NDVI—crop monitoring and early yield assessment of Burkina Faso

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Abstract
On a 1984-1989 series of ARTEMIS-NDVI data derived from the NOAA-AVHRR sensor a case study on crop monitoring and early crop yield forecasting was elaborated for the provinces of Burkina Faso. In order to remove residual effects of clouds and other atmospheric influences on 10-day maximum NDVI images, a conditional temporal interpolation method was applied. Various NDVI regression parameters were compared. For the seven northern provinces, a simple linear regression based on averaged maximum 10-daily or monthly NDVI values proved to be superior to regressions based on the integrated NDVt and on NDVI increments. Multiple regressions led to significantly higher correlation coefficients, but only towards the end of the growing season (up to r2 = 087). The simple linear regression was also found valid for a part of the central and southern provinces. The yields of the majority of the provinces however was best approximated using one second-order polynomial equation. A test of the regressions on 1989 data showed a forecast error percentage of less than 15 per cent for half of the 30 provinces in August, approximately 2 months before harvest. In the other half of the provinces, high forecast errors occurred mainly due to a locust invasion, excessive rainfall in August and drought in September, after the time of the forecast. Therefore correction factors for the occurrence of extreme pest and other problems have to be included in the model in close cooperation with the relevant organizations. Some of these problems could however be assessed indirectly from the NDVI dynamics.