Some scholars also correlated the atmospheric and climatic parameters such as rainfall, temperature, and evapotranspiration to prepare vegetation index ( Cihlar, Laurent, & Dyer,В 1991 Gray & Tapley,В 1985 Sarkar & Kafatos,В 2004). Land use/land cover classification in a forest dominant region is also done by using NDVI as it has helped in assessing the biomass and separated the vegetation from the other land uses ( Myneni, Los, & Tucker, 1996 Tucker & Sellers,В 1986, Cuomo, Lanfredi, Lasaponara, Macchiato, & Simoniello,В 2001 Lanfredi, Lasaponara, Simoniello, Cuomo, & Macchiato,В 2003 Telesca & Lasaponara,В 2006 Telesca etВ al.,В 2008). Nowadays, NDVI has been used as a basis for the assessment of quantity and quality of green space such as LAI ( Kross, McNairn, Lapen, Sunohara, & Champagne,В 2015), fractional vegetative cover ( Xiao & Moody,В 2005 Zhang etВ al.,В 2006), biomass assessment ( Fensholt,В 2004 Telesca etВ al.,В 2008), and broad assessment of environmental change and its impact ( Pettorelli etВ al.,В 2005). Red and near-infrared bands of a spectral stratum are used in the NDVI analysis ( Bannari, Morin, Bonn, & Huete,В 1995). NDVI is widely acknowledged as one of the most recurrently used techniques for assessing vegetation cover. Gitelson, ViГ±a, Ciganda, Rundquist, and Arkebauer (2005) reported that canopy chlorophyll content would vary widely during the growing season, and therefore an adaptive remote sensing technique should be able to widely monitor the dynamic range of chlorophyll content. Furthermore, chlorophyll content varies by time and is dependent on rainfall amount and its distribution. This analysis shows that the time of the GNDVI peak occurrence and the patterns of GNDVI changes vary with rainfall distribution. While the GNDVI had a single peak in the years 2008вЂ“16, it revealed a double peak in the year 2017 due to different rainfall distribution in the area, as was noted for the NDVI.

A recent study has shown that the GNDVI was the most effective index for determination of corn water stress ( Genc et al., 2013). In this regard, the actual photochemical activity of chlorophyll is reduced due to water deficit in plants ( Pettigrew, 2004 Souza, Machado, Silva, LagГґa, & Silveira, 2004) and the reflectance may also be influenced under these conditions ( Clay, Kim, Chang, Clay, & Dalsted, 2006). The GNVDI values did not exceed 0.37 in 2008, which implied crops had lower chlorophyll content and higher water stress than the other years, possibly due to late rainfall occurrence ( Fig. Conversely, the minimum peak of the GNDVI was observed in 2008, which was followed by a low value of ER index (lower ER implies lower mean daily rainfall).

The peak values of the GNDVI reached 0.5 and a little higher in the years 2013вЂ“вЂ“10 in which rainfall events began early in fall and uniformly continued until April ( Fig.

Indeed, the weighing average of the GNDVI of all pixels in an image was determined as the representative GNDVI of the image and the maximum GNDVI value among the various dates in a year was recognized as the peak value. The highest peak of the GNDVI occurred in 2009вЂ“вЂ“14 ( Fig. Previous studies in this region have also reported that the end-season stage and senescence of wheat occur in late May ( Shahrokhnia & Sepaskhah, 2013). It decreased in late May as a result of crop senescence. The GNDVI variations showed almost a very trend to the NDVI ( Fig. Mohammad Hossein Shahrokhnia, Seyed Hamid Ahmadi, in Spatial Modeling in GIS and R for Earth and Environmental Sciences, 2019 2.3.1.3 Green Normalized Difference Vegetation Index

Nir color index of the sun