Impact of Climate Change on Soil Properties of Farms in Southern Guinea Savanna Using Machine Learning and Remote Sensing Approach.

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dc.contributor.authorAlabi, K.O.
dc.contributor.authorArebishola, Y.I
dc.contributor.authorAfe, A.I
dc.contributor.authorSaka, H.A.
dc.date.accessioned2026-02-24T13:04:16Z
dc.date.available2026-02-24T13:04:16Z
dc.date.issued2026-02-02
dc.description.abstractThis study quantitatively assessed the impact of climate change on soil properties in Asomu Farm and Malete Teaching and Research Farm using remote sensing and machine learning approaches. Landsat 8 satellite imagery from 2018, 2020, and 2024 was processed to derive the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Soil Moisture Index (SMI). The NDVI increased from 0.23 in 2018 to 0.42 in 2024, indicating improved vegetation density and health. Correspondingly, LST decreased from 35.6°C in 2018 to 32.1°C in 2024, suggesting a reduction in surface heat stress over time. SMI values revealed a consistent improvement in soil moisture levels, rising from 0.31 in 2018 to 0.47 in 2024. These changes suggest that the soil in both locations has become more resilient to moisture loss, likely due to increased vegetation cover and possibly improved rainfall patterns. Machine learning algorithms were applied to identify patterns and correlations between NDVI, LST, and SMI, enhancing the prediction of soil condition trends under varying climatic scenarios. The study concludes that integrating remote sensing with machine learning offers a reliable, data-driven approach to monitor the effects of climate change on soil systems. The quantitative results provide a strong basis for recommending climate-smart agriculture and adaptive land management practices in semi-arid regions of Nigeria.
dc.identifier.issnISSN: 1595-6121
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/6536
dc.language.isoen
dc.publisherNigerian Journal of Soil and Environmental Research.
dc.titleImpact of Climate Change on Soil Properties of Farms in Southern Guinea Savanna Using Machine Learning and Remote Sensing Approach.
dc.typeArticle
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