Advancing Drought Modeling: Harnessing Innovative GEE-derived Parameters for Improved Fusion-based Analysis at Regional Scale

This study proposes a novel fusion-based approach using Google Earth Engine (GEE) to accurately model distinct drought phenomena in various environmental regions, with Iran as a case study. The method integrates Environmental Parameters (EPs) - Vegetation (Veg), Temperature (Tem), Humidity (Hum), and Evaporation-Transpiration (ET) - with auxiliary parameters such as land-cover, topography, and wind-speed to improve modeling accuracy. A 39-year (1982-2020) time series of 14 Remote Sensing (RS) indices and 18 Ground-Based (GB) drought indices were used as input and output, respectively, for training and testing machine learning algorithms in three phases. The results obtained during the test period (2015-2020) from each phase were employed in the next phase based on a hereditary procedure to evaluate the potential of the proposed indices and their associated EPs (group of indices). The Consolidated Fusion-Based Drought Model (CFDM) was developed as the final product, which demonstrated superior accuracy and stability compared to other models, with an overall accuracy (OA) higher than 90% for all GB indices. As a result, the CFDM has utilized for generating drought maps in Iran. Furthermore, the effectiveness of the simultaneous use of auxiliary parameters and EPs was demonstrated through the Prevalent Fusion-Based Drought Model (PFDM). Our approach entails a systematic framework that incorporates innovative parameters (indices/indicators) to accurately model drought phenomena. This can contribute to the development of effective management strategies.