## 4 Discussions
### Evaluation of NIR\({}_{v}\) for Drought Monitoring  
NIR\({}_{v}\), a newly proposed index which aims to address the mixed pixel problem and aspects of photosynthetic capacity [54], is highly correlated with SIF and can be used as the proxy of GPP ([PERSON] et al., 2021) [55]. It has been demonstrated that NIR\({}_{v}\) and SIF exhibit similar performances for estimating GPP [56], while their differences have not been systematically explored under drought stress [57]. Generally, SIF is strongly driven by PAR or APAR (Figure 7), and NIR\({}_{v}\) is theoretically independent of PAR or APAR. SIF is less impacted by the soil background, and similarly, NIR\({}_{v}\) also has the potential for disentangling the vegetation signal from the soil's brightness and capturing the depth distributions of the canopy's photosynthetic capacity. Therefore, many studies have been conducted. For example, [PERSON] et al. [58] demonstrated that NIR\({}_{v}\) and SIF were complementary for GPP estimation; that is, NIR\({}_{v}\) outperformed the other proxies in non-evergreen sparsely vegetated areas, whereas SIF performed best in high-productivity areas. The main reason for this lies in that the inherent noise associated with SIF retrieval [59] hindered the capturing of GPP dynamics. Aside from that, other NIR\({}_{v}\)-based variablessuch as NIR\({}_{v}\)Rad, defined as the product of observed NIR radiance and the NDVI, has great potential for estimating the GPP from satellite remote sensing data [60]. As for drought monitoring, there are many indices for monitoring thoughts in different scales, and each index has its advantages and limitations. For example, [PERSON] et al. [61] provided a systematic study on evaluating the single drought indices, combined drought indices, and in situ drought indices for monitoring droughts across China. The results indicated that different drought indices have specific performance for drought monitoring in different land use types in China, and this research provided an effective way to explore the potential of different drought indices for monitoring droughts. [PERSON] et al. [62] utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) EVI products to calculate the Vegetation Condition Monitoring Index (VCI), which was proven to be suited for monitoring drought disasters. Meanwhile, for NIR\({}_{v}\), for instance, during the 2012 North American drought, NIR\({}_{v}\) rapidly declined in parallel with the GPP, while the NDVI showed little deviation compared with non-drought years [63]. During the 2018 drought in northwestern Europe, strong reductions in SIF and NIR\({}_{v}\) were observed, corresponding to the occurrence of reduced precipitation [64]. [PERSON] et al. [24] selected a severe drought event in 2016 over the Hulun Buir grasslands as an example and explored the potential of SIF and NIR\({}_{v}\) for drought detection and monitoring. Specifically, many parameters, including the VIs, self-calibrating PDSI, precipitation, SM, surface water storage, and land surface temperature were acquired to detect and characterize the drought event. Meanwhile, according to the composition of SIFtoc, the satellite SIF was used to calculate SIF\({}_{\text{yield}}\) (normalized by APAR), the physiological SIF emission yield, and SIFtot. The results indicated that NIR\({}_{v}\) outperformed other VIs in drought monitoring, with the largest decrease among the VIs, which was consistent with our study. For lag time analysis, similar results showing that SIF had a shorter lag time than the VIs in responding to meteorological droughts were found. Considering the relationship between NIR\({}_{v}\) and SM, [PERSON] et al. [65] used two satellite-derived vegetation indices, NIR\({}_{v}\) and Vegetation Optical Depth (VOD), and two SM networks for the 2018 agricultural drought in a lowland area. The results indicated that NIR\({}_{v}\) had the potential to infer the critical SM content.