Improving Spectral Crop Coefficient Approach with Raw Image Digital Count Data to Estimate Crop Water Use

Abstract:

Water resources and agricultural applications require knowledge of crop water use (CWU) over a range of spatial and temporal scales. Due to the spatial density of meteorological stations, the resolution of CWU estimates based on these data is fairly coarse and not particularly suitable or reliable for water resources planning, irrigation scheduling and decision making. Various methods have been developed for quantifying CWU of agricultural crops. In this study, an improved version of the spectral crop coefficient which includes the effects of stomatal closure is applied. Raw digital count (DC) data in the red, near-infrared, and thermal infrared (TIR) spectral bands of Landsat-7 and Landsat-8 imaging sensors are used to construct the TIR–ground cover (GC) pixel data distribution and estimate the effects of stomatal closure. CWU is then estimated by combining results of the spectral crop coefficient approach and the stomatal closer effect. To test this approach, evapotranspiration was measured in 5 agricultural fields in the semi-arid Texas High Plains during the 2013 and 2014 growing seasons and compared to corresponding estimated values of CWU determined using this approach. The results showed that the estimated CWU from this approach was strongly correlated (R² = 0.79) with observed evapotranspiration. In addition, the results showed that considering the stomatal closer effect in the proposed approach can improve the accuracy of the spectral crop coefficient method. These results suggest that the proposed approach is suitable for operational estimation of evapotranspiration and irrigation scheduling where irrigation is used to replace the daily CWU of a crop.