Crop Yield Estimation Using NOAA − AVHRR Data and Meteorological Data in the Eastern Wimmera ( South Eastern Australia )

Having an estimate of final yield early in the growing season can be a powerful management and economic tool for the farming community. Therefore the possibility of using temporarily high resolution remote sensing data in combination with daily meteorological data for crop yield prediction on a close to field scale has been investigated for one of the main cropping areas in south−eastern Australia. The lack of rainfall in semi−arid to semi−humid climate of this region is one of the major limiting factors to crop growth. The relation of different parameters, such as the “Normalized Differential Vegetation Index” (NDVI), the date of the commencement of the grainfilling stage (GF), the water−Stress Degree Days” index (SDD), as well as the growing season rainfall, to yield of canola, wheat and cereals (wheat and barley) have been examined. Using information from 1995 to 1997, a crop yield estimation model on the basis of a multiple linear regression model has been developed and evaluated. The following paper reports the results of a study carried out at the Commonwealth Scientific and Industrial Research Organization (CSIRO), Aspendale, in collaboration with the Department of Natural Resources & Environment (NRS), Melbourne, and the University of Munich. 1. USING REMOTELY SENSED DATA FOR CROP YIELD FORECASTING AS AN AGRICULTURAL MANAGEMENT TOOL For farmers in the eastern Wimmera (Victoria, Australia) it is important to estimate final yield early in the growing season. Using their knowledge and experience about the local conditions they are able to estimate yield to a certain extent. Having reliable predictions of yield on a close to paddock scale could support their management and economic decisions. A basic parameter for crop yield, especially in semi−humid to semi−arid regions is growing season rainfall (GSR). Therefore, the GSR is often used by farmers to estimate final yield. Also, remotely sensed data have proved to be a good source of information for agricultural applications, in particular for yield estimation. Amongst others, M. S. RASMUSSEN (1997) and S. MOULIN et al. (1998) give a good review over past and present trends. One of the most common approaches is the use of vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) as a measure for plant growth and development. M.S. RASMUSSEN (1992) examined the integral of NDVI from data of the Advanced Very High Resolution Radiometer (AVHRR) over the phenological stages of reproduction of wheat for 4 km2 areas. N.A. QUARMBY et al. (1993) also investigated AVHRR−data and created 4 years time−series of NDVI. They point out the importance of the grainfilling period of wheat for final yield. M.P. CABEZÓN and J.C. TAYLOR (1994) analyzed correlations of multiple linear regressions using variant combinations of rainfall parameters and vegetation indices as independent variables. R.C. G. SMITH et al. (1991) found, that in a mediteranean−type environment the vegetation index − yield relationship does not explain yield variations significantly better than the rainfall − yield relationship over the growing season. However, they also point out that a combination of such information in a multiple linear regression model do improve the correlation significantly. Since S.D. JACKSON et al. (1977) it is known, that observations in the thermal band of the EM−spectrum can be used as an indicator for the plants’ water−stress. They use the difference of observations of daily surface temperature and air temperature for deriving the water−stress index “Stress Degree Days” (SDD). To better take into account the course of vegetation development, temporarily high resolution remote sensing data have to be applied. Only frequent observations allow the examination of the crop’s response to changing agrometeorological conditions. Such data can be obtained from the AVHRR−sensor onboard the polar orbiting Aigner, Edgar International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 19 satellites of the NOAA−series. The high temporal resolution, however is at the expense of the spatial resolution. The AVHRR−sensor has a spatial resolution of 1.1 km in nadir at a temporal resolution of one daytime overpass. One of the objectives of this research was to take advantage of the AVHRR’s high temporal resolution at the largest possible scale. Another objective was to develop a method to predict crop yield, that meets the requirements of the local farmers in the Eastern Wimmera.