Unraveling the time-scale teleconnections between soil moisture and vegetation

Identifying causal relations from observational data is key to understand Earth system interactions. Extensions to spatio-temporal analysis at different scales are of vital importance for better understanding dynamical phenomenon of natural complex systems. Soil moisture-vegetation interactions constitute a central part of ecosystem functioning and health. Here we are interested in uncovering (potentially nonlinear) spatio-temporal causal relations at different time scales between two relevant Earth observation variables: soil moisture (SM) and vegetation optical depth (VOD). To aboard the complexity data problem, we extract relevant and expressive feature components with the nonlinear kernel-based dimensional reduction method ROCK-PCA in [1]. The method yields the main modes of variability of the variables that are then used to study causal relations. To infer causality relations we use the cross-information kernel Granger causality (XKGC) method introduced in [2], which accounts for nonlinear cross-relations between the involved variables and generalizes nonlinear GC methods. Results are succesfully compared to standard correlation analysis, transfer entropy and convergent cross-mapping alternative methods. In general XKGC identifies a sparser connectivity than correlation. Also, well-known wet and dry patterns are identified as reported in the literature, but other interesting unreported connections and spatio-temporal SM<-->VOD emerge.

REFERENCES
[1] D. Bueso, M. Piles and G. Camps-Valls, "Nonlinear PCA for Spatio-Temporal Analysis of
Earth Observation Data," in IEEE Transactions on Geoscience and Remote Sensing, accepted (2020).
[2] Brajard, J., Charantonis, A., Chen, C., & Runge, J. (Eds.). (2019). Proceedings of the
9th International Workshop on Climate Informatics: CI 2019 (No. NCAR/TN-561+PROC).