With population growth, the demand for land resources is expected to increase significantly in the coming decades. Maintaining the integrity of soil distribution requires a remarkable amount of work to deal with agricultural extension. Salinity intrusion monitoring is a crucial process, which directly affects sustainable development, especially in areas affected by global warming and in coastal zones. In recent years, various studies have used the soil-water salinity data to evaluate the spatiotemporal increase in salinity intrusion. This study aims to establish a novel framework for monitoring salinity intrusion using remote sensing and machine learning. It focuses on the salinity intrusion in soil, which affects water availability, food security, human health, etc. Numerous algorithms have been implemented to find the best solution for this issue, including Xgboost (XGR), Gaussian processes, support vector regression, deep neural networks, and the grasshopper optimization algorithm (GOA). A total of 143 samples collected from 2016 to 2020 at 39 measurement stations were divided into two sets: 70% training and 30% testing. Thirty-one independent variables were used to develop the model. Vietnam's Mekong Delta, where the salinity intrusion problem is becoming increasingly serious due to global warming and demographics, was selected as the study area. Each of the proposed models was compared and evaluated by applying various statistical indices such as the root mean square error, coefficient of determination (R²), and mean absolute error. The results show that the prediction model was built successfully by wielding data from the implemented salinity measurement stations, and the XGR-GOA model was better than the other models (R² = 0.86, RMSE = 0.076, and MAE = 0.065). This finding demonstrates the feasibility of estimating and monitoring salinity intrusion in data-limited regions by integrating optical satellite images and machine learning, which are easily and cost-effectively obtainable. The proposed conceptual methodology in our study is novel and provides additional useful information for the monitoring and management of salinity intrusion not only in Vietnam's Mekong Delta, but also in other sites that have similar natural and anthropological conditions.

随着人口增长，预计未来几十年对土地资源的需求将显着增加。保持土壤分布的完整性需要大量的工作来处理农业推广。盐度入侵监测是一个至关重要的过程，它直接影响可持续发展，特别是在受全球变暖影响的地区和沿海地区。近年来，各种研究使用土壤-水盐度数据来评估盐度入侵的时空增加。本研究旨在建立一个使用遥感和机器学习监测盐度入侵的新框架。它侧重于土壤中的盐分入侵，这会影响水资源的可用性、粮食安全、人类健康等。已经实施了许多算法来找到解决此问题的最佳方法，包括 Xgboost (XGR)、高斯过程、支持向量回归、深度神经网络和蚱蜢优化算法 (GOA)。2016-2020年在39个测量站采集的143个样本共分为两组：70%训练和30%测试。使用三十一个自变量来开发模型。越南的湄公河三角洲，由于全球变暖和人口统计，盐分入侵问题变得越来越严重，被选为研究区域。通过应用各种统计指标，如均方根误差、决定系数（R 和蚱蜢优化算法（GOA）。2016-2020年在39个测量站采集的143个样本共分为两组：70%训练和30%测试。使用三十一个自变量来开发模型。越南的湄公河三角洲，由于全球变暖和人口统计，盐分入侵问题变得越来越严重，被选为研究区域。通过应用各种统计指标，如均方根误差、决定系数（R 和蚱蜢优化算法（GOA）。2016-2020年在39个测量站采集的143个样本共分为两组：70%训练和30%测试。使用三十一个自变量来开发模型。越南的湄公河三角洲，由于全球变暖和人口统计，盐分入侵问题变得越来越严重，被选为研究区域。通过应用各种统计指标，如均方根误差、决定系数（R 由于全球变暖和人口统计，盐分入侵问题变得越来越严重，被选为研究区域。通过应用各种统计指标，如均方根误差、决定系数（R 由于全球变暖和人口统计，盐分入侵问题变得越来越严重，被选为研究区域。通过应用各种统计指标，如均方根误差、决定系数（R² )，平均绝对误差。结果表明，利用已实施盐度测量站的数据成功建立了预测模型，XGR-GOA模型优于其他模型（R ²  = 0.86，RMSE = 0.076，MAE = 0.065）。这一发现证明了通过整合光学卫星图像和机器学习来估计和监测数据有限地区盐度入侵的可行性，这些方法易于且经济高效。我们研究中提出的概念方法是新颖的，不仅为越南湄公河三角洲的盐度入侵监测和管理提供了额外的有用信息，而且为其他具有类似自然和人类学条件的地点提供了有用的信息。