Agriculture is the largest consumer of water in the world through irrigation. Improving water use efficiency in agriculture is an immediate requirement of human society to maintain global food security, preserve the quality and quantity of water resources, and to reduce poverty, migration and conflict between countries. Effective cropland management requires an improved understanding of crop responses to water stress. More specifically, a comprehensive assessment of the spatio-temporal dynamics of crop evapotranspiration (ET) is essential to improve irrigation water use efficiency and to minimize water consumption for agricultural purposes.

For the understandingof drought stress, one of the essential parameters is the Land Surface Temperature (LST). The LST is the radiative skin temperature of the earth's surface, which is calculated using thermal infrared radiation satellites. Observations of LST are obtained from many global remote sensing satellites, including the Copernicus’ Sentinel-3 (SLSTR) mission.

To monitor drought stress, it is necessary to understand the energy fluxes over the earth's surface with surface energy balance (SEB) models, which use LST as one of the main drivers, providing the most important lower boundary condition in  SEB models. LST is critical for partitioning ET between evaporation and versus transpiration.

Current research will use satellite based LST and ET as input to a coupled energy-water balance based hydrological model for crop water accounting and estimation of irrigation water requirements. Moreover, the new improved multi-sensor LST climate data records (CDR) produced within the CCI+ LST project will be investigated. This data provides an unprecedented opportunity to confront LST related uncertainties in SEB modeling, and help obtain the strength and uncertainties of SEB models for operational mapping of water stress in arid and semi-arid ecosystems. To date, the estimation of EE, ET, and water-stress (ST) in arid and semi-arid ecosystems has been severely hindered by the lack of long-term, accurate and stable LST CDRs.