A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan

Three existing models are coupled to assess crop development and forecast yield in the largest contiguous irrigation network in the world: the Indus Basin in Pakistan. Monteith’s model is used for the calculation of absorbed photosynthetically active radiation (APAR), the Carnegie Institution Stanford model is used for determining the light use efficiency, and the surface energy balance algorithm for land (SEBAL) is used to describe the spatio-temporal variability in land wetness conditions. The new model requires a crop identification map and some standard meteorological measurements as inputs. The conversion of above ground dry biomass into crop yield has been calibrated through harvest indices and the values obtained are compared with the international literature. The computations were executed in a GIS environment using 20 satellite measurements of the advanced very high resolution radiometer (AVHRR) to cover an annual crop rotation cycle. The validation with district data revealed a root mean square error of 525, 616, 551 and 13,484 kg ha-1 for wheat, rice, cotton and sugarcane yield, respectively. The model performs satisfactorily for wheat, rice and sugarcane, and poorly for cotton. It is expected that the accuracy of the model applied to 1.1 km pixels decreases with the increasing number of crops occurring within a given pixel. Although AVHRR is basically too coarse a resolution for field scale crop yield estimations, the results provides yield predictions to policy makers in Pakistan with a spatial detail that is better than the traditional district level data. The gaps between the average and the maximum yield are 1075 and 1246 kg ha-1 for wheat and rice, respectively. Future work should rely on the integration of high and low resolution images to estimate field scale crop yields.