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A meta‐analysis data‐driven approach is developed to represent the soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. The new model is tested across a bare soil database composed of more than 30 sites around the world, a clay fraction range of 0.02–0.56, a sand fraction range of 0.05–0.92, and about 30,000 acquisition times. SEE is modeled using a soil resistance (rss) formulation based on surface soil moisture (θ) and two resistance parameters rss,refand θefolding. The data‐driven approach aims to express both parameters as a function of observable data including meteorological forcing, cut‐off soil moisture value θ1/2at which SEE=0.5, and first derivative of SEE at θ1/2, named Δθ1/2−1. An analytical relationship between (rss,ref;θefolding)and (θ1/2;Δθ1/2−1)is first built by running a soil energy balance model for two extreme conditions with rss= 0 and rss∼∞using meteorological forcing solely, and by approaching the middle point from the two (wet and dry) reference points. Two different methods are then investigated to estimate the pair (θ1/2;Δθ1/2−1)either from the time series of SEE and θobservations for a given site, or using the soil texture information for all sites. The first method is based on an algorithm specifically designed to accomodate for strongly nonlinear SEE(θ)relationships and potentially large random deviations of observed SEE from the mean observed SEE(θ). The second method parameterizes θ1/2as a multi‐linear regression of clay and sand percentages, and sets Δθ1/2−1to a constant mean value for all sites. The new model significantly outperformed the evaporation modules of ISBA (Interaction Sol‐Biosphère‐Atmosphère), H‐TESSEL (Hydrology‐Tiled ECMWF Scheme for Surface Exchange over Land), and CLM (Community Land Model). It has potential for integration in various land‐surface schemes, and real calibration capabilities using combined thermal and microwave remote sensing data. The new soil resistance model is based on soil moisture and two observable parametersModels are tested using a data set composed of more than 30 contrasted sitesOne resistance parameter is significantly correlated with both sand and clay fractions

During the 1999/2000 agricultural seasons, an experiment was carried out on winter wheat fields in the semiarid Yaqui Valley (Northwest Mexico). This data set was used to calibrate the evolution of the leaf area index (LAI) simulated by STICS, which was found to be in excellent agreement with estimates obtained from field reflectance measurements. After calibration, STICS was able to simulate satisfactorily the seasonal levels and trends observed in net radiation, soil moisture and evapotranspiration, but the crop temperature was overestimated by about 2.5 °C. On a larger scale, STICS was run on 16 fields with contrasting management practices. The simulations indicate that yield predictability is significantly lower for later sowing dates, consistent with observations. The seasonal variations of field and satellite data (Landsat-ETM+, Terra-MODIS and VEGETATION) NDVI were very close. However, some difficulties were noted: saturation of NDVI at high LAI values and smoothed variability on a 1-km spatial scale, as well as the need for a sound methodology for processing satellite data.