Evapotranspiration Retrieval Using S-SEBI Model with Landsat-8 Split-Window Land Surface Temperature Products over Two European Agricultural Crops

Crop evapotranspiration (ET) is a key variable within the global hydrological cycle to account for the irrigation scheduling, water budgeting, and planning of the water resources associated with irrigation in croplands. Remote sensing techniques provide geophysical information at a large spatial scale and over a relatively long time series, and even make possible the retrieval of ET at high spatiotemporal resolutions. The present short study analyzed the daily ET maps generated with the S-SEBI model, adapted to Landsat-8 retrieved land surface temperatures and broadband albedos, at two different crop sites for two consecutive years (2017-2018). Maps of land surface temperatures were determined using Landsat-8 Collection 2 data, after applying the split-window (SW) algorithm proposed for the operational SW product, which will be implemented in the future Collection 3. Preliminary results showed a good agreement with ground reference data for the main surface energy balance fluxes R-n and LE, and for daily ET values, with RMSEs around 50 W/m(2) and 0.9 mm/d, respectively, and high correlation coefficient (R-2 = 0.72-0.91). The acceptable uncertainties observed when comparing with local ground data were reaffirmed after the regional (spatial resolution of 9 km) comparison with reanalysis data obtained from ERA5-Land model, showing a StDev of 0.9 mm/d, RMSE = 1.1 mm/d, MAE = 0.9 mm/d, and MBE = -0.3 mm/d. This short communication tries to show some preliminary findings in the framework of the ongoing Tool4Extreme research project, in which one of the main objectives is the understanding and characterization of the hydrological cycle in the Mediterranean region, since it is key to improve the management of water resources in the context of climate change effects.

Automated summary

This study analyzed crop evapotranspiration using remote sensing techniques and model, and compared the results with ground reference data and reanalysis data. The findings showed good agreement and acceptable uncertainties.