1. Introduction
Semi-arid regions cover about 15% of the Earth’s land surface and the spatial and temporal patterns of rainfall in these regions are very variable, which causes drastic vari-ability in the spatiotemporal distribution, production, and development of vegetation [
1,
2]. Over recent decades, many semi-arid and dryland habitats have faced greater pressures from human-induced activities and climate change [
3,
4,
5]. To better understand climatic change and anthropogenic impacts on dryland and semi-arid ecosystems, information on the vegetation’s spatiotemporal variability is the key source. Satellite-based knowledge is an important tool in tracking vegetation variability and dynamics, with the potential for broad spatial coverage and regular explanations [
6,
7]. The dataset of the spectral vegetation index is particularly well-related to the leaf area index (LAI), the abundance of chlorophyll, the absorption of gross-primary-production (GPP), and photosynthetically active radiation (PAR) [
8,
9,
10]. To monitor semi-arid and dryland vegetation activity and spot changes in growth and phenology of vegetation [
11,
12,
13], vegetation indices time-series have often been used [
14,
15,
16,
17].
The polar-orbiting MODIS (Moderate Resolution Imaging Spectroradiometer) sensor allows for the monitoring and measurement of vegetation and environmental indices. MODIS-derived data have a superior spatial and radiometric resolution than that of the Advanced Very High-Resolution Radiometer (AVHRR) sensor and thus provides an enhanced radiometric, spatial, and spectral representation of surface vegetation conditions [
18,
19]. Among the generally used datasets in the monitoring of vegetation dynamics, the normalized vegetation difference index (NDVI) time-series is noteworthy. Nevertheless, the NDVI has some restrictions relating to soil reflectance, affecting the index and contributing to different index values for various conditions of soil and moisture being observed for similar biophysical properties of the canopy [
20]. A variable of soil adjustment ‘L’ was proposed to interpret the nonlinear, first-order, variance in radiative transmission through a canopy in the spectrum’s red and NIR (near-infrared) zones. Through this, another index (i.e., SAVI, soil adjusted vegetation index) was obtained [
21]. Subsequently, other soil-adjusted indices (e.g., the OSAVI, optimized soil adjusted vegetation index and the MSAVI, modified soil adjusted vegetation index) were established to optimize soil effects [
22,
23].
In addition, to optimize the vegetation signal, the enhanced vegetation index (EVI) with enhanced sensitivity was developed in high-biomass areas to provide better vegetation monitoring by decoupling the background signal of the canopy and the atmospheric effects [
19]. The EVI has been shown to be highly linear and closely correlated with phase and amplitude measurements of the seasonal eddy flux tower photosynthesis, surrounding a wider range of leaf area index (LAI) retrievals [
24]. Vegetation indices (VIs) assessment plays a vital role in assessing the growth of vegetation in biomass diversity. EVI and NDVI obtained from MODIS surface reflectance, modified for the NBAR (nadir bidirectional reflectance distribution function), had better accuracy compared to the leaf area index when assessing the start of the season in large coniferous leaf forests [
25,
26]. The EVI for monitoring the phenology of rice paddy in Asia during the monsoon was a successful vegetation index compared to in situ data [
27]. Among EVI, green-red-vegetation-index (GRVI), and NDVI, it was observed that the best vegetation index was to extract seasonal variations in the GRVI, which was influenced by variations in leaf colors [
28,
29,
30]. To track vegetation phenology and operation across a range of habitats, 2-band enhanced vegetation index (EVI2) was observed to be a comparatively improved index than the NDVI because of its insensitivity to context reflection [
31]. A new generalized differential vegetation index (GDVI) was established [
18]. In vegetation communities such as forest, cropland, savanna, shrubland, and grassland, the ability of NDVI time-series has been verified for the varied semi-arid and arid climates to obtain seasonal and inter-annual variability [
15]. SAVI was also used for evaluating vegetation development in the semi-arid region of Mexico, which is closely related to NDVI [
32,
33].
The NDVI and EVI derived from MODIS exhibited a highly linear correlation with gross primary product (GPP) compared to in situ flux quantities in the Sahel’s semi-arid atmosphere [
17,
34,
35]. Despite these applications, it was suggested that the detection findings of VIs in semi-arid and arid lands should be taken carefully because of the high uncertainty of VIs in thinly vegetative areas, complex vegetation composition, and landscape heterogeneity structure [
36,
37]. Herein, the performance of vegetation indices SAVI, NDVI, and EVI are assessed for vegetation variability and dynamics in the semi-arid and arid environment of the province of Punjab, Pakistan. We derived NDVI, EVI, and SAVI and related them to their spatiotemporal variability throughout the research area, along with transects because phenological transitions in summer and winter are important factors for changeable crop development and plant growth in spring and autumn [
25]. In the present study, two phenological metrics (i.e., for the start of the season and end of the season) were calculated from the MODIS-derived time-series vegetation indices and compared for each vegetation type. Furthermore, the appropriateness of vegetation indices for monitoring the complex vegetation cover in the province of Punjab was evaluated.
4. Discussion
This approach is used to map and identify vegetation variability distributions.
4.1. VI Variability
Various factors including clouds and shadow-polluted pixels, time-based interpolation, smoothing, spatial filtering, cropping assumptions, and mixed pixels can influence the VI values. NDVI and SAVI are more prone to atmospheric effects than EVI, induced by cloud presence. The impurity of the peaks of the remaining clouds makes a big difference in NDVI values. Some limitations in the MODIS cloud mask exist even after using it to decrease cloud properties on per-pixel surface reflectance [
54,
55]. Vegetation shifts are misidentified with EVI, degraded by the presence of clouds and aerosols [
56]. The reliability of quantified vegetation variations mainly depends on how image classification incorporates atmospheric and cloud transmission information. Affected pixels may be treated by additional masking [
56], which targets the resulting inconsistencies through quantifying vegetation variability using the NDVI time series. High VI variation (>40%) can be seen in areas near water bodies, and low vegetation occurs near built-up areas (
Figure 4) [
54,
55]. VI variation (10–30%) occurs in the vicinity of the river, and the majority of cropland areas occur in the study area. This variation may be due to a combination of minor crop fields. In sparsely vegetated areas, in particular, EVI has more coverage than SAVI and NDVI. The soil composition of these regions largely consists of dry sand. NDVI has been described as vulnerable to the spectral effect of soil moisture and texture in the spaces between desert grassland and shrubland [
57,
58]. The great annual diversity observed from EVI may be correlated with a difference in soil history at low levels of VIs.
4.2. Detection of Phenology Metrics
Using TIMESAT’s curve-fitting approach with an equal seasonal amplitude threshold (40%), different VI dynamic phenological metrics were detected for Punjab Province (
Figure 6). Despite the indecision of the VIs time-series, TIMESAT has obtained good results from the three VIs for most vegetation types. The results of the three VIs in this study follow that of previous studies [
12,
29], demonstrating their efficiency for phenological metrics recognition in arid and semi-arid environments. There are, however, still some inconsistencies in the findings of phenology detection. In our analysis, the variability at the end of the season observed from MODIS VIs was greater than that at the start of the season (
Table 4), consistent with earlier studies [
59,
60].
The greatest variance in phenological metrics was observed from the NDVI time series. The overestimation of the end of the season exhibited high variability rates because of its vulnerability to atmospheric conditions and soil composition. The difference between histogram dispersals of phenological metrics of estimated VIs may be due to their varying sensitivities to soil context variations. Likewise, in the drylands of Arizona, USA, the spatiotemporal variability of soil surface phenology removed from NDVI was higher than EVI [
61]. The difference in highest greenness between NDVI and EVI was related to the physiological features of the vegetation types due to their different sensitivities [
36]. More comprehensive information is required for the improved assessment of the suitability of VIs in a dryland environment for phenological detection. Our understanding of ecosystem processes over the arid landscape would be useful for evaluating satellite-derived phenological metrics with measurements of flux tower footprint CO
2 or in situ VI extents [
62,
63].
In the present research, the limitation of data acquisition throughout situ precludes further review. Furthermore, in our study, we compared only three widely used VIs. Furthermore, the utility of other vegetation indices such as the optimized soil adjusted vegetation index (OSAVI), EVI2, and MSAVI, and their combined use can be of significance for complex vegetation cover investigations in the arid and semi-arid regions.
One of the limitations of the approach taken in this study is that it is rigid (i.e., it assumes that the time series oscillates at a regular interval over the year). Additionally, remote sensing vegetation indices were more closely associated with the formation of the canopy structure in the spring, and persistence after photosynthesis ceased in autumn. Additionally, low fractional vegetation cover in the south of the study area was a primary limitation so site-specific circumstances such as the considerable spatial heterogeneity of pixels generated by the sparse vegetation were a source of uncertainty. Sites with short growth seasons and a scarcity of high-quality observations would make it even more difficult to extract seasonal patterns. Moreover, the spatial patterns of the VI-derived phenology agreed well with the timing of the start, end, and length of season, but uncertainties appeared in areas with limited seasonality expressed in the satellite signal and systematic biases.
5. Conclusions
An approach was implemented to identify vegetation variability and its dynamics by comprehensively using three vegetation indices (EVI, SAVI, and NDVI) estimated through MODIS time-series data in the Punjab Province of Pakistan. The varying associations of NDVI, EVI, and SAVI were estimated in different vegetation forms. A minor correlation exists between NDVI, EVI, and SAVI, with Pearson’s correlation coefficients ranging from 0.42 to 0.50. Due to the blue band reflectance disturbance, EVI typically showed high uncertainties in sparse vegetation areas of grassland and forest. The EVI time-series showed the highest inter-annual variability in Punjab from 2000 to 2019, with 14.28% of the total region showing higher variability than 40%. For most vegetation types, the phenological metrics of season-start and season-end generated by the NDVI, SAVI, and EVI were consistent. The greatest deviations from phenological metrics were obtained from the NDVI time-series (33.4 days at the start of the season and 40.7 days at the end of the season), suggesting soil context-sensitivity and atmospheric effects of the index. The annual mean VI images displayed parallel spatial arrangements of vegetation conditions with fluctuating levels. Major steps include using the temporal interpolation algorithm to remove contaminated pixels to reconstruct MODIS NDVI time-series data. The EVI time-series showed greater inter-annual variations from 2000 to 2019 compared to SAVI and NDVI. Given the high vegetation variability in northern Punjab, the high variability in EVI values could be due to variations in soil background properties. In the future, to assess the climatic effect on semi-arid, arid, and rainfed areas’ vegetation dynamics, satellite data will be assessed along with climate data for the region and other larger areas.