Forecasting Vegetation Health in the MENA Region by Predicting Vegetation Indicators with Machine Learning Models

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R
²
> 0.95) in the testing and (R
²
> 0.93) in the evaluation process.