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Cotton yield varies spatially within a field. The variability can be caused by various production inputs such as soil properties, water management, and fertilizer application. Airborne multispectral imaging is capable of providing data and information to study effects of the inputs on yield qualitatively and quantitatively in a timely and cost-effective fashion. A 10-ha cotton field with irrigation and non-irrigation 2x2 blocks was used in this study. Six nitrogen application treatments were randomized with 2 replications within each block. As plant canopy was closed, airborne multispectral images of the field were acquired using a 3-CCD MS4100 camera. The images were processed to generate various vegetation indices. The vegetation indices were evaluated for best performance to characterize yield. The effect of irrigation on vegetation indices was significant. Models for yield estimation were developed and verified by comparing the estimated and actual yields. Results indicated that ratio of vegetation index (RVI) had a close relationship with yield (R2=0.47). Better yield estimation could be obtained using a model with RVI and soil EC (electrical conductivity) measurements of the field as explanatory variables (R2=0.53). This research demonstrates the capability of aerial multispectral remote sensing in estimating cotton yield variation, and providing methods for similar studies.