Evapotranspiration estimates in a traditional irrigated area in semi-arid Mediterranean. Comparison of four remote sensing-based models

Jamal El Farkh, Vincent Simonneaux, Lionel Jarlan, Jamal Ezzahar, Gilles Boulet, Adnane Chakir, Salah Er-Raki

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Quantification of actual crop evapotranspiration (ETa) over large areas is a critical issue to manage water resources, particularly in semi-arid regions. In this study, four models driven by high resolution remote sensing data were intercompared and evaluated over an heterogeneous and complex traditional irrigated area located in the piedmont of the High Atlas mountain, Morocco, during the 2017 and 2018 seasons: (1) SAtellite Monitoring of IRrigation (SAMIR) which is a software-based on the FAO-56 dual crop coefficient water balance model fed with Sentinel-2 high-resolution Normalized Difference Vegetation Index (NDVI) to derive the basal crop coefficient (Kcb); (2) Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) which is a surface energy balance model fed with land surface temperature (LST) derived from thermal data provided from Landsat 7 and 8; (3) a modified version of the Shuttleworth–Wallace (SW) model which uses the LST to compute surface resistances and (4) METRIC-GEE which is a version of METRIC model (“Mapping Evapotranspiration at high Resolution with Internalized Calibration”) that operates on the Google Earth Engine platform, also driven by LST. Actual evapotranspiration (ETa) measurements from two Eddy-Covariance (EC) systems and a Large Aperture Scintillometer (LAS) were used to evaluate the four models. One EC was used to calibrate SAMIR and SPARSE (EC1) which were validated using the second one (EC2), providing a Root Mean Square Error (RMSE) and a determination coefficient (R) of 0.53 mm/day (R=0.82) and 0.66 mm/day (R=0.74), respectively. SW and METRIC-GEE simulations were obtained respectively from a previous study and Google Earth Engine (GEE), therefore no calibration was performed in this study. The four models predict well the seasonal course of ETa during two successive growing seasons (2017 and 2018). However, their performances were contrasted and varied depending on the seasons, the water stress conditions and the vegetation development. By comparing the statistical results between the simulation and the measurements of ETa it has been shown that SAMIR and METRIC-GEE are the less scattered and the better in agreement with the LAS measurements (RMSE equal to 0.73 and 0.68 mm/day and R equal to 0.74 and 0.82, respectively). On the other hand, SPARSE is less scattered (RMSE = 0.90 mm/day, R = 0.54) than SW which is slightly better correlated (RMSE = 0.98 mm/day, R = 0.60) with the observations. This study contributes to explore the complementarities between these approaches in order to improve the evapotranspiration mapping monitored with high-resolution remote sensing data.
Original languageEnglish (US)
Pages (from-to)107728
JournalAgricultural Water Management
StatePublished - May 31 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-06-13
Acknowledgements: This research was conducted within the Joint International Laboratory TREMA (https://lmi-trema.ma). Setup was funded by CNRST SAGESSE Project and German Cooperation Giz within the frame of the Hydraulic Basin Agency of the Tensift (ABHT). The authors wish to thank the projects: RISE-H2020-ACCWA (grant agreement no: 823965), PHC TBK/18/61, PRIMA-IDEWA, PRIMA-ALTOS and ERANETMED03-62 CHAAMS for partly funding the experiments.


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