Forecasting rainfed sorghum yield using satellite-derived vegetation indices with limited ground-based information in Gadarif region, eastern Sudan - Bashir, M.A.; Tanakamaru, H.; Tada, A.; Hassan, A.E.; Khalid, A.E.; Sirelkhatim, H.A. (المجلة الزراعية; مجلد 17 عدد 2 (2019), 2021)
A practical crop growth and yield monitoring system based on satellite data is required and fundamental not only for precision farming, but also very useful for global food security enhancement. This study was performed to determine the optimal vegetation index and also to identify the best time for making a reliable crop yield forecast in one of the major sorghum-growing region (Gedarif State, Sudan). The study was also aimed to develop a simple yield prediction model which was later validated using an official yield data acquired during 2013 and 2014 cropping seasons from the Department of Information System and Statistical Analysis of the State Ministry of Agriculture, Gedarif State. The study used NASA’s multi-temporal MODerate resolution Imaging Spectroradiometer (MODIS) land products with limited ground information. Relationship between sorghum yield and crop reflectance indicated that normalized difference vegetation index (NDVI) at the third dekad of September (Sep.III) is the most appropriate to develop sorghum yield prediction model with higher R 2 value of 0.77 (p<0.05) compared to other vegetation indices (normalized ratio vegetation index, NRVI and soil-adjusted vegetation index, SAVI). The plotting of estimated yield against actual yield during 2013 and 2014 cropping seasons revealed strong positive and linear correlations (R 2 = 0.64 (p=0.06) and 0.74 (p<0.05), respectively with average R 2 = 0.71 (p<0.001) for both seasons. This study concluded that a good prediction of rainfed sorghum yield could be achieved more than 30 days before harvesting with quick, accurate and cost-effective method compared to traditional field surveys.