Implementing the Remotely Sensed Evaporative Stress Index Globally Using MODIS Day/Night Land-surface Temperatures
Monday, 15 December 2014
The utility and reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, precipitation-based indices only reflect one component of the surface hydrologic cycle, and cannot readily capture non-precipitation based moisture inputs to the land-surface system (e.g., irrigation, shallow groundwater tables) that may temper drought impacts or variable rates of water consumption across a landscape. As global drought monitoring exercises, such as the Global Drought Information System, continue to expand, a need for tools that complement precipitation-based indicators will also grow. Here we describe a global implementation of the remotely sensed Evaporative Stress Index (ESI) based on anomalies in actual-to-reference evapotranspiration (ET) ratio. For ESI implementations to date, actual ET has been derived via energy balance using the morning land-surface temperature (LST) rise observed with geostationary satellites. In comparison with vegetation indices, LST is a fast-response variable, with the potential for providing early warning of crop stress reflected in increasing canopy temperatures. Our initial work has mainly focused on regional implementations of ESI (e.g., North America, Brazil, Africa) and a global ESI product has not been yet been evaluated. As the global constellation of geostationary sensors continue to mature, some limitations still exist which hamper an implementation of ESI using only geostationary LST. Therefore, a new regression-based methodology which uses twice-daily observations of LST from polar orbiting sensors (such as the Moderate Resolution Imaging Spectrometer - MODIS and the Visible Infrared Imaging Radiometer Suite - VIIRS) has been developed to estimate mid-morning LST needed for ESI from a single sensor. This new global ESI dataset will be evaluated over the 2000-2014 time period against currently used global drought indicators, with a special focus on evolution of ESI during significant droughts in the period of record.