## 1 Introduction  
Drylands cover nearly 40 percent of global land surface [1, 2]. Rainfall is low and highly variable in these lands, which results in dramatic variation of temporal development and growth cycle of vegetation from year to year. Many dryland ecosystems have experienced increased threats from climate change and human-induced activities over recent decades [3, 4, 5, 6]. Knowledge of the spatial and temporal dynamics of vegetation is essential to understand the impacts of climate change and anthropic activities on dryland ecosystems. With the capability for large spatial coverage and frequent observations, satellite data have played an important role in monitoring vegetation dynamics [7, 8, 9]. In particular, the spectral vegetation index dataset is known to be well-related to leaf area index (LAI), chlorophyll abundance, absorption of photosynthetically active radiation (fPAR) and gross primary production (GPP) [10, 11]. For dryland vegetation activity monitoring, time series of vegetation indices have often been used to detect variability of vegetation activity [12, 13, 14] and changes of vegetation phenology [15, 16]. The polar orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the National Aeronautics and Space Administration's Terra and Aqua platforms enable sound environmental monitoring in general and the calculation of vegetation indices, as they offer a radiometric and spatial resolution superior to that of, for example, the Advanced Very High Resolution Radiometer (AVHRR) sensor [17]. Vegetation Indices (VIs) derived from MODIS data represent improved spatial, spectral, and radiometric representations of surface vegetation conditions [18]. Among the most commonly used datasets in vegetation dynamic monitoring are the Normalized Difference Vegetation Index (NDVI) time series. However, the NDVI does have some limitations related to soil background brightness, which has an impact on the index and leads to the fact that, for similar canopy biophysical properties, different index values can be found for different soil and moisture conditions [19]. In order to overcome this problem, [PERSON] [20] proposed a soil-adjustment factor, L, to account for the first-order, non-linear, differential radiative transfer through a canopy in the near infrared (NIR) and red regions of the spectrum, and obtained a soil-adjusted vegetation index (SAVI). After that, soil-adjusted indices such as the modified soil-adjusted vegetation index (MSAVI) and optimized soil-adjusted vegetation index (OSAVI) were developed for optimal adjustment of soil effects [21, 22]. Moreover, the Enhanced Vegetation Index (EVI) was developed to optimize the vegetation signal with improved sensitivity in high-biomass regions and to provide improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmospheric influences [17]. The EVI has shown to be strongly linearly related and highly synchronized with seasonal eddy flux tower photosynthesis measurements in terms of phase and amplitude, encompassing a broader range in LAI retrievals [23]. The evaluation of VIs is an important and necessary process when assessing the vegetation development in different biomes. [PERSON] _et al._[24] pointed out that both the NDVI and EVI derived from MODIS surface reflectance adjusted for the nadir bidirectional reflectance distribution function (NBAR) had a better accuracy than LAI in estimating the onset dates of greenness in deciduous broadleaf forest. [PERSON] _et al._[25] found the EVI to be the most effective vegetation index for rice paddy phenology monitoring in monsoon Asia when it was compared with _in situ_ data. Based on ground observations, [PERSON] _et al._[26, 27] examined the NDVI, EVI, and the green-red ratio vegetation index (GRVI) and found that the GRVI was the best at capturing the seasonal changes in photosynthetic capacity, as green and red reflectances are strongly influenced by changes in leaf pigments.