Applying Tipping Point Theory to Remote Sensing Science to Improve Early Warning Drought Signals for Food Security

Abstract

Famines have long been associated with drought. With the severity of droughts growing in association with climate change, there is increasing pressure to do a better job predicting famines and delivering international aid to avert human suffering and civil instability. We examine recent advances in remote sensing technology, focusing on the latency, historical availability and spatial and temporal scales of the data these satellites provide. Because of their global coverage, seven variables derived from satellite observations emerge as especially pertinent to drought and famine: precipitation (TRMM/GPM), groundwater (GRACE/GRACE-FO), snow (MODIS), soil moisture (SMOS, SMAP, Sentinel-1), evapotranspiration (MODIS, ECOSTRESS), vegetation health (Landsat, AVHRR, MODIS, SPOT) and chlorophyll fluorescence (OCO-2). We discuss tipping point theory as a possible framework for taking advantage of long time series of these satellite data where they exist in order to enhance the effectiveness of existing famine early warning systems.