Advanced satellite-based remote sensing and data analytics for precision water resource management and agricultural optimization
Abstract This study presents a novel integration of the Water Ratio Index (WRI), Normalized Difference Chlorophyll Index (NDCI), Land Use/Land Cover (LULC) mapping, and Cellular Automata–Markov (CA–Markov) modeling with temperature fluctuations to monitor irrigated land dynamics using high-resolutio...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-13167-0 |
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| Summary: | Abstract This study presents a novel integration of the Water Ratio Index (WRI), Normalized Difference Chlorophyll Index (NDCI), Land Use/Land Cover (LULC) mapping, and Cellular Automata–Markov (CA–Markov) modeling with temperature fluctuations to monitor irrigated land dynamics using high-resolution (30m) satellite imagery in South Africa’s North West Province between 2016 to 2023, revealing critical challenges to agricultural sustainability and water resource management. Satellite imagery and geospatial analysis show irrigated lands concentrated in the region, which declined from 25,732 km2 to 24,322 km2, while urbanization expanded built-up areas from 4,146 to 6,581 km2, competing for arable land. The CA–Markov model predicts further agricultural loss by the year 2033, with barren land dominating (62.54%) and water bodies shrinking to 1.72%, worsening water scarcity. WRI values dropped from 0.40 in 2016 to 0.28 in 2023, reflecting increasing water stress, while temperatures rose sharply in summer, peaks up to 35.99 °C in 2023, intensifying evapotranspiration and irrigation demands. The study identifies institutional barriers such as biased subsidies, poor rural infrastructure, and climate extremes as key drivers of irrigation decline, mirroring global patterns in arid regions. The integration of the CA Markov model with WRI and temperature trends provides a robust framework for adaptive land-use planning, emphasizing stakeholder engagement and technology adoption to mitigate climate impacts and ensure food-water security in this vulnerable semi-arid region. This manuscript reflects the multi-dimensional approach to synthesizes multi-index, multi-temporal remote sensing analysis to deliver both spatial and predictive insights. This multi-model fusion bridges the gap between biophysical water availability, vegetation health, land transition trends, and future irrigation scenarios, offering a more holistic and scalable solution for water-scarce regions, driven by climate change provides critical insights into the interplay of water supply, land suitability, and climate variability, offering a foundation for adaptive strategies that support food security, livelihoods, and environmental sustainability in vulnerable regions. |
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| ISSN: | 2045-2322 |