Remotely sensed data can be quickly and economically acquired, which is useful for in-season crop management. A multispectral remote sensing study of water and N relations in precision agriculture of cotton (Gossypium hirsutum L.) has shown the positive relationships between plant reflectance, spectral index, irrigation amount, soil water content, N uptake, and lint yield, measured in a center pivot irrigated field on the southern High Plains of Texas. In this two-year (1998-1999) study, the treatments consisted of irrigation at 50% and 75% calculated cotton potential evapotranspiration (ET), and N application at rates of 0, 90 and 135 kg ha ^-1 arranged in an incomplete block (ICB) design. The multispectral plant and soil reflectance properties were investigated in eight discrete wavelengths ranging between 447 and 1752 nm. Cotton reflectance in the nearinfrared (NIR)band and normalized difference vegetative index (NDVI), measured on average twice per week, were related to total N uptake, plant biomass, and lint yield, measured in a 10-day interval during the growing season. The weekly patterns of soil/cotton multispectral reflectance in different spectral bands reflect temporal plant development status across the season. Based on the significant correlations between plant reflectance, NDVI, and lint yield, we attempted to predict cotton lint growth using the NDVI-days concept. The NDVI-days modeling predicts cotton lint yield based on in-season evaluation of NDVI against growing season days. The plant/soil spectral index is useful to determine real-time status of plant development, and to predict crop yield.