Integration of thermal and optical models for mapping crop evapotranspiration in California.

The retrieval of land-surface temperature (LST) from thermal infrared (TIR) remote sensing has been shown to be a valuable tool in surface energy balance models for estimating evapotranspiration (ET) However, it is difficult to monitor daily evapotranspiration with passive sensors such as Landsat, the revisit time (8 days) can be insufficient to detect changes in surface moisture or crop phenology, particularly in regions with persistent overcast conditions. To address this challenge, the easy implementation of the common gap-filling methods, such as interpolating the ratio of actual to reference ET (ET/ETo) across image acquisition days, offers advantages in an operational context, but might not fully account for the non-linear dynamics of ET, especially over sparse-canopy irrigated crops. For this purpose, combining thermal and optical models can provide a more comprehensive and accurate estimate of crop evapotranspiration, by taking advantage of the operational use of dense time series of Earth Observation (EO) data by using different satellite platforms. In this study, we integrate the Dis-ALEXI model based on Landsat optical and thermal data and the Shuttleworth-Wallace (SW) ET model based on Sentinel-2 optical data. The aim is to evaluate the integration of the two frameworks, and to determine the advantages of using high temporal observations in estimating ET instead of interpolated ones. The approach proposed in this study has been evaluated using flux tower observations in California vineyards and almond orchards, respectively in the GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) and the T-REX (Tree crop Remote sensing of Evapotranspiration eXperiment) projects.