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Project description: We propose to develop a global agricultural monitoring tool, with a focus on providing early warning of developing vegetation stress at relatively high spatial resolution (5-km) for agricultural decision makers and stakeholders. This tool is based on remotely sensed estimates of evapotranspiration retrieved via energy balance principles using observations of land-surface temperature (LST). The Evaporative Stress Index (ESI) represents anomalies in the ratio of actual-to-potential ET generated with the thermal remote-sensing based Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model. The LST inputs to ESI have been shown to provide early warning information about the development of vegetation stress, with stress-elevated canopy temperatures observed well before a decrease in greenness is detected in remotely sensed vegetation indices.


Whereas many drought indicators based on precipitation or atmospheric conditions capture meteorological drought, the ESI is one of few indicators of agricultural drought that reveals actual vegetation stress conditions realized on the ground. As a diagnostic indicator of actual ET, the ESI requires no information regarding antecedent precipitation or soil moisture storage capacity—the current available moisture to vegetation is deduced directly from the remotely sensed LST signal. This signal also inherently accounts for both precipitation and non-precipitation related inputs/sinks to the plant-available soil-moisture pool (e.g., irrigation, tile drainage), which can modify crop response to rainfall anomalies. Independence from precipitation data is a benefit for global agricultural monitoring applications due to sparseness in existing ground-based precipitation networks and time delays in public reporting. Even as satellite precipitation monitoring has closed some of the observational gaps, these data are usually provided at coarse resolution with their accuracy dependent upon extensive calibration with ground-based precipitation estimates.


End users/partners: National Drought Mitigation Center, USDA Foreign Agricultural Service,

International Center for Biosaline Agriculture’s MENA Regional Drought Management System,

G20 GeoGLAM Crop Monitor Initiative for the Agricultural Information System (AMIS), NASA SERVIR, Agriculture and Agri-Food Canada (AAFC)


Data sources, models, technology: The primary input to the original ALEXI modeling system is the time-differential change in mid-morning LST, typically obtained from geostationary satellites. To facilitate global mapping applications, new methods have been developed to estimate the mid-morning change from day-night temperature differences available from a single polar-orbiting thermal infrared sensor. In addition, a new cloud gap-filling technique using Ka-band retrievals of LST allows coverage in persistently cloudy equatorial regions. The project will exploit several NASA and NOAA Earth Science research datasets including: 1) land-surface products from the MODIS instruments on NASA’s Terra and Aqua satellites and the VIIRS on the Suomi National Polar-orbiting Partnership (NPP) platform, 2) microwave Ka-band observations from a number of national and international platforms, and 3) meteorological information from  the NOAA Climate Forecast System Reanalysis modeling systems, which provide operational production and retrospective analyses back to 1979.


Relevant project websites/links: Global ESI in NASA SERVIR Data Catalogue: