LARS Tech Report Number
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 which are capable of providing accurate estimates of crop condition and yields. Initial tests of this concept using data acquired in field experiments included: different planting dates, populations, row widths, and soil types, at the Purdue Agronomy Farm are described.
The spectral variable greenness was associated with approximately 80 percent of the variation in LAI and percent soil cover for both corn and soybean canopies. The proportions of solar radiation intercepted (SRI) by corn canopies were estimated using either measured LAI or greenness. The proportions of SRI of soybeans were estimated using either measured percent cover or greenness. Both estimates when accumulated over the growing season accounted for approximately 65 percent of the variation in corn 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 corn grain yield were 0.68 for ECG models using greenness to estimate SRI. Similar relationships were developed for soybean yields.
We concluded that the concept of estimating intercepted solar radiation using spectral data represents a viable approach for merging spectral and meteorological data for crop yield models. The concept appears to be extendable to large areas by using Landsat MSS or Thematic Mapper data along with daily meteorological data and could form the basis for a future crop production forecasting system.
Date of this Version