Untapping the potential of geostationary EO data to understand drought impacts with XAI

Ecosystems are affected by extreme climate conditions such as droughts worldwide but we still lack understanding of the involved dynamics. Which factors render an ecosystem more resilient, and on which temporal scales do weather patterns affect vegetation state and physiology? Traditional approaches to tackle such questions involve assumption-based land surface modeling or inversions. Machine learning (ML) methods can provide a complementary perspective on how ecosystems respond to climate in a more data-driven and assumption-free manner. However, ML depends heavily on data, and commonly used observations of vegetation at best contain one observation per day, but most products are provided at 16-daily to monthly temporal resolution. This masks important processes at sub-monthly time scales. In addition, ML models are inherently difficult to interpret, which still limits their applicability for process understanding.

In the present study, we combine modern deep learning models in the time domain with observations from the geostationary Meteosat Second Generation (MSG) satellite, centered over Africa. We model fractional vegetation cover (representing vegetation state) and land surface temperature (as a proxy for water stress) from MSG as a function of meteorology and static geofactors. MSG collects observations at sub-daily frequency, rendering it into an excellent tool to study short- to mid-term land surface processes. Furthermore, we use methods from explainable ML for post-hoc model interpretation to identify meteorological drivers of vegetation dynamics and their interaction with key geofactors.

From the analysis, we expect to gather novel insights into ecosystem response to droughts with high temporal fidelity. Drought response of vegetation can be highly diverse and complex especially in arid to semi-arid regions prevalent in Africa. Also, we assess the potential of explainable machine learning to discover new linkages and knowledge and discuss potential pitfalls of the approach. Explainable machine learning, combined with potent deep learning approaches and modern Earth observation products offers the opportunity to complement assumption-based modeling to predict and understand ecosystem response to extreme climate.