Abstract Excess fertilizer use has increased crop yields at the cost of disrupting nutrient cycles and contaminating waterways. This research harnesses satellite remote sensing and geospatial modeling to enable sustainable nutrient management. MODIS-derived Normalized Difference Vegetation Index (NDVI) tracks crop growth to optimize fertilizer timing and application rates. Geospatiotemporal analysis of terrain, land use, soils, and hydrology data delineates nutrient distribution pathways to mitigate pollution. Kriging (Gaussian Process Regression) and conditional simulation were used to model soil nutrient distributions. The framework enhances productivity while minimizing agriculture’s environmental footprint through precision management. Geospatiotemporal techniques provide stakeholders with actionable insights, reducing costs and protecting water resources. This methodology offers a practical solution for balancing agricultural intensification and stewardship.
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Akanbi et al. (2024) studied this question.
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