If agronomic variables related to vigor and yield of crops could be reliably estimated from multispectral data, then crop growth and yield models could be implemented for large areas. The objectives of these experiments were to determine relationships of key agronomic characteristics and spectral properties of crops and to integrate spectral and meteorological data for forecasting crop yields. Reflectance data of corn, wheat, and soybeans were acquired with radiometers that simulate Landsat MSS and TM sensors. The spectral indices, greenness, and normalized difference were highly correlated with leaf area index (LAI) and absorbed photosynthetically active radiation (APAR). Grain yields were more highly related to APAR cumulated during the growing season than to maximum LAI or LAI duration. A model which simulated the daily effects of weather and APAR on growth accounted for 85% of the variation in corn yields. The concept of estimating agronomic characteristics using spectral data represents a viable approach for merging spectral and meteorological data for crop forecasting models
