PredRNNv2-based drought prediction using Vegetation Health Index (VHI)

Droughts are expected to increase in both frequency and severity, exacerbated by rising global temperatures associated with climate change. These trends pose serious threats to the agricultural sector, directly impacting food production and security. Moreover, increasing drought incidence increases the risks associated with agricultural and forestry disasters, including reduced crop yields, soil degradation, and wildfires. Given these challenges, the ability to accurately monitor and predict drought conditions is critical. Effective drought forecasting plays an important role in establishing agricultural and water management policies and enabling better handling of the impacts of these events. This will enable timely and informed decisions to ensure that appropriate measures are in place to mitigate the adverse impacts of drought on ecosystems, food supplies and overall environmental health. The development and improvement of tools for drought time series forecasting is therefore essential to ongoing efforts to adapt to and mitigate the impacts of climate change. This study introduces a model designed to predict Vegetation Health Index (VHI) time series data using the Predictive Recurrent Neural Network Version 2 (PredRNN-V2). The VHI, which effectively integrates land surface temperature and vegetation status, has been widely used in drought assessment. The study focuses on South Korea, utilizing long-term weekly VHI data from NOAA for short-term prediction. The PredRNN-V2 model utilizes a network of interconnected spatio-temporal LSTM cells to learn and predict the temporal and spatial characteristics of time series images. This architecture can properly handle the complex spatial and temporal dynamics inherent in satellite-based drought data and can therefore be an effective tool for drought prediction.

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2022R1I1A1A01073185)