OPENAIRE OpenAire Sygma HAL INRAE Bielefeld Academic Search Engine (BASE) HAL Sorbonne Université UnpayWall HAL-INSU HAL-IRD Horizon / Pleins textes Crossref Microsoft Academic Graph European Union Open Data Portal Agricultural and Forest Meteorology Open Access 2018-04-19 2018-11-12 Abstract Modeling soil evaporation has been a notorious challenge due to the complexity of the phenomenon and the lack of data to constrain it. In this context, a parsimonious model is developed to estimate soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. It uses a soil resistance driven by surface (0–5 cm) soil moisture, meteorological forcing and time (hour) of day, and has the capability to be calibrated using the radiometric surface temperature derived from remotely sensed thermal data. The new approach is tested over a rainfed semi-arid site, which had been under bare soil conditions during a 9-month period in 2016. Three calibration strategies are adopted based on SEE time series derived from (1) eddy-covariance measurements, (2) thermal measurements, and (3) eddy-covariance measurements used only over separate drying periods between significant rainfall events. The correlation coefficients (and slopes of the linear regression) between simulated and observed (eddy-covariance-derived) SEE are 0.85, 0.86 and 0.87 (and 0.91, 0.87 and 0.91) for calibration strategies 1, 2 and 3, respectively. Moreover, the correlation coefficient (and slope of the linear regression) between simulated and observed SEE is improved from 0.80 to 0.85 (from 0.86 to 0.91) when including hour of day in the soil resistance. The reason is that, under non-energy-limited conditions, the receding evaporation front during daytime makes SEE decrease at the hourly time scale. The soil resistance formulation can be integrated into state-of-the-art dual-source surface models and has calibration capabilities across a range of spatial scales from spaceborne microwave and thermal data. 550 0207 environmental engineering Soil resistance 02 engineering and technology Remote sensing 15. Life on land calibration surface temperature [SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces, environment Surface temperature remote sensing Calibration [SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology soil resistance Soil moisture [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces soil moisture environment Soil evaporation Merlin, Olivier, Olivera-Guerra, Luis Enrique, Aït Hssaine, Bouchra, Amazirh, Abdelhakim, Rafi, Zoubair, Ezzahar, Jamal, Gentine, Pierre, Khabba, Said, Gascoin, Simon, Er-Raki, Salah, 2018-06-01 journalpaper Abstract Modeling soil evaporation has been a notorious challenge due to the complexity of the phenomenon and the lack of data to constrain it. In this context, a parsimonious model is developed to estimate soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. It uses a soil resistance driven by surface (0–5 cm) soil moisture, meteorological forcing and time (hour) of day, and has the capability to be calibrated using the radiometric surface temperature derived from remotely sensed thermal data. The new approach is tested over a rainfed semi-arid site, which had been under bare soil conditions during a 9-month period in 2016. Three calibration strategies are adopted based on SEE time series derived from (1) eddy-covariance measurements, (2) thermal measurements, and (3) eddy-covariance measurements used only over separate drying periods between significant rainfall events. The correlation coefficients (and slopes of the linear regression) between simulated and observed (eddy-covariance-derived) SEE are 0.85, 0.86 and 0.87 (and 0.91, 0.87 and 0.91) for calibration strategies 1, 2 and 3, respectively. Moreover, the correlation coefficient (and slope of the linear regression) between simulated and observed SEE is improved from 0.80 to 0.85 (from 0.86 to 0.91) when including hour of day in the soil resistance. The reason is that, under non-energy-limited conditions, the receding evaporation front during daytime makes SEE decrease at the hourly time scale. The soil resistance formulation can be integrated into state-of-the-art dual-source surface models and has calibration capabilities across a range of spatial scales from spaceborne microwave and thermal data. A phenomenological model of soil evaporative efficiency using surface soil moisture and temperature data 10.1016/j.agrformet.2018.04.010 https://doi.org/10.1016/j.agrformet.2018.04.010 645642