Submitted to: Irrigation Science
Publication Type: Peer reviewed journal
Publication Acceptance Date: 6/13/2003
Publication Date: 8/14/2003
Citation: Hunsaker, D.J., Pinter Jr, P.J., Barnes, E.M., Kimball, B.A. 2003. Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index. Irrigation Science. 22:95-104 Interpretive Summary: Dependable estimates of the amount of water used in crop evapotranspiration (ET), which is the combined crop transpiration and evaporation from the soil, are needed for determining the proper timing and amounts for crop irrigations. Crop coefficient (Kc) curves have been developed for many different crops and are widely used in irrigation scheduling to estimate the daily ET use from planting through harvest. However, the Kc curves that are presently available are fairly generalized and often fail to give good estimates of the actual daily ET use, which can lead to improper irrigation scheduling and poor water management. For example, generalized Kc curves presently cannot be routinely used to account for crop ET changes caused by differences in crop development due to nutrient deficiencies or insect pressures. This study presents a method to estimate a Kc curve for cotton from remote sensing measurements of crop reflectances, which can indicate differences in crop development and can be routinely measured either on the ground, in the air, or by satellite. The Kc estimated from the crop reflectance is expected to provide an estimate of crop water use based on the actual crop condition. This technique potentially offers cotton farmers, irrigation consultants, and government agencies a means to realistically determine daily ET occurring within a cotton field.
Technical Abstract: Crop coefficients are a widely used and universally accepted method for estimating the crop evapotranspiration (ETc) component in irrigation scheduling programs. However, uncertainties of generalized basal crop coefficient (Kcb) curves can contribute to ETc estimates that are substantially different from actual ETc. Limited research with corn has shown improvements to irrigation scheduling due to better water use estimation and more appropriate timing of irrigations when Kcb estimates derived from remotely-sensed multispectral vegetation indices (VIs) were incorporated with irrigation scheduling algorithms. The purpose of this article was to develop and evaluate Kcb estimation based on observations of the normalized-difference vegetation index (NDVI) for a full-season cotton grown in the desert southwestern U.S. The Kcb data used in developing the relationship with NDVI were derived from back-calculations of the FAO-56 dual crop coefficient procedures using field data obtained during two cotton experiments conducted during 1990 and 1991 at a site in central Arizona. The estimation model consisted of two regression relations: a linear function of Kcb versus NDVI (r2 = 0.97, n = 68) used to estimate Kcb from early vegetative growth to effective full cover, and a multiple regression of Kcb as a function of NDVI and cumulative growing-degree-days (GDD) [r2 = 0.82, n = 64] used to estimate Kcb after effective full cover was attained. The NDVI for cotton at effective full cover was 0.80; this value was used to mark the point at which the model transferred from the linear to the multiple regression function. An initial evaluation of the performance of the model was made by incorporating Kcb estimates, based on NDVI measurements and the developed regression functions, within the FAO-56 dual procedures and comparing the estimated ETc with field observations from two cotton plots collected during an experiment in central Arizona in 1998. Preliminary results indicate that the ETc based on the NDVI-Kcb model provided close estimates of actual ETc.