A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset

Earlier research has shown that the Normalized Difference Drought Index (NDDI), combining information from both NDVI and NDMI, can be an accurate early indicator of drought conditions. NDDI is computed with information from visible, near-infrared, and short-wave infrared channels, and demonstrates increased sensitivity as a drought indicator than other indices. In this work, we aim to determine whether NDDI can serve as an early indicator of drought or dramatic environmental change, by computing NDDI using data from landscapes around bodies of water in Europe, which are not as drought-prone as the central US grasslands where NDDI was initially evaluated on. We use the dataset SEN2DWATER (SEN2DWATER: A Novel Multitemporal Dataset and Deep Learning Benchmark For Water Resources Analysis), a 2-Dimensional spatiotemporal dataset created from multispectral Sentinel-2 data collected over water bodies from July 2016 to December 2022. SEN2DWATER contains data from all 13 bands of Sentinel-2, making it a suitable dataset for our research. We leverage two CNNs, each learning trends in NDVI and NDMI values respectively using time series of images obtained from the SEN2DWATER dataset. By using the CNNs outputs, the predicted NDVI and NDMI values, we propose to compute a predicted NDDI, with the goal of investigating its accuracy. Preliminary results show that NDDI can be effectively forecasted with good accuracy by using ML methods, and the SEND2DWATER dataset could allow to calculate NDDI as a useful method for predicting climate and ecological change. Moreover, such predictions could be highly useful also in mitigating, or even preventing, any harmful effects of climate and ecological change, by supporting policy decisions.

SEN2DWATER: A Novel Multispectral and Multitemporal Dataset and Deep Learning Benchmark for Water Resources Analysis

Climate change has caused disruption in certain weather patterns, leading to extreme weather events like flooding and drought in different parts of the world. In this paper, we propose machine learning methods for analyzing changes in water resources over a time period of six years, by focusing on lakes and rivers in Italy and Spain. Additionally, we release open-access code to enable the expansion of the study to any region of the world. We create a novel multispectral and multitemporal dataset, SEN2DWATER, which is freely accessible on GitHub. We introduce suitable indices to monitor changes in water resources, and benchmark the new dataset on three different deep learning frameworks: Convolutional Long Short Term Memory (ConvLSTM), Bidirectional ConvLSTM, and Time Distributed Convolutional Neural Networks (TD-CNNs). Future work exploring the many potential applications of this research is also discussed.