Recognition of different yield potentials among rain-fed wheat fields before harvest using remote sensing

An algorithm was proposed to classify wheat fields into high-productive and low-productive classes using satellite images according to a threshold value for rain-fed wheat ETa in the anthesis stage. Since the classification process was based on the ETa values in the rain-fed wheat pixels, an algorithm was proposed to map wheat fields using the combination of MODIS and Landsat-8 images. The wheat area mapping method included two major processes in which the first one used a step by step elimination process of non-wheat pixels which did not follow the standard wheat vegetation index time series; the second one used supervised classification methods for detecting the rain-fed wheat pixels. The assessment of the wheat area map was performed statistically using surveyed wheat plots. After detecting the wheat parcels, the threshold value was determined using a frequency analysis on actual evapotranspiration values in the parcels. The rain-fed wheat ETa values estimated from the SEBAL algorithm and were compared to the results from the Eagleman-Affholder method and MOD16A2 products. To assess if the wheat fields in productivity classes have been categorized correctly, yield values in the two classes were compared with each other. The rain-fed wheat yield was estimated using the light use efficiency model and compared to provincial census data for accuracy assessment. Results showed that the overall accuracy, Kappa coefficient, and F1 score values for the 250-m resolution map from the MODIS images were 82, 0.61, and 0.71, respectively. In the same order, these statistics for the 30-m resolution map from the Landsat-8 images were 92, 0.62, and 0.77, respectively. Both the SEBAL and the Eagleman-Affholder methods closely estimated the average wheat ETa value in the anthesis stage equal to 2.4 mm/day. The mean of the rain-fed wheat yield value from the LUE model in 2015 was 10% lower than the census data. Relying on the low amount of the absolute error between the LUE model and the census data, the mean of the yield values in both high-productive and low-productive classes were compared with together. Results showed that the mean amount of yield in high-productive class was 33% (271 kg/ha) more than that of in low-productive class.