Ali Awais, Jat Baloch Muhammad Yousuf, Naveed Muhammad, Nigar Anam, Almalki Abdulrahman Seraj, Rasool Ayesha Ghulam, Gedfew Meseret Abeje, Arafat Ahmed A
Department of Geography, Government Graduate College Gojra, Toba Tek Singh, Pakistan.
School of Environmental Science and Engineering, Shandong University, Qingdao, 266237, People's Republic of China.
Sci Rep. 2025 Jul 28;15(1):27527. doi: 10.1038/s41598-025-13167-0.
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 km to 24,322 km, while urbanization expanded built-up areas from 4,146 to 6,581 km, 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.
本研究提出了一种将水比指数（WRI）、归一化差异叶绿素指数（NDCI）、土地利用/土地覆盖（LULC）制图以及细胞自动机 - 马尔可夫（CA - Markov）模型与温度波动进行新颖整合的方法，利用2016年至2023年期间南非西北省的高分辨率（30米）卫星图像监测灌溉土地动态，揭示了农业可持续性和水资源管理面临的严峻挑战。卫星图像和地理空间分析表明，该地区灌溉土地集中，面积从25,732平方千米降至24,322平方千米，而城市化使建成区面积从4,146平方千米扩大到6,581平方千米，与耕地形成竞争。CA - Markov模型预测到2033年农业损失将进一步加剧，荒地占主导（62.54%），水体面积缩小至1.72%，水资源短缺状况恶化。WRI值从2016年的0.40降至2023年的0.28，反映出水分胁迫加剧，而夏季气温急剧上升，2023年峰值高达35.99°C，加剧了蒸散和灌溉需求。该研究确定了诸如补贴偏向、农村基础设施薄弱和极端气候等制度障碍是灌溉面积减少的主要驱动因素，这与干旱地区的全球模式相符。将CA - Markov模型与WRI和温度趋势相结合，为适应性土地利用规划提供了一个强大的框架，强调利益相关者的参与和技术采用，以减轻气候影响并确保这个脆弱半干旱地区的粮食 - 水安全。本手稿反映了一种综合多指标、多时间遥感分析的多维方法以提供空间和预测性见解。这种多模型融合弥合了生物物理水资源可利用性、植被健康、土地转变趋势和未来灌溉情景之间的差距，为受气候变化驱动的缺水地区提供了一个更全面且可扩展的解决方案，深入洞察了供水、土地适宜性和气候变异性之间的相互作用，为支持脆弱地区粮食安全、生计和环境可持续性的适应性策略奠定了基础。
