Author: Prasanta Das, Nazrul Islam and Dibyendu Mukhopadhyay
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Soil fertility is a critical component for ensuring sustainable agriculture and food security. This study assesses the spatial variability of soil fertility across Koch Bihar district, West Bengal, using a cloud-based geospatial approach via Google Earth Engine (GEE). The analysis utilized SoilGrids250m data (0–30 cm depth) and Landsat 8 reflectance imagery to extract key physical and chemical parameters, including pH, SOC, nitrogen (N), phosphorus (P), potassium (K), bulk density (BD), CEC, and soil texture (sand, silt, clay). A Fuzzy-AHP-based Soil Fertility Index (SFI) was developed using min–max normalization and expert-weighted indicators in Google Earth Engine. Results revealed that 28.59% of the district falls under high fertility (SFI: 0.61–0.80), 23.11% under moderate (0.41–0.60), and 21.19% under very high fertility (0.81–1.00), together covering ~73% of the region. Sitalkuchi (96.3%), Mathabhanga I (94.1%), and Mekhliganj (82.3%) showed the highest fertile zones, while Sitai, Cooch Behar II and Mathabhanga II have shown considerable portions of land under low to moderate fertility categories.. These spatial disparities underline the need for location-specific nutrient management and crop planning. The findings support policy interventions for precision agriculture, sustainable land use, and informed input allocation. Future research should incorporate time-series monitoring and field-based validation to enhance the accuracy of fertility predictions
Soil Fertility Index, Koch Bihar, Google Earth Engine, Fuzzy AHP, Sustainable Agriculture
This study has successfully demonstrated the integration of remote sensing and geospatial technologies—specifically Google Earth Engine (GEE)—in mapping and assessing soil fertility across the Koch Bihar district, West Bengal. By leveraging the high-resolution SoilGrids250m dataset and empirical estimations from Landsat 8 surface reflectance data, key soil parameters such as pH, SOC, BD, texture, nitrogen, CEC, phosphorus, and potassium have been spatially analyzed over a merged topsoil depth of 0–30 cm. The adoption of digital soil mapping (DSM) techniques, supported by Fuzzy AHP weighting and min–max normalization, has enabled the creation of a continuous Soil Fertility Index (SFI) surface, which has been classified into five distinct fertility classes. The results have revealed a predominantly fertile landscape, with approximately 73% of the district falling under moderate to very high fertility zones. Blocks like Sitalkuchi, Mekhliganj, and Mathabhanga-I have shown remarkably high fertility, making them suitable for intensive agriculture with minimal fertilizer input. In contrast, Sitai, Mathabhanga II, and parts of Cooch Behar II have been characterized by lower fertility due to suboptimal soil texture, reduced macro-nutrient levels, and acidic pH conditions. These findings have reinforced the critical role of soil physical and chemical properties—such as texture, organic carbon, pH, and nutrient content—in defining soil fertility and agricultural potential. This research has underscored the efficiency of GEE as a scalable and cost-effective platform for regional soil assessment, especially in heterogeneous landscapes like the Himalayan foothills. The digital SFI maps produced have provided a robust spatial framework for identifying nutrient-deficient zones, enabling site-specific nutrient management strategies. In sum, while Koch Bihar has possessed a strong agronomic base due to its fertile soils, targeted interventions are needed in low-fertility pockets to ensure balanced and sustainable agricultural development. These insights have paved the way for precision farming and informed land-use planning, contributing to long-term soil health and productivity in the region
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Prasanta Das, Nazrul Islam and Dibyendu Mukhopadhyay (2025). Cloud-Based Geospatial Mapping of Soil Properties Using Google Earth Engine: A Case Study of Koch Bihar District, West Bengal, India. International Journal of Theoretical & Applied Sciences, 17(2): 80–99
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