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The objective of this paper is to study, on a weekly basis, the explanatory power of one satellite-based measurement, the Normalized Difference Vegetation Index (NDVI), for wheat yield modelling in 40 census agricultural regions (CAR) in the Canadian Prairies during the whole growing season using 16 years of NOAA AVHRR satellite data (between 1987 and 2002). We also explore the relative value of NDVI compared with a land-based measurement, the Cumulative Moisture Index (CMI). By developing a series of weekly wheat yield models over the course of the growing season, we are able to determine the accuracy of different models. Our findings indicate that NDVI possesses explanatory power 4 weeks earlier in the season than CMI. [ABSTRACT FROM AUTHOR]

Currently, a model (referred to as a monthly model) employing monthly temperature and precipitation data is used by the Canadian Wheat Board to estimate spring wheat yields for the Canadian Prairies. The model uses a cumulative moisture index as an explanatory variable. In this paper, the performance of the monthly model was improved by first developing a daily model by employing daily, instead of monthly, data for the 1975-1996 period and then by developing a hybrid model which incorporated into the daily model an additional variable derived from the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR)-based composited Normalized Difference Vegetation Index (NDVI) data for the 1987-1996 period. Out of the seven variables derived, two variables - the average NDVI during the heading phenological-phase and the average NDVI during the entire growing season - were found to be the best. The start and the end of the heading phase were estimated using a biometeorological time scale model. The performance of models was tested on five crop districts (1b, 3bn, 4b, 6a, and 9a) of Saskatchewan on the basis of coefficient of determination, R[sup 2]. While using 1975-1996 data, the values for R[sup 2] were 0.43, 0.82, 0.73, 0.71 and 0.00 in the case of the daily model as opposed to 0.20, 0.71, 0.57, 0.58, and 0.00 in the case of the monthly model for districts 1b, 3bn, 4b, 6a, and 9a, respectively. While using 1987-1996 data, the values of R[sup 2] were 0.79, 0.96, 0.83, 0.95, and 0.39 in the case of the hybrid model as opposed to 0.13, 0.70, 0.75, 0.50, and 0.00 in the case of the monthly model for districts 1b, 3bn, 4b, 6a, and 9a, respectively. For district 9a, which experiences an adequate supply of soil moisture, the concept of cumulative soil moisture index was not found to hold well for yield estimation. [ABSTRACT FROM AUTHOR]