Spectral Estimates of Solar Radiation Intercepted by Corn Canopies1

Abstract

AbstractAs the need for timely information on worldwide crop production intensifies the role of remote sensing is becoming more prominent. If agronomic variables related to yield could be reliably estimated from multispectral satellite data, then crop growth and yield models could be implemented for large areas. The objective of this experiment was to develop methods for combining spectral and meteorological data in crop yield models that are capable of providing accurate estimates of crop condition and yields. Initial tests of this concept using data acquired in field experiments at the Purdue Agronomy Farm, West Lafayette, Ind., are presented. Reflectance factor data were acquired with a Landsat band radiometer throughout two growing seasons for corn (Zea mays L.) canopies differing in planting dates, populations, and soil types (Typic Argiaquoll and Udollic Ochraqualf). Agronomic data collected to coincide with the spectral data included leaf area index (LAI), biomass, development stage, and final grain yields. The spectral variable greenness was associated with 76% of the variation in LAI over all treatments. Single observations of LAI or greenness were found to have limited value in predicting corn yields. The proportions of solar radiation intercepted (SRI) by these canopies were estimated using either measured LAI or greenness. Both estimates, when accumulated over the growing season, accounted for approximately 65% of the variation in yields. The Energy Crop Growth (ECG) variable was used to evaluate the daily effects of solar radiation, temperature, and moisture stress on corn yields. Coefficients of determination for grain yields were 0.67 for the ECG model using measured LAI to estimate SRI, and 0.68 for the ECG model using greenness to estimate SRI. We conclude that this concept of estimating intercepted solar radiation using spectral data represents a viable approach for merging spectral and meteorological data in crop yield models. The concept appears to be extendable to large areas by using Landsat MSS data along with daily meteorological data and could form the basis for a future crop production forecasting system.