Despite satellite imagery is being used to identify suitable areas for desert locust, there is a lack of automatized and operational procedures in Near Real Time (NRT). The aim of this study was to assess the capacity of Soil Moisture Near Real Time Neural Network Level 2 product (MIR_SMNRT2) from the Soil Moisture and Ocean Salinity satellite (SMOS) to predict nymphs of desert locust. We used soil moisture time series (between 2016 and 2019) to build 6 machine learning models (logistic regression model “glm”, eXtreme Gradient Boosting “xgbTree”, Weighted k-Nearest Neighbors “kknn”, Feed-Forward Neural Networks and Multinomial Log-Linear Models “nnet”, support vector machine radial “svmRadial”, and random forest “rf”) over the entire recession area. Model results proved that spatial and/or temporal constraints in data sampling conditioned the predictive capacity of the selected machine learning algorithms. Furthermore, we used a forward selection procedure to evaluate the impact that time series data exert on modelling. Our results suggest that soil moisture data retrieved between 95 and 12 days (before the sighting) provided sufficient information to achieve acceptable predictive performances. This methodology can improve current preventive and control operations, it is site-specific, and could be used to other pests.

尽管卫星图像被用于确定适合沙漠蝗虫的区域，但缺乏近实时 (NRT) 的自动化和操作程序。本研究的目的是评估土壤水分和海洋盐度卫星 (SMOS) 的土壤水分近实时神经网络 2 级产品 (MIR_SMNRT2) 预测沙漠蝗若虫的能力。我们使用土壤水分时间序列（2016 年至 2019 年之间）构建了 6 个机器学习模型（逻辑回归模型“glm”、极限梯度提升“xgbTree”、加权 k-最近邻“kknn”、前馈神经网络和多项对数对数-整个衰退区域的线性模型“nnet”、支持向量机径向“svmRadial”和随机森林“rf”）。模型结果证明，数据采样中的空间和/或时间限制会影响所选机器学习算法的预测能力。此外，我们使用前向选择程序来评估时间序列数据对建模的影响。我们的结果表明，在 95 到 12 天（目击前）检索到的土壤水分数据提供了足够的信息来实现可接受的预测性能。这种方法可以改进当前的预防和控制操作，它是针对特定地点的，可用于其他害虫。我们的结果表明，在 95 到 12 天（目击前）检索到的土壤水分数据提供了足够的信息来实现可接受的预测性能。这种方法可以改进当前的预防和控制操作，它是针对特定地点的，可用于其他害虫。我们的结果表明，在 95 到 12 天（目击前）检索到的土壤水分数据提供了足够的信息来实现可接受的预测性能。这种方法可以改进当前的预防和控制操作，它是针对特定地点的，可用于其他害虫。