## 4 Discussion
### 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.