Namibia's Omusati region, a semiarid agroecological zone, faces intensifying agricultural droughts driven by climate change, erratic rainfall, and land degradation. With 70 % of its population dependent on rain-fed subsistence farming, these droughts threaten food security, livelihoods, and ecological stability, highlighting the need for predictive tools to support sustainable land management. This study employs deep learning models to analyse remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), and Global Land Data Assimilation System (GLDAS) data to predict drought drivers, such as the normalized difference vegetation index (NDVI), evapotranspiration, land surface temperature, rainfall, soil moisture, and leaf area index. Trend analysis revealed a drought index increase from 0.3 (2003–2015) to over 0.7 by 2022, with tree cover decreasing to 0.08 % by 2023 and bare ground expanding, indicating severe ecological degradation. Projections indicate an 8 % increase in drought probability by 2024, endangering 81 % of farmland. Convolutional neural networks (CNNs), long short-term memory networks (LSTMs), identify the NDVI and land surface temperature as key predictors, whereas Convolutional LSTM (ConvLSTM) provides spatial insight into high-risk zones, although with lower accuracy. These findings can guide targeted strategies for adopting drought-resistant crops, water-efficient irrigation, and soil conservation in vulnerable areas. We recommend integrating AI-driven drought forecasts into Namibia’s climate and land policies through real-time monitoring, community-led adaptation and interagency coordination to increase on-ground resilience.

中文翻译：

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在纳米比亚 Omusati 地区实施深度学习算法，对农业干旱进行建模，以实现可持续土地管理

纳米比亚的奥穆萨蒂地区是一个半干旱的农业生态区，由于气候变化、降雨不稳定和土地退化，农业干旱加剧。由于 70% 的人口依赖雨养自给农业，这些干旱威胁着粮食安全、生计和生态稳定，凸显了需要预测工具来支持可持续土地管理。本研究采用深度学习模型分析来自中分辨率成像光谱仪 （MODIS）、气候灾害组红外降水站 （CHIRPS） 和全球土地数据同化系统 （GLDAS） 数据的遥感数据，以预测干旱驱动因素，例如归一化差异植被指数 （NDVI）、蒸散、地表温度、降雨量、土壤水分和叶面积指数。趋势分析显示，到 2022 年，干旱指数从 0.3（2003-2015 年）增加到 0.7 以上，到 2023 年树木覆盖率下降到 0.08%，裸露地面扩大，表明生态退化严重。预测表明，到 2024 年，干旱的可能性将增加 8%，危及 81% 的农田。卷积神经网络 （CNN） 和长短期记忆网络 （LSTM） 将 NDVI 和地表温度确定为关键预测因子，而卷积 LSTM （ConvLSTM） 提供对高风险区域的空间洞察，但准确性较低。这些发现可以指导在脆弱地区采用抗旱作物、节水灌溉和土壤保护的针对性策略。我们建议通过实时监测、社区主导的适应和机构间协调，将人工智能驱动的干旱预测整合到纳米比亚的气候和土地政策中，以提高实地复原力。