Energy Balance Estimation of Evapotranspiration for Wheat Grown Under Variable Management Practices in Central Arizona

Estimating and monitoring the spatial distribution of evapotranspiration (ET) over irrigated crops is becoming increasingly important for managing crop water requirements under water scarce conditions. The usual point-based approaches for estimating ET, however, do not provide enough data for precision farming applications, whereby irrigation schedules could be customized by crop conditions at sub-field scales. Needed in addition are spatially distributed ET modeling approaches, obtainable only through remote sensing, which can observe ET-related surface properties such as vegetation density and surface temperature. Although research using remote sensing to estimate ET has been pursued for many years, there are still few ground-validated, full-season ET studies at fine spatial scales. In this study, we assessed the ability of a remote sensing model to retrieve daily ET throughout an entire growing season for wheat. Image data with 0.5 m resolution were collected in 2005 over an irrigation scheduling research site in central Arizona (Maricopa). The 1.3 ha study area (FISE05) contained 32 leveled, flood-irrigated plots with treatments for irrigation scheduling, planting density, and fertilization. Daily ET was modeled using a two-source energy balance (TSEB) approach and airborne image observations in visible, near-infrared, and thermal infrared wavelengths for six dates throughout the growing season. Using independent soil water depletion observations, modeled daily ET values were accurate to within 0.4 mm d-1 for most of the FISE05 season and sensitive to changes in wheat canopy changes. Late-season ET estimates were less satisfactory, with accuracies to within 1.3 mm d-1. The significance of these results was supported by verifying agreement between ground and airborne estimates of vegetation indices, wheat canopy cover, and surface temperature. Results from FISE05 also showed that the value of this particular implementation of ET remote sensing was limited by inadequate temporal sampling, seasonal dependence of vegetation density estimators, and uncertain parameterization for the late-season. In each case, modeled and projected daily ET estimate errors could exceed 1 mm d-1. These ET/energy balance results can be improved, however, by revising the vegetation density estimators and by combining episodic high-spatial-resolution image data with continuous high-temporal-resolution ground data.