Recent Advances in Deep Learning for Spatio-Temporal Drought Monitoring, Forecasting and Model Understanding

Droughts constitute one of the costliest natural hazards and have seriously destructive effects on the ecological environment, agricultural production and socio-economic conditions. Their elusive and subjective definition, due to the complex physical, chemical and biological processes of the Earth system they involve, makes their management an arduous challenge to researchers, as well as decision and policy makers. We present here our most recent advances in machine learning models in three complementary lines of research about droughts: monitoring, forecasting and understanding. While monitoring or detection is about gaining the time series of drought maps and discovering underlying patterns and correlations, forecasting or prediction is to anticipate future droughts. Last but not least, understanding or explaining models by means of expert-comprehensible representations is equally important as accurately addressing these tasks, especially for their deployment in real scenarios. Thanks to the emergence and success of deep learning, all of these tasks can be tackled by the design of spatio-temporal data-driven approaches built on the basis of climate variables (soil moisture, precipitation, temperature, vegetation health, etc.) and/or satellite imagery. The possibilities are endless, from the design of convolutional architectures and attention mechanisms to the use of generative models such as Normalizing Flows (NFs) or Generative Adversarial Networks (GANs), trained both in a supervised and unsupervised manner, among others. Different application examples in Europe from 2003 onwards are provided, with the aim of reflecting on the possibilities of the strategies proposed, and also of foreseeing alternatives and future lines of development. For that purpose, we make use of several mesoscale (1 km) spatial and 8 days temporal resolution variables included in the Earth System Data Cube (ESDC) [Mahecha et al., 2020] for drought detection, while high resolution (20 m, 5 days) Sentinel-2 data cubes, extracted from the extreme summer track in EarthNet2021 [Requena-Mesa et al., 2021], are considered for forecasting.

 

References

Mahecha, M. D., Gans, F., Brandt, G., Christiansen, R., Cornell, S. E., Fomferra, N., ... & Reichstein, M. (2020). Earth system data cubes unravel global multivariate dynamics. Earth System Dynamics, 11(1), 201-234.

Requena-Mesa, C., Benson, V., Reichstein, M., Runge, J., & Denzler, J. (2021). EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1132-1142).