Developing Corn Regional Crop Coefficients Using a Satellite-Based Energy Balance Model (ReSET-Raster) in the South Platte River Basin of Colorado

Abstract

Improved irrigation management depends on accurate irrigation scheduling that should be based on estimates of daily crop water requirements. Traditional methods for estimating daily crop water requirements use a generic crop coefficient (Kc) that is based on point measurement and is developed for crops growing under optimal conditions that do not reflect actual growing conditions in regular fields. Therefore, daily water requirements based on these coefficients are not always accurate, require using stress coefficients, and obtaining crop coefficients under actual growing condition using traditional point measurement approaches, such as lysimeters or soil moisture monitoring, is impractical. Surface energy balance models can be used to directly estimate actual evapotranspiration (ET) in many fields, and these estimates can be used to develop regional crop coefficients that reflect the actual growing conditions. The objective of this study is to develop regional remote sensing–based corn Kcs for the South Platte River basin in Colorado. The ReSET-Raster surface energy balance model is used to estimate the actual ET for irrigated grain corn in the South Platte River basin of Colorado. The study covered five growing seasons (2001, 2004, 2005, 2006, and 2007) and a total of 104 Landsat 5 and 7 images were used. Normalized difference vegetation index (NDVI) computed from Landsat reflectance bands was used to separate the fields into two sets based on the crop planting date: (1) on-time planting, and (2) late planting. For each of these two sets of fields, a Kc curve was developed using remote sensing data, and a third Kc curve was developed using all fields. Growers can select the Kc curve to use based on their knowledge of their planting date. The three sets of crop coefficients were compared to traditional Kc curves. The differences between the comparison curves and the remote sensing–based Kc highlight the importance of developing regional and planting date–based crop Kc curves using crops under actual growing conditions.