## 4 Discussion  
### Ground Interpretations of the Satellite-Observed Vegetation Phenology  
Although the five selected sites exhibited distinct seasonality revealed by eddy covariance flux measurements of vegetation production (GPP), both satellite-based SIF and EVI generally captured seasonal dynamics and inter-annual variations over a variety of biomes (Figures 3 and 4). In comparison with the greenness-based vegetation index, GOME-2 SIF displayed more consistently a seasonal profile with tower-based GPP (Figure 4). Especially, there was a significant \"hysteresis effect\" during the senescent period of EVI relative to that of GPP over water-limited ecosystems (Hummock grasslands and shrublands), consistent with previous findings [4]. In the wet year of 2016-2017, there was a considerably delayed senescence of EVI over two southern sites (AU-ASM and AU-TTE), relative to those of GPP and SIF (Figure 3). Besides, we found that the autumn senescence rates of EVI were considerably slower than the spring green-up rates of EVI over these arid/semiarid biomes (Figure 7); however, RAU and RSP of SIF were generally comparable. Aside from the slow chlorophyll degradation leading to a gradual decrease of EVI [42], another possible reason causing the discrepancies between EVI and SIF during the brown-down phase is that the rapid decline in solar radiation in the arid southern NATT gave rise to the swiftly  
Figure 8: Relationships between climatic–environmental drivers and vegetation variables of (**a1–a4**) EVI, (**b1–b4**) SIF, (**c1–c4**) SIF\({}_{\mathrm{PAR}}\), and (**d1–d4**) SIF\({}_{\mathrm{yield}}\) among four major biome types across the NATT during 2014–2019. (\(p\)-value \(<0.001\)).  
dropping SIF signals (Figure 4f). After removing the impact of the PAR on SIF, there was a remarkably enhanced correlation between EVI and SIF\({}_{\text{PAR}}\) across southern water-limited biomes (Figure 8b3-b4).  
In contrast to GOME-2 SIF, EVI had an improved capability (higher \(r^{2}\)) of tracking the interannual and seasonal variations in GPP over most sites (except AU-Stp, Figure 3), probably owing to the substantial footprint mismatch between flux tower measurement and satellite observations (EVI: \(\sim\)5 km, SIF: \(\sim\)50 km). Moreover, as compared with the reflectance-based vegetation index, the high retrieval noises over the low-productivity region resulted in more erratic SIF signals [43], giving rise to a relatively weaker correlation with tower-based GPP. Given the sparse spatial resolution of satellite-based SIF data, [PERSON] et al. (2019) utilized a high-resolution contiguous SIF product (SIF\({}_{\text{COCO2\_005}}\)) over the NATT and found that SIF\({}_{\text{COCO2\_005}}\) outperformed EVI at AU-ASM with a stronger temporal consistency with tower-based GPP [23]. However, this up-scaled dataset was generated by native OCO-2 SIF signals along with MODIS reflectance through a machine learning method [32], and it therefore contains information of canopy structure and chlorophyll content, like greenness-based indices. Even though EVI showed a tighter correlation with the GPP (higher \(r^{2}\)) than SIF\({}_{\text{COCO2\_005}}\) at AU-Dry and AU-Stp in [PERSON] et al.'s (2019) studies, congruent with our results [23], nevertheless, the high spatiotemporal SIF data derived from TROPOMI onboard the Sentinel-5 Precursor launched in 2017 exhibiting striking consistency with field measurement has great potential of characterizing the phenological dynamics of dryland vegetation in the future [44].