spatially explicit soil fertility maps using field data, laboratory soil test results, and ancillary geospatial variables. By capturing spatial variability in soil nutrients, these maps support data-driven nutrient management and soil health restoration efforts. A case study from a humid tropical region in Kerala, India, demonstrates the practical application of RS and GIS in mapping soil chemical properties and nutrient distribution at the block level. A comprehensive soil analysis of Irikkur revealed that 99.75% of the samples were acidic, primarily due to high rainfall, acidic fertilizer use, and plant root activity under humid tropical conditions, leading to nutrient leaching. Liming is recommended to address calcium (Ca) and magnesium (Mg) deficiencies and enhance nutrient availability. High organic carbon content was observed in 94.4% of samples, attributed to abundant litter fall and rapid decomposition. Phosphorus (P) was found to be high in 70.63% of the samples, while potassium (K) was low in 45%, medium in 39%, and high in 16%. Deficiencies were noted in 56.87% for Ca and 71.88% for Mg, whereas 95.63% of samples had sufficient sulfur (S). Micronutrient analysis showed adequate levels of zinc (Zn) in all samples, while boron (B) was deficient in 34.06%. Most samples had sufficient iron (Fe), copper (Cu), and manganese (Mn). All chemical properties, except pH, showed positive skewness and high kurtosis. Soil productivity was considered suboptimal based on chemical properties, potentially affecting root development and nutrient uptake. Spatial analysis showed low variance in pH, organic carbon, and electrical conductivity, with high variance in Ca. Kriging was more effective than IDW for spatial prediction, with strong spatial dependency for Zn and B, and moderate for other parameters.

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Soil Fertility Assessment and Mapping for Sustainable Soil Conservation Using Remote Sensing and GIS Techniques—A Case Study

  • 章节

  • 发表日期：

    2026年

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  • P. Sruthi,
  • U. Surendran,
  • P. Raja,
  • Girish Gopinath

摘要

Modern remote sensing (RS) technologies, combined with Geographic Information Systems (GIS), have revolutionized agricultural monitoring and resource management by enabling site-specific observations and decision-making. This chapter explores the integration of digital soil mapping (DSM), geostatistical techniques, and GIS tools for soil fertility evaluation and conservation planning. DSM facilitates the generation of spatially explicit soil fertility maps using field data, laboratory soil test results, and ancillary geospatial variables. By capturing spatial variability in soil nutrients, these maps support data-driven nutrient management and soil health restoration efforts. A case study from a humid tropical region in Kerala, India, demonstrates the practical application of RS and GIS in mapping soil chemical properties and nutrient distribution at the block level. A comprehensive soil analysis of Irikkur revealed that 99.75% of the samples were acidic, primarily due to high rainfall, acidic fertilizer use, and plant root activity under humid tropical conditions, leading to nutrient leaching. Liming is recommended to address calcium (Ca) and magnesium (Mg) deficiencies and enhance nutrient availability. High organic carbon content was observed in 94.4% of samples, attributed to abundant litter fall and rapid decomposition. Phosphorus (P) was found to be high in 70.63% of the samples, while potassium (K) was low in 45%, medium in 39%, and high in 16%. Deficiencies were noted in 56.87% for Ca and 71.88% for Mg, whereas 95.63% of samples had sufficient sulfur (S). Micronutrient analysis showed adequate levels of zinc (Zn) in all samples, while boron (B) was deficient in 34.06%. Most samples had sufficient iron (Fe), copper (Cu), and manganese (Mn). All chemical properties, except pH, showed positive skewness and high kurtosis. Soil productivity was considered suboptimal based on chemical properties, potentially affecting root development and nutrient uptake. Spatial analysis showed low variance in pH, organic carbon, and electrical conductivity, with high variance in Ca. Kriging was more effective than IDW for spatial prediction, with strong spatial dependency for Zn and B, and moderate for other parameters.