Using ESAP software for predicting the spatial distributions of NDVI and transpiration of cotton

Abstract

Observations of the normalized difference vegetation index (NDVI) from aerial imagery can be used to infer the spatial variability of basal crop coefficients (Kcb), which in turn provide a means to estimate variable crop water use within irrigated fields. However, monitoring spatial Kcb at sufficient temporal resolution using only aerial acquisitions would likely not be cost-effective for growers. In this study, we evaluated a model-based sampling approach, ESAP (ECe Sampling, Assessment, and Prediction), aimed at reducing the number of seasonal aerial images needed for reliable Kcb monitoring. Aerial imagery of NDVI was acquired over an experimental cotton field having two treatments of irrigation scheduling, three plant density levels, and two N levels. During both 2002 and 2003, ESAP software used input imagery of NDVI on three separate dates to select three ground sampling designs having 6, 12, and 20 sampling locations. On three subsequent dates during both the years, NDVI data obtained at the design locations were then used to predict the spatial distribution of NDVI for the entire field. Regression of predicted versus imagery observed NDVI resulted in r2 values from 0.48 to 0.75 over the six dates, where higher r2 values occurred for predictions made near full cotton cover than those made at partial cover. Prediction results for NDVI were generally similar for all three sample designs. Cumulative transpiration (Tr) for periods from 14 to 28 days was calculated for treatment plots using Kcb values estimated from NDVI. Estimated cumulative Tr using either observed NDVI from imagery or predicted NDVI from ESAP procedures compared favorably with measured cumulative Tr determined from soil water balance measurements for each treatment plot. Except during late season cotton senescence, errors in estimated cumulative Tr were between 3.0% and 7.3% using observed NDVI, whereas they were they were between 3.4% and 8.8% using ESAP-predicted NDVI with the 12 sample design. Thus, employing a few seasonal aerial acquisitions made in conjunction with NDVI measurements at 20 or less ground locations optimally determined using ESAP, could provide a cost-effective method for reliably estimating the spatial distribution of crop water use, thereby improving cotton irrigation scheduling and management.Remote sensing Crop coefficients Irrigation management Crop water use