# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 4 Discussion
### Sensitivity Analysis of Vegetation Response to Drought  
The ecological environment of YRB is fragile and drought events occur frequently. Drought can easily lead to decreased transpiration of vegetation, decreased photosynthetic rate, gradually slowed down or even stagnant growth and development of vegetation,  
Figure 10: Correlation between SPEI and NDVI (**a**) cropland, (**b**) grassland and (**c**) forestland; correlation between SPEI and SIF (**d**) cropland, (**e**) grassland and (**f**) forestland.  
decreased biomass and even led to plant death [9; 46]. NDVI is an important indicator to reflect the growth status and vegetation coverage of vegetation. SIF reflects the photosynthesis of vegetation. NDVI and SIF monitored by remote sensing data can reflect the growth status of vegetation [47]. In order to analyze the difference between NDVI and SIF in detecting the impact of drought in YRB, the study compared the correlation analysis results between NDVI, SIF and SPEI in annual and different seasons (Figure 9). The significant positive correlation areas of SIF-SPEI in spring, summer and autumn (R > 0, \(p\) < 0.05) were 7.00%, 28.49% and 2.28% respectively, which were higher than the significant positive correlation areas of NDVI-SPEI (spring: 1.79%; summer: 20.72%; autumn: 1.13%). The significant positive correlation area of SIF-SPEI in summer (R > 0, \(p\) < 0.05) accounted for 28.49%, which was significantly higher than that in other seasons and higher than that in NDVI-SPEI. This result is consistent with the conclusion of sun et al. [48], that is, for temperate grassland, the correlation between SIF and SPEI, PDSI and soil moisture in July and August is much higher than that in other months, and for alpine grassland, the correlation is higher in June and July. Meanwhile, due to high temperature and heat in summer, drought has become one of the environmental stresses faced by plant growth. The high temperature in summer accelerates the physiological activities of plants, thus accelerating transpiration, causing the absorption and supply of roots to be insufficient for the evaporation of plants, resulting in water loss, destroying the water balance of plants, and promoting plants to wither [49; 50].  
In different land cover types, farmland is more vulnerable to drought than grassland and forest, and farmland SIF has a higher response to SPEI (0.3424, \(p\) < 0.01). Therefore, we conclude that SIF is more responsive to drought than NDVI, which is consistent with the previous research conclusion, that is, SIF is a more effective drought response index than NDVI [24]. Similarly, [PERSON] et al. [25] showed that SIF can timely capture the damage to wheat when high temperature stress occurs, half a month earlier than the traditional NDVI and EVI. [PERSON] et al. used multi-source spaceborne SIF to analyze the response of dryland vegetation in Australia to severe extreme drought in the past 20 years, indicating that SIF has a good ability to accurately track the changes in dryland vegetation heterogeneity caused by drought [51]. [PERSON] et al. [23] confirms that SIF is superior to NDVI in detecting the impact of drought, and our conclusion was consistent with him.  
Our research also shows that SIF represents vegetation photosynthesis, has the potential to better monitor vegetation growth, and the response of SIF to drought is higher than NDVI [41]. Solar induced chlorophyll fluorescence (SIF) remote sensing is a new remote sensing technology developed rapidly in recent years [52]. The close relationship between SIF and photosynthetic process makes it an effective probe to indicate the Photosynthetic Changes of vegetation. GOSIF describes the photosynthesis of vegetation, continuously covering the world with high spatial resolution. Therefore, GOSIF has broader application prospects and greater potential in vegetation monitoring, global terrestrial carbon cycle, drought stress, carbon neutralization, etc. [35].