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