Abstract

This paper aims to offer an improved version of the new composite drought monitoring index (CDMI) and to test its applicability in the context of Tensift watershed in Morocco. A synergistic approach incorporating the remote sensing techniques, hydrometeorological data, simulated data and agricultural statistics was used for this purpose. After assessing the performance of CDMI, estimated Soil Moisture Anomaly Indicator (ISMA) was processed, validated and incorporated to the composite model. Random Forest algorithm was used to determine the weight of composite model components. Apart from comparative mapping, Pearson's correlation statistical analysis, linear regression and dependency tests were used to assess the performance of the improved composite model (CDMIa_RF). The result show that CDMIa_RF is better correlated with several indices such as: the Standardized Precipitation Index (SPI), (R²=0.74); Hydrological Drought Index (R²=0.70); grain productivity (R²=0.70), CDMI (R²=0.95), Vegetation Health Index (VHI), (R²=0.87), and Normalized Vegetation Supply Water Index (NVSWI), (R²= 0.85).

摘要

本文旨在提供新的综合干旱监测指数 (CDMI) 的改进版本，并测试其在摩洛哥 Tensift 流域的适用性。为此目的采用了一种结合了遥感技术、水文气象数据、模拟数据和农业统计数据的协同方法。在评估了 CDMI 的性能后，估计的土壤水分异常指标 (ISMA) 被处理、验证并合并到复合模型中。随机森林算法用于确定复合模型组件的权重。除了比较映射之外，还使用 ​​Pearson 的相关统计分析、线性回归和依赖性测试来评估改进的复合模型 (CDMIa_RF) 的性能。² =0.74); 水文干旱指数（R ² =0.70）；谷物生产力 (R ² =0.70)、CDMI (R ² =0.95)、植被健康指数 (VHI) (R ² =0.87) 和标准化植被供水指数 (NVSWI) (R ² = 0.85)。