Rapid and accurate estimation of Ground Cover -GC- at regional and global scales for 6 agricultural management application is only possible by using remote sensing -RS-. In 7 this paper, we used two Vegetation Indices -VIs- including the Perpendicular 8 Vegetation Index -PVI- and Normalized Difference Vegetation Index -NDVI- for 9 estimating GC. Since the parameters of the bare soil line have an important role in 10 calculating GC based on PVI, this line was extracted based on the Red-NIRmin method 11 with different intervals -0.0001, 0.0005 and 0.001-. In addition to traditional measures 12 such as Root Mean Square Error -RMSE-, the sensitivity analysis -S- was also used to 13 sharp the accuracy of the models estimations. The results indicated that the PVI-based 14 method, in contrast to the NDVI-based approach, had a better performance in 15 estimating GC of wheat. The highest correlation between the observed GC and the 16 estimated GC based on PVI method was achieved in interval length of 0.0005 -R217 =0.91- with RMSE equal to 8.82. This regression line -GCEST=-3.47+0.96GCOBS- 18 was not significantly different from the 1:1 line. As expected, the best estimation was 19 achieved when the sensitivity of estimated GC based on PVI -length of the interval: 20 0.0005- was almost constant and low compared to the other models.

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